Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents

Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field

Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia

Siting and capacity configurations of static transfer switches for three-phase unbalance governance in rural distribution networks: a bi-level optimization programme

Introduction:Mitigating three-phase unbalance in rural distribution networks is a significant challenge, especially with the integration of photovoltaic and energy storage systems (PESS). While Distributed Static Transfer Switches (STS) offer a promising solution by regulating load phase sequences, conventional approaches are costly and inefficient, limiting large-scale implementation.Methods:To address these limitations, we propose a bi-level optimization model for the siting and capacity optimization of STS in rural networks. The upper-layer model focuses on minimizing investment and maintenance costs for STS, while considering branch loss reduction and three-phase unbalance mitigation. The lower-layer model aims to minimize three-phase unbalance in daily operations, with the integration of PESS. We use the hyperparameter alternating iteration (HAI) method to iteratively refine the bi-level model and obtain optimal planning and operational solutions.Results:We applied the proposed model to an IEEE-13 benchmark case study. The results demonstrated that the bi-level optimization approach effectively reduced three-phase unbalance in the rural distribution system while minimizing STS planning costs.Discussion:This innovative approach provides a cost-effective and efficient solution for mitigating three-phase unbalance in rural distribution networks, enhancing the feasibility of large-scale STS deployment. The integration of PESS further contributes to system stability, making this model a robust tool for future network planning

A Statistical Investigation of the Earthquake Predictions Using LURR

In terms of the spatial scanning of LURR (Load-Unload Response Ratio), we have been predicting the seismic tendency within the next year for the mainland of China from 1995 to 2003. In order to make the quantitative retrospective assessment of LURR metho

Improved reinforcement learning path planning algorithm integrating prior knowledge.

In order to realize the optimization of autonomous navigation of mobile robot under the condition of partial environmental knowledge known. An improved Q-learning reinforcement learning algorithm based on prior knowledge is proposed to solve the problem of slow convergence and low learning efficiency in mobile robot path planning. Prior knowledge is used to initialize the Q-value, so as to guide the agent to move toward the target direction with a greater probability from the early stage of the algorithm, eliminating a large number of invalid iterations. The greedy factor ε is dynamically adjusted based on the number of times the agent successfully reaches the target position, so as to better balance exploration and exploitation and accelerate convergence. Simulation results show that the improved Q-learning algorithm has a faster convergence rate and higher learning efficiency than the traditional algorithm. The improved algorithm has practical significance for improving the efficiency of autonomous navigation of mobile robots

Functional Nanomaterials: From Structures to Biomedical Applications

In recent decades, a number of functional nanomaterials have attracted a great amount of attention and exhibited excellent performance for biomedical and pharmaceutical applications [...