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Background: The comorbidities of ischemic stroke (IS) are increasing worldwide. This study aimed to quantitatively assess the effect of different types of comorbidity on readmission and hospitalization expenditures of patients with IS. Methods: A retrospective observational study was conducted from the basic insurance claims database of a large city in China, between January 1, 2018, and May 31, 2022. We identified patients with IS aged 18 years and over, who experienced the first episode of IS and had one-year follow-up records. This study divided eighteen different comorbid conditions into two categories (concordant comorbidity and discordant comorbidity) and the IS patients were further categorized into four groups. Multivariable logistic regression models and generalized linear models with log-link and gamma distribution were to estimate the effect of different comorbidity groups on one-year readmission rates and annual hospitalization expenditures. Results: In total, 99,649 adult patients with IS were identified. Approximately 94.0 % of patients with IS had at least one comorbidity, and 63.8 % reported concordant comorbidity only. Patients with IS had a readmission rate of 26.7 %, and the mean of annual hospitalization expenditure and annual hospitalization out-of-pocket expenditure (OOPE) were 28086.6 Chinese Yuan (CNY) and 8267.3 CNY, respectively. After adjustment for covariates, the concordant comorbidity-only group had the highest readmission rate, annual hospitalization expenditure, and OOPE compared with the other groups, furthermore, these results increased as the number of comorbidity increased and had statistically significant positive associations. Conclusions: The readmission and annual hospitalization expenditures of patients with IS were associated with different comorbidities. Concordant comorbidity increased hospital readmission risk and health expenditures. To better manage the comorbidities of patients with IS, especially concordant comorbidities, it is necessary to establish a routine care strategy specifically for comorbid conditions.

Abstract Background Hyperspectral techniques have aroused great interest in non-invasively measuring periodontal tissue hemodynamics. However, current studies mainly focused on three typical inflammation stages (healthy, gingivitis and periodontitis) and practical approaches for using optical spectroscopy for early and precisely detection of periodontal inflammation at finer disease stages have not been well studied. Methods This study provided novel spectroscopic insights into periodontitis at different stages of disease, and developed six simple but physically meaning hemodynamic spectral indices (HSIs) including four spectral absorption depths of oxyhemoglobin ( $$D_{{{\text{HbO}}_{2} }}$$ D HbO 2 ), deoxyhemoglobin ( $$D_{{{\text{Hb}}}}$$ D Hb ), total hemoglobin ( $$t{\text{Hb}}$$ t Hb ) and tissue water ( $$D_{{{\text{water}}}}$$ D water ), and two normalized difference indices of oxyhemoglobin( $$ND{\text{HbO}}_{2} I$$ N D HbO 2 I ) and deoxyhemoglobin ( $$ND{\text{Hb}}I$$ N D Hb I ) from continuum-removal spectra (400–1700 nm) of periodontal tissue collected from 47 systemically healthy subjects over different severities from healthy, gingivitis, slight, moderate to severe periodontitis for early and precision diagnostics of periodontitis. Typical statistical analyses were conducted to explore the effectiveness of the proposed HSIs. Results $$D_{{{\text{Hb}}}}$$ D Hb and $$t{\text{Hb}}$$ t Hb exerted significant increasing trends as inflammation progressed, whereas $$D_{{{\text{HbO}}_{2} }}$$ D HbO 2 exhibited significant difference (P

Autoencoders (AEs) have gained widespread application in the field of hyperspectral anomaly detection, largely due to their notable effectiveness in efficiently reconstructing backgrounds within hyperspectral images (HSIs). However, the absence of prior knowledge and constraints imposed by spectral information capacity hinder the accuracy of anomaly detection by allowing AEs to reconstruct both anomalous targets and backgrounds simultaneously. To address this limitation, a spatial–spectral cross-guided masked autoencoder (SSCMAE) has been proposed. The guided mask is generated based on the spectral difference between the anomaly and the background. This mask effectively suppresses the reconstruction of anomalous targets while enhancing the accuracy of background reconstruction. Moreover, a dual-branch structure operates, encompassing spatial and spectral dimensions, effectively capturing the inherent three-dimensional characteristics present in HSIs. Ingeniously designed cross-connection layers within the architecture enhance the spatial and spectral branches' capability of extracting internal spatial and spectral features of images. In order to capture a more comprehensive range of background features, a lightweight three-dimensional convolutional autoencoder is introduced. This addresses the issue of local feature loss during background reconstruction and overcomes the limitations that visual transformers face when learning local image structures. The proposed method has been systematically compared against several advanced methods on six real-world datasets. The results explicitly demonstrate the efficacy and superior performance of the presented SSCMAE approach.

Hyperspectral images are valuable for precise land cover change detection in a consistent area over time. Nevertheless, supervised methods for hyperspectral change detection face limitations due to insufficient labeled samples. Additionally, current deep learning-based approaches often neglect critical feature interactions across temporal, spatial, and spectral domains. To address these challenges, we introduce a semisupervised model called the HyperMatch-based Cross Temporal and Spatial Interaction Transformer (CTSIT) for hyperspectral change detection. The key contributions of this study are as follows: Introduction of the HyperMatch training schedule, which relies on weak-to-strong consistency, to enhance feature extraction using both labeled and unlabeled data. Then, the contribution of the Cross Temporal Bidirectional Attention module to emphasize bidirectional temporal interactions and resemblances. Finally, Introduction of the Spatial and Spectral Attention module, which includes the Dense Cross Spatial Attention and Spectral Attention modules, to capture long-range densely spatial interactions and internal spectral similarities. Extensive comparative experiments conducted on three mainstream hyperspectral change detection datasets confirm the effectiveness and superiority of the proposed HyperMatch-based CTSIT in utilizing both labeled and unlabeled samples. For instance, the training ablation demonstrates that this method significantly outperforms most existing state-of-the-art hyperspectral change detection methods, even with a significantly smaller number of samples.

Iron overload occurs due to excessive iron intake compared to the body’s demand, leading to iron deposition and impairment of multiple organ functions. Our previous study demonstrated that chronic oral administration of ferric citrate (FC) caused colonic inflammatory injury. However, the precise mechanism underlying this inflammatory response remains unclear. The current study aims to investigate the mechanism by which iron overload induced by FC exposure leads to colonic inflammation. To accomplish this, mice were orally exposed to three different concentrations of FC (71 mg/kg/bw (L), 143 mg/kg/bw (M) and 286 mg/kg/bw (H)) for continuous 16 weeks, with the control group receiving ultrapure water (C). Exposure to FC caused disturbances in the excretory system, altered colonic flora alpha diversity, and enriched pathogenic bacteria, such as Mucispirillum, Helicobacter, Desulfovibrio, and Shigella. These changes led to structural disorders of the colonic flora and an inflammatory response phenotype characterized by inflammatory cells infiltration, atrophy of intestinal glands, and irregular thickening of the intestinal wall. Mechanistic studies revealed that FC-exposure activated the NF-κB signaling pathway by up-regulating TLR4, MyD88, and NF-κB mRNA levels and protein expression. This activation resulted in increased production of pro-inflammatory cytokines, further contributing to the colonic inflammation. Additionally, in vitro experiments in SW480 cells confirmed the activation of NF-κB signaling pathway by FC exposure, consistent with the in vivo findings. The significance of this study lies in its elucidation of the mechanism by which iron overload caused by FC exposure leads to colonic inflammation. By identifying the role of pathogenic bacteria and the NF-κB signaling pathway, this study could potentially offer a crucial theoretical foundation for the research on iron overload, as well as provide valuable insights for clinical iron supplementation.

Background A hospital group is an organizational integration strategy that has recently been widely implemented in Chinese urban health systems to promote integrated care. This study aims to evaluate the effect of hospital group on integrated care from the perspectives of both patients and care professionals. Methods Two cross-sectional surveys were conducted in Shenzhen city of China, in June 2018 and July 2021. All 30 community health stations (CHSs) in the hospital group were included in the intervention group, with 30 CHSs in the same district selected as the control group by simple random sampling. All care professionals within both the intervention and the control groups were invited to participate in the surveys. Twelve CHSs were selected from 30 CHSs in the intervention and the control groups by simple random sampling, and 20 patients with type 2 diabetes mellitus (T2DM) were selected from each of these selected CHSs to participate in the survey by systematic sampling. The Chinese version Rainbow Model of Integrated Care Measurement Tool (C-RMIC-MT) was used to assess integrated care. Propensity score matching and difference-in-differences regression (PSM-DID) were used to evaluate the effect of the hospital group on integrated care. Results After matching, 528 patients and 1896 care professionals were included in the DID analysis. Results from care professionals indicated that the hospital group significantly increased technical competence of the health system by 0.771 points, and cultural competence by 1.423 points. Results from patients indicated that the hospital group significantly decreased organizational integration of the health system by 0.649 points. Conclusion The results suggests that the effect of the hospital group on integrated care over and above routine strategies for integrated care is limited. Therefore, it is necessary to pay attention to implementing professional, clinical and other integration strategies beyond establishing hospital groups, in urban Chinese health systems.

Abstract Background Diabetes increases the risk of hypertension morbidity, but whether this association is varied with glycemic control remains unknown. We aimed to examine the association of glycemic control with hypertension among individuals with diabetes. Methods Data was from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2018. Participants were categorized as having adequate glycemic control (HbA1c

As the largest manufacturing country, China is striving to improve the development quality of its power industry with the goal of Carbon Peaking and Carbon Neutrality, in order to sustain its high-quality economic growth. In this regard, it is of importance to reveal both the regional development level of China’s power sector and its characteristics in terms of inspiring the next improvement direction. Motived by this purpose, this paper constructs an evaluation indicator system from three dimensions at the province level based on the connotation of high-quality development of the power industry (HDPI). Next, it calculates the HDPI indexes of 30 provinces and explore their development trend and spatial pattern. The results indicate that the total comprehensive performance of all regions was improved in general in the recent decade, but the spatial distribution characteristics of clean, low-carbon, safe and efficient are different. In the aspects of improvement space in future, not only do actively ameliorate the related management regimes or technical fields so as to improve the corresponding indicators’ value, but also passively rely on the macro-development such as China’s urbanization level improvement, technological level improvement, and industrial structure upgrading as usual.

To achieve the goals of carbon peaking and carbon neutrality and maintain high-quality economic growth, China is currently striving to improve the quality of development of its power sector. In this regard, revealing the regional differences and evolutionary trends in the development quality of China’ power sector has a high value to inspire the next improvement direction toward how to integrate regional power recourses to an overall optimization level. Motived by this purpose, this paper uses the entropy method to evaluate the comprehensive and subsystem indices of the development quality of the power industry, and reveals their regional differences and evolutionary trends with the help of the Dagum Gini coefficient and Kernel density estimation methods. The findings show that: There are obvious regional differences in the development quality of China’s power industry, and the differences are steadily declining in all regions except the West. Regional differences are mainly derived from inter-regional differences, with the largest inter-regional differences in the East-Northeast region. Intra-regional differences show a distribution pattern of East > West > Northeast > Center.

IntroductionTo promote patients’ referral across healthcare institutions and integrated care delivery, we identified predictors of physicians’ behaviour and intention to refer patients in a county medical consortium in China on the basis of the theory of planned behaviour (TPB).MethodsThis census-based cross-sectional study was conducted in Yangxi Hospital Group (YHG). All physicians in county hospitals and township health centres were invited to participate. Structural equation modelling was employed to analyse the relationships between referral intention and behaviour and other TPB variables in the group of whole participants and in sub-groups.ResultsIn total, 330 physicians participated in this study. One-third of participants were general practitioners, and half of them were from county hospitals. Referral behaviour of females (χ2 = 20.372, p

Introduction: Circular RNAs (circRNAs) regulatory network is important in human cancer. We, therefore, mapped the regulatory networks driven by circRNA in luminal-subtype breast cancer.Methods: Breast cancer-related microarray datasets from GEO database were analyzed for the differentially expressed circRNAs, miRNAs, and mRNAs. The potential downstream RNAs were collected using Circular RNA Interactome or Targetscan database. Protein-protein interaction (PPI) analysis was performed for the filtered genes to identify hub genes. The functions were annotated by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. CircRNA-miRNA-mRNA networks were mapped using Cytoscape software. Hsa_circ_0086735-miR-1296-5p-STAT1 axis was used for verification. The expression levels of hsa_circ_0086735, miR-1296-5p, and STAT1 mRNA were confirmed by qRT-PCR in luminal-subtype tissues and cell lines. The interactions among them were verified by Luciferase reporter assay and RNA pull-down assay. Cell proliferation and apoptosis were assayed. Overall and distant metastasis-free survival was analyzed.Results: A total of 70 genes were finally targeted and enriched in multi-process and multi-pathway. Networks containing 96 circRNA-miRNA-mRNA axes were constructed. Hsa_circ_0086735 and STAT1 mRNA was upregulated in luminal breast cancer, while miR-1296-5p was downregulated. Hsa_circ_0086735-miR-1296-5p-STAT1 axis promotes breast cancer progression and contributes to tamoxifen resistance. High hsa_circ_0086735 was associated with poor overall and distant metastasis-free survival.Discussion: This study identified the hsa_circ_0086735-miR-1296-5p-STAT1 as an important regulatory axis in luminal-subtype breast cancer, aiding to determine potential therapeutic targets.

Benshan tea is a kind of oolong tea, and Benshan (Camellia sinensis) tea tree originates from Anxi County of Fujian Province in China, which is a national tea tree breed. Tea processing is the key to the formation of its odor characteristics. It is extremely important to step by step analyze effects of tea processing on aroma intensity and the formation of odor characteristics for optimizing tea processing process and improving tea quality. The results of this study showed that processing resulted in a significant increase in the content of volatile compounds in tea leaves, i.e., from 25.213 μg/kg to 111.223 μg/kg, in which the volatile compounds were mainly terpenoids. Secondly, the analysis found that 20 kinds of key compounds constituted to odor characteristics of Benshan tea leaves, among which geraniol, trans-β-ionone, gerol, citronellol, benzeneacetaldehyde, and trans-nerolidol were the most key six. Floral and fruity aromas, especially floral aroma, mainly formed odor characteristics of Benshan tea after processing, while floral aroma mainly came from the contribution of geraniol, which was the foremost compound in the formation of floral aroma of Benshan tea.

Abstract This paper investigates the accuracy of rotor‐locked transient‐response (RLTR)‐based vibration model for switched reluctance machines (SRM). RLTR is stator vibration stimulated by abrupt change of radial force when turning off one phase of SRM with rotor being locked, the total vibration of SRM can be modelled as linear superposition of RLTR series. An electromechanical model is built to clarify the generation of SRM vibration, it is revealed that some velocity‐depend terms in electromechanical model would be neglected when rotor is locked, it leads to inevitable difference of RLRT between rotor‐locked condition and rotational condition, the difference is illustrated through multi‐physics simulation. A harmonic radial force model is proposed to quantitatively evaluate the accuracy of RLTR‐based vibration model, radial force spectrums with locked rotor and rotational rotor are compared to predict relative error between RLTR‐based vibration and actual vibration. The influence of turn‐off angle and velocity on accuracy of RLTR‐based vibration model is also analysed. The accuracy of RLTR‐based vibration model is evaluated through experiment on 3‐phase 12/8 SRM, the results showed that: (1) the accuracy of RLTR‐based vibration model is impacted by turn‐off angle and velocity; (2) relative error between RLTR‐based vibration and actual vibration can be predicted using harmonic radial force model. This paper illustrates the generation mechanism of modelling error for commonly used RLTR‐based vibration model of SRM, the proposed error prediction method could lead to improved accuracy on SRM vibration model through error compensation.

This work aims to investigate the effects of welded Thermal Contact Resistance (TCR) on heat transfer for rectangular fins applied in passive nuclear plant. The dimensionless equations are established to describe fin efficiency and overall surface efficiency when subjected to TCR. An experimental set-up using a hot box are developed， and 18 experiments of fins with bilateral fillet welding (BFW) and unilateral penetration welding (UPW) are performed to obtain the range of TCR. Results show that the TCR values are about 0.006 and 0.002 (m2 K)/W for BFW and UPW, respectively. Dimensionless fin efficiency ηf' and overall surface efficiency η0' are governed by several dimensionless parameters that group thermal resistance, important material properties, and geometric parameters of the fin or substrate. TCR has a remarkable effect in the range of 10−4

Hyperspectral images can assist change-detection methods in precisely identifying differences in land cover in the same region at different observation times. However, the difficulty of labeling hyperspectral images restricts the number of training samples for supervised change-detection methods, and there are also complex real influences on hyperspectral images, such as noise and observation directions. Furthermore, current deep-learning-based change-detection methods ignore the feature reusage from receptive fields with different scales and cannot effectively suppress unrelated spatial–spectral dependencies globally. To better handle these issues, a contrastive self-supervised two-domain residual attention network (TRAMNet) with a random augmentation pool is proposed for hyperspectral change detection. The contributions of this article are summarized as follows. (1) To improve the feature extraction from hyperspectral images with random Gaussian noise and directional information, a contrastive learning framework with a random data augmentation pool and a soft contrastive loss function (SCLF) is proposed. (2) The multi-scale feature fusion module (MFF) is provided to achieve feature reusage from different receptive fields. (3) A two-domain residual attention (TRA) block is designed to suppress irrelated change information and extract long-range dependencies from both spectral and spatial domains globally. Extensive experiments were carried out on three real datasets. The results show that the proposed TRAMNet can better initialize the model weights for hyperspectral change-detection task and effectively decrease the need for training samples. The proposed method outperforms most existing hyperspectral change-detection methods.

Abstract Breast cancer (BC) is one of the most fatal diseases among women all over the world. Non-coding RNAs including circular RNAs (circRNAs) have been reported to be involved in different aspects during tumorigenesis and progression. In this study, we aimed to explore the biological functions and underlying mechanism of circRPPH1 in BC. Candidate circRNAs were screened in dataset GSE101123 from Gene Expression Omnibus (GEO) database and a differentially expressed circRNA, circRPPH1, was discovered in BC. CircRPPH1 expression was higher in the cancerous tissue compared to paired adjacent tissue. Further in vitro and in vivo experiments indicated that circRPPH1 acted as an oncogene in BC. In addition, circRPPH1 was mainly localized in cytoplasm and played the role of miR-512-5p sponge. By sequestering miR-512-5p from the 3′-UTR of STAT1, circRPPH1 inhibited the suppressive role of miR-512-5p, stabilized STAT1 mRNA in BC and finally affected BC progression. In conclusion, these findings indicated that circRPPH1 acted as an oncogene and regulated BC progression via circRPPH1-miR-512-5p-STAT1 axis, which might provide a potential therapeutic target for BC treatment.

BackgroundExisting research indicates that job satisfaction has effects on job performance, but little evidence exists about the mechanism through which the satisfaction-performance association operates. This study aims to examine the effect of job satisfaction on job performance in a district-level health care system of China and to explore the effect mechanism mediated by organizational commitment and burnout.MethodsCluster sampling was used in this study. All healthcare professionals in the Nanshan Medical Group, who were at work in the last 3 months and able to complete online questionnaire independently were invited to participate the anonymous online survey. Job satisfaction, organizational commitment, burnout and job performance were measured by tools, which have been validated in China. Descriptive statistics were used for the socio-demographic variables and the four job psychological variables. Pearson correlation coefficients was used to determine associations among each of the psychological variables. Linear regression was used to examine association between job performance and other three psychological variables. PROCESS macro was used to examine the mediation effects of organizational commitment and burnout on the association between job satisfaction and performance.ResultsIn total, 1,200 healthcare professionals completed the anonymous online survey. Job satisfaction, organizational commitment, and job performance were positively correlated with one another, with burnout negatively correlated with them. Linear regression revealed that demographic characteristics, job satisfaction, organizational commitment, and burnout explained 5, 6, 2, and 9% of the variance in job performance. Path analysis showed that the coefficient of the direct effect of job satisfaction on job performance was 0.18, the coefficients of the indirect effects of job satisfaction on job performance through organizational commitment and burnout were 0.17 and 0.37, respectively. The coefficients of the indirect effects of organizational commitment on job performance through burnout was −0.04, but it was not significant.ConclusionIt is promising to improve job performance of providers in Chinese healthcare systems by improving job satisfaction and reducing burnout. Tailored support policies for female healthcare professionals, appropriate incentive mechanisms and improving multidisciplinary healthcare delivery are potential to improve job performance of healthcare professionals in integrated healthcare systems.

When an accident occurs in a nuclear power plant, the main control room (MCR) is isolated while initiating emergency measures. To keep the indoor temperature from increasing to dangerous levels, heating loads need to be mitigated by heavy concrete insulation, on which rectangular fins are installed to improve the heat transfer. However, the effect of optimized fins for the thermal environment has not been investigated. In this study, a parametric model based on COMSOL Multiphysics is proposed to describe the dynamic heat transfer process of a fin-concrete heat sink and validated by an experiment on a 1200 mm × 1200 mm × 610 mm module. A COBYLA algorithm has been implemented to optimize the fin parameters combination with different steel consumption. Results show that both steel consumption and fin structure parameters need to be taken into consideration when optimizing. The optimal heat transfer increases by 2.84%, 7.33%, and 9.26%. The maximum indoor temperature is 0.77, 1.85, and 2.39 °C lower than the prototype when the steel consumption is 100, 150, and 200 kg/m2, respectively. Furthermore, the heat storage will improve as increasing the steel consumption if the fin structure is optimized. And it does not necessarily so without optimization. Finally, the optimization potential increases with the decrease in fins space, the increase in fin height, and the increase in fin thickness. In the case of only taking reducing the maximum indoor temperature into account, reducing fins space and increasing fin height are the superior choices for the optimal design of heat sink.

BackgroundChina has stepped into an era of aging society, where the impending considerable economic burden attributed to high prevalence of dementia in the elderly appears to be one of the most important health and social issues to deal with for the country. However, population-based quantification and projections for the economic burden of dementia in China are lacking for further health action and policy making.ObjectiveTo estimate and predict the costs of managing dementia in the elderly population aged 60 and above from 2010 to 2050 in China.MethodsData were collected from a six-province study (n = 7072) and other multiple sources for calculation of the economic burden of dementia. With the convincing data from published studies, we quantified and projected the costs attributed to dementia in China from 2010 to 2050.ResultsThe national cost of dementia in 2010 was estimated to be US$22.8 billion by the opportunity cost method and US$26.4 billion by the proxy method. In 2050, the costs would increase to US$372.3 billion by the opportunity cost method and US$430.6 billion by the proxy method, consuming 0.53% and 0.61% of China's total GDP, respectively. A series of sensitivity analyses showed that the changes in the proportions of informal caregiving led to the most robust changes in the total burden of care for dementia in China.ConclusionDementia represents an enormous burden on China's population health and economy. Due to the changes in policies and population structure, policymakers should give priority to dementia care.

Abstract Background The original Rainbow Model of Integrated Care Measurement Tool (RMIC-MT) is based on the Rainbow Model of Integrated Care (RMIC), which provides a comprehensive theoretical framework for integrated care. To translate and adapt the original care provider version of the RMIC-MT and evaluate its psychometric properties by a pilot study in Chinese primary care systems. Methods The translation and adaptation process were performed in four steps, forward and back-translation, experts review and pre-testing. We conducted a cross-sectional study with 1610 community care professionals in all 79 community health stations in the Nanshan district. We analyzed the distribution of responses to each item to study the psychometric sensitivity. Exploratory factor analysis with principal axis extraction method and promax rotation was used to assess the construct validity. Cronbach’s alpha was utilized to ascertain the internal consistency reliability. Lastly, confirmation factor analysis was used to evaluate the exploratory factor analysis model fit. Results During the translation and adaptation process, all 48 items were retained with some detailed modifications. No item was found to have psychometric sensitivity problems. Six factors (person- & community-centeredness, care integration, professional integration, organizational integration, cultural competence and technical competence) with 45 items were determined by exploratory factor analysis, accounting for 61.46% of the total variance. A standard Cronbach’s alpha of 0.940 and significant correlation among all items in the scale (> 0.4) showed good internal consistency reliability of the tool. And, the model passed the majority of goodness-to-fit test by confirmation factor analysis. Conclusions The results showed initial satisfactory psychometric properties for the validation of the Chinese RMIC-MT provider version. Its application in China will promote the development of people-centered integrated primary care. However, further psychometric testing is needed in multiple primary care settings with both public and private community institutes.

The main control rooms of passive nuclear plants must maintain a habitable environment once station-blackout accidents occur. In this condition, the heavy fin-concrete ceiling on the room will remove heat from kind of heat sources and protect from overheating environment. However, the dynamic thermal process of finned concrete ceiling has not been studied in various passive cooling measures. In this study, an efficient parametric resistance–capacitance thermal network model is established to describe dynamic thermal responses of heavy fin-concrete layer, and validated by dynamic temperature change data of a hot-box experiment for 24 h. Series indicators were deduced by analytic solutions. Based on the validated model and indicators, factor analysis was conducted to obtain details of effect of finned-plate geometric parameters and heat source intensity. The results show that initial 2-h thermal process can be divided into two stage, “transient stage” and “quasi-stable stage”. The transient-stage thermal process is determined by source flux and finned coefficient of finned plate. The quasi-stable thermal process depends on thermal mass of the finned plate and heat source flux. Furthermore, there exists a linear relationship between air temperature and heat source flux. Finned plate layout can be design according to the factor assessment results.

Change detection methods using hyperspectral remote sensing can precisely identify differences of the same area at different observing times. However, due to massive spectral bands, current change detection methods are vulnerable to unrelatedspectral and spatial information in hyperspectral images with the stagewise calculation of attention maps. Besides, current change methods arrange hidden change features in a random distribution form, which cannot express a class-oriented discrimination in advance. Moreover, existent deep change methods have not fully considered the hierarchical features’ reuse and the fusion of the encoder–decoder framework. To better handle the mentioned existent problems, the parallel spectral–spatial attention network with feature redistribution loss (TFR-PS2ANet) is proposed. The contributions of this article are summarized as follows: (1) a parallel spectral–spatial attention module (PS2A) is introduced to enhance relevant information and suppress irrelevant information in parallel using spectral and spatial attention maps extracted from the original hyperspectral image patches; (2) the feature redistribution loss function (FRL) is introduced to construct the class-oriented feature distribution, which organizes the change features in advance and improves the discriminative abilities; (3) a two-branch encoder–decoder framework is developed to optimize the hierarchical transfer and change features’ fusion; Extensive experiments were carried out on several real datasets. The results show that the proposed PS2A can enhance significant information effectively and the FRL can optimize the class-oriented feature distribution. The proposed method outperforms most existent change detection methods.

Field phenotyping is a crucial process in crop breeding, and traditional manual phenotyping is labor-intensive and time-consuming. Therefore, many automatic high-throughput phenotyping platforms (HTPPs) have been studied. However, existing automatic phenotyping methods encounter occlusion problems in fields. This paper presents a new in-field interactive cognition phenotyping paradigm. An active interactive cognition method is proposed to remove occlusion and overlap for better detectable quasi-structured environment construction with a field phenotyping robot. First, a humanoid robot equipped with image acquiring sensory devices is designed to contain an intuitive remote control for field phenotyping manipulations. Second, a bio-inspired solution is introduced to allow the phenotyping robot to mimic the manual phenotyping operations. In this way, automatic high-throughput phenotyping of the full growth period is realized and a large volume of tiller counting data is availed. Third, an attentional residual network (AtResNet) is proposed for rice tiller number recognition. The in-field experiment shows that the proposed method achieves approximately 95% recognition accuracy with the interactive cognition phenotyping platform. This paper opens new possibilities to solve the common technical problems of occlusion and observation pose in field phenotyping.

Introduction: The original Rainbow Model of Integrated Care Measurement Tool (RMIC-MT) is based on the Rainbow Model of Integrated Care (RMIC), which provides a comprehensive theoretical framework for integrated care. The aim of this paper is to modify the original patient version of the RMIC-MT for the Chinese primary care context and validate its psychometric properties. Methods: The translation and adaptation processes were performed in four steps, forward and back-translation, experts review and pre-testing. We conducted a cross-sectional study with 386 patients with diabetes attending one of 20 community health stations in the Nanshan district. We analyzed the distribution of responses to each item to study the psychometric sensitivity. Exploratory factor analysis with principal axis extraction method was used to assess the construct validity. Confirmation factor analysis was used to evaluate model fit of the modified version. Cronbach’s alpha was used to ascertain the internal consistency reliability. Results: During the translation and adaptation process, all 24 items were retained with some detailed modifications. No item was found to have psychometric sensitivity problems. Five factors (person-centeredness, clinical integration, professional integration, team-based coordination, organizational integration) with 15 items were determined by exploratory factor analysis, accounting for 53.51% of the total variance. Good internal consistency was achieved with each item correlated the highest on an assigned subscale and Cronbach’s alpha score of 0.890. Moderately positive associations (r≥ 0.4, p

Abstract Background Depression is regarded as a major public health concern in our society. While living arrangements as a structural factor of social support may contribute to older adults’ depression. Our study aims to investigate the association between living arrangements and depressive symptoms among older adults in the whole China, and to explore whether such influences differ by genders. Methods Data were obtained from the 2015 China Health and Retirement Longitudinal Study. The sample was comprised of 6001 individuals aged ≥60 years. Depressive symptoms were measured by the 10-item Short-Form Center for Epidemiological Studies Depression. Independent variables were divided into 4 groups, considering living with/without a spouse and living with/without a child. The multivariate logistic regression was used to estimate the relationship between living arrangements and depressive symptoms in four models. Results Compared with living only with a spouse, people living with a spouse and child, or living alone were more likely to have depressive symptoms (odds ratio = 1.23 95% CI 1.06–1.42 and 1.40 95% CI 1.03–1.92, respectively). Women were more associated with depressive symptoms (odds ratio = 2.13), but there were no significant associations between living arrangements and depressive symptoms among women. Men living with a spouse and a child had stronger positively depressive symptoms (odds ratio = 1.37). Conclusions Older adults living alone, or living with both a child and spouse were more likely to have depressive symptoms. It is important to provide more social services for those older adult, particularly for men living with a spouse and child.

Motor fault diagnosis based on deep learning frameworks has gained much attention from academic research and industry to guarantee motor reliability. Those methods are commonly under two default assumptions: 1) massive labeled training samples and 2) the training and test data share a similar distribution under unvarying working conditions. Unfortunately, these assumptions are nearly invalid in a real-world scenario, where the signals are unlabeled and the working condition changes constantly, resulting in the diagnosis models of the previous studies that always fail in classifying the unlabeled data in real applications. To deal with those issues, in this paper, we propose a novel feature adaptive motor fault diagnosis using deep transfer learning to improve the performance by transferring the knowledge learned from labeled data under invariant working conditions to the unlabeled data under constantly changing working conditions. A convolutional neural network (CNN) is adopted as the base framework to extract multi-level features from raw vibration signals. Then, the regularization term of maximum mean discrepancy (MMD) is incorporated in the training process to impose constraints on the CNN parameters to reduce the distribution mismatch between the features in the source and target domains. To verify the effectiveness of our proposal, data from the motor tests of European driving cycle (NEDC) for simulating the real working scenario and the motor tests under invariant working conditions are, respectively, conducted as the target domain and the source domain. The results show that the proposal presents higher diagnosis accuracy for the unlabeled target data than other methods, and it is of applicability to bridge the discrepancy between different domains.

AimsThe aim of this study was to determine the association between social determinants of health and direct economic burden on Chinese middle-aged and elderly individuals living with diabetes in China.MethodsThis study used data from the baseline wave of The China Health and Retirement Longitudinal Study (CHARLS) database, covering 17,708 middle-aged and elderly residents in China. The population with diabetes was grouped into those diagnosed with diabetes mellitus (DDM) and those undiagnosed with diabetes mellitus (UDM). Direct economic cost data, including total direct medical costs (TC) and out-of-pocket (OOP) payments, were extracted as outcome variables. A two-part model was applied to analyze the association between social determinants of health and direct economic burden.ResultsIn our analysis, we included 958 patients with DDM and 1,285 patients with UDM. The mean TC and OOP payments were 11,193 CNY (US $1,733; 6.46 CNY = 1 USD) and 7,266 CNY (US $1,125) in DDM patients, and 3,700 CNY (US $573) and 3,060 CNY (US $474) in UDM patients. Rural-urban status (pConclusionAlthough progress has been made in the development of current health policies intended to contain the direct economic burden of diabetes, the gaps in that burden in populations with different social characteristics remains a burning issue. More policy breakthroughs are needed to achieve health equity.

The COVID-19 outbreak spread rapidly throughout the globe, with worldwide infections and deaths continuing to increase dramatically. To control disease spread and protect healthcare workers, accurate information is necessary. We searched PubMed and Google Scholar for studies published from December 2019 to March 31, 2020 with the terms “COVID-19,” “2019-nCoV,” “SARS-CoV-2,” or “Novel Coronavirus Pneumonia.” The main symptoms of COVID-19 are fever (83–98.6%), cough (59.4–82%), and fatigue (38.1–69.6%). However, only 43.8% of patients have fever early in the disease course, despite still being infectious. These patients may present to clinics lacking proper precautions, leading to nosocomial transmission, and infection of workers. Potential COVID-19 cases must be identified early to initiate proper triage and distinguish them quickly from similar infections. Early identification, accurate triage, and standardized personal protection protocols can reduce the risk of cross infection. Containing disease spread will require protecting healthcare workers.

The large vertical mill has complicated structure and tens of thousands of parts, which is a critical grinding equipment for slag and cinder. As large vertical mill always works in severe conditions, the on-line monitoring, timely fault diagnosis, and trend prediction are very important guarantees for the safe service and saving maintaining costs. To address this issue, the health management system for large vertical mill is developed. More specifically, in order to manage reservoirs of state-related running data, the intrinsic physic data, and diagnosis knowledge base, an entity-relationship-model-based database is first constructed. Based on the fault diagnosis reasoning of experts, the fault tree is developed and the fault diagnosis rules are derived. Especially, a hybrid condition prognosis method based on backtracking search optimization algorithm and neural network is developed, and in comparison with traditional back propagation neural network and ant colony neural network, the developed backtracking search optimization algorithm and neural network gets superior hybrid prediction performance in prediction accuracy and training efficiency. Finally, the health management system, including the functions of condition monitoring, fault diagnosis, and trend prediction for large vertical mill is implemented using Microsoft Visual Studio C # and Microsoft SQL Server.

BackgroundIllness-related costs experienced by tuberculosis patients produce a severe economic impact on households, especially poor families. Few studies have investigated the full costs, including direct and indirect costs, at the patient and household levels in south-east China.MethodsA case follow-up study was conducted in the Bao'an district of Shenzhen City, China. Eligible new and previously treated individuals with pulmonary tuberculosis (TB) during January 1st 2013 to June 30th 2013 were enrolled. Medical and non-medical costs as well as income loss were calculated in diagnosis and treatment periods, respectively. Factors associated with costs due to TB diagnosis, treatment and TB care (diagnosis + treatment) were explored respectively with a linear regression model.ResultsOf the total 514 TB patients enrolled, 95% were from the migrant population, and 65% were males, with a mean age of 32.25 (±10.11). The median costs due to TB diagnosis and TB treatment were 79 United States dollar (USD), 748USD (6.2897 China Yuan (CNY) = 1USD, 2013) per patient, respectively. The median costs due to TB care (diagnosis and treatment) per patient was 1218USD, corresponding to 26% of patients' annual income pre-illness. Those who visited more times to health facilities, hospitalized, received higher education, or occupied in national civil servant/services/retired staff might expense more before diagnosis. Costs due to TB treatment was significantly higher among migrant patients, sputum smear positive patients, and widowed/divorced population. Factors associated with less total costs were native patients, fewer times of visiting to health-care facilities and those with no hospitalization history due to TB.ConclusionsAlthough a free TB control policy is in force, patients with TB are still facing a heavy economic burden. More available interventions to reduce the financial burden on tuberculosis patients are urgently needed.

This paper presents a multi-physics modeling of a switched reluctance motor (SRM) drive system. The proposed framework includes a 2-D finite element model to simulate the magnetic field characteristics, and a multi-physics mechatronic model to simulate its electric field and controller properties. The obtained magnetic characteristics are used in the analytical modeling in the form of a lookup table. Dynamic performance parameters for SRM are directly calculated from the co-simulation platform. Current, torque, flux, and radial force under various operational conditions can then be simulated to evaluate the dynamic performance of a switched reluctance drive system. The control algorithm is then realized based on the co-simulation platform. Methodology to evaluate the dynamic performance for the SRM is shown in this paper. A three-phase 12/8 1.5 Kw SRM is used in this paper. Static magnetic characteristics and dynamic performance parameters for the SRM are shown and discussed in this paper.

Objective This study was performed to assess the reliability and validity of the Chinese version of the Snizek-revised Hall’s Professionalism Inventory Scale (C-SR-HPIS). Methods Exploratory factor analysis and confirmatory factor analysis were used to evaluate the construct validity of the C-SR-HPIS. The average variance extracted (AVE) and square root of the AVE were calculated and correlation analyses were performed to test the convergent validity and discriminant validity, respectively. Cronbach’s alpha (α) coefficient was used to test the internal consistency reliability. Results Data for 355 clinical nurses in mainland China were collected. Five factors were extracted, accounting for 58.86% of the total explained variance, and 20 items were selected for the C-SR-HPIS. The confirmatory factor analysis suggested good fitness of the modified model. The AVE was acceptable for convergent validity. The square roots of the AVE of the five factors were larger than their correlation coefficients with other factors, showing suitable discriminant validity. Cronbach’s α coefficient of internal consistency reliability of the overall scale was 0.76, indicating good reliability of the scale. Conclusions This study demonstrated good reliability and validity of the C-SR-HPIS and provides a quantitative tool for the assessment of nursing professionalism in China.

Powdery mildew is a common disease in plants, and it is also one of the main diseases in the middle and final stages of cucumber (Cucumis sativus). Powdery mildew on plant leaves affects the photosynthesis, which may reduce the plant yield. Therefore, it is of great significance to automatically identify powdery mildew. Currently, most image-based models commonly regard the powdery mildew identification problem as a dichotomy case, yielding a true or false classification assertion. However, quantitative assessment of disease resistance traits plays an important role in the screening of breeders for plant varieties. Therefore, there is an urgent need to exploit the extent to which leaves are infected which can be obtained by the area of diseases regions. In order to tackle these challenges, we propose a semantic segmentation model based on convolutional neural networks (CNN) to segment the powdery mildew on cucumber leaf images at pixel level, achieving an average pixel accuracy of 96.08%, intersection over union of 72.11% and Dice accuracy of 83.45% on twenty test samples. This outperforms the existing segmentation methods, K-means, Random forest, and GBDT methods. In conclusion, the proposed model is capable of segmenting the powdery mildew on cucumber leaves at pixel level, which makes a valuable tool for cucumber breeders to assess the severity of powdery mildew.

Motor fault diagnosis has gained much attention from academic research and industry to guarantee motor reliability. Generally, there exist two major approaches in the feature engineering for motor fault diagnosis: (1) traditional feature learning, which heavily depends on manual feature extraction, is often unable to discover the important underlying representations of faulty motors; (2) state-of-the-art deep learning techniques, which have somewhat improved diagnostic performance, while the intrinsic characteristics of black box and the lack of domain expertise have limited the further improvement. To cover those shortcomings, in this paper, two manual feature learning approaches are embedded into a deep learning algorithm, and thus, a novel fault diagnosis framework is proposed for three-phase induction motors with a hybrid feature learning method, which combines empirical statistical parameters, recurrence quantification analysis (RQA) and long short-term memory (LSTM) neural network. In addition, weighted batch normalization (BN), a modification of BN, is designed to evaluate the contributions of the three feature learning approaches. The proposed method was experimentally demonstrated by carrying out the tests of 8 induction motors with 8 different faulty types. Results show that compared with other popular intelligent diagnosis methods, the proposed method achieves the highest diagnostic accuracy in both the original dataset and the noised dataset. It also verifies that RQA can play a bigger role in real-world applications for its excellent performance in dealing with the noised signals.

Introduction: Emerging from the epidemiological transition and accelerated aging process, China’s fragmentated healthcare systems struggle to meet the demands of the population. On Sept 1st 2017, China’s National Health and Family Planning Commission encouraged all cities to learn from the Luohu model of integration adopted in Luohu as an approach to meeting these challenges. In this paper, we study the integration process, analyze the core mechanisms, and conduct preliminary evaluations of integrated policy development in the Luohu model. Policy development: The Luohu hospital group was established in Aug 2015, consists of five district hospitals, 23 community health stations and an institute of precision medicine. The group adopted a series of professional, organizational, system, functional and normative strategies for integrated care, which was provided for the residents of Luohu, especially for the elderly population and patients with chronic conditions. According to a preliminary evaluation of the past two years, the Luohu model showed improvement in the structure and process towards integrated care. New preventive programs conducted in the hospital group resulted in changes of disease incidence. Residents were more satisfied with the Luohu model. However, spending exceeded the global budget for health insurance because of short-term increases in the demand for health care. Lessons learned: First, engagement of multiple stakeholders is essential for the design and implementation of reform. Second, organizational integration is a prerequisite for integrated care in China. Third, effective care integration requires alignment with payment reforms. Fourth, normative integration could promote collaboration in an integrated healthcare system. Conclusion: Core strategies and mechanisms of the Luohu model will promote integrated care in urban China and other countries facing the same challenges. However, it is necessary to study the effects of the Luohu model over the long term and continue to strive for integrated care.

Sandwich panels are commonly used across industries for their ability to bear structural and thermal loads. In this paper, a panel chamber matching apparatus was designed to investigate the thermal performance of eight steel-based panels by exposing them to an impinging jet at approximately 550 °C for 30 min. Three types of low-cost materials (polycrystalline filaments, silica aerogel, and aluminum silicate) were used as the insulation core. The temperature of the panel surfaces was measured, as well as the metallic fasteners, including bolts, nails, battens, seams, and angle iron, to examine their thermal bridge effects. Major conclusions include the following: first, the maximum temperature on the impinged surface was consistent among all 20 cases, whereas that of the surface under free convection varied, ranging from 41 to 120 °C, depending on the core and thermal bridges. Second, most of the highest temperatures on opposite surfaces were caused by a section of bare angle iron, and this bridging effect could be significantly reduced by up to 50 °C using a few layers of cloth, although the improvement could be temporary. Bolts and nails were less effective as thermal bridges, while the battens could be more effective. Third, the estimated heat flux of all specimens ranged from 167 to 331 W·m−2.

Visual based route and boundary detection is a key technology in agricultural automatic navigation systems. The variable illumination and lack of training samples has a bad effect on visual route detection in unstructured farmland environments. In order to improve the robustness of the boundary detection under different illumination conditions, an image segmentation algorithm based on support vector machine was proposed. A superpixel segmentation algorithm was adopted to solve the lack of training samples for a support vector machine. A sufficient number of superpixel samples were selected for extraction of color and texture features, thus a 19-dimensional feature vector was formed. Then, the support vector machine model was trained and used to identify the paddy ridge field in the new picture. The recognition F1 score can reach 90.7%. Finally, Hough transform detection was used to extract the boundary of the ridge field. The total running time of the proposed algorithm is within 0.8 s and can meet the real-time requirements of agricultural machinery.

BackgroundThe association of pulse pressure and peripheral arterial disease (PAD) has seldom been examined using a prospective design. This study aimed to investigate the association of pulse pressure with PAD incidence in an elderly general population.MethodsWe utilized data from a cohort conducted in Beijing with additionally 2-year follow-up time. PAD was defined as an ankle brachial index value

Considering the complexity structure of wind turbines, it is difficult to establish an accurate model for wind turbines. This article proposed an improved model based on the vibration signal analysis to investigate the onshore wind turbine dynamic behavior. The model is formulated using the Euler–Lagrangian approach in which the dynamic interaction effects between the nacelle and the tower and the effects between the tower and the foundation are considered. The nacelle is assumed as a mass with 2 degrees of freedom at the top of a flexible tower. And the tower is considered as a beam with elastic end support. The foundation stiffness can influence the dynamic response of wind turbines due to the soft soils surrounding the tower base. A rotational spring and a lateral spring derived from the foundation are used to reflect the interaction between the tower and the foundation. Finally, a comparison with existing National Renewable Energy Laboratory 5-MW baseline wind turbine model is performed. And a field experiment has been implemented to validate the model. The vibration signal of the nacelle and the tower is analyzed to determine the model parameters. Results show that the proposed model is accurate and practical for dynamic property analysis of wind turbine.

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

This paper deals with mobility prediction of LiFeMnPO_4 batteries for an emission-free Electric Vehicle. The data-driven model has been developed based on empirical data from two different road types –highway and local streets –and two different driving modes – aggressive and moderate. Battery State of Charge (SoC) can be predicted on any new roads based on the trained model by selecting the drving mode. In this paper, the performance of Adaptive Recurrent Neural Network (ARNN) and regression is evaluated using two benchmark data sets. The ARNN model at first estimates the speed profile of the new road based on slope and then both slope and speed is going to be used as the input to estimate battery current and SoC. Through comparison it is found that if ARNN system is appropriately trained, it performs with better accuracy than Regression in both two road types and driving modes. The results show that prediction SoC model follows the Columb-counting SoC according to the road slope.

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.