Association of Victimization by Sex among Public Facing Bus and Subway Transit Workers, New York City.

Federal data indicate that assaults on transit workers resulting in fatalities or hospitalizations tripled between 2008 and 2022. The data indicated a peri-pandemic surge of assault-related fatalities and hospitalizations, but assaults with less dire outcomes were not recorded. In collaboration with the Transport Workers Union, Local 100, we conducted an online survey in late 2023 through early 2024 of New York City public-facing bus and subway workers that focused on their work experiences during the 2020-2023 period of the COVID-19 pandemic. Items for this analysis on victimization included measures of physical and sexual assault/harassment, verbal harassment/intimidation, theft, and demographic characteristics (e.g., sex, race, work division). We estimated separate modified Poisson models for each of the four outcomes, yielding prevalence ratios (PRs) and 95% confidence intervals (CIs). Potential interactions between variables with strong main effects in the adjusted model were further examined using product terms. Among 1297 respondents, 89.0% reported any victimization; respondents also reported physical assault (48.6%), sexual assault/harassment (6.3%), verbal harassment/intimidation (48.7%), and theft on the transit system (20.6%). Physical assault was significantly more common among women in the bus division compared to female subway workers, male bus workers, and male subway workers (adjusted PR (aPR) = 3.54; reference = male subway workers; Wald test p < .001). With the same reference group, sexual assault/harassment was more frequently reported among female subway workers (aPR = 5.15; Wald test, p < .001), but verbal assault/intimidation and experiencing theft were least common among women in the bus division (aPR = 0.22 and 0.13, respectively; Wald tests, p < .001). These data point to the need for greater attention to record and report on victimization against workers in both buses and subway.

Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks.

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy.

Research on Blockchain-Enabled Smart Grid for Anti-Theft Electricity Securing Peer-to-Peer Transactions in Modern Grids.

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower-upper decomposition (LUD), lower-upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods.

Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction.

The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation.

Trophy, souvenir, or simple theft? Taking items from the victim in sexual homicide.

Although most people have heard the terms 'souvenirs', 'trophies', and 'mementos', discussed in books and movies on the true crimes of sexual murderers, limited research has delved into the phenomenon of theft in sexual homicide (SH). Using a sample of 762 SH cases coming from the Sexual Homicide International Database, the current study examines the crime-commission process of the pre-crime, crime, and post-crime phases of sexual homicide offenders (SHOs) who engaged in theft during a SH. Additionally, this study seeks to determine if a specific type of SHO engages in this behaviour over others. Results from the sequential logistic regression indicate that victims who are 16 years or older, were strangers to the SHO, and were sex workers were more likely to be victims of theft. Additionally, results indicate that the presence of sadism made it more likely the SHO would engage in theft from the victim and/or crime scene. Findings suggest there is a group of SHOs who engage in theft not for monetary purposes but due to the paraphilia of the offender. These findings can inform the police investigation of these crimes.

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns.

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

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.

Healthcare provider data breaches - framework for crisis communication and support of patients and healthcare workers in mental healthcare.

Increasing numbers of healthcare data breaches highlight the need for structured organisational responses to protect patients, trainees and psychiatrists against identity theft and blackmail. Evidence-based guidance that is informed by the COVID-19 pandemic response includes: timely and reliable information tailored to users' safety, encouragement to take protective action, and access to practical and psychological support. For healthcare organisations which have suffered a data breach, insurance essentially improves access to funded cyber security responses, risk communication and public relations. Patients, trainees and psychiatrists need specific advice on protective measures. Healthcare data security legislative reform is urgently needed.

'I like you so . . . ': how transgressor and interviewer likeability and familiarity influence children's disclosures.

This study examined how children's age and their ratings of the likeability of a transgressor (E1) and an interviewer (E2) influenced their testimonies after witnessing a theft. Children (N = 152; ages 7-13 years) witnessed E1 steal $20 from a wallet. E1 then asked the children to lie and say that they did not take the money. Children were interviewed about their experience with E1 and completed two questionnaires about E1 and E2. Children who reported higher likeability scores with E1 were more likely to attempt to conceal the theft and more willing to keep it a secret. Children who reported higher likeability scores with E2 were more likely to indirectly disclose the theft. Age also played a role in children's ability to maintain their concealment. Results have important implications for professionals who interview children and suggest that more research is needed to examine ways to increase children's comfort with interviews/interviewers.

Trends and Sources of Crime Guns in California: 2010-2021.

Firearm-related interpersonal violence is a leading cause of death and injury in cities across the United States, and understanding the movement of firearms from on-the-books sales to criminal end-user is critical to the formulation of gun violence prevention policy. In this study, we assemble a unique dataset that combines records for over 380,000 crime guns recovered by law enforcement in California (2010-2021), and more than 126,000 guns reported stolen, linked to in-state legal handgun transactions (1996-2021), to describe local and statewide crime gun trends and investigate several potentially important sources of guns to criminals, including privately manufactured firearms (PMFs), theft, and "dirty" dealers. We document a dramatic increase over the decade in firearms recovered shortly after purchase (7% were recovered within a year in 2010, up to 33% in 2021). This corresponds with a substantial rise in handgun purchasing over the decade, suggesting some fraction of newly and legally acquired firearms are likely diverted from the legal market for criminal use. We document the rapid growth of PMFs over the past 2-3 years and find theft plays some, though possibly diminishing, role as a crime gun source. Finally, we find evidence that some retailers contribute disproportionately to the supply of crime guns, though there appear to be fewer problematic dealers now than there were a decade ago. Overall, our study points to temporal shifts in the dynamics of criminal firearms commerce as well as significant city variation in the channels by which criminals acquire crime guns.

Impact of alcohol home delivery and other methods of obtaining alcohol in young adults.

AIMS: To estimate the use of home alcohol delivery and other methods of obtaining alcohol, rates of ID checking for home alcohol delivery purchases, and associations with alcohol-related consequences. METHODS: Surveillance data from 784 lifetime drinkers participating in the 2022 Rhode Island Young Adult Survey were used. The method of obtaining alcohol (e.g. type of purchase, gifted, theft) was assessed. The Alcohol Use Disorders Identification Test, Brief Young Adults Alcohol Consequences Questionnaire, and a drinking and driving question were used to measure high-risk drinking behaviors, experiencing negative alcohol questions, and history of drinking and driving. Logistic regression models adjusting for sociodemographic variables were used to estimate main effects. RESULTS: About 7.4% of the sample purchased alcohol through a home delivery or to-go purchase; 12.1% of participants who obtained alcohol this way never had their ID checked during the purchase attempt, and 10.2% of these purchases were completed by participants under the legal purchase age. Home delivery/to-go purchases were associated with high-risk drinking. Alcohol theft was associated with high-risk drinking, experiencing negative alcohol consequences, and drinking and driving. CONCLUSIONS: Home alcohol delivery and to-go purchases may provide an opportunity for underage access to alcohol, but their current use as a method of obtaining alcohol is rare. Stronger ID checking policies are needed. Alcohol theft was linked to several negative alcohol outcomes, and home-based preventive interventions should be considered.

Hybrid CNN-Transformer Network for Electricity Theft Detection in Smart Grids.

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.

Intangible Identity Theft and Intimate Partner Violence.

While tougher domestic violence laws and protective orders are frequently credited with attenuating intimate partner violence (IPV), one unexplored explanation for this observed reduction is that intimate partner abusers are shifting their abusive behavior to intangible identity theft to thwart legal mechanisms traditionally used to deter IPV. Unlike the monetary motive associated with document identity theft, intangible identity theft is committed by someone with a preexisting grievance against the victim because the theft's primary purpose is to tarnish the victim's reputation. Results from a multilevel analysis show that a woman has a lower probability of being a victim of an intimate rather than nonintimate partner crime in cities with a higher intangible identity theft rate. Such a finding suggests that intangible identity theft may be a form of intimate partner abuse with few adverse consequences for offenders because identity thieves are rarely arrested and prosecuted. Nevertheless, the current study is only preliminary. Further research is needed before our findings and conclusions can be universally accepted.

Technology-facilitated abuse among Americans age 50 and older: a latent class analysis.

Technology-facilitated abuse (TFA) is of growing concern and is a form of elder abuse. There is limited TFA research in general population samples in the U.S. among older adults. Researchers conducted a survey of behavior-based forms of TFA experiences in a nationally representative sample of n = 1,011 U.S. adults ages 50 and older. Within this sample, 63.8% of respondents reported some experience of TFA during their lifetime. Latent class analyses were applied to understand the pattern of older adults' exposure to ten different forms of TFA resulting in three classes distinguished by the number of different TFA types experienced: low TFA (55%), low-mid TFA (40%) and high TFA (5%). Socio-economic characteristics associated with these TFA profiles, as well as perpetrator relationship, post-TFA behaviors, and resulting harms associated with the TFA experiences, were examined to inform research, prevention, and intervention activities. Attention across different sectors to TFA among older adults is needed.

The Organised Theft of Medicines: a Study of the Methods for Stealing and Reselling Medicines and Medical Devices in the EU and Beyond.

The theft of medicines is a significant component of the illicit trade in pharmaceutical products. Besides small-scale thefts committed for personal usage, organised criminal networks are increasingly targeting high-priced medical products, either to reintroduce them into the legal supply chain or sell them on the black market. This crime has considerable implications that extend beyond the value of the stolen goods, including harmful impacts on citizens' health, legitimate companies, and national health systems. However, knowledge on organised theft of medicines remains limited. This paper employs a crime script analysis approach, based on interviews with relevant stakeholders and case studies retrieved across European countries, to examine the most common modi operandi in the organised theft of medicines and medical devices. Potential policy implications are also discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s10610-023-09546-w.

Bias in Stroke Evaluation: Rethinking the Cookie Theft Picture.

Despite a current emphasis on equity in stroke care, one of the most common stroke assessment tools that is used both nationally and internationally, includes an anachronistic image that projects cultural, linguistic, and socioeconomic bias. This image, titled The Cookie Theft picture, is included in the National Institutes of Health Stroke Scale and was originally developed in 1972. Now, 50 years later, it does not reflect our current diverse, linguistically rich, and multicultural patient population.

Wage theft and life expectancy inequities in the United States: A simulation study.

Wage theft - employers not paying workers their legally entitled wages and benefits - costs workers billions of dollars annually. We tested whether preventing wage theft could increase U.S. life expectancy and decrease inequities therein. We obtained nationally representative estimates of the 2001-2014 association between income and expected age at death for 40-year-olds (40 plus life expectancy at age 40) compiled from tax and Social Security Administration records, and estimates of the burden of wage theft from several sources, including estimates regarding minimum-wage violations (not paying workers the minimum wage) developed from Current Population Survey data. After modeling the relationship between income and expected age at death, we simulated the effects of scenarios preventing wage theft on mean expected age at death, assuming a causal effect of income on expected age at death. We simulated several scenarios, including one using data suggesting minimum-wage violations constituted 38% of all wage theft and caused 58% of affected workers' losses. Among women in the lowest income decile, mean expected age at death was 0.17 years longer in the counterfactual scenario than observed (95% confidence interval [CI]: 0.11-0.22), corresponding to 528,685 (95% CI: 346,018-711,353) years extended in the total 2001-2014 age-40 population. Among men in the lowest decile, the estimates were 0.12 (95% CI: 0.07-0.17) and 380,502 (95% CI: 229,630-531,374). Moreover, among women, mean expected age at death in the counterfactual scenario increased 0.16 (95% CI: 0.06-0.27) years more among the lowest decile than among the highest decile; among men, the estimate was 0.12 (95% CI: 0.03-0.21).

How cunning is the puppet-master? Cestode-infected fish appear generally fearless.

Trophically transmitted parasites have life cycles that require the infected host to be eaten by the correct type of predator. Such parasites should benefit from an ability to suppress the host's fear of predators, but if the manipulation is imprecise the consequence may be increased predation by non-hosts, to the detriment of the parasite. Three-spined sticklebacks (Gasterosteus aculeatus) infected by the cestode Schistocephalus solidus express reduced antipredator behaviours, but it is unknown whether this is an example of a highly precise manipulation, a more general manipulation, or if it can even be attributed to mere side effects of disease. In a series of experiments, we investigated several behaviours of infected and uninfected sticklebacks. As expected, they had weak responses to simulated predatory attacks compared to uninfected fish. However, our results suggest that the parasite induced a general fearlessness, rather than a precise manipulation aimed at the correct predators (birds). Infected fish had reduced responses also when attacked from the side and when exposed to odour from a fish predator, which is a "dead-end" for this parasite. We also tested whether the reduced anti-predator behaviours were mere symptoms of a decreased overall vigour, or due to parasite-induced hunger, but we found no support for these ideas. We propose that even imprecise manipulations of anti-predator behaviours may benefit parasites, for example, if other behaviours are altered in a way that increases the exposure to the correct predator.

GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection.

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU-BiLSTM Model with Feature Engineering-Based Preprocessing.

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples' nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model's efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.