Early Postoperative Patient-Reported Outcomes After Thoracoscopic Segmentectomy Versus Lobectomy for Small-Sized Peripheral Non-small-cell Lung Cancer.

BACKGROUND: Patient-reported outcomes are critical for obtaining valuable patient insight into different surgical treatment options with comparable clinical outcomes. This study aimed to compare early postoperative patient-reported symptoms and functioning between thoracoscopic segmentectomy and lobectomy for small-sized (≤ 2 cm) peripheral non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: This study included 110 patients who underwent thoracoscopic segmentectomy or lobectomy for peripheral NSCLC ≤ 2 cm in a multicenter prospective longitudinal study (CN-PRO-Lung 1). Symptom severity, functional status, and short-term clinical outcomes were compared between the groups. Symptom severity and functional status were measured using the MD Anderson Symptom Inventory-Lung Cancer at baseline, daily post-surgery, and weekly post-discharge for up to 4 weeks. Both the proportion of moderate-to-severe scores and mean scores on a 0-10 scale were compared between the groups. RESULTS: Overall, 48 and 62 patients underwent thoracoscopic segmentectomy and lobectomy, respectively. No significant between-group differences were found in the severity of the top five symptoms (coughing, shortness of breath, pain, fatigue, and disturbed sleep) or in the impairment of all six function items (work, walking, general activity, enjoyment of life, mood, and relations with others) during both the 6-day postoperative hospitalization and the 4-week post-discharge (all p > 0.05). Short-term clinical outcomes of postoperative hospital stay, operative time, drainage time, postoperative in-hospital oral morphine equivalent dose, and complication rate were also comparable (all p > 0.05). CONCLUSIONS: In patients with peripheral NSCLC ≤ 2 cm, thoracoscopic segmentectomy and lobectomy might produce comparable symptom burden and functional impairment during the early postoperative period.

Patient-Reported Outcomes of Video-Assisted Thoracoscopic Surgery Versus Thoracotomy for Locally Advanced Lung Cancer: A Longitudinal Cohort Study.

BACKGROUND: The effects of video-assisted thoracoscopic surgery (VATS) and traditional thoracotomy with respect to patient-reported outcomes (PROs) have only been assessed for early-stage lung cancer. This study was a longitudinal PRO assessment to compare the effects of these surgeries for locally advanced (stage II and III) lung cancer from the patients' perspective. METHODS: We investigated lung cancer patients from a previous prospective, multicentre study. Longitudinal data of clinical characteristics and PROs were collected. PROs were obtained preoperatively, daily in the hospital postoperatively, and weekly up to 4 weeks after discharge or the beginning of postoperative adjuvant therapy. Symptoms and impact on daily functioning and quality of life (QOL) were assessed by using the MD Anderson Symptom Inventory for lung cancer and a single-item QOL scale. Trajectories of PROs over the investigation period were compared. RESULTS: Overall, 117 primary lung cancer patients (stage II or III), including 63 and 54 patients who underwent VATS and traditional thoracotomy, respectively, were included. During postoperative hospitalization, VATS patients reported milder disturbed sleep (p = 0.048), drowsiness (p = 0.008), and interference with activity (p = 0.001), as well as better work ability (p < 0.0001), walking ability (p < 0.0001), and life enjoyment (p = 0.004). Post-discharge, VATS patients had less distress (p = 0.039), milder pain (p = 0.006), better work ability (p = 0.001), and better QOL (p = 0.047). CONCLUSIONS: Locally advanced lung cancer patients who underwent VATS had lower postoperative symptom burden, less daily function interference, and better QOL than those who underwent thoracotomy.

Fault-Tolerant Fuzzy-Resilient Control for Fractional-Order Stochastic Underactuated System With Unmodeled Dynamics and Actuator Saturation.

This article is considered on underactuated fractional-order stochastic systems (FOSSs) with actuator saturation and incrementally conic nonlinear terms, whose fractional-order α ∈ (0,1) . First, to bring FO dynamic signals, solving the unmodeled dynamics, in the meantime, the saturated nonlinear term of the control input is taken into account. At the time, to cope with the stability issue of FOSS under such situation, the fault tolerant resilient controller based on underactuated condition is designed. Then, according to the method of the Lyapunov and It∧ o differential formulation to design proper multiple Lyapunov-Krasovskii (L-K) functions, such that, a novel sufficient condition of the robustly asymptotically stability of fuzzy FOSS under underactuated conditions is rigorously proved in terms of linear matrix inequality (LMI). Furthermore, in order to research the mean square stability of the above-mentioned system, so the solution of FOSS is obtained to achieve this purpose. By applying the above method, which is proposed in this work that the controlled system can be obtained with faster response and higher control accuracy. At last, to display the superiority of the above-mentioned scheme is effective, tethered satellite system and numerical results are presented.

Online lithium-ion battery intelligent perception for thermal fault detection and localization.

-Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The improved LBIP-V2 performs better than LBIP-V1 in most cases. We tested the performance of LBIP on the single-cell battery dataset, the 1P3S battery pack dataset, and the flattened 1P3S battery pack dataset. The results show that the recognition accuracy of LBIP exceeded 95 %. At the same time, we simulated the failure of the 1P3S battery pack within 0-15 min and tested the effectiveness of LBIP in real-time battery fault diagnosis. The results indicate that LBIP can effectively respond to online faults with a confidence level of over 98 %.

A Study on the Critical Saturation Response Characteristics of Simple and Sandwich Cylindrical Shells under Long-Duration Blast Loading.

Experimental research and numerical simulations of the structural response to shock waves with pulse durations of hundreds of milliseconds, or even seconds, are extremely challenging. This paper takes typical single-layer and sandwich cylindrical shells as the research objects. The response rules of cylindrical shells under long-duration blast loadings were studied. The results show that when the pulse duration is greater than or equal to 4~5 times the first-order period of the structure, the maximum response of the structure tends to be consistent, that is, the maximum response of the cylindrical shells with different vibration shapes shows a saturation effect as the pulse duration increases. This study established the relationship between the saturation loading time and the inherent characteristics of the structure. It was found that the saturation effect was applicable under the following conditions, including different load waveforms, elastic-plastic deformation of the structure, and the loading object being a sandwich shell. This will help transform the long-duration explosion wave problem into a finite pulse-duration shock wave problem that can be realized by both experiments and numerical simulations.

Suicidal ideation and suicide attempts among students aged 12 to 24 after the lifting of COVID-19 restrictions in China: prevalence and associated factors.

Objective: To investigate the prevalence and associated factors of suicidal ideation and suicide attempts among adolescent and young adults in China from December 14, 2022 to February 28, 2023, when COVID-19 restrictions were lifted. Methods: Students in middle and high schools and colleges and universities in the province of Sichuan, China were asked to complete on-line cross-sectional surveys. Information was collected about sociodemographics, experiences related to the COVID-19 pandemic, suicidal ideation and suicide attempts. Participants also filled out the Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7 and the Social Support Rate Scale surveys. Factors associated with suicidal ideation or suicide attempts were explored using logistic regression. Results: Of the 82,873 respondents (aged 12 to 24 years), 21,292 (25.7%) reported having thought of suicide at least once in their lifetime, 10,382 (12.5%) reported having thought about suicide within the previous 12 months, and 1,123 (1.4%) reported having attempted it within the previous 12 months. Risk of lifetime suicidal ideation was higher among middle school students than among older students. Risk of suicidal ideation and risk of suicide attempts correlated directly with severity of symptoms of depression and anxiety, and inversely with level of social support. Greater risk of suicidal ideation and suicidal attempts was associated with: being female, living in an urban environment, attending a boarding school, currently being in love, having parents who divorced or remarried, having parents who exhibit non-authoritative parenting behavior, having higher family income, having been COVID-19 infected, having been quarantined for a long time, and being dissatisfied with one's education. Conclusions: Suicidal ideation and suicide attempts remain prevalent among young people in China. The potential associated factors identified in our study may be useful for targeting appropriate psychosocial interventions and developing mental health policies.

The mutual overlapping impact of stress and infection on mental health problems in adolescents and youths during and after COVID-19 pandemic in China.

BACKGROUND: It is unclear about the mutual impact of COVID-19 related psychological stress and infection on mental health of adolescent and youth students. This study aimed to explore the mutual impact of COVID-19 related psychological stress and infection on mental health problems among students. METHODS: This study was conducted from December 14, 2022 to February 28, 2023 in Sichuan, China. Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, PTSD Checklist for DSM-5, Insomnia Severity Index, and Internet Addiction Test were used. Participants were grouped by COVID-19 infection and psychological stress level. The differences among groups were compared, and logistic regression analysis was used to investigate risk factors for depression, anxiety, PTSD and insomnia among groups. RESULTS: Of 90,118 participants, 82,873 (92.0 %) finished the questionnaires and were included in the study. Of 82,873 participants, 33,314 (40.2 %) reported to be infected with COVID-19. Participants had depression symptoms (38.1 %), anxiety symptoms (31.8 %), PTSD (33.9 %), insomnia (34.0 %), and internet addiction (60.3 %). Compared with participants uninfected with low psychological stress level, the risk for symptoms of depression, anxiety, PTSD and insomnia increased by 9.6 %, 12.3 %, 6.6 %, and 12.0 % in participants infected with low psychological stress level (p < 0.001), 106.8 %, 125.9 %, 125.2 %, and 95.7 % in participants uninfected with high psychological stress level (p < 0.001), and 147.3 %, 161.1 %, 158.7 %, and 141.0 % in participants infected with high psychological stress level (p < 0.001). LIMITATION: This study is a cross-sectional design, and no causal associations should be inferred. Infection status was based on self-report of participants with infectious symptoms. CONCLUSION: COVID-19 related psychological stress and infection per se have mutually overlapping impacts on mental health problems among students. Further health policies and psychosocial interventions should be developed to reduce mutually overlapping impact and improve the long-term mental health among students.

Spontaneous Hemothorax by Pulmonary Arteriovenous Malformation during Pregnancy.

Background: Pulmonary arteriovenous malformation (PAVM) is a rare vascular malformation that may cause hemothorax, especially during pregnancy. Case Description: A 25-year-old woman presented sudden-onset left chest pain, dizziness, and dyspnea in the 27th week of gestation. Computed tomography angiography showed left pleural effusion with complete hemithorax opacification and an aneurysmal PAVM. She exhibited hemorrhagic shock and received emergency exploratory video-assisted thoracic surgery. A ruptured PAVM was identified and stopped by wedge resection in the upper lobe of the left lung. The patient's postoperative clinical course was uncomplicated. She subsequently delivered a healthy live baby vaginally at 41 weeks gestation. Conclusion: PAVM should be considered in pregnant women with hemothorax. Emergency thoracoscopic surgery is the best treatment option.

Adaptive Event-Triggered Time-Varying Output Bipartite Formation Containment of Multiagent Systems Under Directed Graphs.

The time-varying output bipartite formation containment (TVOBFC) problem for linear multiagent systems (MASs) under directed graphs is an important problem. However, the methods in existing works rely on the global information of the MASs or do not use event-triggered communication. This article investigates two kinds of TVOBFC problems for heterogeneous linear MASs under signed digraphs by event-triggered communication. For the first case where leaders have the same dynamics, the innovative fully distributed event-triggered protocol for the follower is proposed. In this case, the followers form the preset formation shape. For the second case where leaders have different dynamics, the leaders are divided into two groups. One group can directly obtain the output information of the virtual leader, while the other group cannot. In order to make leaders achieve the formation shape and track the virtual leader, two kinds of innovative observers are designed for two kinds of leaders to estimate the state of the virtual leader, and the control protocol is designed for each leader based on the designed observers. Then, the control law for each follower is designed to solve the formation containment problem. Finally, two examples are introduced to illustrate the main results.

Finite-Time Event-Triggered Output Consensus of Heterogeneous Fractional-Order Multiagent Systems With Intermittent Communication.

The finite-time output consensus (FTOC) issue of heterogeneous fractional-order multiagent systems (HFO-MASs) is investigated in this article. First, a new principle of finite-time convergence for absolutely continuous functions is developed if a fractional derivative inequality is satisfied. Next, in order to remove the assumption that the leader's system matrix is known to all agents in previous studies, a distributed adaptive finite-time observer is designed, which can estimate not only the leader's state but also the leader's system matrix. Then, a novel finite-time event-triggered compensator with intermittent communication is constructed to estimate the leader's state by introducing a dynamic threshold for a novel triggering function. In this case, the high frequency triggering is restrained and the triggering number is significantly reduced. The Zeno behavior does not exist by choosing parameters appropriately. In addition, two finite-time control strategies are constructed based on the above distributed observer and event-triggered compensator, respectively, to achieve output consensus in finite time. The feasibility of the proposed method is ensured by the comprehensive theoretical demonstration of the finite-time consensus stability and the analysis of the Zeno behavior. Finally, the examples are given to demonstrate the conclusion.

Cooperative Containment Control for Multiagent Systems With Reduced-Order Protocols.

This article addresses the problem of containment control for continuous-time multiagent systems. A containment error is first given to show the coordination between the outputs of leaders and followers. Then, an observer is designed based on the neighbor observable convex hull state. Under the assumption that the designed reduced-order observer is subject to external disturbances, a reduced-order protocol is designed to realize the containment coordination. In order to ensure the designed control protocol can achieve the effect of the main theories, a corresponding Sylvester equation is given with a novel approach which proves that the Sylvester equation is solvable. Finally, a numerical example is given to verify the validity of the main results.

Distributed Observer-Based Robust Fault Estimation Design for Discrete-Time Interconnected Systems With Disturbances.

This article focuses on the distributed robust fault estimation problem for a kind of discrete-time interconnected systems with input and output disturbances. For each subsystem, by letting the fault as a special state, an augmented system is constructed. Particularly, the dimensions of system matrices after augmentation are lower than some existing related results, which may help to reduce calculation amount, especially, for linear matrix inequality-based conditions. Then, a distributed fault estimation observer design scheme that utilizes the associated information among subsystems is presented to not only reconstruct faults, but also suppress disturbances in the sense of robust H∞ optimization. Besides, to improve the fault estimation performance, a common Lyapunov matrix-based multiconstrained design method is first given to solve the observer gain, which is further extended to the different Lyapunov matrices-based multiconstrained calculation method. Thus, the conservatism is reduced. Finally, simulation experiments are shown to verify the validity of our distributed fault estimation scheme.

Effects of internet addiction and academic satisfaction on mental health among college students after the lifting of COVID-19 restrictions in China.

Background: Internet addiction (IA) among students, worsened by Coronavirus disease 2019 (COVID-19) pandemic, has become a social problem with the digitalization of school learning and many aspects of daily life. However, few studies on IA have been conducted among students after the lifting of COVID-19 restrictions in China. Method: This large-sample, cross-sectional, online survey was conducted to explore the characteristics of IA and the association among IA, academic satisfaction, and mental health problems from December 14, 2022 to February 28, 2023 in Sichuan, China. All participants (N = 22,605) were students in colleges and universities, recruited via their teachers and professors. Results: Of all the participants, 14,921 (66.0%) participants had IA. Participants with IA were more likely to have depression symptom, anxiety symptom, insomnia, and lifetime suicidal ideation. In addition, participants with severe IA had significantly higher rates of mental health problems (e.g., depression, anxiety, insomnia, and suicidal ideation) than those with mild IA. A significant IA-by-academic satisfactory-interaction on mental health was identified: participants with higher level of IA showed particularly severe symptom of depression, anxiety and insomnia when affected by low satisfactory of academy (p < 0.001). Conclusion: This study reveals that IA has a significantly negative impact on mental health among college students after the lifting of COVID-19 restrictions in China. IA and academic satisfaction have interactive impacts on mental health problems among students. Further educational and health policies and psychosocial interventions should be developed to reduce IA and enhance academic satisfaction for improving students' mental health.

Academic performance and mental health among Chinese middle and high school students after the lifting of COVID-19 restrictions.

Background: Although the COVID-19 pandemic has greatly changed the way students studied, it is still unknown about the impact of the COVID-19 pandemic on students' academic performance and mental health. Objective: To explore the academic performance and mental health status of middle and high school students after the lifting of COVID-19 restrictions in China. Methods: An online survey was conducted in Sichuan province, China from Dec 14, 2022 to Feb 28, 2023. All participants were students in middle and high schools, recruited via their teachers. The general information, COVID-19-related information, and academic performance were collected. The Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Internet Addiction Test (IAT) were used to assess the mental health problems. Results: Of 60,268 participants, 36,247 (60.2%) middle and high school students reported that their studies were affected by the COVID-19 pandemic, and 24,864 (41.2%) reported that their academic performance had worsened. The prevalence of depression and anxiety symptoms was 38.4 and 32.7%, respectively. There was a significant association between academic performance change and mental health problems. The logistic regression analysis showed that improved academic performance was a protective factor for depression, and declined academic performance was a risk factor for depression and anxiety. Being COVID-19 infected, family members being infected, with quarantine experience, and with COVID-19-related stigma were risk factors for depression and anxiety. Conclusion: Academic studies and mental health status of middle and high school students in Sichuan, China have been negatively impacted by the COVID-19 pandemic, even after the lifting of COVID-19 restrictions. Students' academic performance, academic concerns, and mental health status should be considered for educational policymakers and institutions to improve students' academic studies and mental well-being.

Observations on Detonation Growth of Lead Azide at Microscale.

Lead azide (LA) is a commonly used primary explosive, the detonation growth of which is difficult to study because it is so sensitive and usually has a small charge size in applications. We used photon Doppler velocimetry (PDV) and calibrated polyvinylidene fluoride (PVDF) gauges to reveal the detonation growth in LA, which was pressed in the confinements with controlled heights. The particle-velocity profiles, output pressure, unsteady detonation velocity, reaction time, and reaction-zone width were obtained and analyzed. Three phases of detonation propagation of LA microcharges are discussed. The volume reactions occur at the beginning of detonation in LA microcharges without forming complete shock profiles. Then the shock front is fast with a slow chemistry reaction zone, which is compressed continuously between the height of 0.8 mm and 2.5 mm. Finally, the steady detonation is built at a height of 2.5 mm. The stable detonation velocity and CJ pressure are 4726 ± 8 m/s and 17.12 ± 0.22 GPa. Additionally, the stable reaction zone time and width are 44 ± 7 ns and 148 ± 11 µm. The detailed detonation process has not previously been quantified in such a small geometry.

Optimal Consensus Control Design for Multiagent Systems With Multiple Time Delay Using Adaptive Dynamic Programming.

In this article, a novel data-based adaptive dynamic programming (ADP) method is presented to solve the optimal consensus tracking control problem for discrete-time (DT) multiagent systems (MASs) with multiple time delays. Necessary and sufficient conditions of the corresponding equivalent time-delay system are provided on the basis of the causal transformations. Benefitting from the construction of tracking error dynamics, the optimal tracking problem can be transformed into settling the Nash-equilibrium in the graphical game, which can be completed by solving the coupled Hamilton-Jacobi (HJ) equations. An error estimator is introduced to construct the tracking error of the MASs only using the input and output (I/O) data. Therefore, the designed data-based ADP algorithm can minimize the cost functions and ensure the consensus of MASs without the knowledge of system dynamics. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

Distributed Bipartite Adaptive Event-Triggered Fault-Tolerant Consensus Tracking for Linear Multiagent Systems Under Actuator Faults.

This article considers the distributed bipartite adaptive event-triggered fault-tolerant consensus tracking issue for linear multiagent systems in the presence of actuator faults based on the output feedback control protocol. Both time-varying additive and multiplicative actuator faults are taken into account in the meantime. And the upper/lower bounds of actuator faults are not required to be known. First, the state observer is designed to settle the occurrence of unmeasurable system states. Two kinds of event-triggered mechanisms are then developed to schedule the interagent communication and controller updates. Next, with the developed event-triggered mechanisms, a novel observer-based bipartite adaptive control strategy is proposed such that the fault-tolerant control problem can be addressed. Compared with some related works on this topic, our control scheme can achieve the intermittent communication and intermittent controller updates, and the more general actuator faults and network topology are considered. It is proved that the exclusion of Zeno behavior can be realized. Finally, three illustrative examples are given to demonstrate the feasibility of the main theoretical findings.

Data-model alliance network for the online multi-step thermal warning of energy storage system based on surface temperature diffusion.

As an important type of energy storage, battery energy storage systems have been widely used. However, there are frequent cases of battery explosion due to high temperature. To address this issue, researches have been carried out either in the model-driven or the data-driven aspects to predict the temperature of the battery. In this paper, a two-node electrothermal model and a multi-scale long short-term memory network are established formulating a data-model alliance network (DMAN) for surface temperature diffusion. An improved adaptive boosting algorithm is then employed to enhance the bridge of the two models. Integrating a data-model alliance module (DMAM) and multi-step-ahead thermal warning network (MATWN), this DMAN provides an advanced online multi-step-ahead thermal warning structure to achieve early warning of temperature crossing. Experimental results verify the progressiveness of the proposed technique.