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.

Illegal synthetic dyes in spices: a Singapore case study.

Some synthetic dyes are fraudulently added into spices to appeal visually to consumers. Food regulations in several countries, including the United States, Australia, Japan and the European Union, strictly prohibit the use of unauthorised synthetic dyes in food. Nevertheless, illegal practices persist, where spices contaminated with potentially carcinogenic dyes have been documented, posing potential health risks to consumers. In the present study, 14 synthetic dyes were investigated through liquid chromatography/tandem mass spectrometry in 252 commercially available spices in the Singapore market. In 18 out of these (7.1%) at least 1 illegal dye was detected at concentrations ranging from 0.010 to 114 mg/kg. Besides potential health risks, presence of these adulterants also reflects the economic motivations behind their fraudulent use. Findings in the present study further emphasise the need for increased public awareness, stricter enforcement, and continuous monitoring of illegal synthetic dyes in spices to ensure Singapore's food safety.

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.

Insider Threats to the Military Health System: A Systematic Background Check of TRICARE West Providers.

Background: To address the pandemic, the Defense Health Agency (DHA) expanded its TRICARE civilian provider network by 30.1%. In 2022, the DHA Annual Report stated that TRICARE's provider directories were only 80% accurate. Unlike Medicare, the DHA does not publicly reveal National Provider Identification (NPI) numbers. As a result, TRICARE's 9.6 million beneficiaries lack the means to verify their doctor's credentials. Since 2013, the Department of Health and Human Services' (HHS) Office of Inspector General (OIG) has excluded 17,706 physicians and other providers from federal health programs due to billing fraud, neglect, drug-related convictions, and other offenses. These providers and their NPIs are included on the OIG's List of Excluded Individuals and Entities (LEIE). Patients who receive care from excluded providers face higher risks of hospitalization and mortality. Objective: We sought to assess the extent to which TRICARE screens health care provider names on their referral website against criminal databases. Methods: Between January 1-31, 2023, we used TRICARE West's provider directory to search for all providers within a 5-mile radius of 798 zip codes (38 per state, ≥10,000 residents each, randomly entered). We then copied and pasted all directory results' first and last names, business names, addresses, phone numbers, fax numbers, degree types, practice specialties, and active or closed statuses into a CSV file. We cross-referenced the search results against US and state databases for medical and criminal misconduct, including the OIG-LEIE and General Services Administration's (GSA) SAM.gov exclusion lists, the HHS Office of Civil Rights Health Insurance Portability and Accountability Act (HIPAA) breach reports, 15 available state Medicaid exclusion lists (state), the International Trade Administration's Consolidated Screening List (CSL), 3 Food and Drug Administration (FDA) debarment lists, the Federal Bureau of Investigation's (FBI) list of January 6 federal defendants, and the OIG-HHS list of fugitives (FUG). Results: Our provider search yielded 111,619 raw results; 54 zip codes contained no data. After removing 72,156 (64.65%) duplicate entries, closed offices, and non-TRICARE West locations, we identified 39,463 active provider names. Within this baseline sample group, there were 2398 (6.08%) total matches against all exclusion and sanction databases, including 2197 on the OIG-LEIE, 2311 on the GSA-SAM.gov list, 2 on the HIPAA list, 54 on the state Medicaid exclusion lists, 69 on the CSL, 3 on the FDA lists, 53 on the FBI list, and 10 on the FUG. Conclusions: TRICARE's civilian provider roster merits further scrutiny by law enforcement. Following the National Institute of Standards and Technology 800, the DHA can mitigate privacy, safety, and security clearance threats by implementing an insider threat management model, robust enforcement of the False Claims Act, and mandatory security risk assessments. These are the views of the author, not the Department of Defense or the US government.

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.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys.

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Induced erotomania by online romance fraud - a novel form of de Clérambault's syndrome.

BACKGROUND: Although the impact of internet usage on mental health is extensively documented, there is a notable scarcity of reports in the literature concerning internet-induced erotomania. Erotomania is a rare and likely underdiagnosed delusional disorder. It is characterized by an irrational belief held by the affected persons that someone of higher socioeconomic status harbor romantic feelings toward them. Here, we describe the psychopathology of erotomanic delusion induced by online romantic fraud in a female patient. Employing this case as a focal point, we illuminate novel aspects of erotomania that warrant attention and examination. CASE PRESENTATION: We present a compelling case involving a 70-year-old married Caucasian woman diagnosed with medically controlled persistent depressive disorder for several years. The intricacies of her condition became evident as she became deeply engrossed in online profiles featuring the image of a renowned musician, inadvertently falling victim to an online romantic fraud. Subsequently, this distressing experience triggered the emergence of erotomanic delusions and a suicide attempt. The patient's history reveals an array of medical conditions and stressful life events, contributing to her vulnerability. The diagnosis of erotomanic delusional disorder, dysthymia, and mild cognitive impairment with cerebral vascular background was established. Treatment involved her previous antidepressant with low-dose risperidone, alongside supportive individual and group therapy. Her delusion showed remission four weeks later, prompting her discharge for outpatient follow-up. Although she retained some false beliefs, the intensity of the symptoms had notably diminished and her functionality improved. CONCLUSION: This case underscores the complex interplay between mental health, online activities, and the consequences of delusions, including suicidal thoughts, shedding light on the need for a comprehensive approach in addressing such challenging psychiatric scenarios.

Integrated DNA barcoding methods to identify species in the processed fish products from Chinese market.

DNA-based methods are reliable for a precise identification of species in processed products. In this study, we assessed five typical DNA extraction methods from multiple aspects. Full-length and mini-length DNA barcoding were performed to detect the species substitution and mislabeling of 305 processed fish products from the Chinese market covering six processed fish products. The salt extraction method that exhibited the best overall performance was applied. All samples were successfully extracted; however, only 19.3 % of samples could be amplified using the full-DNA barcode primer set, and 90.2 % of samples could be amplified using the newly designed mini-DNA barcode primer sets (401 and 320 bp). Overall, the molecular identification results revealed that 36.4 % (111/305) of the samples were inconsistent with the labels, with commercial fraud observed in all six types of processed fish products. The survey findings provide technical references for effective fish authentication monitoring, offering insights into the seafood safety in markets.

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.

How widespread use of generative AI for images and video can affect the environment and the science of ecology.

Generative artificial intelligence (AI) models will have broad impacts on society including the scientific enterprise; ecology and environmental science will be no exception. Here, we discuss the potential opportunities and risks of advanced generative AI for visual material (images and video) for the science of ecology and the environment itself. There are clearly opportunities for positive impacts, related to improved communication, for example; we also see possibilities for ecological research to benefit from generative AI (e.g., image gap filling, biodiversity surveys, and improved citizen science). However, there are also risks, threatening to undermine the credibility of our science, mostly related to actions of bad actors, for example in terms of spreading fake information or committing fraud. Risks need to be mitigated at the level of government regulatory measures, but we also highlight what can be done right now, including discussing issues with the next generation of ecologists and transforming towards radically open science workflows.

Improved organic and pesticide-free rice (Oryza sativa L.) authentication based on multiple stable isotope ratio analysis and rice milling state.

This study looked at the application of multiple bulk stable isotope ratio analysis to accurately authenticate organic rice and counteract organic fraud within the expanding global organic market. Variations of δ13C, δ15N, δ18O, and δ34S in organic, pesticide-free, and conventional rice were assessed across different milling states (brown, milled, and bran). Individual stable isotope ratio alone such as δ15N demonstrated limited capacity to correctly differentiate organic, pesticide-free, and conventional rice. A support vector machine model-incorporating δ13C, δ15N, δ18O, and δ34S in milled rice-yielded overall predictability (95%) in distinguishing organic, pesticide-free, and conventional rice, where δ18O emerged as the pivotal variable based on the feature weights in the SVM model. These findings suggest the potential of multi-isotope and advanced statistical approaches in combating organic fraud and ensuring authenticity in the food supply chain.

Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders.

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Anticounterfeiting and Fraud Mitigation Solutions for High-value Food Products.

Globalization and the increasing complexity of supply chains have allowed food fraud to expand to a great extent. Some of the most serious effects of these deceitful activities are damage to a brand's reputation and trust, economic losses, and public health risks. The usual victims of food fraud are dairy, meat, fish, and seafood products, as well as fats/oils and alcoholic drinks. The purpose of this review paper is to present an updated analysis of the currently available anticounterfeit technologies and their application to the four most fraud-affected food supply chains. An assessment that was conducted to determine when the adoption of a combination of technologies could enhance food safety and brand protection is also provided. The obtained results indicate that electronic and data-driven technologies (RFID devices and digital traceability systems) are still in their infancy in the food sectors that are subjected the most to fraudulent activities. Research is necessary to develop innovative digital and physical technologies to "outsmart" such fraudsters and to prevent their illicit actions in the food sector.

Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis.

In addressing the generalization issue faced by data-driven methods in food origin traceability, especially when encountering diverse input variable sets, such as elemental contents (C, N, S), stable isotopes (C, N, S, H and O) and 43 elements measured under varying laboratory conditions. We introduce an innovative, versatile deep learning-based framework incorporating explainable analysis, adept at determining feature importance through learned neuron weights. Our proposed framework, validated using three rice sample batches from four Asian countries, totaling 354 instances, exhibited exceptional identification accuracy of up to 97%, surpassing traditional reference methods like decision tree and support vector machine. The adaptable methodological system accommodates various combinations of traceability indicators, facilitating seamless replication and extensive applicability. This groundbreaking solution effectively tackles generalization challenges arising from disparate variable sets across distinct data batches, paving the way for enhanced food origin traceability in real-world applications.

The court's judgment on the true identity of the author responsible for identity fraud and the consequent batch retraction of Iranian's papers in the journal of diagnostic pathology.

This letter concerns retracted papers published in the Journal of Diagnostic Pathology, where my name was misused as the author or corresponding author without my permission or knowledge. Considering that all misconducts were directed by an author during initial manuscripts' submissions, I opened a case in Iran's Cyber Police (FATA) to unravel the true identity of the submitting author. After Cyber Police's report revealed the true identity of the submitting author, the court started a thorough investigation and finally convicted the submitting author for identity fraud and data forgery through creating and using fake email addresses.

Commentary: Fifty years exploring pharmacology with Sam Enna.

The passing of Sam Enna in June of 2023 is major loss to the world of pharmacology. While best known for his extensive research activities in the area of γ-aminobutyric acid (GABA) pharmacology, Sam devoted much of his professional time to teaching and as an Editor in Chief for the legacy journals - the Journal of Pharmacology and Experimental Therapeutics (JPET - 1998-2003); Pharmacology & Therapeutics (P & T - 2003-2023) and Biochemical Pharmacology (BCP -2003-2023) - increasing the volume of submissions for all three journals and their Impact Factors while decreasing the time for peer review and publication. Sam was a well-respected consultant in the CNS area for the biopharmaceutical industry and served as Secretary General and President of the International Union of Basic and Clinical Pharmacology where his efforts were focused on sustaining research integrity, particularly in the areas of data reproducibility and fraud. This Commentary provides a personal overview of Sam's 50-year career in pharmacology and briefly updates topics that were of keen interest to Sam including: developments on the continuing reproducibility crisis where systematic fraud continues to proliferate now reaching industrial scale proportions, aided and abetted by paper mills, AI and the erosion of meritocratic norms; and the fall and rise of CNS drug discovery.

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection.

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model's performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.

A soft voting ensemble learning approach for credit card fraud detection.

With the advancement of e-commerce and modern technological development, credit cards are widely used for both online and offline purchases, which has increased the number of daily fraudulent transactions. Many organizations and financial institutions worldwide lose billions of dollars annually because of credit card fraud. Due to the global distribution of both legitimate and fraudulent transactions, it is difficult to discern between the two. Furthermore, because only a small proportion of transactions are fraudulent, there is a problem of class imbalance. Hence, an effective fraud-detection methodology is required to sustain the reliability of the payment system. Machine learning has recently emerged as a viable substitute for identifying this type of fraud. However, ML approaches have difficulty identifying fraud with high prediction accuracy, while also decreasing misclassification costs due to the size of the imbalanced data. In this research, a soft voting ensemble learning approach for detecting credit card fraud on imbalanced data is proposed. To do this, the proposed approach is evaluated and compared with numerous sophisticated sampling techniques (i.e., oversampling, undersampling, and hybrid sampling) to overcome the class imbalance problem. We develop several credit card fraud classifiers, including ensemble classifiers, with and without sampling techniques. According to the experimental results, the proposed soft-voting approach outperforms individual classifiers. With a false negative rate (FNR) of 0.0306, it achieves a precision of 0.9870, recall of 0.9694, f1-score of 0.8764, and AUROC of 0.9936.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae.

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

AI, doping and ethics: On why increasing the effectiveness of detecting doping fraud in sport may be morally wrong.

In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The first argument in favour of this conclusion is that using AI to make a non-ideal antidoping policy even more effective can be morally wrong. Whether the increased effectiveness is morally wrong depends on whether you believe that the current antidoping system administrated by the World Anti-Doping Agency is already morally wrong. The second argument is based on the possibility of scenarios in which a more effective AI system may be morally worse than a less effective but non-AI system. We cannot, of course, conclude that the increased effectiveness of doping detection is always morally wrong. But our point is that whether the introduction of AI to increase detection of doping fraud is a moral improvement depends on the moral plausibility of the current system and the distribution of harm that will follow from false positive and false negative errors.