The ecology of lichenicolous lichens: a case-study in Italy.

This paper, with Italy as a case-study, provides a general overview on the ecology of lichenicolous lichens, i.e. those which start their life-cycle on the thallus of other lichens. It aims at testing whether some ecological factors do exert a positive selective pressure on the lichenicolous lifestyle. The incidence of some biological traits (photobionts, growth-forms and reproductive strategies) in lichenicolous and non-lichenicolous lichens was compared, on a set of 3005 infrageneric taxa potentially occurring in Italy, 189 of which are lichenicolous. Lichenicolous lichens have a much higher incidence of coccoid (non-trentepohlioid) green algae, crustose growth-forms and sexual reproduction. A matrix of the 2762 species with phycobionts and some main ecological descriptors was subjected to ordination. Lichenicolous lichens occupy a well-defined portion of the ecological space, tending to grow on rocks in dry, well-lit habitats where a germinating spore is likely to have a short life-span, at all altitudes. This corroborates the hypothesis that at least some of them are not true "parasites", as they are often called, but gather the photobionts - which have already adapted to local ecological conditions - from their hosts, eventually developing an independent thallus.

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.

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.

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.

Reducing Employees' Time Theft through Leader's Developmental Feedback: The Serial Multiple Mediating Effects of Perceived Insider Status and Work Passion.

Time theft, especially with the shift to remote work during the pandemic, is an increasing challenge for organizations. Existing studies demonstrate that both authoritarian leadership and laissez-faire leadership can exacerbate time theft, putting leaders in a behavioral dilemma of neither being strict nor lenient. Additionally, the pervasive and covert nature of time theft diminishes the effectiveness of subsequent corrective actions. Our study aims to investigate how to prevent time theft by mitigating employees' inclinations. Based on role theory, our study examines whether supervisor developmental feedback can encourage employees to perform work roles more appropriately. To uncover the complicated internalization process of role expectation, our study incorporates perceived insider status and work passion as serial mediators and considers the boundary effect of leaders' word-deed consistency. In Study 1, a survey of 402 employees revealed that supervisor developmental feedback can negatively predict employee time theft through employees' perceived insider status and work passion. Study 2 employs the same sample to further identify three topics of supervisor developmental feedback: skill learning, attitude learning, and social learning. Moreover, serial multiple mediating effects are affirmed across topics. The findings suggest that providing feedback on employees' learning and growth is an effective approach to prevent time theft.

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.

Condición de discapacidad y victimización por robo en pobladores de Perú / Disability status and robbery victimization in Peruvian villager

Fundamentos: alrededor del 15% de la población mundial tiene algún grado de discapacidad. La violencia y el crimen afectan primordialmente a la región de américa latina, especialmente a perú. El objetivo de este estudio fue determinar la asociación entre la condición de discapacidad y la victimización por robo en pobladores peruanos durante 2017. Métodos: se realizó un estudio transversal de análisis secundario de datos de la encuesta nacional especializada sobre victimi-zación (enevic) 2017. La variable independiente fue la condición de discapacidad y la variable dependiente fue la victimización por robo; además, se incluyeron variables de confusión. Para demostrar la asociación se realizó una regresión de poisson y se calcularon razones de prevalencia (rp) con sus intervalos de confianza al 95% (ic95%).resultados: se incluyeron los registros de 32.199 peruanos de dieciocho o más años. Las personas con discapacidad tuvieron un 24% menos probabilidad de ser víctimas de robo que las personas sin discapacidad (rp=0,76; ic95%: 0,61-0,95), ajustado por las variables de confusión. Sin embargo, esta asociación solo fue estadísticamente significativa en las mujeres, adultos mayores y en el estrato socioeconómico alto. Conclusiones: en perú, las personas con discapacidad tienen menor probabilidad de ser víctimas de robo que las personas sin discapacidad, aunque solamente si son mujeres, adultos mayores y provienen de un nivel socioeconómico alto. En los demás grupos poblacionales, las probabilidades de sufrir de este hecho de victimización serían semejantes entre las personas con y sin discapacida.(AU)

Background: about 15% of the world’s population has some degree of disability. Violence and crime primarily affect the latin american region, especially peru. This study aimed to determine the association between disability status and robbery victimization in peruvian villagers in 2017. Methods: a cross-sectional study of secondary data analysis from the national specialized victimization survey (enevic) 2017 was conducted. The independent variable was disability status, and the dependent variable was robbery victimization; in addition, confounding variables were included. Poisson regression was performed to demonstrate the association, and prevalence ratios (pr) with their 95% confidence intervals (95%ci) were calculated.results: records of 32,199 peruvians aged 18 years or older were included. People with disabilities were 24% less likely to be rob-bery victims than people without disabilities (pr=0.76; 95%ci: 0.61-0.95), adjusted for confounding variables. However, this association was only statistically significant in women, older adults, and the high socioeconomic stratum. Conclusions: in peru, people with disabilities are less likely to be robbery victims than people without disabilities. However, only if they are women, older adults, and come from a high socioeconomic level. In the other population groups, the probabilities of suffering this victimization would be similar between people with and without disabilities.(AU)

[Disability status and robbery victimization in Peruvian villagers]. / Condición de discapacidad y victimización por robo en pobladores de Perú.

OBJECTIVE: About 15% of the world's population has some degree of disability. Violence and crime primarily affect the Latin American region, especially Peru. This study aimed to determine the association between disability status and robbery victimization in Peruvian villagers in 2017. METHODS: A cross-sectional study of secondary data analysis from the National Specialized Victimization Survey (ENEVIC) 2017 was conducted. The independent variable was disability status, and the dependent variable was robbery victimization; in addition, confounding variables were included. Poisson regression was performed to demonstrate the association, and prevalence ratios (PR) with their 95% confidence intervals (95%CI) were calculated. RESULTS: Records of 32,199 Peruvians aged 18 years or older were included. People with disabilities were 24% less likely to be robbery victims than people without disabilities (PR=0.76; 95%CI: 0.61-0.95), adjusted for confounding variables. However, this association was only statistically significant in women, older adults, and the high socioeconomic stratum. CONCLUSIONS: In Peru, people with disabilities are less likely to be robbery victims than people without disabilities. However, only if they are women, older adults, and come from a high socioeconomic level. In the other population groups, the probabilities of suffering this victimization would be similar between people with and without disabilities.

OBJECTIVE: Alrededor del 15% de la población mundial tiene algún grado de discapacidad. La violencia y el crimen afectan primordialmente a la región de América Latina, especialmente a Perú. El objetivo de este estudio fue determinar la asociación entre la condición de discapacidad y la victimización por robo en pobladores peruanos durante 2017. METHODS: Se realizó un estudio transversal de análisis secundario de datos de la Encuesta Nacional Especializada sobre Victimización (ENEVIC) 2017. La variable independiente fue la condición de discapacidad y la variable dependiente fue la victimización por robo; además, se incluyeron variables de confusión. Para demostrar la asociación se realizó una regresión de Poisson y se calcularon razones de prevalencia (RP) con sus intervalos de confianza al 95% (IC95%). RESULTS: Se incluyeron los registros de 32.199 peruanos de dieciocho o más años. Las personas con discapacidad tuvieron un 24% menos probabilidad de ser víctimas de robo que las personas sin discapacidad (RP=0,76; IC95%: 0,61-0,95), ajustado por las variables de confusión. Sin embargo, esta asociación solo fue estadísticamente significativa en las mujeres, adultos mayores y en el estrato socioeconómico alto. CONCLUSIONS: En Perú, las personas con discapacidad tienen menor probabilidad de ser víctimas de robo que las personas sin discapacidad, aunque solamente si son mujeres, adultos mayores y provienen de un nivel socioeconómico alto. En los demás grupos poblacionales, las probabilidades de sufrir de este hecho de victimización serían semejantes entre las personas con y sin discapacidad.

RNN-BiLSTM-CRF based amalgamated deep learning model for electricity theft detection to secure smart grids.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

Purchaser, firearm, and retailer characteristics associated with crime gun recovery: a longitudinal analysis of firearms sold in California from 1996 to 2021.

BACKGROUND: Firearm violence is a major cause of death and injury in the United States. Tracking the movement of firearms from legal purchase to use in crimes can help inform prevention of firearm injuries and deaths. The last state-wide studies analyzing crime gun recoveries used data from over 20 years ago; thus, an update is needed. METHODS: We used data for 5,247,348 handgun and 2,868,713 long gun transactions and law enforcement recoveries from California crime gun recovery (2010-2021) and California's Dealer Records of Sales records. Covariates included characteristics of dealership sales, firearms and their transactions, and purchaser's demographic characteristics, purchasing history, criminal history (from firearm purchaser criminal history records), and neighborhood socioeconomic status. Analyses for handguns and long guns was conducted separately. In multivariable analysis, we included correlates into a Cox proportional hazard model accounting for left truncation and clustering between the same firearm, purchaser, dealerships, and geographic location. Covariates that remained significant (P < 0.05) were retained. For handguns, we evaluated associations of violent and weapons crimes separately. In supplementary analyses, we examined interactions by purchasers' race and ethnicity. RESULTS: In total, 38,441 handguns (0.80%) and 6,806 long guns (0.24%) were recovered in crimes. A firearm dealer's sales volume, percent of transactions that were denials, pawns, pawn redemptions, and firearms that became crime guns were each positively associated with firearm recovery in crime. Handguns that were inexpensive, larger caliber, and that had been reported lost or stolen were positively associated with recovery in crimes. Purchaser characteristics associated with crime gun recovery included: being younger, female, Black, Hispanic, Native American or Pacific Islander, or other race/ethnicity (vs white), having previous arrests, living in close proximity to the firearm dealership, and living in a more socially vulnerable census tract. Associations with race and ethnicity were modified by previous infraction-only arrests. CONCLUSIONS: This study confirms that many previously studied correlates of firearm recovery are still relevant today. We were able to expand on previous research by examining novel associations including purchasers' criminal history and previous firearm transaction history. These results provide evidence that can be used to disrupt firearm use in 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.

Identity Theft, Deep Brain Stimulation, and the Primacy of Post-trial Obligations.

Patient narratives from two investigational deep brain stimulation trials for traumatic brain injury and obsessive-compulsive disorder reveal that injury and illness rob individuals of personal identity and that neuromodulation can restore it. The early success of these interventions makes a compelling case for continued post-trial access to these technologies. Given the centrality of personal identity to respect for persons, a failure to provide continued access can be understood to represent a metaphorical identity theft. Such a loss recapitulates the pain of an individual's initial injury or illness and becomes especially tragic because it could be prevented by robust policy. A failure to fulfill this normative obligation constitutes a breach of disability law, which would view post-trial access as a means to achieve social reintegration through this neurotechnological accommodation.

A case of suspected chimpanzee scavenging in the Issa Valley, Tanzania.

Like humans, chimpanzees (Pan troglodytes) are well known for their vertebrate and invertebrate hunting, but they rarely scavenge. In contrast, while hunting and meat consumption became increasingly important during the evolution of the genus Homo, scavenging meat and marrow from carcasses of large mammals was also likely to be an important component of their subsistence strategies. Here, we describe a confrontational scavenging interaction between an adult male chimpanzee from the Issa Valley and a crowned eagle (Stephanoaetus coronatus), which resulted in the chimpanzee capturing and consuming the carcass of a juvenile bushbuck (Tragelaphus scriptus). We describe the interaction and contextualize this with previous scavenging observations from chimpanzees.

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.

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.

A novel data balancing approach and a deep fractal network with light gradient boosting approach for theft detection in smart grids.

Electricity theft is the largest type of non-technical losses faced by power utilities around the globe. It not only raises revenue losses to the utilities but also leads to lethal fires and electric shocks at distribution side. In the past, field operation groups were sent by the utilities to conduct inspections of suspicions electric equipments stated by the public. Advanced metering infrastructure based recent development in the smart grids makes it easy to detect electricity thefts. However, the conventional supervised learning techniques have low theft detection performance mainly due to imbalance datasets available for training. Therefore, in this paper, we develop a novel theft detection model with twofold contribution. A unique hybrid sampling technique named as hybrid oversampling and undersampling using both classes (HOUBC) is proposed to balance the dataset. HOUBC first performs undersampling and then oversampling using both the majority (normal) and minority (theft) classes. A new deep learning method, fractal network is applied with light gradient boosting method to extract and learn important characteristics from electricity consumption profiles for identifying electricity thieves. The proposed model relies on smart meter's data for theft detection and hence, a rapid and widespread adaption of this model is feasible, which shows its main advantage. The performance of the model is evaluated with real-world smart meter's data, i.e., state grid corporation of China. Comprehensive simulation results describe the effectiveness of the proposed model against conventional schemes in terms of electricity theft detection.

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.

'Eye-Tracking' with Words for Alzheimer's Disease Detection: Time Alignment of Words Enunciation with Image Regions During Image Description Tasks.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in cognitive decline, dementia, and eventually death. Diagnosing early signs of AD can help clinicians to improve the quality of life. OBJECTIVE: We developed a non-invasive approach to help neurologists and clinicians to distinguish probable AD patients and healthy controls (HC). METHODS: The patients' gaze points were followed based on the words they used to describe the Cookie Theft (CT) picture description task. We hypothesized that the timing of words enunciation aligns with the participant's eye movements. The moments that each word was spoken were then aligned with specific regions of the image. We then applied machine learning algorithms to classify probable AD and HC. We randomly selected 60 participants (30 AD and 30 HC) from the Dementia Bank (Pitt Corpus). RESULTS: Five main classifiers were applied to different features extracted from the recorded audio and participants' transcripts (AD and HC). Support vector machine and logistic regression had the highest accuracy (up to 80% and 78.33%, respectively) in three different experiments. CONCLUSIONS: In conclusion, point-of-gaze can be applied as a non-invasive and less expensive approach compared to other available methods (e.g., eye tracker devices) for early-stage AD diagnosis.

Factores sociodemográficos en la dinámica del comportamiento delictivo: análisis descriptivo de criminalidad en Colombia, año 2022 / Socio-demographic factors in the dynamics of criminal behaviour: descriptive analysis of criminality in Colombia, year 2022 / Fatores sociodemográficos na dinâmica do comportamento criminal: análise descritiva da criminalidade na Colômbia, ano de 2022

El presente estudio de carácter descriptivo y analítico tiene como objetivo principal presentar el comportamiento criminal en Colombia para el 2022, desde un enfoque cuantitativo empleado para la extracción, análisis e interpretación de los registros administrativos del Sistema de Información Estadístico, Delincuencial, Contravencional y Operativo (SIEDCO), constituyéndose como un insumo para aquellos interesados en el estudio de la dinámica criminal, así como para quienes se encargan de diseñar estrategias para la contención del delito y la generación de política pública en materia de seguridad. En este sentido y en el marco de las dinámicas sociodemográficas, en una primera parte se aborda de manera general el proceso de homogenización de los registros administrativos llevado a cabo por la Policía Nacional y la Fiscalía General de la Nación. Y en una segunda parte, con especial énfasis en el homicidio intencional, se presenta el análisis de la información que permitió identificar las principales variables que influyen en la comisión del delito, de acuerdo con las cifras contenidas en el SIEDCO, en el periodo comprendido entre el 1 de enero y el 31 de diciembre de 2022, comparado con la misma temporalidad del 2021, en el que se detallan los delitos que afectan la integridad personal y el patrimonio económico de quienes habitan el territorio colombiano; se hallaron incrementos considerables en estos y se resaltan los factores de oportunidad para su comisión, situación contraria a la que se evidenció sobre las afectaciones a la vida y la integridad, conjunto de conductas que, según lo registrado, decrecieron en el periodo analizado. Finalmente, se ofrece un aporte a la contención desde la actividad de policía y una serie de conclusiones que permitan ampliar la visión sobre los diversos fenómenos y enriquecer la generación de conocimiento en el campo de la criminología.

The main objective of this descriptive and analytical study is to present criminal behaviour in Colombia for 2022, from a quantitative approach used for the extraction, analysis and interpretation of the administrative records of the Statistical, Criminal, Contraventional and Operational Information System (SIEDCO), constituting an input for those interested in the study of criminal dynamics, as well as for those responsible for designing strategies for the containment of crime and the generation of public policy on security. In this sense, and within the framework of socio-demographic dynamics, the first part of the paper deals in a general way with the process of homogenisation of administrative records carried out by the National Police and the Attorney General's Office. The second part, with special emphasis on intentional homicide, presents the analysis of the information that made it possible to identify the main variables that influence the commission of the crime, according to the figures contained in SIEDCO, in the period between 1 January and 31 December 2022, compared with the same period in 2021, in which the crimes that affect the personal integrity and economic patrimony of those who live in Colombian territory are detailed; considerable increases were found in these and the factors of opportunity for their commission are highlighted, contrary to the situation that was evidenced in the affectations to life and integrity, a group of conducts that, according to what was recorded, decreased in the period analysed. Finally, we offer a contribution to containment from the police activity and a series of conclusions that allow us to broaden the vision of the diverse phenomena and enrich the generation of knowledge in the field of criminology.

O principal objetivo deste estudo descritivo e analítico é apresentar o comportamento criminal na Colômbia para 2022, a partir de uma abordagem quantitativa utilizada para a extração, análise e interpretação dos registros administrativos do Sistema de Informação Estatística, Criminal, Contravencional e Operacional (SIEDCO), constituindo um insumo para os interessados no estudo da dinâmica criminal, bem como para os responsáveis pela elaboração de estratégias para a contenção do crime e a geração de políticas públicas de segurança. Nesse sentido, e dentro da estrutura da dinâmica sociodemográfica, a primeira parte do artigo trata de forma geral do processo de homogeneização dos registros administrativos realizado pela Polícia Nacional e pela Procuradoria Geral da República. A segunda parte, com ênfase especial no homicídio doloso, apresenta a análise das informações que permitiram identificar as principais variáveis que influenciam o cometimento do crime, de acordo com os números contidos no SIEDCO, no período entre 1º de janeiro e 31 de dezembro de 2022, em comparação com o mesmo período de 2021, no qual são detalhados os crimes que afetam a integridade pessoal e o patrimônio econômico daqueles que vivem em território colombiano; Neles foram encontrados aumentos consideráveis e são destacados os fatores de oportunidade para seu cometimento, ao contrário da situação que se evidenciou nas afetações à vida e à integridade, grupo de condutas que, segundo o que foi registrado, diminuiu no período analisado. Finalmente, oferecemos uma contribuição para a contenção da atividade policial e uma série de conclusões que nos permitem ampliar a visão dos diversos fenômenos e enriquecer a geração de conhecimento no campo da criminologia.