Search Results (644)

Introduction: Cardiovascular diseases (CVDs) are a vast and widespread problem around the world, responsible for around one third of global deaths, of which 85% were due to heart attack and stroke in 2022. There are a lot of well-established risk factors for CVDs, including smoking, diabetes mellitus, obesity, poor diet, alcohol use, and sedentary lifestyle. Psychiatric disorders, however, are not among those frequently cited. Over a billion people worldwide suffer from some kind of mental disorder, with anxiety and depression being among the leading causes of long-term disability. All-cause death is significantly elevated in individuals with all mental health disorders. Methods: This narrative review aims to provide details on the selected psychiatric disorders and their pharmacotherapy with regard to the risk of developing cardiac illness by reviewing the available literature and the 2025 ESC Clinical Consensus Statement on mental health and cardiovascular disease. Results: Primary and secondary prevention of cardiovascular complications in the psychiatric disease population is an essential component in clinical healthcare. Conclusions: Taking all into account, it is essential to underline the role of the activation of the sympathetic nervous system and chronic inflammation, ultimately leading to metabolic syndrome in individuals with mental disorders, as well as an increase in residual cardiovascular risk and the development of CVDs, thereby worsening their long-term prognosis. In view of risky lifestyle behaviors in this population, it is essential to screen proactively, mitigate risk factors, consider the role of pharmacotherapy, and, if needed, initiate appropriate treatment. Full article

While Mongolian university students have been exposed to rapid sociocultural changes, also shifting their sexual norms and substance use behaviors, there is limited empirical evidence on them. Therefore, we examined associations between substance use (smoking, drinking, illicit drug use), sexual behaviors (sexual intercourse, casual and unprotected sex), sexual beliefs, social support, and psychological factors (self-efficacy, optimism, and general well-being). Using a cross-sectional design, we employed self-administered online questionnaires in a sample of Mongolian undergraduate students (N = 396; aged 18–24 years; 49.2% males). More than 62% reported ever drinking, 40.4% ever smoking, 23.0% illicit drug use; 64.9% was the lifetime prevalence of sexual intercourse, 41.2% reported engagement in casual sex, and 29.5% had engaged in unprotected sex. Differences by sex were significant with a higher prevalence among males, except for current illicit drug use and unprotected sex. Substance use was significantly associated with an increased likelihood of having sexual intercourse and participating in casual sex. While higher levels of well-being, self-efficacy, optimism, and social support from family and friends were significantly linked to ever being engaged in sexual intercourse, these positive constructs did not prevent them from risky sexual behaviors. Sex differences in substance use, sexual behaviors, and beliefs support the influence of the prevailing social expectations and traditional gender role perceptions in Mongolia. These findings emphasize the importance of comprehensive health education and supportive psychosocial environments. Full article

Despite extensive research on traffic safety, the complex, non-linear spatial discrepancy between risky driving and actual accidents remains a significant challenge to quantify within diverse urban contexts. This study investigates the non-linear relationship between grid-level risky driving patterns and traffic accident occurrence in Daejeon, Korea, examining how these associations vary across different urban contexts. Using data collected from July 2023 to June 2024, the analysis incorporates GPS-based risky driving indicators, including rapid acceleration, deceleration, and sudden maneuvers from general passenger vehicles, thereby overcoming the limitations of previous studies reliant on commercial vehicle data. By adopting an H3-based spatial grid system, the study classifies areas into four quadrants based on median values of risky behaviors and accident counts, further categorizing them into “Matched” and “Mismatched” types to identify spatial discrepancies. Furthermore, the Explainable Artificial Intelligence (XAI) technique is employed to integrate regional variables—including population density, land use, and transport infrastructure—to uncover the key drivers of accident risks. Providing a significant methodological improvement over traditional linear models, the findings demonstrate that identical driving behaviors can yield different safety outcomes depending on local environmental interactions. Specifically, while driver behavioral factors directly explain accident frequency in matched regions, accident risks in mismatched regions are more significantly shaped by spatial environmental factors, such as green spaces and commercial land use, which override direct behavioral impacts. This study provides a robust framework for developing data-driven, region-specific traffic intervention strategies, including context-aware advanced driver assistance systems (ADAS) and spatially tailored traffic calming, to enhance urban safety. Full article

Background and Objectives: Sleep is a vital psychological function for health and well-being in all age groups, from children to adolescents, to adults and the elderly, and impacts quality of life. This study evaluated temporal changes in sleep quality and lifestyle behaviors among medical students in North-Western Romania (Transylvania) between the COVID-19 pandemic and the post-pandemic period. Materials and Methods: A cross-sectional design was employed involving 709 medical students assessed during the first pandemic wave (2020) and the 2023–2024 academic year. Online questionnaires collected data on demographics, body mass index (BMI), substance use, and physical activity. Sleep quality was measured using the validated Athens Insomnia Scale (AIS), and multiple linear regression was performed to identify predictors of sleep outcomes. Results: Post-pandemic data revealed a significant decline in sleep quality, with female gender and lower academic performance identified as significant predictors of insomnia symptoms (R² of 0.258, p < 0.05). While physical activity levels improved significantly in 2024 compared to the confinement period, this was accompanied by increased fast-food consumption and a rise in overweight and obesity rates. Conversely, illicit drug use decreased, and alcohol consumption patterns shifted, characterized by reduced weekly frequency among females but persistent binge drinking episodes. Conclusions: The transition to post-pandemic education yielded mixed health outcomes; while physical activity rebounded, sleep quality and nutritional status deteriorated. These findings highlight the necessity for university-based interventions focusing on sleep hygiene, nutrition, and stress management to support the well-being of medical students. Full article

We study an infinite-horizon consumption–investment problem in which an investor endogenously manages a consumption comfort zone above a fixed subsistence benchmark. Consumption can move freely within the prevailing admissible interval, while upward expansions of the upper endpoint are irreversible and costly. This captures downward rigidity not through a single ratcheting reference level but through the endogenous management of a sustainable expenditure range. Using the dual martingale method together with singular stochastic control, we reduce the problem to a one-sided singular control problem for the comfort-zone width process. The associated dual Hamilton–Jacobi–Bellman equation becomes a gradient-constrained free-boundary problem, which admits a one-dimensional reduction under CRRA utility. We characterize the optimal comfort-zone expansion rule, consumption policy, risky portfolio rule, and value function. Economically, the model implies infrequent upward revisions of the sustainable consumption ceiling, smoother consumption than in the frictionless Merton benchmark, and state-dependent portfolio behavior. A key implication of the additive specification is that proportional consumption flexibility shrinks as the upper endpoint rises, so higher consumption states become endogenously tighter and require a larger wealth buffer to sustain. The infinite-horizon formulation is interpreted as a stationary benchmark that isolates the economics of costly lifestyle upgrading. Full article

Decision-making under time pressure has been associated with reduced deliberation and increased sensitivity to contextual cues such as framing. This study investigates how time pressure reshapes information processing in risky decision-making and which types of information receive greater attention when cognitive resources are constrained. Two experiments examined the combined effects of time pressure, spatial position, and presentation order on framing effects, integrating behavioral risk-choice measures with gaze-based indices of attention allocation. The results show that time pressure significantly reduces fixation counts and fixation durations, suggesting more restricted information search. Moreover, time pressure enhances frame-consistent risk preferences, with contextual presentation factors further shaping decision outcomes. Specifically, under time pressure and loss framing, stronger risk seeking emerged when the certain option was presented second. Overall, these findings suggest that time pressure not only amplifies framing effects in risky decision-making but also is associated with changes in attentional allocation patterns and increased reliance on contextual cues underlying framed choices. This study highlights how the temporal and spatial characteristics of information presentation shape decision processes under temporal constraint and provides theoretical and practical implications for decision-making under pressure. Full article

This study identifies and explains road safety behavior patterns in Colombia using explainable artificial intelligence (XAI). Based on 9232 records and 38 variables from the Territorial Survey of Road Safety Behavior, the CRISP-DM methodology was applied, including data cleaning, normalization, encoding, and feature selection. XGBoost, Random Forest, Bagging, and AdaBoost models were evaluated, incorporating three domain-specific indices: Distraction Index (DI), Risky Road Interaction Index (RRI), and Normative Compliance Index (NCI). AdaBoost achieved the best overall balance (Precision = 0.78; Recall = 0.75; F1-score = 0.77), simultaneously reducing false positives and false negatives. SHAP analysis revealed that environmental and infrastructure factors (lighting, traffic signals, intersections, congestion, perceived crime) explain more variance than self-reported behaviors (mobile phone use, alcohol consumption, speeding). The complementary indices indicated above-average distraction levels, high exposure to risky interactions, and low compliance in specific segments. These findings enable the prioritization of targeted interventions (improvements in lighting and crossings, focused enforcement, and educational campaigns) and support operation with thresholds adjusted to error costs, providing traceable decision support for public road safety policies. Overall, the proposed approach integrates prediction and explainability to enable actionable decisions and continuous monitoring aimed at reducing traffic accidents. Full article

Methamphetamine (METH) use is highly prevalent among people with HIV (PWH) and those at risk and may contribute to overall worse health outcomes. Poorer health-related problems may be mediated by METH enhancing risky decision-making among PWH. While both METH and HIV are known to have overlapping and deleterious effects on the frontostriatal neural circuitry essential for decision-making, few studies have examined their combined effects. Eighty-eight participants stratified by HIV and a history of METH use disorder completed a risky decision-making paradigm, which involved choosing among safe (20¢) and risky (40¢/80¢ win or loss) choices, during blood-oxygen level-dependent functional magnetic resonance imaging (fMRI). Linear mixed-effects models were used to assess voxelwise differences in group and choice constrained to the anterior cingulate cortex (ACC), insula, and striatum. Despite similar choice behavior across groups, PWH and a history of METH use disorder had greater activation of the ACC and caudate than either condition alone (i.e., HIV+/METH− and HIV−/METH+), which was similar to seronegative, non-using controls. Within the ACC in particular, these differences may have been driven by safe choices. A longer estimated duration of HIV infection was associated with greater ACC activation to risky choices for PWH regardless of METH use history. These findings suggest that PWH and a history of METH use disorder may exhibit compensatory activation of regions associated with decision-making in the context of rewards and that the effects of HIV and past METH use might not be additive. Full article

This study investigates the probability of consumer default across both secured and unsecured assets, with a particular focus on borrower behavior and the role of moral hazard in shaping individual credit risk. It examines how different borrower decisions, such as investing in secured and unsecured projects after loan disbursement, affect default outcomes, especially under limited lender supervision. The Ornstein–Uhlenbeck process is used to capture the dynamics of risky asset returns and identifies the conditions under which borrowers are likely to switch from safer to riskier investments. We assume that borrowers may allocate loan funds to both secured and unsecured projects, thereby recognizing that credit risk assessment inherently involves behavioral factors that are difficult to quantify. Monte Carlo simulations are used to assess how return volatility influences borrower decision-making, showing that higher uncertainty increases the probability of returns exceeding the repayment obligation, thereby incentivizing risk-shifting behavior. The results indicate that unsecured lending is more exposed to strategic risk shifting and experiences more frequent and severe default outcomes than secured lending. As a result, this study recommends that microfinance institutions prioritize collateral-backed lending as a more effective strategy for mitigating credit risk and reducing exposure to borrower opportunism. Full article

The effective recognition of risky driving behaviors holds technical potential for supporting accident prevention and sustainable transportation. However, existing intelligent algorithms for optimizing deep learning models in this field often suffer from slow convergence and high errors. This study proposes a novel hybrid model (ICPO-XGBoost) for risky driving behavior classification. The improved crested porcupine optimizer (ICPO) was developed using logistic-tent composite mapping for population initialization, a hybrid mechanism combining refraction opposition-based learning and Cauchy mutation to avoid local optima, and an adaptive variable spiral search with inertia weight to balance global and local search. The ICPO was then employed to optimize the hyperparameters of the XGBoost classifier. The ICPO demonstrated superior optimization accuracy and convergence speed compared to benchmark algorithms. The ICPO-XGBoost model achieved accuracy, precision, recall, and F1 scores of 96.2%, 95.4%, 95.8%, and 95.6%, respectively, for classifying and identifying risky driving behaviors. Compared to various benchmark models, these results represent increases of 12.7–24.8%, 14.8–31.8%, 14.9–31.0%, and 15.0–32.4%, respectively. For specific driving behavior categories (normal driving, slow driving, short-distance tailgating, sudden acceleration/deceleration, frequent lane changing, and forced lane changing), the precision, recall, and F1 scores of the ICPO-XGBoost model fell within the ranges of 84.8–99.2%, 87.5–100.0%, and 86.2–99.2%, respectively. Compared to benchmark models, these metrics show increases of 1.5–75.8%, 5.8–68.1%, and 3.3–72.6%, respectively. Notably, the model significantly improved accuracy in identifying sudden acceleration/deceleration behaviors. The results of this model facilitate the classification and early warning of risky driving behaviors, thereby reducing the frequency of such behaviors, lowering the risk of traffic accidents, and enhancing road traffic safety. Full article

Investment in risky financial assets plays a crucial role in individual wealth accumulation and broader financial market development. However, existing research has primarily emphasized financial literacy while giving limited attention to behavioral mechanisms that may weaken its influence on investment behavior. In particular, hyperbolic discounting, reflecting time-inconsistent preferences that favor immediate rewards over long-term gains, may constrain the effective translation of financial knowledge into forward-looking financial decisions. Against this background, this study examines whether hyperbolic discounting mediates the association between financial literacy and investment in risky assets using large-scale survey data from Japan’s Money and Life survey. Employing regression-based mediation analysis within a cross-sectional framework, the results indicate that financial literacy is strongly and positively associated with risky asset investment, while hyperbolic discounting exerts a statistically significant but economically small mediating effect that slightly attenuates this relationship. The findings suggest that cognitive financial capability remains the dominant driver of participation in risky financial markets, whereas present-biased preferences play a secondary behavioral role. These results provide important implications for investors, educators, and policymakers by highlighting that policies aimed at improving financial literacy are likely to yield substantial investment benefits, while complementary interventions addressing behavioral biases may offer additional, though more modest, gains in promoting long-term, forward-looking financial decision-making. Full article

The sum of diseases has been associated with many personal, behavioral, and psychological variables as well as with many biochemical, inflammatory, oxidative stress, and epigenetic biomarkers. However, the search for the association between some of these biomarkers and illness count is limited, particularly in Mexican individuals. (1) To determine the associations between personal, behavioral, biochemical, and molecular factors with the illness count in Mexican individuals, globally and segmented by sex, and (2) to determine the intercorrelation among the studied biomarkers. Mexican adults were invited to participate, and many personal, psychological, and biochemical variables were measured; in addition, the systolic blood pressure, body mass index, and waist-to-hip ratio were obtained. The self-report of 28 health conditions was measured, and the detection of 3 (diabetes, hypertension, and dyslipidemia) conditions was obtained with biochemical analyses and blood pressure measurement; with these reports, we obtained the variable illness count. A total of 157 individuals were included, of whom 83 (52.9%) were women; the median age and range were 24 (18–58) years old, and all participants were Mexican mestizo individuals. Women showed a higher number of self-reported/detected diseases than men. The multivariate analyses revealed that female sex, age, having children, risky eating behavior, poor sleep quality, systolic blood pressure, and lower levels of IL-10 were significantly correlated with the illness count. In the women’s sample, lower levels of IL-10, less free time, monthly earnings, and depression were positively correlated with illness count. In the men’s sample, the age, systolic blood pressure, poor sleep quality, 8-OHdG, IL-6, and plateletcrit, in addition to positive relations with others, were variables positively correlated with illness count. In the correlations of the studied biomarkers, we found that IL-10, TNF-α, and IL-8 showed a high positive correlation among them; in addition, inflammatory and oxidative stress biomarkers showed low significant correlations among them. Many personal, biochemical, and psychological factors are associated with the number of diseases, while the associated biomarkers differed in each sex, highlighting the role of IL-10, 8-OHdG, IL-6, and plateletcrit. Full article

This study examines the application of ensemble machine learning methods for identifying and flagging potentially risky transactions in military public procurement in Ukraine, a sector characterized by elevated financial and security sensitivity and limited capacity for comprehensive ex post control. Using an integrated dataset of procurement procedures conducted between 2021 and 2025, enriched with 56 financial, economic, and behavioral indicators of suppliers, the study develops and compares standard logistic and LASSO-penalized regression as econometric benchmarks, Random Forest, XGBoost, XGBoost with SMOTE balancing, and CatBoost classification models. The target variable is defined on the basis of officially detected violations identified through state monitoring. Model performance is evaluated using standard binary classification metrics, with particular emphasis on recall. Model uncertainty and predictive robustness are addressed through partial dependence analysis, temporal stability assessment, and out-of-sample residual diagnostics. The results indicate that the CatBoost model demonstrates the most balanced performance across evaluation measures. Feature importance analysis identifies expected contract value, procurement method, CPV code, and suppliers’ financial capacity as significant determinants of procurement-related risk. The findings provide empirical evidence on the usefulness of risk-oriented machine learning tools in supporting earlier detection and monitoring of irregularities in military procurement. Full article

Modern multi-Unmanned Aerial Vehicle (UAV) attacks pose significant challenges to existing counter-UAV frameworks due to their agility, irregular spatial formations, and increasing reliance on intelligent evasive behaviors. This paper proposes a unified interception architecture that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for multi-target grouping, a deceptive waypoint sequencing (DWS) mechanism for adversarial evasion, and a robust sliding-mode backstepping controller augmented with extended state observers (ESOs) for precise tracking under disturbances. DBSCAN enables real-time clustering of attacking UAVs without prior knowledge of the number of formations, producing dynamic centroids that serve as tactical interception references. To counter risky attackers capable of predicting defender trajectories, a novel DWS strategy introduces centroid-relative waypoints that preserve mission objectives while reducing trajectory predictability. Lyapunov-based analysis is developed for stability, guaranteeing uniform ultimate boundedness of the tracking errors. The proposed approach achieves successful interception in both scenarios, with an interception time of 7 s and final interception error of $0.023$ m in the single-UAV case, and an interception time of 8 s with final interception error of $0.050$ m in the multiple-UAV case, whereas the PID baseline fails to achieve interception under the same conditions. Extensive simulations involving single and multi-cluster engagements demonstrate that the proposed strategy achieves fast, accurate, and deception-resilient interception, outperforming the conventional PID approach in the presence of disturbances, nonlinearities, and dynamic swarm configurations. The obtained results show the effectiveness of integrating adaptive clustering, deceptive planning, and robust nonlinear control for modern UAV–UAV defensive operations. Full article

Background: Since the onset of the HIV epidemic, over 40 million individuals have died from AIDS-related illnesses, leading to nearly 14 million children aged 0–17 losing one or both parents to AIDS by 2022. In 2023, Namibia had 250,000 vulnerable children and 72,000 children aged 0–17 orphaned due to HIV and AIDS. Without parental support, orphaned and vulnerable children (OVC) face heightened risks, including neglect, distress, and compromised decision-making. These vulnerabilities can increase their susceptibility to risky behaviors, such as sexual experimentation. This study used data from the Project HOPE Namibia (PHN) OVC program to assess HIV testing rates and associated factors among OVC. Methods: This retrospective cross-sectional secondary analysis study used data from PHN’s OVC program implemented from 1 August 2023 to 30 November 2024. Data were analyzed using Chi-square tests and binomial and multinomial logistic regression. Results: Among the 16,995 participants included in this analysis, 15,014 (88.3%) participants had ever been tested for HIV (95% confidence interval (CI): 87.8–88.8%). Participants with an increased likelihood of having ever tested for HIV included those who had been in the program for 0–6 months (adjusted odds ratio (AOR) = 1.31, 95% CI (1.17–1.47)), and those from households experiencing little or moderate hunger (AOR = 1.29, 95% CI (1.12–1.50), AOR = 1.51, 95% CI (1.33–1.72), respectively. Conclusions: A multi-pronged approach involving all stakeholders is required to increase HIV testing among OVC. Such an approach should include community-based HIV testing, providing male-friendly healthcare services, and reducing household hunger through economically empowering vulnerable households. Full article