Robust chemical analysis with graphene chemosensors and machine learning.

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications1-4. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring5,6, healthcare diagnostics7 and industrial process control8. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance9,10. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated by non-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.

Organized crime in the fisheries sector threatens a sustainable ocean economy.

The threat of criminal activity in the fisheries sector has concerned the international community for a number of years. In more recent times, the presence of organized crime in fisheries has come to the fore. In 2008, the United Nations General Assembly asked all states to contribute to increasing our understanding the connection between illegal fishing and transnational organized crime at sea. Policy-makers, researchers and members of civil society are increasing their knowledge of the dynamics and destructiveness of the blue shadow economy and the role of organized crime within this economy. Anecdotal, scientific and example-based evidence of the various manifestations of organized crime in fisheries, its widespread adverse impacts on economies, societies and the environment globally and its potential security consequences is now publicly available. Here we present the current state of knowledge on organized crime in the fisheries sector. We show how the many facets of organized crime in this sector, including fraud, drug trafficking and forced labour, hinder progress towards the development of a sustainable ocean economy. With reference to worldwide promising practices, we highlight practical opportunities for action to address the problem. We emphasize the need for a shared understanding of the challenge and for the implementation of intelligence-led, skills-based cooperative law enforcement action at a global level and a community-based approach for targeting organized crime in the supply chain of organized criminal networks at a local level, facilitated by legislative frameworks and increased transparency.

Online engagement with 2020 election misinformation and turnout in the 2021 Georgia runoff election.

Following the 2020 general election, Republican elected officials, including then-President Donald Trump, promoted conspiracy theories claiming that Joe Biden's close victory in Georgia was fraudulent. Such conspiratorial claims could implicate participation in the Georgia Senate runoff election in different ways-signaling that voting doesn't matter, distracting from ongoing campaigns, stoking political anger at out-partisans, or providing rationalizations for (lack of) enthusiasm for voting during a transfer of power. Here, we evaluate the possibility of any on-average relationship with turnout by combining behavioral measures of engagement with election conspiracies online and administrative data on voter turnout for 40,000 Twitter users registered to vote in Georgia. We find small, limited associations. Liking or sharing messages opposed to conspiracy theories was associated with higher turnout than expected in the runoff election, and those who liked or shared tweets promoting fraud-related conspiracy theories were slightly less likely to vote.

The role of corruption in global food systems: a systematic scoping review.

BACKGROUND: Corruption exists at all levels of our global society and is a potential threat to food security, food safety, equity, and social justice. However, there is a knowledge gap in the role and impact of corruption within the context of the global food system. We aimed to systematically review empirical literature focused on corruption in the global food system to examine how it is characterized, the actors involved, its potential impacts, and the solutions that have been proposed to address corruption in the food system. METHODS: We used a systematic scoping review methodology. Terms combining corruption and the food system were searched in Scopus, PubMed, Web of Science, PsycInfo and Econlit, in October 2021. Two screeners applied a priori selection criteria to screen the articles at the title and abstract and full-text levels. Data was extracted into a charting form and thematically synthesized to describe the types of corruption in the food system, the actors involved, how corruption impacts the food system, and potential solutions. Sankey diagrams and narrative summaries were developed to summarize the included studies and findings. RESULTS: From the 238 included records, five main types of corruption were identified in the global food system: bureaucratic corruption, fraud, bribery, organized crime, and corporate political activity. These different types of corruption spanned across various food system areas, from policy and governance structures to food environments, and involved a wide range of actors. More powerful actors like those in public and private sectors tended to instigate corruption in the food system, while community members and primary producers tended to be impacted by it. The impacts of corruption were mostly negative and corruption was found to undermine food system governance and regulatory structures; threaten health, safety, and food security; and lead or contribute to environmental degradation, economic loss, erosion of trust, social inequities, and decreased agricultural productivity. While solution-oriented literature was limited, the essential role of strong governance,  use of technology and predictive modelling methods to improve detection of corruption, and organizational approaches to problem solving were identified. CONCLUSION: Our review findings provide researchers and policymakers with a comprehensive overview of corruption in the global food system, providing insights to inform a more holistic approach to addressing the issue. Addressing corruption in the food system is an essential element of supporting the transition to a more healthy, equitable and sustainable global food system.

Protection of the human gene research literature from contract cheating organizations known as research paper mills.

Human gene research generates new biology insights with translational potential, yet few studies have considered the health of the human gene literature. The accessibility of human genes for targeted research, combined with unreasonable publication pressures and recent developments in scholarly publishing, may have created a market for low-quality or fraudulent human gene research articles, including articles produced by contract cheating organizations known as paper mills. This review summarises the evidence that paper mills contribute to the human gene research literature at scale and outlines why targeted gene research may be particularly vulnerable to systematic research fraud. To raise awareness of targeted gene research from paper mills, we highlight features of problematic manuscripts and publications that can be detected by gene researchers and/or journal staff. As improved awareness and detection could drive the further evolution of paper mill-supported publications, we also propose changes to academic publishing to more effectively deter and correct problematic publications at scale. In summary, the threat of paper mill-supported gene research highlights the need for all researchers to approach the literature with a more critical mindset, and demand publications that are underpinned by plausible research justifications, rigorous experiments and fully transparent reporting.

Unlocking food safety: a comprehensive review of South Africa's food control and safety landscape from an environmental health perspective.

Food fraud (often called fake food in South Africa) the deliberate misrepresentation or adulteration of food products for financial gain, is a growing problem in South Africa (SA) with severe public health and financial consequences for consumers and businesses. The recent public outcry against food fraud practices especially in communities that have lost loved ones due to the consumption of allegedly adulterated foodstuffs, highlights the grave danger that food fraud poses to consumers and the potential for significant reputational damage to food manufacturers. Despite the risks, food fraud often goes undetected, as perpetrators are becoming increasingly sophisticated. The precise magnitude of food fraud remains obscure, as incidents that do not cause consumer illnesses are frequently unreported and, as a result, are not investigated. Food fraud costs the global economy billion annually. This cost is borne by consumers, businesses, and the government. Food fraud can occur at any stage of the food supply chain, from production to processing to retailing or distribution. This is due in part to the limitations of current analytical methods, which are not always able to detect food fraud. This review of food fraud in SA looks at several factors that may be contributing to epidemic of food fraud, including inadequate penalties, inadequate government commitment, a complex labelling regulation, emerging threats such as e-commerce, and shortage of inspectors and laboratories. The review recommends establishing a single food control/safety authority, developing more food safety laboratories, and adopting innovative technologies to detect and prevent food fraud. SA faces a serious food fraud crises unless decisive action is taken.

What are people's attitudes toward medical insurance fraud whistleblowers? a study from China.

PURPOSE: Medical insurance fraud has caused huge losses to countries around the world, and public reporting has become an important means to combat medical insurance fraud. The attitude of medical insurance fraud whistleblowers affects people's reporting behavior, and understanding people's attitude toward medical insurance fraud whistleblowers provides a basis for further improving the system and policy of public participation in medical insurance fund supervision. METHODS: We adopted the questionnaire method to conduct a national cross-sectional survey of the Chinese public and analyzed the data using Chi-square tests, Fisher's exact tests, and binary logistic regression models. RESULTS: A total of 837 respondents were included, and 81.8% of the population had a supportive attitude toward medical insurance fraud whistleblowers, with gender, whether they had used medical insurance reimbursement, and present life satisfaction being statistically significant (P < 0.05). CONCLUSION: The public is generally supportive of medical insurance fraud whistleblowers, and women, those who have used medical insurance for reimbursement, and those who are satisfied with their lives are more likely to be supportive of medical insurance fraud whistleblowers.

The challenges associated with the prevention of smuggling and counterfeiting health goods in Iran.

BACKGROUND: Smuggling health goods given the importance and critical nature of health services should be undeniably addressed and controlled by all countries. This issue is especially more widespread in developing countries with more damaging consequences. This paper therefore aims to identify and analyze the challenges of preventing smuggling of health goods in Iran. METHOD: Within this qualitative study, we conducted face-to-face, semi-structured interviews with 30 purposefully recruited key informants and stakeholders in the detection, prevention, and combating of health goods smuggling. Each interview was analyzed thematically, using an inductive approach to generate codes, then categorized and presented in the form of main themes and sub-themes. Maxqda 11 assisted in coding, analysis, and data management. RESULTS: Three main themes emerged representing the challenges of prevention of smuggling in Iran in the areas of anti-smuggling policy development, including categories of inefficient policy and plan, and failure to reach agenda; policy implementation; categorized into actors, resources and instruments, and implementation guarantee; and finally monitoring and evaluation; including, procedures and practices, and the role of surveyors. CONCLUSION: Prevention of smuggling health goods proves to be a highly complex, challenging, and multi-faceted practice. Therefore, strengthening policy-making, regulatory frameworks, and facilitation functions about smuggling, counterfeiting, and corruption should be promoted in parallel.

What influences the public's willingness to report health insurance fraud in familiar or unfamiliar healthcare settings? a cross-sectional study of the young and middle-aged people in China.

INTRODUCTION: Young and middle-aged people are important participants in the fight against health insurance fraud. The study aims to investigate the differences in their willingness to report health insurance fraud and the factors influencing it when it occurs in familiar or unfamiliar healthcare settings. METHODS: Data were obtained from a validated questionnaire from 828 young and middle-aged people. McNemar's test was used to compare the public's willingness to report under the two scenarios. Chi-square tests and multiple logistic regression analysis were used to analyze the determinants of individuals' willingness to report health insurance fraud in different scenarios. RESULTS: Young and middle-aged people were more likely to report health insurance fraud in a familiar healthcare setting than in an unfamiliar one (McNemar's χ²=26.51, P < 0.05). Their sense of responsibility for maintaining the security of the health insurance fund, the government's openness about fraud cases, and the perception of their ability to report had significant positive effects on the public's willingness to report in both settings (P < 0.05). In a familiar healthcare setting, the more satisfied the public is with government measures to protect whistleblowers, the more likely they are to report (OR = 1.44, P = 0.025). Those who perceive the consequences of health insurance fraud to be serious are more likely to report than those who perceive the consequences to be less serious (OR = 1.61, P = 0.042). CONCLUSION: Individuals are more likely to report health insurance fraud in familiar healthcare settings than in unfamiliar ones, in which their awareness of the severity of the consequences of health insurance fraud and their perceived risk after reporting it play an important role. The government's publicizing of fraud cases and enhancing the public's sense of responsibility and ability to maintain the safety of the health insurance fund may be a way to increase their willingness to report, regardless of whether they are familiar with the healthcare setting or not.

Automatic Recommender System of Development Platforms for Smart Contract-Based Health Care Insurance Fraud Detection Solutions: Taxonomy and Performance Evaluation.

BACKGROUND: Health care insurance fraud is on the rise in many ways, such as falsifying information and hiding third-party liability. This can result in significant losses for the medical health insurance industry. Consequently, fraud detection is crucial. Currently, companies employ auditors who manually evaluate records and pinpoint fraud. However, an automated and effective method is needed to detect fraud with the continually increasing number of patients seeking health insurance. Blockchain is an emerging technology and is constantly evolving to meet business needs. With its characteristics of immutability, transparency, traceability, and smart contracts, it demonstrates its potential in the health care domain. In particular, self-executable smart contracts are essential to reduce the costs associated with traditional paradigms, which are mostly manual, while preserving privacy and building trust among health care stakeholders, including the patient and the health insurance networks. However, with the proliferation of blockchain development platform options, selecting the right one for health care insurance can be difficult. This study addressed this void and developed an automated decision map recommender system to select the most effective blockchain platform for insurance fraud detection. OBJECTIVE: This study aims to develop smart contracts for detecting health care insurance fraud efficiently. Therefore, we provided a taxonomy of fraud scenarios and implemented their detection using a blockchain platform that was suitable for health care insurance fraud detection. To automatically and efficiently select the best platform, we proposed and implemented a decision map-based recommender system. For developing the decision-map, we proposed a taxonomy of 102 blockchain platforms. METHODS: We developed smart contracts for 12 fraud scenarios that we identified in the literature. We used the top 2 blockchain platforms selected by our proposed decision-making map-based recommender system, which is tailored for health care insurance fraud. The map used our taxonomy of 102 blockchain platforms classified according to their application domains. RESULTS: The recommender system demonstrated that Hyperledger Fabric was the best blockchain platform for identifying health care insurance fraud. We validated our recommender system by comparing the performance of the top 2 platforms selected by our system. The blockchain platform taxonomy that we created revealed that 59 blockchain platforms are suitable for all application domains, 25 are suitable for financial services, and 18 are suitable for various application domains. We implemented fraud detection based on smart contracts. CONCLUSIONS: Our decision map recommender system, which was based on our proposed taxonomy of 102 platforms, automatically selected the top 2 platforms, which were Hyperledger Fabric and Neo, for the implementation of health care insurance fraud detection. Our performance evaluation of the 2 platforms indicated that Fabric surpassed Neo in all performance metrics, as depicted by our recommender system. We provided an implementation of fraud detection based on smart contracts.

Healthcare insurance fraud detection using data mining.

BACKGROUND: Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. METHODOLGY: In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. RESULTS: Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. CONCLUSION: The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

Participant Fraud in Virtual Qualitative Substance Use Research: Recommendations and Considerations for Detection and Prevention Based on a Case Study.

Background: The COVID-19 pandemic has accelerated and amplified the use of virtual research methods. While online research has several advantages, it also provides greater opportunity for individuals to misrepresent their identities to fraudulently participate in research for financial gain. Participant deception and fraud have become a growing concern for virtual research. Reports of deception and preventative strategies have been discussed within online quantitative research, particularly survey studies. Though, there is a dearth of literature surrounding these issues pertaining to qualitative studies, particularly within substance use research. Results: In this commentary, we detail an unforeseen case study of several individuals who appeared to deliberately misrepresent their identities and information during participation in a virtual synchronous qualitative substance use study. Through our experiences, we offer strategies to detect and prevent participant deception and fraud, as well as challenges to consider when implementing these approaches. Conclusions: Without general awareness and protective measures, the integrity of virtual research methods remains vulnerable to inaccuracy. As online research continues to expand, it is essential to proactively design innovative solutions to safeguard future studies against increasingly sophisticated deception and fraud.

Federal Enforcement of Pharmaceutical Fraud under the False Claims Act, 2006-2022.

CONTEXT: The False Claims Act is the US federal government's primary tool for identifying and penalizing pharmaceutical fraud. The Department of Justice uses the False Claims Act to bring civil cases against drug manufacturers that allegedly obtain improper payment from federal programs. METHODS: The authors searched the Department of Justice website for press releases published between 2006 and 2022 that announced fraud actions brought against drug companies. They then used the World Health Organization's Anatomical Therapeutic Classification index to identify the classes of prescription drugs implicated in fraud actions. FINDINGS: During fiscal years 2006-2022, payments by six manufacturers amounted to more than 28% of total payments made as a result of federal False Claims Act actions. Nervous system and cardiovascular drugs were the classes of medications most commonly implicated in alleged fraud. Federal officials most frequently alleged that companies improperly promoted nervous system drugs and paid kickbacks to increase revenues from cardiovascular, antineoplastic and immunomodulating, and alimentary tract and metabolism drugs. CONCLUSIONS: Despite frequent pharmaceutical fraud settlements and penalties, incidence of alleged fraud among drug companies remains high. Alternative methods for preventing and deterring fraud could help safeguard our health systems and promote public health, and policy makers should ensure that effective fraud enforcement complements preventive public health regulation.

Food fraud detection and reporting by food control officers in Finland.

We studied food fraud detection and the reporting of suspected cases using a questionnaire survey and interviews with Finnish food control officers (FCOs). In total, 95 FCOs responded to the questionnaire, and 17 were interviewed. We found that even though many respondents had either suspected (69.2%) or detected (43.4%) food fraud or other food-related crime during the past five years, 46.8% thought they had no realistic chance of detecting food fraud during inspections. Challenges raised by the FCOs we interviewed included inadequate resources (8/17) and difficulties in inspecting documents or establishing their authenticity (14/17). Moreover, many interviewees highlighted difficulties in assessing whether to inform the police about a suspected case (7/17), and 62% (18/29) of respondents who had detected fraud had not reported it to the police. Training in food fraud detection, increased resources and guidelines on reporting suspected food fraud would improve food fraud detection and harmonize reporting.

Food fraud prevention strategies: Building an effective verification ecosystem.

Food fraud is an ever-present threat that regulators, food business operators (FBOs), and consumers need to be aware of, prevent where possible, and address by developing mitigation strategies to detect and reduce its negative consequences. While extant literature focuses on food fraud detection, there is less attention given to prevention strategies, a knowledge gap this review seeks to address. The aim of this review was to consider food-related fraud prevention initiatives, understand what has worked well, and develop a series of recommendations on preventing food fraud, both policy related and for future research. Reactive (including intelligence based) food fraud detection dominates over prevention strategies, especially where financial, knowledge, and time resources are scarce. First-generation tools have been developed for food fraud vulnerability assessment, risk analysis, and development of food fraud prevention strategies. However, examples of integrated food control management systems at FBO, supply chain, and regulatory levels for prevention are limited. The lack of hybrid (public/private) integration of food fraud prevention strategies, as well as an effective verification ecosystem, weakens existing food fraud prevention plans. While there are several emergent practice models for food fraud prevention, they need to be strengthened to focus more specifically on capable guardians and target hardening. This work has implications for policymakers, Official Controls bodies, the food industry, and ultimately consumers who seek to consistently purchase food that is safe, legal, and authentic.