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Background We aimed to explore the long-term caregiving experiences of family caregivers of people with schizophrenia (PwS) in terms of both positive and negative aspects. Method Utilising a purposive sampling method, we conducted in-depth interviews with 20 family caregivers of persons who had suffered from schizophrenia for more than 20 years. We empirically investigated their retrospective experiences of caregiver-patient interactions during a long period of family caregiving. We audio-recorded and transcribed the interviews into text. We thematically coded and analysed the transcribed text using a four-phase method of theme development. Findings Schizophrenia might not only generate a caregiving burden, affect caregivers’ psychological status, and accordingly influence their coping strategies, but also have short- or long-term patient-related consequences. Discussion Family caregivers should develop their stress management skills to cope with relevant life changes and increase their knowledge of the potential psychological consequences for care recipients resulting from negative caregiving strategies during home-based practice. Care recipients with schizophrenia in a relatively stable status should be empowered to take care of themselves. More effective family-based interventions for psychiatric nursing or individualised training for symptom management should be tailored to serve families’ diverse needs.

This study aimed to conduct a quantitative synthesis of the clinical correlates of caregiver burden in schizophrenia studies published in the last two decades. Derived from eight electronic databases, this meta-analytic review revisits 34 English articles published from 2000 to 2020 relevant to family caregiver burden in the schizophrenia field. The Newcastle–Ottawa Scale (NOS) was used to assess study quality. The pooled effect sizes of the selected studies ranged from −0.390 to 0.751. The results indicated a significant association between a heavier burden and disease-related risk factors, including more severe symptoms, greater general psychopathology, greater severity of functional impairment, and longer duration of illness. The results show moderating effects of study characteristics (i.e., study quality, participants, and location) on the correlations between these disease-related risk factors and caregiver burden. This review highlights the roles of study characteristics in affecting the inconsistent results for the effects of disease-related risk factors on caregiver burden in families of patients with schizophrenia. Psychosocial interventions are essential for family caregivers of persons with schizophrenia. Future studies incorporating random samples from both high-income and low-to-middle-income countries will be crucial to understand the effects of cultural contexts on caregiver burden in families of persons with schizophrenia.

PurposeHousehold poverty associated with schizophrenia has been long described. However, the mechanisms by which schizophrenia may have influenced the economic status of a household in rural communities are still unclear. This study aimed to test an integrated model of schizophrenia, social support and caregiving burden on household poverty in a rural community in China.MethodsA mental health survey using identical methods and ICD-10 was conducted in six townships of Xinjin County (population ≥ 15 years old, n = 152,776), Chengdu, China in 2015. Identified persons with schizophrenia (n = 661) and their caregivers completed a joint questionnaire of sociodemographic information, illness conditions, social support and caregiving burden. Descriptive analysis was applied first to give an overview of the dataset. Then, multivariable regression analyses were conducted to examine the associative factors of social support, caregiving burden and household income. Then, structural equation modeling (SEM) was used to estimate the integrated model of schizophrenia, social support, caregiving burden and household income.ResultsHouseholds with patient being female, married, able to work and having better social function were better off. Larger household size, higher social support and lower caregiving burden also had salient association with higher household income. The relationship between schizophrenia and household poverty appeared to be mediated by the impacts of schizophrenia on social support and caregiving burden.ConclusionThere was a strong association between schizophrenia and household poverty, in which social support and caregiving burden may had played significant roles on mediating it. More precise poverty alleviation policies and interventions should focus on supporting recovery for persons with schizophrenia, as well as on increasing social support and on reducing family caregiving burden.

Albeit poverty reduction has been listed as an overarching objective in many countries’ development plans, little is known about how development could shape poverty dynamics in disadvantaged groups. Guided by a modified capability framework, this study aimed to explore the long-term experiences of poverty dynamics in severe mental illness. Semi-structured interviews were carried out with 20 caregivers who provided care for persons with severe mental illness in Chengdu, China. Their perceptions on development, the illness, and social security were addressed. Content analysis was employed to analyze data. Participants experienced an overall improvement of life quality due to changes on urban infrastructure and transformed lifestyle. However, they were more disadvantaged while facing ability-based opportunities. These families were hindered from transferring opportunities into incomes. Negative impacts of the illness were also reflected in multiple stigma and conversion difficulties. Additionally, the high threshold for payment made those inclusive social security policies not inclusive for them. Poverty associated with severe mental illness was unlikely to be alleviated automatically within the process of development. Social isolation and high caregiving burden had aggravated poverty for those disadvantaged families. Poverty alleviation should be closely linked to the improvement in social policies in China.

Although the link between mental health issues and physical functioning is established, research on the patterns of depression trajectories and their long-term impact on functional disabilities in middle-aged and older individuals remains scarce. This study leverages four waves of data from middle-aged and older Chinese adults to investigate how different trajectories of depression affect functional disabilities. Data from four waves of the China Health and Retirement Longitudinal Study (2011, 2013, 2015, and 2018), encompassing 3,126 participants aged 45 and older at baseline, were analyzed. Growth mixture modeling identified distinct patterns of depression trajectories. Bivariate and multivariable linear regression analyses were then applied to examine the long-term effects of these depression trajectories on functional disabilities. Depression trajectories among participants were categorized into four groups: persistently severe (7.7%), increasing (18.6%), decreasing (15.1%), and stable asymptomatic (58.6%). All observed relationships between depression trajectories and functional disabilities were statistically significant. Specifically, those with persistently severe, increasing, or decreasing depressive symptoms reported greater functional disabilities compared to stable asymptomatic individuals. Notably, respondents with increasing or decreasing depressive symptoms showed fewer functional disabilities than those with persistently severe symptoms, with decreasing symptoms associated with lesser disabilities than increasing symptoms. Clinical practitioners and social workers need to intensify efforts to alleviate the impact of chronic depression on daily functioning among middle-aged and older adults. Public health policymakers should prioritize resource allocation towards those with persistently severe depression and functional disabilities in this age group.

Increased risk of multimorbidity has been linked to depressive symptoms, and the onset of multimorbidity can further aggravate these symptoms. However, the lagged relationship between these two factors remains unclear. This study aimed to investigate the bidirectional longitudinal association between multimorbidity and depressive symptoms among middle-aged and older adults in China over time, specifically focusing on gender and urban-rural differences in this relationship. Data from 8692 participants in the China Health and Retirement Longitudinal Study (CHARLS), collected between 2011 and 2020, were analyzed on a biannual basis. The ten-item Center for Epidemiologic Studies Depression Scale (CES-D-10) was utilized to assess depressive symptoms, complemented by self-reported information on 12 chronic diseases to evaluate multimorbidity. Cross-lagged panel models, adjusted for various covariates, were employed to investigate the bidirectional relationship between multimorbidity and depressive symptoms. Furthermore, the analysis examined gender and urban-rural differences across four distinct subgroups: urban men, urban women, rural men, and rural women. A significant bidirectional relationship was identified between multimorbidity and depressive symptoms. Higher levels of multimorbidity were associated with more severe depressive symptoms and vice versa. Path analyses revealed that the influence of multimorbidity on depressive symptoms was stronger than the reverse relationship. Furthermore, subgroup analyses highlighted variability in these associations, with significant bidirectional relationships observed only among rural women across different periods. The findings reveal positive bidirectional associations between multimorbidity and depressive symptoms in middle-aged and older Chinese adults. The results underscore the importance of early monitoring of multimorbidity and depression, especially concerning the mental and physical health of women in rural areas.

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples.

Abstract This study aims to explore the long-term effects of lifestyle-related factors and physical health on life satisfaction among older adults by transitions in mental health conditions. Using data derived from the China Health and Retirement Longitudinal Study (CHARLS), the analytic sample included 643 older adults. Linear regression analyses were used to examine the cross-sectional and longitudinal associations of lifestyle-related factors and physical health with depression risk and life satisfaction in older adults. In this study, sleep duration and multimorbidity was found to be significantly related to baseline and follow-up depressive symptoms in older adults. Compared to non-drinkers, current drinkers reported more severe depressive symptoms. More depressive symptoms were associated with worse impairment in physical function or Activities of Daily Living (IADLs). Among older adults remaining no depressive symptoms at baseline and follow-up, current drinkers tended to have lower life satisfaction than non-drinker. Shorter sleep duration showed a longitudinal correlation with lower life satisfaction. In the subgroups of emerging depression, past drinkers tended to have lower life satisfaction than non-drinkers, and baseline multimorbidity significantly predicted lower subsequent life satisfaction. In conclusion, our findings identified drinking and shorter sleep duration as the lifestyle-related detrimental factors of late-life depression and life satisfaction in Chinese community-dwelling older adults. Other physical-health-related risk factors of depression included worse impairment in physical function or IADLs, and multimorbidity. Our findings have implications for future psychosocial interventions targeted at alleviating depressive symptoms and promoting life satisfaction of the older adults based on their long-term mental and physical health conditions.

In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity and multimodal characteristics. Diffusion models, known for their stable training process and exceptional image generation capabilities, have shown good application potential in this field. However, when dealing with multimodal data, it may prove challenging for the models to fully capture the intricate relationships between the modalities, which may result in incomplete information integration and a small amount of remaining noise in the generated images. To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. To address the differences in modal information between multispectral and hyperspectral images, KANDiff incorporates Kolmogorov–Arnold Networks (KAN) to guide the inputs. It helps the model understand the complex relationships between the modalities by replacing the fixed activation function in the traditional MLP with a learnable activation function. Furthermore, the image generated by the diffusion model may exhibit a small amount of the remaining noise. Convolutional Neural Networks (CNNs) effectively extract local features through their convolutional layers and achieve noise suppression via layer-by-layer feature representation. Therefore, the MergeCNN module is further introduced to enhance the fusion effect, resulting in smoother and more accurate outcomes. Experimental results on the public CAVE and Harvard datasets indicate that KanDiff has achieved improvements over current high-performance methods across several metrics, particularly showing significant enhancements in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), thus demonstrating superior performance. Additionally, we have created an image fusion dataset of the lunar surface, and KANDiff exhibits robust performance on this dataset as well. This work introduces an effective solution for addressing the challenges posed by missing high-resolution hyperspectral images (HRHS) data, which is essential for tasks including landing site selection and resource exploration within the realm of deep space exploration.

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design.