Enhanced battery capacity and cycle life due to suppressed side reactions on the surface of Ni-rich LiNi0.8Co0.1Mn0.1O2 cathode materials coated with Co3(PO4)2.

This study explores a strategy to mitigate capacity fading in secondary batteries, which is primarily attributed to side reactions caused by residual Li impurities (LiOH or Li2CO3) on the surface of Ni-rich LiNi0.8Co0.1Mn0.1O2 (NCM811) layered cathode materials. By applying a 1.5 wt% Co3(PO4)2 coating, we successfully formed a thin and stable LiF cathode-electrolyte interface (CEI) layer, which resulted in decreased battery resistance and enhanced diffusion of Li+ ions within the electrolyte. This coating significantly improved the interface stability of NCM811, leading to superior battery performance. Specifically, the discharge capacity of uncoated NCM811 was 190 mA h g-1 at a charge of 4.3 V and a rate of 0.1C, whereas the 1.5Co3(PO4)2/NCM811 exhibited an increased capacity of 213 mA h g-1. Furthermore, the Co3(PO4)2 coating effectively reduced the levels of LiOH and Li2CO3 on the NCM811 surface to only 0.1 %, thereby minimizing adverse side reactions with the electrolyte salt (LiPF6), cation mixing between Ni2+ and Li+, and defects at the NCM811 interface. As a result, battery lifespan was significantly extended. This study presents a robust approach for enhancing battery stability and performance by efficiently reducing residual Li+ ions on the surface of NCM811 through strategic Co3(PO4)2 coating.

Deep breathing in your hands: designing and assessing a DTx mobile app.

Digital Therapeutics (DTx) are experiencing rapid advancements within mobile and mental healthcare sectors, with their ubiquity and enhanced accessibility setting them apart as uniquely effective solutions. In this evolving context, our research focuses on deep breathing, a vital technique in mental health management, aiming to optimize its application in DTx mobile platforms. Based on well-founded theories, we introduced a gamified and affordance-driven design, facilitating intuitive breath control. To enhance user engagement, we deployed the Mel Frequency Cepstral Coefficient (MFCC)-driven personalized machine learning method for accurate biofeedback visualization. To assess our design, we enlisted 70 participants, segregating them into a control and an intervention group. We evaluated Heart Rate Variability (HRV) metrics and collated user experience feedback. A key finding of our research is the stabilization of the Standard Deviation of the NN Interval (SDNN) within Heart Rate Variability (HRV), which is critical for stress reduction and overall health improvement. Our intervention group observed a pronounced stabilization in SDNN, indicating significant stress alleviation compared to the control group. This finding underscores the practical impact of our DTx solution in managing stress and promoting mental health. Furthermore, in the assessment of our intervention cohort, we observed a significant increase in perceived enjoyment, with a notable 22% higher score and 10.69% increase in positive attitudes toward the application compared to the control group. These metrics underscore our DTx solution's effectiveness in improving user engagement and fostering a positive disposition toward digital therapeutic efficacy. Although current technology poses challenges in seamlessly incorporating machine learning into mobile platforms, our model demonstrated superior effectiveness and user experience compared to existing solutions. We believe this result demonstrates the potential of our user-centric machine learning techniques, such as gamified and affordance-based approaches with MFCC, which could contribute significantly to the field of mobile mental healthcare.

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models.

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives.

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.

Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment.

With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations.

Development of freeway-based test scenarios for applying new car assessment program to automated vehicles.

As automated driving technology continues to develop, studies are being conducted to develop various scenarios for assessing the functional safety, failure safety, and mobility of automated vehicles (AVs). As the commercialization of AVs progresses, it is necessary to develop a set of test scenarios for new car assessment programs (NCAPs), so as to provide information on the safety and reliability of AVs to consumers. To provide valuable information regarding newly emerged AVs to consumers who are willing to purchase them, it is necessary to derive specific and well-defined test scenarios based on the safety-in-use. Accordingly, to apply NCAPs to AVs, this study established test scenarios targeting freeways where AVs were expected to be commercialized. To this end, based on freeway traffic accident data and opinions of traffic safety and AV experts, we derived possible dangerous situations when an AV is maintaining a lane on a freeway. Functional scenarios were defined based on the derived dangerous situations. The priority of the defined functional scenarios was set using the analytic hierarchy process (AHP). Accordingly, this study presents a logical and concrete scenario construction methodology for deriving the ranges and values of test parameters for functional scenarios.

Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process.

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.

Deep Q-network-based traffic signal control models.

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.

[The Reliability and Validity of Korean Version of Wong and Law Emotional Intelligence Scale (K-WLEIS)].

PURPOSE: The aim of this study was to evaluate the reliability and validity of the Korean version of the Wong and Law Emotional Intelligence Scale (K-WLEIS). METHODS: Data were collected from 360 nursing students using a self reported questionnaire. Exploratory and confirmatory factor analysis were used to test construct validity. Convergence validity was identified by correlation with communication competency. Item convergent and discriminant validity were also analyzed. Reliability was evaluated internal consistency and test-retest reliability. RESULTS: The results of exploratory factor analysis showed that the eigen values ranged from 1.34 to 5.86 and 73.2% of the total explained variance. Confirmatory factor analysis showed adequate model fit indices (χ²/df 1.89, RMSEA .07, GFI .89, CFI .95, and TLI .93) and standardized factor loadings (.48 to .87). The average extracted variances (.71 to .79) and composite reliability (.80 to .87) validated convergence and discriminant validity of the items. Test-retest reliability of intra-class correlation coefficient was .90 and the Cronbach's alpha coefficient was .88. CONCLUSION: The K-WLEIS is an appropriate scale for measuring the emotional intelligence of Korean nursing students. Therefore, it is expected that the K-WLEIS will be used for nursing education programs to improve nursing students' emotional intelligence.