Search Results (267)

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

The concept of Brain Health is transforming the neuroscientific landscape, promoting an integrative and preventive approach to care under a unifying vision. This position paper, developed by Swiss junior societies in neurology and psychiatry, presents a trainee perspective on how Brain Health should be addressed from the earliest stages of postgraduate training. It explores current gaps in postgraduate training, including the continued separation of neurology, psychiatry and other specialties involved in brain disorder care, limited interdisciplinary and interprofessional exposure, and gaps in leadership, public health, and advocacy skills. We highlight promising models such as Switzerland’s integrated training components and the proposed “brain medicine” framework, inspired by internal medicine. Additionally, we examine innovative initiatives from trainee associations that promote collaborative learning, advocacy, and Brain Health awareness through academic and creative channels. The paper also stresses the importance of equitable global access to training, the integration of research into clinical education, and the urgent need to address burnout and working conditions among early-career professionals. By reframing trainees not as passive learners but as active agents of change, we call for systemic reforms that support their role in advancing Brain Health. Ultimately, we advocate for the development of international core competencies, adaptable curricula, and structured interdisciplinary pathways that embed Brain Health into every level of medical training. Only through this comprehensive approach can we equip the next generation of clinicians to promote lifelong Brain Health across specialties, systems, and populations. Full article

The rapid integration of artificial intelligence (AI), particularly generative AI (Gen-AI), into higher education presents a critical challenge: preparing students for the complex ethical dilemmas inherent in AI-driven research and practice. Current AI ethics education, however, often remains fragmented, overly theoretical, and disconnected from practical application, leaving a significant gap between knowing ethical principles and acting upon them. To address this pressing issue, this study proposes and validates a full-cycle AI ethics education system designed to bridge this gap. The system integrates three core components: (1) an updated four-dimensional ethics framework focused on Gen-AI challenges (research review, data privacy, algorithmic fairness, intellectual property); (2) a “cognition-behavior” dual-loop training mechanism that combines theoretical learning with hands-on, simulated practice; and (3) a full life-cycle education platform featuring tools like virtual laboratories to support experiential learning. A mixed-methods study with 360 students and 20 instructors demonstrated the system’s effectiveness, showing significant improvement in students’ ethical knowledge, a large effect size in enhancing ethical decision-making capabilities, and high user satisfaction. These findings validate a scalable model for AI ethics education that moves beyond passive instruction toward active, situated learning, offering a robust solution for higher education institutions to cultivate ethical responsibility in the age of Gen-AI. Full article

This study aimed to investigate the effect of antagonist static stretching applied between sets during resistance training on the number of repetitions of leg press exercise in young volleyball players. For this purpose, a total of 16 female active volleyball players (age 15.50 ± 0.52 years; height 167.25 ± 6.10; body mass 57.00 ± 5.98) participated voluntarily. The athletes participating in the study visited the laboratory five times. In the first session, anthropometric measurements were taken. In the second session, their 10 repetition maximums (RTs) were recorded, and in the third session, 10 control RTs were recorded. In the other two sessions, athletes were randomly assigned to two experimental protocol treatments in accordance with the crossover experimental design. In the traditional application, leg press exercise was performed as four sets with their own maximums and 2 min of passive rest between sets. In the experimental application, the participants performed four sets of leg press exercise with ten repetitions of their own maximums until concentric exhaustion, and static hamstring stretching was applied continuously for 30 s over 2 min between sets. All participants participated in both application protocols in different sessions. SPSS 20.0 package programed and GraphPad Prizm 8 graphics program were used for the analysis of all data. Data were analyzed at 0.05 significance level. In the findings obtained, Group* application interaction was found to be statistically significant according to the application and groups (F = 4.198, p = 0.016, η_p² = 0.219). In the leg press repetitions, statistical significance was found in favor of the experimental treatment in the third and fourth sets. This study shows that antagonist static stretching applied between sets positively affects resistance training performance by increasing the number of repetitions in leg press exercise in young female volleyball players. Full article

The Arctic, characterized by extensive ice and snow cover with persistent low solar elevation angles and prolonged polar nights, poses significant challenges for conventional spectral threshold methods in cloud detection and cloud-top thermodynamic phase classification. The study addressed these limitations by combining active and passive remote sensing and developing a machine learning framework for cloud detection and cloud-top thermodynamic phase classification. Utilizing the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud product from 2021 as the truth reference, the model was trained with spatiotemporally collocated datasets from FY3D/MERSI-II (Medium Resolution Spectral Imager-II) and CALIOP. The AdaBoost (Adaptive Boosting) machine learning algorithm was employed to construct the model, with considerations for six distinct Arctic surface types to enhance its performance. The accuracy test results showed that the cloud detection model achieved an accuracy of 0.92, and the cloud recognition model achieved an accuracy of 0.93. The inversion performance of the final model was then rigorously evaluated using a completely independent dataset collected in July 2022. Our findings demonstrated that our model results align well with results from CALIOP, and the detection and identification outcomes across various surface scenarios show high consistency with the actual situations displayed in false-color images. Full article

Background: This study examined the effects of repeated ischemic preconditioning (IPC) combined with normobaric hypoxia on anaerobic performance and physiological stress markers. Methods: Fourteen physically active males (22.3 ± 3.1 years) completed three randomized, single-blind crossover sessions under the following conditions: (1) normoxia (NOR), (2) normobaric hypoxia (HYP; FiO₂ = 14.7%), and (3) hypoxia with IPC (IPC-HYP). Each session included three 30 s cycling Wingate tests separated by four minutes of passive recovery. Blood samples were collected pre-exercise, immediately post-exercise, and 15 min post-exercise to assess lactate, pH, bicarbonate (HCO₃⁻), and creatine kinase (CK) activity. Results: Peak power output was highest under NOR during Wingate II and III. IPC-HYP attenuated the decline in peak power compared to that under HYP (e.g., Wingate II: 15.56 vs. 12.52 W/kg). IPC-HYP induced greater lactate accumulation (peak: 15.45 mmol/L, p < 0.01), more pronounced acidosis (pH: 7.18 post-exercise), and lower bicarbonate (9.9 mmol/L, p < 0.01). CK activity, measured immediately and then 1 h and 24 h post-exercise, was highest under IPC-HYP at 24 h (568.5 U/L). Conclusions: IPC-HYP mitigates the decline in peak anaerobic power observed under hypoxia, despite eliciting greater metabolic and muscular stress. These findings suggest that IPC may enhance physiological adaptation to hypoxic training, potentially improving anaerobic performance. Full article

Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3 L/min, and the response time is under 200 ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100 mL/${\mathrm{cmH}}_2$O and airway resistance from 2.01 to 14.67$\sqrt{{\mathrm{cmH}}_2\mathrm{O}}/(\mathrm{L}/\mathrm{s})$, with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human–machine interaction studies, and simulating pathological respiratory states. Full article

Background: Genetic factors pertinent to Parkinson’s disease (PD) might predispose an individual to post-traumatic stress disorder (PTSD). Humans who are heterozygous for the glucocerebrosidase 1 (GBA) L444P Gaucher mutation have an increased PD risk and elevated levels of alpha synuclein (aSyn). Mice that are heterozygous for the GBA mutation and express aSyn with the A53T mutation show elevated anxiety levels at 20 months of age compared to those expressing only A53T. Objective: This study aims to assess whether A53T and A53T-L444P affect the risk of developing PTSD phenotypes and whether sex and age modulate this risk. Methods: Young (5.1 ± 0.2 months) and older (11.3 ± 0.2 months) A53T and GBA L444P female and male mice were tested for fear learning and memory extinction in the contextual fear conditioning and passive avoidance paradigms. Subsequently, the mice were tested for measures of activity and anxiety in the open field and for depressive-like behavior in the forced swim test. Results: In the contextual fear memory extinction paradigm, only young A53T female mice showed contextual fear memory extinction, while older A53T female mice showed increased activity levels over subsequent days. In the passive avoidance memory paradigm, no mice showed extinction of passive avoidance memory. When the frequency of entering the more anxiety-provoking center of the open field was analyzed, a test history x sex x age interaction was observed. In the forced swim test, test history affected the depressive-like behavior in mice trained; there was more depressive-like behavior in mice trained in the contextual fear memory extinction paradigm than in mice trained in the passive avoidance memory extinction paradigm. Moreover, there was an effect of age with more depressive-like behavior in older than in younger mice, and an effect of genotype with more depressive-like behavior in A53T-L444P compared to A53T mice. When cortical phosphorylated tau (pS 199) levels were analyzed, there was an effects of genotype, a sex x age interaction, and ant age x test history interaction. Conclusions: A53T and A53T-L444P affect the risk of developing PTSD phenotypes. Fear extinction test history, genotype, and age affect depressive-like behavior and tau pathology. Full article

The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement control approaches lack the ability to adapt and effectively improve care recipient comfort. To address these problems, this paper proposes an active, personalized intelligent control method based on neural networks. A muscle activation prediction model is established for the piggyback transfer robot, enabling dynamic adjustments during the care process to improve human comfort. Initially, a kinematic analysis of the piggyback transfer robot is conducted to determine the optimal back-carrying trajectory. Experiments were carried out to measure human–robot contact forces, chest holder rotation angles, and muscle activation levels. Subsequently, an Online Sequential Extreme Learning Machine (OS-ELM) algorithm is used to train a predictive model. The model takes the contact forces and chest holder rotation angle as inputs, while outputting the latissimus dorsi muscle activation levels. The Genetic Algorithm (GA) is then employed to dynamically adjust the chest holder’s rotation angle to minimize the difference between actual muscle activation and the comfort threshold. Comparative experiments demonstrate that the proposed ELM-GA-based active control method effectively enhances comfort during the piggyback transfer process, as evidenced by both subjective feedback and objective measurements of muscle activation. Full article

Surface soil moisture (SSM) is a critical land surface parameter affecting a wide variety of economically and environmentally important processes. Spaceborne microwave remote sensing has been extensively employed for monitoring SSM. Active microwave sensors offering high spatial resolution are typically utilized to capture dynamic fluctuations in soil moisture, albeit with low temporal resolution, whereas passive sensors are typically used to monitor the absolute values of large-scale soil moisture, but offer coarser spatial resolutions (~10 km). In this study, a passive microwave observation system using an X-band microwave radiometer mounted on a drone was established to obtain high-resolution (~1 m) radiative brightness temperature within the observation region. The region was a control experimental field established to validate the proposed approach. Additionally, machine learning models were employed to invert the soil moisture. Based on the site-based validation the trained inversion model performed well, with estimation accuracies of 0.74 and 2.47% in terms of the coefficient of determination and the root mean square error, respectively. This study introduces a methodology for generating high-spatial resolution and high-accuracy soil moisture maps in the context of precision agriculture at the field scale. Full article

This study presents a wearable haptic feedback system designed to support speech training for individuals with speech and hearing impairments. The system provides real-time tactile cues based on detected phonemes, helping users correct their pronunciation independently. Unlike prior approaches focused on passive reception or therapist-led instruction, our method enables active, phoneme-level feedback using a multimodal interface combining audio input, visual reference, and spatially mapped vibrotactile output. We validated the system through three user studies measuring pronunciation accuracy, phoneme discrimination, and learning over time. The results show a significant improvement in word articulation accuracy and user engagement. These findings highlight the potential of real-time haptic pronunciation tools as accessible, scalable aids for speech rehabilitation and second-language learning. Full article

Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating satellite-based soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission into the assessment of seismic landslide occurrence. Using landslide inventories from five major earthquakes (Nepal 2015, New Zealand 2016, Papua New Guinea 2018, Indonesia 2018, and Haiti 2021), a balanced global dataset of landslide and non-landslide cases was compiled. Exploratory analysis revealed a strong association between elevated pre-event soil moisture and increased landslide occurrence, supporting its relevance in seismic slope failure. Moreover, a Random Forest model was trained and tested on the dataset and demonstrated excellent predictive performance. To assess the generalizability of the model, a leave-one-earthquake-out cross-validation approach was also implemented, in which the model trained on four events was tested on the fifth. This approach outperformed comparable models that did not consider soil moisture, such as the United States Geological Survey (USGS) seismic landslide model, confirming the added value of satellite-based soil moisture data in improving seismic landslide susceptibility assessments. Full article

In the age of artificial intelligence (AI), architectural practice faces a paradox of immense potential and creeping standardization. As humans are increasingly relying on AI-generated outputs, architecture risks becoming a spectacle of repetition—a shuffling of data that neither truly innovates nor progresses vertically in creative depth. This paper explores the critical role of data in AI systems, scrutinizing the training datasets that form the basis of AI’s generative capabilities and the implications for architectural practice. We argue that when architects approach AI passively, without actively engaging their own creative and critical faculties, they risk becoming passive users locked in an endless loop of horizontal expansion without meaningful vertical growth. By examining the epistemology of architecture in the AI age, this paper calls for a paradigm where AI serves as a tool for vertical and horizontal growth, contingent on human creativity and agency. Only by mastering this dynamic relationship can architects avoid the trap of passive, standardized design and unlock the true potential of AI. Full article

To establish a theoretical foundation for assessing the dynamic performance of high-speed train catenary systems in wind-prone regions, this study develops a coupled pantograph–catenary model using ANSYS(2022R1) APDL. The dynamic responses of conventional high-speed pantographs traversing both mainline and transition sections are analyzed under varying operational conditions. The key findings reveal that an elevated rated tension in the contact wire and messenger wire reduces the pantograph lift in wind areas with no crosswind compared to non-wind areas, with an average lift reduction of 8.52% and diminished standard deviation, indicating enhanced system stability. Under a 20 m/s crosswind, both tested pantograph designs maintain contact force and dynamic lift within permissible thresholds, while significant catenary undulations predominantly occur at mid-span locations. Active control strategies preserve the static lift force but induce pantograph flattening under compression, reducing aerodynamic drag and resulting in smaller contact force fluctuations relative to normal-speed sections. In contrast, passive control increases static lift, thereby causing greater fluctuations in contact force compared to baseline conditions. The superior performance of active control is attributed to its avoidance of static lift amplification, which dominates the dynamic response in passive systems. Full article