Navigating the challenges of imposter participants in online qualitative research: lessons learned from a paediatric health services study.

BACKGROUND: The growth in online qualitative research and data collection provides several advantages for health service researchers and participants, including convenience and extended geographic reach. However, these online processes can also present unexpected challenges, including instances of participant fraud or scam behaviour. This study describes an incident of participant fraud identified during online focus group discussions and interviews for a PhD health services research project on paediatric neurodevelopmental care. METHODS: We aimed to recruit carers of Australian children with neurodevelopmental disorders. Potential participants were recruited via a publicly available social media advert on Facebook offering $50 AUD compensation. Those who expressed interest via email (n = 254) were sent a pre-interview Qualtrics survey to complete. We identified imposters at an early stage via inconsistencies in their self-reported geographical location and that captured by the survey as well as recognition of suspicious actions before, during and after focus group discussions and interviews. RESULTS: Interest in participation was unexpectedly high. We determined that all potential participants were likely imposters, posing as multiple individuals and using different IP addresses across Nigeria, Australia, and the United States. In doing so, we were able to characterise several "red flags" for identifying imposter participants, particularly those posing as multiple individuals. These comprise a combination of factors including large volumes and strange timings of email responses, unlikely demographic characteristics, short or vague interviews, a preference for nonvisual participation, fixation on monetary compensation, and inconsistencies in reported geographical location. Additionally, we propose several strategies to combat this issue such as providing proof of location or eligibility during recruitment and data collection, examining email and consent form patterns, and comparing demographic data with regional statistics. CONCLUSIONS: The emergent risk of imposter participants is an important consideration for those seeking to conduct health services research using qualitative approaches in online environments. Methodological design choices intended to improve equity and access for the target population may have an unintended consequence of improving access for fraudulent actors unless appropriate risk mitigation strategies are also employed. Lessons learned from this experience are likely to be valuable for novice health service researchers involved in online focus group discussions and interviews.

Gas chromatography ion mobility spectrometry (GC-IMS) dataset of honey samples with different botanical origins.

Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.

Protecting older consumers in the digital age: a commentary on ChatGPT, helplines and the way to prevent accessible fraud.

Older people are often targeted by fraudsters due to their unique characteristics and vulnerabilities. Being a victim of exploitation can lead to negative emotional and financial consequences. The purpose of this commentary is to present ChatGPT's potential to provide accessible information and support, helping older consumers protect themselves when confronted with exploitation, address the limitations of ChatGPT and propose solutions to overcome these limitations. Integrating tailored human and technological solutions, such as helplines, AI chatbots, and involving older adults in development, is crucial. By providing adequate training and support, the goal of ensuring accessibility for all users can be achieved.

Prevention of food fraud and fraud emulation among companies in the supply chain based on a social Co-governance framework.

This study develops a three-party evolutionary game model among upstream raw material producers, midstream food producers, and downstream distributors in the food supply chain, and investigates food fraud and fraud emulation among companies in the same group based on a food safety social co-governance framework. Moreover, the equilibrium points are divided into four scenarios according to the number of groups of companies committing fraud in the supply chain and whether companies in the same group emulate each other's fraudulent behavior. The stability conditions of these scenarios are also discussed and verified by numerical simulation in MATLAB. The results show that the behavioral strategy choices of different groups of food companies in the supply chain are closely related to the level of social co-governance involving the government, market, and consumers. Government regulation, supervision between companies, and consumer reporting can all change companies' behavioral strategies. Although the level of fraud emulation among companies in the same group does not change their behavioral strategy choice, it affects the time it takes for their behavioral strategy to evolve to a stable state. Moreover, the level of social co-governance directly affects companies' behavioral strategy choices at different emulation levels.

Food Fraud Conceptualization: An Exploratory Study with Portuguese Consumers.

Food fraud refers to deceptive practices conducted for economic gain, and incidents of such fraud are often reported in the media and scientific literature. However, little is known about how European consumers perceive food fraud. To address this gap, a study explored Portuguese consumers' knowledge and perceptions of food fraud using qualitative methods such as free word association and semi-structured interviews. For this research, 340 participants were recruited, providing 911 valid words, classified into categories, major categories, and dimensions. Differences between consumers' previous exposure to food fraud and sociodemographic characteristics were explored. Additionally, other thirty-six participants were selected and interviewed, following a semi-structured guide. Interviews were transcribed, coded, and analyzed using a thematic analysis procedure. The results suggest that Portuguese consumers view food fraud as a morally reprehensible deception and are aware of its causes and impacts. However, not all consumers know the different forms of food fraud or the types of products vulnerable to fraud. Among the most repeated words were "deception", "expiration date", and "falsification". Despite this food fraud awareness, most consumers believed they were not exposed to food fraud and stated that they do not conduct daily practices to reduce exposure to it. Following the chi-square and Mann-Whitney tests, significant differences (p ≤ 0.05) were identified between participants exposed and not exposed to food fraud. The study also found that consumers with higher education and self-reported exposure to food fraud had a better understanding of the issue. This study provides insights for quantitative research on consumer perceptions and beliefs about food fraud to explore further vulnerable food categories and types of food fraud in real-world scenarios.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges.

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Parvalbumin Gene: A Valuable Marker for Pike Authentication and Allergen Risk Assessment.

Fish from the pike (Esox) genus are valued in gastronomy for their superior meat quality. However, they can cause allergic reactions in sensitive consumers. This work aimed to fill the gap in the detection of pike allergens using molecular-biological techniques. New, fast, and accurate loop-mediated isothermal amplification (LAMP) and real-time PCR (qPCR) assays were designed to detect pike DNA using the parvalbumin gene as a marker. LAMP was assessed by electrophoresis, SYBR green optical detection, and real-time fluorescence detection. The latter was the most sensitive, detecting as little as 0.78 ng of pike DNA; the qPCR detection limit was 0.1 ng. The LAMP analysis took 20-70 min, which is significantly faster than qPCR. The study provides reliable detection and quantification of the parvalbumin gene in both fresh and processed samples and further highlights the versatility of the use of the parvalbumin gene for the authentication of food products and consumer protection via refined allergen risk assessment that is independent of the type of tissue or food processing method used.

Current trends in retraction of plastic surgery and reconstruction research.

BACKGROUND: Various studies regarding retractions of publications have determined the rate of retraction has increased in recent years. Although this trend may apply to any field, there is a paucity of literature exploring the publication of erroneous studies within plastic and reconstructive surgery. The present study aims to identify trends in frequency and reasons for retraction of plastic and reconstructive surgery studies, with analysis of subspecialty and journals. METHODS: A database search was conducted for retracted papers within plastic and reconstructive surgery. The initial search yielded 2347 results, which were analyzed by two independent reviewers. 77 studies were jointly identified for data collection. RESULTS: The most common reasons for retractions were duplication (n = 20, 25.9 %), request of author (n = 15, 19.5 %), plagiarism (n = 9, 11.6 %), error (n = 9, 11.6 %), fraud (n = 2, 2.6 %), and conflict of interest (n = 1, 1.3 %). 15 were basic science studies (19.4 %), 58 were clinical science studies (75.3 %), and 4 were not categorized (5.2 %). Subspecialties of retracted papers were maxillofacial (n = 29, 37.7 %), reconstructive (n = 17, 22.0 %), wound healing (n = 8, 10.4 %), burn (n = 6, 7.8 %), esthetics (n = 5, 6.5 %), breast (n = 3, 3.9 %), and trauma (n = 1, 1.3 %). Mean impact factor was 2.9 and average time from publication to retraction was 32 months. CONCLUSION: Analysis of retracted plastic surgery studies revealed a recent rise in frequency of retractions, spanning a wide spectrum of journals and subspecialties.

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review.

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Radiology quick cash? Kickbacks, compliance, and consequences.

The Anti-Kickback Statute was passed by Congress in the 1970s to reduce the overuse of government-reimbursed medical services. It attempts to eliminate fraud, abuse, and waste of medical services by outlawing the incentive of personal gain when referring patients for government-funded services. Although safe harbors were written into the law to maintain transactions beneficial to society, they require strict adherence. Anti-Kickback Statute violations are subject to the whistleblower provision of the False Claims Act, and violations can yield significant civil and criminal penalties.

Death anxiety and older adults' vulnerability to fraud: the mediating role of materialism.

Death anxiety arousal, provoked by anticipating self-nonexistence, may be used as a fraud tactic by scammers on older adults; however, little is known about how it affects older adults' decision making when confronted with a scam and the mechanisms underlying these effects. This study used a questionnaire survey and experimental design to examine them. In Study 1, 307 older adults in China completed questionnaires. The results showed a positive link between death anxiety and vulnerability to fraud, partially mediated by materialism. In Study 2, 82 older adults in China were randomly assigned to the mortality salience group and control group to examine whether death anxiety arousal can increase older adults' vulnerability to fraud and the mediating role of materialism. The results indicated that death anxiety and materialism increase the risk of consumer products and services fraud; therefore, targeting these risk factors might protect older adults from fraud.

William Lawrence Tower's Beetles: Experimental Evolution and the Manipulation of Inheritance.

William Lawrence Tower's work on the evolution of the Colorado Potato Beetle (Leptinotarsa decemlineata), documenting the environmental induction of mutation and speciation, made him a leading figure in experimental genetics during the first decade of the 20th century. His research program served as a model for other experimental evolution studies seeking to demonstrate the environmental modification of inheritance. Tower enjoyed the support of influential figures in the field, despite well-known problems that plagued Tower's earlier academic career. The validity of his genetic work, and other findings reported by Tower, were later challenged. The Tower affair illustrates how questionable and possibly fraudulent scientific practices can be tolerated to explore certain experimental directions and theoretical frameworks, particularly at the frontier of expanding disciplines. When needed, those explorations can be forestalled or extinguished by exploiting conspicuous vulnerabilities of rogue practitioners. In Tower's case, both unrefuted allegations of scientific misconduct and personal problems dissolved his institutional support, leading to a swift ouster from academic science. Tower's downfall discredited soft inheritance and neo-Lamarckian conceptions in the field of experimental genetics, facilitating the discipline's embrace of a hard inheritance model that featured a hereditary material resistant to environmental modification.

Fingerprinting of Octane Rating Gasoline and its Contaminants Using Synchronous Fluorescence Spectroscopy.

Fuel fraud has proliferated due to underlying economic advantage in nearly every nation. For the purpose of detecting adulteration and providing real-time quality assurance, non-destructive oil analysis is crucial. This paper reports the simple approach for fingerprinting undiluted petroleum products including gasoline from various brands, diesel, and kerosene oil in comparison with organic solvents using synchronous fluorescence spectroscopy and hierarchical cluster analysis. Fluorescence-based successful detection of adulterated samples is demonstrated in imported RON 92 gasoline, synthetically adulterated with kerosene oil (KO) in proportions up to 70%. Compared to gasoline, kerosene oil has a lower relative poly aromatic hydrocarbons, as the amount of kerosene oil (KO) increases, the KO peak at 352 nm rises, but the gasoline's peak intensity decreases in the range of 371-500 nm. It is noteworthy that imported fuel grades RON 92 and RON 95 are comparable to each other and surprisingly clustered with RON 91 from the Attock refinery presenting concerns about quality. Similarly, the Shell website mentions that Shell V-Power is RON 99 but interestingly it clusters with retail fuel samples acquired from PSO filling stations and PSO RON 95 showing disagreement with the claim that the fuel is high octane. Another use for this technique in oil exploration was the detection of adulterants and successfully spotted methanol, ethanol, and kerosene oil in the tainted samples. These findings suggest SFS as an accurate, and low-cost testing tool for gasoline fingerprinting and contamination screening.

Data mining algorithm in the identification of accounting fraud by smart city information technology.

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

cor1 Gene: A Suitable Marker for Identification of Opium Poppy (Papaver somniferum L.).

This paper discusses the development of rapid, reliable, and accurate polymerase chain reaction (PCR) assays for detecting opium poppy (Papaver somniferum L.) in food. Endpoint, quantitative, and digital PCRs were compared based on the amplification of a newly developed DNA marker targeting the NADPH-dependent codeinone reductase (COR) gene. Designed assays were shown to be highly specific and sensitive in discriminating opium poppy from other plant species, even in heat-treated and food samples. Digital PCR was the most sensitive, with a detection limit of up to 5 copies, i.e., approximately 14 pg of target DNA per reaction. Quantitative and digital PCR further allowed the quantification of opium poppy in up to 1.5 ng and 42 pg (15 copies) of target DNA in a sample, respectively. In addition, two duplex PCRs have been developed for the simultaneous detection of opium poppy DNA and representatives of (i) the Papaveraceae family or (ii) the Plantae kingdom. Finally, all designed assays were successfully applied for analysis of 15 commercial foodstuffs; two were suspected of being adulterated. The study results have an important impact on addressing food fraud and ensuring the safety and authenticity of food products. Beyond food adulteration, the study may also have significant implications for forensics and law enforcement.

Health insurance fraud detection based on multi-channel heterogeneous graph structure learning.

Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.

Validation of the brief version of the Susceptibility to Persuasion-II scale and evaluation of measurement invariance across age and gender in the Canadian general population.

Measurement tools that can assess personality traits rendering people more susceptible to engagement with and compliance in scams can help identify at-risk populations. The brief, 30-item version of the Susceptibility to Persuasion-II (StP-II-B) scale is a recently developed instrument for assessing 10 personality traits that play a role in scam compliance; however, psychometric evidence supporting the use of this scale is limited. This study aimed to validate the StP-II-B by examining its internal consistency reliability, factor structure, as well as age- and gender-related measurement invariance with a sample of 1287 Canadians aged 16 years and older. Confirmatory factor analysis supported a 10-factor structure identified in previous research. Good internal consistency reliability was obtained for each of the 10 subscales. This 10-factor structure was found to be invariant across age and gender at configural, metric, and scalar levels, suggesting that the StP-II-B was conceptualized in the same way across age and gender and that meaningful comparisons of factor scores could be made. Age and gender differences were found in most factors, with younger individuals and men scoring higher than older individuals and women. This study supports the use of the StP-II-B as a valid and reliable scale for measuring personality traits associated with scam compliance in the Canadian general population and offers insights into age and gender cohorts that may be at higher risk of scam victimization.

Novel foods, old issues: Metabarcoding revealed mislabeling in insect-based products sold by e-commerce on the EU market.

Insects intended for human consumption are considered Novel Foods according to EU legislation. marketed in form of powders, bars, snacks are increasingly available on the EU market, especially on e-commerce. The commercial form and the way of distribution make IBPs particularly prone to mislabeling. Literature concerning the mislabeling occurrence in IBPs is extremely scarce. In this study, 46 processed IBPs were collected on nine EU e-commerce platforms (e-CO) to be authenticated by metabarcoding. A 200 bp region from 16S rRNA gene was used as molecular target. Sequencing data were processed using DADA2 R package, and sequences were taxonomically assigned through BLAST analysis against GenBank. Procedural blanks and positive controls were included in the analysis, and threshold values were established to filter the final data. The mislabeling rate (i. e. the mismatch between the species declared on the IBP label and the species identified by metabarcoding) was calculated. Overall, a high mislabeling rate (33.3 %) was observed, although this percentage is influenced by the e-CO platform and the insect species, with A. domesticus particularly involved. The use of species not listed in authorized Novel Food (e. g. Gryllus locorojo), and/or the partial replacement of high value species with lower value species was highlighted for the first time in processed IBPs. The presence of insect pests was also detected. Metabarcoding was confirmed as an effective tool for IBPs authentication. Also, outcomes from this study can provide useful data on the main issues involving the EU IBPs' market, that can represent an incentive to reinforce both official controls and FBO's self-controls on these poorly investigated products.

Enhancing fraud detection in banking by integration of graph databases with machine learning.

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.