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The school–vacation cycle may have impacts on the psychological states of adolescents. However, little evidence illustrates how transition from school to vacation impacts students’ psychological states (e.g. depression and anxiety).
Aims
To explore the changing patterns of depression and anxiety symptoms among adolescent students within a school–vacation transition and to provide insights for prevention or intervention targets.
Method
Social demographic data and depression and anxiety symptoms were measured from 1380 adolescent students during the school year (age: 13.8 ± 0.88) and 1100 students during the summer vacation (age: 14.2 ± 0.93) in China. Multilevel mixed-effect models were used to examine the changes in depression and anxiety levels and the associated influencing factors. Network analysis was used to explore the symptom network structures of depression and anxiety during school and vacation.
Results
Depression and anxiety symptoms significantly decreased during the vacation compared to the school period. Being female, higher age and with lower mother's educational level were identified as longitudinal risk factors. Interaction effects were found between group (school versus vacation) and the father's educational level as well as grade. Network analyses demonstrated that the anxiety symptoms, including ‘Nervous’, ‘Control worry’ and ‘Relax’ were the most central symptoms at both times. Psychomotor disturbance, including ‘Restless’, ‘Nervous’ and ‘Motor’, bridged depression and anxiety symptoms. The central and bridge symptoms showed variation across the school vacation.
Conclusions
The school–vacation transition had an impact on students’ depression and anxiety symptoms. Prevention and intervention strategies for adolescents’ depression and anxiety during school and vacation periods should be differentially developed.
The flexible delivery of single-frequency lasers is far more challenging than that of conventional lasers due to the onset of stimulated Brillouin scattering (SBS). Here we present the successful delivery of 100 W single-frequency laser power through 100 m of anti-resonant hollow-core fiber (AR-HCF) in an all-fiber configuration, with the absence of SBS. By employing a custom-designed AR-HCF with a mode-field diameter matching that of a large-mode-area panda fiber, the system achieves high coupling efficiency without the need for free-space components or fiber post-processing. The AR-HCF attains a transmission efficiency of 92%, delivering an output power of 100.3 W with a beam quality factor (M2) of 1.22. The absence of SBS is confirmed through monitoring backward light, which shows no increase in intensity. This all-fiber architecture ensures high stability, compactness and efficiency, potentially expanding the application scope of single-frequency lasers in high-precision metrology, optical communication, light detection and ranging systems, gravitational wave detection and other advanced applications.
The emerging era of big data in radio astronomy demands more efficient and higher-quality processing of observational data. While deep learning methods have been applied to tasks such as automatic radio frequency interference (RFI) detection, these methods often face limitations, including dependence on training data and poor generalization, which are also common issues in other deep learning applications within astronomy. In this study, we investigate the use of the open-source image recognition and segmentation model, Segment Anything Model (SAM), and its optimized version, HQ-SAM, due to their impressive generalization capabilities. We evaluate these models across various tasks, including RFI detection and solar radio burst (SRB) identification. For RFI detection, HQ-SAM (SAM) shows performance that is comparable to or even superior to the SumThreshold method, especially with large-area broadband RFI data. In the search for SRBs, HQ-SAM demonstrates strong recognition abilities for Type II and Type III bursts. Overall, with its impressive generalization capability, SAM (HQ-SAM) can be a promising candidate for further optimization and application in RFI and event detection tasks in radio astronomy.
This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed-loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.
This study demonstrates a kilowatt-level, spectrum-programmable, multi-wavelength fiber laser (MWFL) with wavelength, interval and intensity tunability. The central wavelength tuning range is 1060–1095 nm and the tunable number is controllable from 1 to 5. The wavelength interval can be tuned from 6 to 32 nm and the intensity of each channel can be adjusted independently. Maximum output power up to approximately 1100 W has been achieved by master oscillator power amplifier structures. We also investigate the wavelength evolution experimentally considering the difference of gain competition, which may give a primary reference for kW-level high-power MWFL spectral manipulation. To the best of our knowledge, this is the highest output power ever reported for a programmable MWFL. Benefiting from its high power and flexible spectral manipulability, the proposed MWFL has great potential in versatile applications such as nonlinear frequency conversion and spectroscopy.
The reactive Navier–Stokes equations with adaptive mesh refinement and a detailed chemical reactive mechanism (11 species, 27 steps) were adopted to investigate a detonation engine considering the injection and supersonic mixing processes. Flame acceleration and deflagration-to-detonation transition (DDT) in a premixed/inhomogeneous supersonic hydrogen–air mixture with and without transverse jet obstacles were addressed. Results demonstrate the difficulty in undergoing DDT in the premixed/inhomogeneous supersonic mixture within a smooth chamber. By contrast, multiple transverse jets injected into the chamber aid detonation transition by introducing perturbed vortices, shock waves and a suitable blockage ratio. Increasing distance between the leading shock and the flame tip impedes detonation transition due to an insufficient blockage ratio. The extremely perturbed distributions of fuel-lean and fuel-rich mixtures lead to more complicated flame structures. Also, a larger flame thickness appears in the inhomogeneous mixture compared with the premixed mixture, resulting in a lower combustion temperature. The key findings are that the DDT, detonation quenching and reinitiation are generated in the inhomogeneous supersonic mixture, but both DDT mechanisms are ascribed to a strong Mach stem with the Zel'dovich gradient mechanism. Additionally, the obtained results demonstrate that an intensely fuel-lean mixture (equivalence ratio = 0.15) results in a partially decoupled flame front. However, detonation reinitiation and subsequent self-sustained detonation occur when a fierce shock wave propagates through a highly sensitive mixture, even within a smaller and elongated area. Moreover, the inhomogeneous mixture also augments the propagation speed and detonation cell structure instabilities and delays the sonic point resulting from the extending non-equilibrium reaction.
We demonstrated a method to improve the output performance of a Ti:sapphire laser in the long-wavelength low-gain region with an efficient stimulated Raman scattering process. By shifting the wavelength of the high-gain-band Ti:sapphire laser to the long-wavelength low-gain region, high-performance Stokes operation was achieved in the original long-wavelength low-gain region of the Ti:sapphire laser. With the fundamental wavelength tuning from 870 to 930 nm, first-order Stokes output exceeding 2.5 W was obtained at 930–1000 nm, which was significantly higher than that directly generated by the Ti:sapphire laser, accompanied by better beam quality, shorter pulse duration and narrower linewidth. Under the pump power of 42.1 W, a maximum first-order Stokes power of 3.24 W was obtained at 960 nm, with a conversion efficiency of 7.7%. Furthermore, self-mode-locked modulations of first- and second-order Stokes generation were observed in Ti:sapphire intracavity solid Raman lasers for the first time.
Unpredictability is a core but understudied dimension of adversities and has been receiving increasing attention recently. The effects of unpredictability on psychopathology and the underlying neural mechanisms, however, remain unclear. It is also unknown how unpredictability interacts with other dimensions of adversities in predicting brain development and psychopathology of youth.
Methods
We applied cluster robust standard errors to examine how unpredictability was associated with the developmental changes in resting-state functional connectivity (rsFC) of large-scale brain networks implicated in psychopathology, as well as the moderating role of deprivation, using data from the Adolescent Brain Cognitive Development (ABCD) study, which included four measurements from baseline (mean ± s.d. age, 119.13 ± 7.51 months; 2815 females) to 3-year follow-up (N = 5885).
Results
After controlling for threat, unpredictability was associated with a smaller increase in rsFC within default mode network (DMN) and a smaller decrease in rsFC between cingulo-opercular network (CON) and DMN. Neighborhood educational deprivation moderated the associations between unpredictability and changes in rsFC within DMN and fronto-parietal network (FPN), as well as between CON and DMN. A smaller decrease in rsFC between CON and DMN mediated the association between unpredictability and externalizing problems. Neighborhood educational deprivation moderated the indirect pathway from unpredictability to externalizing problems via a smaller decrease in CON-DMN rsFC.
Conclusions
Our findings shed light on the neural mechanisms underlying the associations between unpredictability and adolescents' psychopathology and the moderating role of deprivation, highlighting the significance of providing stable environment and abundant educational opportunities to facilitate optimal development.
Psychotic-like experiences (PLEs), especially for persistent PLEs, are highly predictive of subsequent mental health problems. Hence, it is crucial to explore the psychopathological associations underlying the occurrence and persistence of PLEs. This study aimed to explore the above issues through a longitudinal dynamic network approach among PLEs and psychological and psychosocial factors.
Methods
A total of 3,358 college students completed two waves of online survey (from Oct 2021 to Oct 2022). Socio-demographic information was collected at baseline, and PLEs, depressive and anxiety symptoms, and adverse life events were assessed in both waves. Cross-lagged panel network analyses were used to establish networks among individuals with baseline PLEs as well as those without.
Results
At baseline, 455(13.5%) students were screened positive for PLEs. Distinct dynamic network structures were revealed among participants with baseline PLEs and those without. While ‘psychomotor disturbance’ had the strongest connection with PLEs in participants with baseline PLEs, ‘suicide/self-harm’ was most associated with PLEs in those without. Among all three subtypes of PLEs, bizarre experiences and persecutory ideation were the most affected nodes by other constructs in participants with baseline PLEs and those without, respectively. Additionally, wide interconnections within the PLEs construct existed only among participants without baseline PLEs.
Conclusions
The study provides time-variant associations between PLEs and depressive symptoms, anxiety symptoms, and adverse life events using network structures. These findings help to reveal the crucial markers of the occurrence and persistence of PLEs, and shed high light on future intervention aimed to prevent and relieve PLEs.
Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice (Oryza sativa L.) fields has been increasing, likely due to changing paddy-farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil-flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, nonstructural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchymal fibers, with lignin content also increasing. After 90 d, the lignified sclerenchymal fibers and lignin contents were 4.0 and 1.1 times higher than those in the no soil-flooding treatment. Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil-flooding conditions by adopting a low-oxygen quiescence strategy, which involves morphological adaptations through the development of lignified sclerenchymal fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.
With the development of overall design methodologies for hypersonic vehicles and their propulsion systems, nozzles should expand airflow in a short length and provide sufficient thrust. Therefore, the large expansion ratio single expansion ramp nozzle (LSERN) is widely used. The form of the overexpanded flow field in the nozzle is complex, under the conditions of nozzle start-up, low speed and low nozzle pressure ratio (NPR), thereby negatively influencing the entire propulsion system. Thus, the nozzle flow separation pattern and the key factors affecting the flow separation pattern also deserve considerable attention. In this study, the design of SERN is completed using the cubic curve design method, and the model is numerically simulated for specific operating conditions to study the flow separation patterns and the transition processes of different patterns. Furthermore, the key factors affecting the various flow separation patterns in the nozzle are investigated in detail. Results show that the LSERN in different NPRs appeared in two types of restricted shock separation (RSS) pattern and free shock separation (FSS) pattern, as well as their corresponding flow separation pattern transition processes. The initial expansion angle and the nozzle length affect the range of NPRs maintained by the FSS pattern. The initial expansion angle affects the pattern of flow separation, whereas the nozzle length remarkably influences the critical NPR during transition.
For a class of robustly transitive diffeomorphisms on ${\mathbb T}^4$ introduced by Shub [Topologically transitive diffeomorphisms of $T^4$. Proceedings of the Symposium on Differential Equations and Dynamical Systems (Lecture notes in Mathematics, 206). Ed. D. Chillingworth. Springer, Berlin, 1971, pp. 39–40], satisfying an additional bunching condition, we show that there exists a $C^2$ open and $C^r$ dense subset ${\mathcal U}^r$, $2\leq r\leq \infty $, such that any two hyperbolic points of $g\in {\mathcal U}^r$ with stable index $2$ are homoclinically related. As a consequence, every $g\in {\mathcal U}^r$ admits a unique homoclinic class associated to the hyperbolic periodic points with index $2$, and this homoclinic class coincides with the whole ambient manifold. Moreover, every $g\in {\mathcal U}^r$ admits at most one measure of maximal entropy, and every $g\in {\mathcal U}^{\infty }$ admits a unique measure of maximal entropy.
Background: Healthcare-associated central line associated bloodstream infection (HA-CLABSI) surveillance is important for monitoring healthcare-associated infections (HAIs) and evaluating effectiveness of infection prevention (IP) measures. However, implementing it is a laborious and time-consuming approach. Exclusive focus on central lines neglects HAI risk due to peripheral vascular catheters. This study aimed to assess whether HA-CLABSI incidence could be inferred from HA-bloodstream infection (BSI) trends and explore shift to HA-BSI surveillance. Methods: The study was performed in a Singaporean tertiary care hospital. Electronic medical records review was performed to determine whether positive blood cultures met Centers for Disease Control/National Health Safety Network (CDC/NHSN) definitions for HA-CLABSI and HA-BSI. Incident episodes of HA-BSI were included (excluding positive cultures repeated within 14 days). Incident organisms were explored to identify common causative pathogens (excluding same organisms isolated from cultures repeated within 14 days). CLABSI and BSI occurring ≥72hrs after admission were considered healthcare-associated. Patients under oncology or hematology service were considered immunocompromised. Incidence rates (IR) per 10,000 patient-days, patient characteristics and causative pathogens were compared between both indicators. Results: From January 2022 to October 2023, mean IR for HA-CLABSI was 0.63 (n=68) and for HA-BSI was 10.06 (n=1094). Median age of patients with HA-CLABSI was 66 years and HA-BSI was 68 years. HA-CLABSI and HA-BSI were more common in males (60.86% & 58.68%). Median duration between admission to HA-CLABSI was 20 days and to HA-BSI was 12 days. Median duration between central line insertion to HA-CLABSI was 16 days. Of 1094, 631 (57.7%) patients had vascular catheter(s) (i.e., IV cannula, port-a-cath, peripherally-inserted central catheter or central line) inserted at time of HA-BSI diagnosis, of whom 46 (7.3%) patients had CLABSI ±2days from positive blood culture. There was no significant correlation between monthly aggregate data from these indicators (Spearman’s correlation coefficient= 0.36, p-value=0.1). Predominant organisms causing HA-CLABSI and HA-BSI were gram negative bacteria (GNB, 40% & 57.21%), gram positive bacteria (24.71% & 22.23%), and fungi. Common GNB in CLABSI patients were Pseudomonas spp. and Stenotrophomonas maltophilia (8.24%), followed by Serratia marcescens and Klebsiella pneumoniae (5.88%). The frequent GNB in HA-BSI patients were Escherichia coli (15.4%), Klebsiella pneumonia (12.68%), and Pseudomonas spp. (6.69%). Common multi-drug resistant organisms were vancomycin-resistant Enterococcus faecium (10.59% & 3.69%) and methicillin-resistant Staphylococcus aureus (10.59% & 3.07%). Conclusion: HA-BSI did not correlate with HA-CLABSI. HA-BSI reflects heterogenous population outcomes. For utilization as surveillance indicator, further assessment on exclusion criteria is required to improve specificity.
Salvia miltiorrhiza is an outcrossing and perennial herb native to China. Although well-known for its medicinal value, there is a lack of knowledge regarding its natural population genetics. Here, we used 12 microsatellite markers to investigated population genetic diversity and structure of 215 samples from populations naturally distributed in central eastern China. A moderate level of genetic diversity was detected probably due to the over-mining of its roots. The allelic richness (AR) ranged from 3.034 to 4.889 with an average of 3.891. Moreover, pairwise estimates of FST among the populations of S. miltiorrhiza varied from 0.036 to 0.312 and two clusters were obtained by STRUCTURE and discriminant analysis of principal components. It is likely that the genetic differentiation of these two clusters was formed during glacial periods. Our result provides insights into the conservation of this valuable medicinal plant.
Real-time reverse-transcriptase polymerase chain reaction (RT-PCR) has been the gold standard for diagnosing coronavirus disease 2019 (COVID-19) but has a lag time for the results. An effective prediction algorithm for infectious COVID-19, utilized at the emergency department (ED), may reduce the risk of healthcare-associated COVID-19.
Objective:
To develop a prototypic prediction model for infectious COVID-19 at the time of presentation to the ED.
Material and methods:
Retrospective cohort study of all adult patients admitted to Singapore General Hospital (SGH) through ED between March 15, 2020, and December 31, 2022, with admission of COVID-19 RT-PCR results. Two prediction models were developed and evaluated using area under the curve (AUC) of receiver operating characteristics (ROC) to identify infectious COVID-19 patients (cycle threshold (Ct) of <25).
Results:
Total of 78,687 patients were admitted to SGH through ED during study period. 6,132 of them tested severe acute respiratory coronavirus 2 positive on RT-PCR. Nearly 70% (4,226 of 6,132) of the patients had infectious COVID-19 (Ct<25). Model that included demographics, clinical history, symptom and laboratory variables had AUROC of 0.85 with sensitivity and specificity of 80.0% & 72.1% respectively. When antigen rapid test results at ED were available and added to the model for a subset of the study population, AUROC reached 0.97 with sensitivity and specificity of 95.0% and 92.8% respectively. Both models maintained respective sensitivity and specificity results when applied to validation data.
Conclusion:
Clinical predictive models based on available information at ED can be utilized for identification of infectious COVID-19 patients and may enhance infection prevention efforts.
Contra-posing panel data on the incidence of pulmonary tuberculosis (PTB) at the provincial level in China through the years of 2004–2021 and introducing a geographically and temporally weighted regression (GTWR) model were used to explore the effect of various factors on the incidence of PTB from the perspective of spatial heterogeneity. The principal component analysis (PCA) was used to extract the main information from twenty-two indexes under six macro-factors. The main influencing factors were determined by the Spearman correlation and multi-collinearity tests. After fitting different models, the GTWR model was used to analyse and obtain the distribution changes of regression coefficients. Six macro-factors and incidence of PTB were both correlated, and there was no collinearity between the variables. The fitting effect of the GTWR model was better than ordinary least-squares (OLS) and geographically weighted regression (GWR) models. The incidence of PTB in China was mainly affected by six macro-factors, namely medicine and health, transportation, environment, economy, disease, and educational quality. The influence degree showed an unbalanced trend in the spatial and temporal distribution.
We formulate a centrally planned portfolio selection problem with the investor and the manager having S-shaped utilities under a recently popular first-loss contract. We solve for the closed-form optimal portfolio, which shows that a first-loss contract can sometimes behave like an option contract. We propose an asymptotic approach to investigate the portfolio. This approach can be adopted to illustrate economic insights, including the fact that the portfolio under a convex contract becomes more conservative when the market state is better. Furthermore, we discover a means of Pareto improvement by simultaneously considering the investor’s utility and increasing the manager’s incentive rate. This is achieved by establishing the collection of Pareto points of a single contract, proving that it is a strictly decreasing and strictly concave frontier, and comparing the Pareto frontiers of different contracts. These results may be helpful for the illustration of risk choices and the design of Pareto-optimal contracts.
Tuberculosis (TB) infection prevention and control (IPC) in healthcare facilities is key to reducing transmission risk. A framework for systematically improving TB IPC through training and mentorship was implemented in 9 healthcare facilities in China from 2017 to 2019.
Methods:
Facilities conducted standardized TB IPC assessments at baseline and quarterly thereafter for 18 months. Facility-based performance was assessed using quantifiable indicators for IPC core components and administrative, environmental, and respiratory protection controls, and as a composite of all control types We calculated the percentage changes in scores over time and differences by IPC control type and facility characteristics.
Results:
Scores for IPC core components increased by 72% during follow-up when averaged across facilities. The percentage changes for administrative, environmental, and respiratory protection controls were 39%, 46%, and 30%, respectively. Composite scores were 45% higher after the intervention. Overall, scores increased most during the first 6 months. There was no association between IPC implementation and provincial economic development or volume of TB services.
Conclusions:
TB IPC policies and practices showed most improvement early during implementation and did not differ consistently by facility characteristics. The training component of the project helped increase the capacity of healthcare professionals to manage TB transmission risks. Lessons learned here will inform national TB IPC guidance.
Here, we report the generation of MeV alpha-particles from H-11B fusion initiated by laser-accelerated boron ions. Boron ions with maximum energy of 6 MeV and fluence of 109/MeV/sr@5 MeV were generated from 60 nm-thick self-supporting boron nanofoils irradiated by 1 J femtosecond pulses at an intensity of 1019 W/cm2. By bombarding secondary hydrogenous targets with the boron ions, 3 × 105/sr alpha-particles from H-11B fusion were registered, which is consistent with the theoretical yield calculated from the measured boron energy spectra. Our results demonstrated an alternative way toward ultrashort MeV alpha-particle sources employing compact femtosecond lasers. The ion acceleration and product measurement scheme are referential for the studies on the ion stopping power and cross section of the H-11B reaction in solid or plasma.