Search Results (386)

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article

Introduction: Sexual minority men (SMM) are at increased risk for psychosocial stressor exposure, substance use, and poor mental health relative to heterosexual men. While the burden of mental health is growing in the United States, among SMM these trends are increasing at a greater rate, driving health disparities. Methods: Framed within a minority stress framework, these analyses examine how stressors explain substance use and poorer mental health over time. Participants were asked questions on stressor exposure (stigma, discrimination, internalized homophobia, perceived stress), mental health (anxiety, depression, PTSD), and substance use (alcohol to intoxication, club drugs, poly club drugs) over 36 months among 528 SMM in NYC. Results: Perceived stress increased frequency of all substance use, whereas discrimination decreased days of club and poly club drug use. Depression severity predicted increased days of club drug and poly club drug use. PTSD severity predicted increased days of club drug and poly club drug use. Conclusion: We are able to expand on the literature with granular substance use data to highlight associations with stressors and mental health. These findings support an increased need for systematic policy solutions and public health interventions to address drivers of substance use disparities among young SMM. Full article

The growing complexity of modern manufacturing has intensified the need for precise control of residual stresses to ensure structural reliability, dimensional stability, and material performance. This study conducts a bibliometric review using data from Scopus and Web of Science, covering publications from 2019 to 2024. Residual stress research in metal manufacturing has gained prominence, particularly in relation to welding, additive manufacturing, and machining—processes that induce significant stress gradients affecting mechanical behavior and service life. Emerging trends focus on simulation-based prediction methods, such as the finite element method, heat treatment optimization, and stress-induced defect prevention. Key thematic clusters include process-induced microstructural changes, mechanical property enhancement, and the integration of modeling with experimental validation. By analyzing the evolution of research output, global collaboration networks, and process-specific contributions, this review provides a comprehensive overview of current challenges and identifies strategic directions for future research in residual stress management in advanced metal manufacturing. Full article

The constitutive model is widely employed to characterize the rheological properties of metallic materials under high-temperature conditions. It is typically derived from a series of high-temperature tests conducted at varying deformation temperatures, strain rates, and strains, including hot stretching, hot compression, separated Hopkinson pressure bar testing, and hot torsion. The original experimental data used for establishing the constitutive model serves as the foundation for developing phenomenological models such as Arrhenius and Johnson–Cook models, as well as physical-based models like Zerilli–Armstrong or machine learning-based constitutive models. The resulting constitutive equations are integrated into finite element analysis software such as Abaqus, Ansys, and Deform to create custom programs that predict the distributions of stress, strain rate, and temperature in materials during processes such as cutting, stamping, forging, and others. By adhering to these methodologies, we can optimize parameters related to metal processing technology; this helps to prevent forming defects while minimizing the waste of consumables and reducing costs. This study provides a comprehensive overview of commonly utilized experimental equipment and methods for developing constitutive models. It discusses various types of constitutive models along with their modifications and applications. Additionally, it reviews recent research advancements in this field while anticipating future trends concerning the development of constitutive models for high-temperature deformation processes involving metallic materials. Full article

Radon anomalies have long been explored as potential geochemical precursors to seismic activity due to their responsiveness to subsurface stress variations. However, before this study, the scientific progression of this research domain had not been systematically examined through a quantitative lens. This study presents a comprehensive bibliometric analysis of 379 articles published between 1977 and 2025 and indexed in Scopus and Web of Science. Utilizing the Bibliometrix R-package and its Biblioshiny interface, the analysis investigates temporal publication trends, leading countries, institutions, international collaboration networks, and thematic evolution. The results reveal a marked increase in research output since 2010, with China, India, and Italy emerging as the most prolific contributors. Thematic mapping indicates a shift from conventional geochemical monitoring toward the integration of artificial intelligence techniques, such as decision trees and neural networks, for anomaly detection and predictive modeling. Notwithstanding this methodological evolution, core research themes remain centered on radon concentration monitoring and the analysis of environmental parameters. Overall, the findings highlight the coexistence of traditional and emerging approaches, emphasizing the importance of standardized methodologies and interdisciplinary collaboration. This bibliometric synthesis provides strategic insights to inform future research and strengthen the role of radon monitoring in seismic early warning systems. Full article

This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

The strength parameters of fault gouge are critical factors that influence sealing capacity and fault reactivation in underground gas storage reservoirs. This study investigates the shear characteristics of remolded fault gouge under varying hydro-mechanical conditions, focusing on the coupled influence of water content and cementation. Sixty fault gouge samples are prepared using a mineral mixture of quartz, montmorillonite, and kaolinite, with five levels of water content (10–30%) and three cementation degrees (0%, 1%, 3%). Direct shear tests are conducted under four normal stress levels (100–400 kPa), and microstructural characteristics are examined using SEM. The results show that shear strength and cohesion exhibit a non-monotonic trend with water content, increasing initially and then decreasing, while the internal friction angle decreases continuously. Higher cementation degrees not only enhance shear strength and reduce the softening effect caused by water but also shift the failure mode from ductile sliding to brittle, cliff-type rupture. Moreover, clay content is found to modulate the degree—but not the trend—of strength parameter responses to water and cementation variations. Based on the observed mechanical behavior, a semi-empirical shear strength prediction model is developed by extending the classical Mohr–Coulomb criterion with water–cementation coupling terms. The model accurately predicts cohesion and internal friction angle as functions of water content and cementation degree, achieving strong agreement with experimental results (R² = 0.8309 for training and R² = 0.8172 for testing). These findings provide a practical and interpretable framework for predicting the mechanical response of fault gouge under complex geological conditions. Full article

Background: Pulmonary embolism (PE) increases right ventricular (RV) afterload, potentially leading to myocardial stress and electrocardiographic abnormalities. Although QTc prolongation has been suggested as a marker of RV dysfunction, its prevalence, clinical significance, and prognostic value in acute PE remain poorly defined. Objective: The objective of this study is to evaluate the prevalence and clinical implications of QTc prolongation in patients with intermediate–high and high-risk acute PE. Methods: We retrospectively analyzed 95 consecutive patients admitted with intermediate–high or high-risk PE between September 2021 and December 2023. QTc prolongation was defined as ≥470 ms in males and ≥480 ms in females. Clinical, imaging, and laboratory data were compared between patients with normal and prolonged QTc intervals. QTc was assessed at admission, after treatment, and prior to discharge. Results: QTc prolongation was observed in 28.4% of patients at presentation. This group had significantly higher lactate levels (2.3 vs. 1.8 mmol/L, p = 0.03) and a non-significant trend toward elevated troponin and lower oxygen saturation. No differences were observed in echocardiographic or CT-based RV dysfunction parameters. QTc values normalized by discharge irrespective of treatment modality. There was no association between QTc prolongation and in-hospital or long-term mortality. A trend toward more aspiration thrombectomy was noted in the prolonged QTc group (29.6% vs. 11.8%, p = 0.06). Conclusions: QTc prolongation is common in acute intermediate–high and high-risk PE and may reflect transient myocardial stress. While not predictive of clinical outcomes, it should be considered in the differential diagnosis of QTc prolongation in patients presenting with dyspnea and chest pain. Full article

Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. Full article

Background/Objectives: Psychological distress is common among university students and often co-occurs with unhealthy lifestyle patterns. However, most studies examine physical activity (PA) and dietary intake (DI) in isolation, overlooking how these behaviors interact under stress. This study aimed to identify and compare integrated PA and DI behavior patterns among students with low vs. high psychological distress. Methods: A cross-sectional case–control design was used with 209 students (aged 19–21). Questionnaires included the International Physical Activity Questionnaire (IPAQ), Questionnaire of Eating Behavior (QEB), and Depression Anxiety Stress Scales-21 items (DASS-21). Behavioral patterns were assessed using a cophylogenetic approach (tanglegrams, cophenetic statistics), and predictive behaviors were analyzed using stepwise logistic regression. Results: Permutational Multivariate Analysis of Variance (PERMANOVA) revealed significant group differences in PA–DI structure (F = 3.91, R² = 0.0185, p = 0.001). Tanglegram and PACo analyses showed tighter PA–DI alignment in high-distress individuals, suggesting more rigid, compensatory behavior profiles. Logistic regression identified vigorous PA (OR = 1.80, 95% CI: 1.33–2.50, p < 0.001) and fast food intake (OR = 1.43, 95% CI: 1.05–1.98, p = 0.026) as significant distress indicators. Sweets intake showed a non-significant trend (OR = 1.33, p = 0.064). Conclusions: Students with higher psychological distress exhibit complex lifestyle co-patterns combining risk (e.g., fast food) and compensatory behaviors (e.g., vigorous PA). Health promotion should address PA and DI jointly, and screening for distress should be integrated into student wellness programs. Full article

Clay is a widely used material with unique properties that vary depending on water content and applied pressure. In this study, the electromechanical behavior of clay samples from Afyonkarahisar, Turkey, is investigated by examining the relationship between electrical resistivity, water content, and mechanical loading under uniaxial pressure. The samples with a water content of 10%, 20%, and 30% were tested using a uniaxial loading machine in accordance with ASTM D 2216 and the Turkish standard TS 1900-1. The analysis included measurements of stress, deformation, and electrical conductivity of the soil. A comparative assessment of samples with varying water content revealed that at low moisture levels (10%), the specific electrical resistivity initially decreases due to soil compaction and reduced porosity. However, as stress increases further, resistivity rises significantly as microcracks begin to develop, disrupting conductive pathways. In contrast, at higher water contents (20% and 30%), resistivity consistently decreases with increasing stress, while conductivity increases markedly. This indicates that at elevated saturation levels, the presence of water facilitates charge carrier mobility through ionic conduction, resulting in lower resistivity and higher conductivity. Comparisons with previous studies on clays such as bentonite and kaolinite reveal similar qualitative trends, although differences in the rate of resistivity change suggest a distinct mineralogical influence in Afyonkarahisar clay. This study contributes to a deeper understanding of the geotechnical behavior of this regional clay and supports more accurate performance predictions in engineering and construction applications. Full article

This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. Smartwatches, equipped with sensors for physiological signals such as heart rate, heart rate variability, electrodermal activity, and skin temperature, have demonstrated promise in detecting and predicting stress and mood fluctuations in both clinical and everyday contexts. This review emphasizes the need for interdisciplinary collaboration to advance technological precision, ethical data handling, and user experience design. Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). Furthermore, it identifies performance trends and challenges across lab-based vs. real-world deployments, emphasizing the trade-off between generalizability and personalization in model design. Full article

Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. Grid search optimization was employed to fine-tune the hyperparameters of each model, while model evaluation was conducted using 10-fold cross-validation and six performance metrics. Across all the diagnostic approaches, ensemble models, namely random forest, XGBoost, and LightGBM, consistently outperformed non-ensemble models. Notably, random forest and LightGBM classifiers demonstrated the most robust and superior performance across all schemes, achieving accuracy, precision, recall, and F1 scores between 0.99 and 1, along with Matthew correlation coefficient values exceeding 0.98 in all cases. This robustness suggests that ensemble models are effective at capturing complex decision boundaries and relationships among gas ratio features. Furthermore, beyond numerical classification, the integration of physicochemical and dielectric properties in this study revealed degradation signatures that strongly correlate with thermal fault indicators. Particularly, the CIGRÉ-based classification using a random forest classifier demonstrated high sensitivity in detecting thermally stressed units, corroborating trends observed in chemical deterioration parameters such as interfacial tension and CO₂/CO ratios. Access to over 80 years of operational data provides a rare and invaluable perspective on the long-term performance and degradation of power equipment. This extended dataset enables a more accurate assessment of ageing trends, enhances the reliability of predictive maintenance models, and supports informed decision-making for asset management in legacy power systems. Full article