As sea waves break, a bubble layer forms beneath the sea surface. The bubble scattering affects sound propagation, thus influencing the accuracy of sound field prediction. This paper aims to ...investigate the effects of bubble scattering on the statistical characteristics of the sound field, the distribution of transmission loss (TL), and the average scattering attenuation in shallow water. A bubble layer model based on the bubble spectrum and a parallel Parabolic Equation (PE) model are combined to calculate and analyse the sound field in the marine environment with bubbles. The effects of the bubble layer are then compared with those of the fluctuant sea surface. The results show that the bubble scattering causes additional energy loss and random fluctuations of the sound field. The TL distribution properties and the average scattering attenuation are related to the wind speed, range, frequency, and source position relative to the negative gradient sound speed layer in shallow water. The comparison demonstrates that the random variation caused by the fluctuation of the sea surface is more significant than that caused by bubbles, and the energy loss caused by bubble scattering is more significant than the fluctuant sea surface under strong wind conditions.
Acute haemorrhagic conjunctivitis (AHC) outbreaks are reported frequently in China. However, the transmissibility of AHC remains unclear. This study aimed to calculate the transmissibility of the ...disease with and without interventions. An AHC outbreak dataset from January 2007 to December 2016 in different schools was built in Hunan Province. A Susceptible-Infectious-Recovered (SIR) model was adopted to calculate the effective reproduction number (R
) of AHC. R
was divided into two parts (R
and R
) where R
and R
represent the uncontrolled and controlled R
, respectively. Based on R
and R
, an index of effectiveness of countermeasures (I
) was developed to assess the effectiveness of countermeasures in each outbreak. During the study period, 34 AHC outbreaks were reported in 20 counties of 9 cities in Hunan Province, with a mean total attack rate of 7.04% (95% CI: 4.97-9.11%). The mean R
of AHC outbreaks was 8.28 (95% CI: 6.46-10.11). No significance of R
was observed between rural and urban areas (t = -1.296, P = 0.205), among college, secondary, and primary schools (F = 0.890, P = 0.459), different levels of school population (F = 0.738, P = 0.538), and different number of index cases (F = 1.749, P = 0.180). The most commonly implemented countermeasures were case isolation, treatment, and health education, followed by environment disinfection, symptom surveillance, and school closure. Social distance, prophylaxis, and stopping eye exercises temporary were implemented occasionally. The mean value of R
was 0.16 (range: 0.00-1.50). The mean value of I
was 97.16% (range: 71.44-100.00%). The transmissibility of AHC is high in small-scale outbreaks in China. Case isolation, treatment, and health education are the common countermeasures for controlling the disease.
ObjectivesThis study measures the differences in inpatient performance after a points-counting payment policy based on diagnosis-related group (DRG) was implemented. The point value is dynamic; its ...change depends on the annual DRGs’ cost settlements and points of the current year, which are calculated at the beginning of the following year.DesignA longitudinal study using a robust multiple interrupted time series model to evaluate service performance following policy implementation.SettingTwenty-two public general hospitals (8 tertiary institutions and 14 secondary institutions) in Wenzhou, China.InterventionThe intervention was implemented in January 2020.Outcome measuresThe indicators were case mix index (CMI), cost per hospitalisation (CPH), average length of stay (ALOS), cost efficiency index (CEI) and time efficiency index (TEI). The study employed the means of these indicators.ResultsThe impact of COVID-19, which reached Zhejiang Province at the end of January 2020, was temporary given rapid containment following strict control measures. After the intervention, except for the ALOS mean, the change-points for the other outcomes (p<0.05) in tertiary and secondary institutions were inconsistent. The CMI mean turned to uptrend in tertiary (p<0.01) and secondary (p<0.0001) institutions compared with before. Although the slope of the CPH mean did not change (p>0.05), the uptrend of the CEI mean in tertiary institutions alleviated (p<0.05) and further increased (p<0.05) in secondary institutions. The slopes of the ALOS and TEI mean in secondary institutions changed (p<0.05), but not in tertiary institutions (p>0.05).ConclusionsThis study showed a positive effect of the DRG policy in Wenzhou, even during COVID-19. The policy can motivate public general hospitals to improve their comprehensive capacity and mitigate discrepancies in treatment expenses efficiency for similar diseases. Policymakers are interested in whether the reform successfully motivates hospitals to strengthen their internal impetus and improve their performance, and this is supported by this study.
•Tissue signals bordering tumor are valuable in predicting tumor prognosis.•Post-treatment images are of great significance for prognosis prediction.•Assessing images of tumor after each treatment ...course is a possible prospect.•The post-treatment images predict the prognosis better than pre-treatment images do.
We aimed to predict the prognosis of advanced nasopharyngeal carcinoma (stage Ⅲ-Ⅳa) using Pre- and Post-treatment MR images based on deep learning (DL).
A total of 206 patients with primary nasopharyngeal carcinoma who were diagnosed and treated at the Renmin Hospital of Wuhan University between June 2012 and January 2018 were retrospectively selected. A rectangular region of interest (ROI), which included the tumor area, surrounding tissues and organs, was delineated on each Pre- and Post-treatment MR image. Two Inception-Resnet-V2 based transfer learning models, named Pre-model and Post-model, were trained with the Pre-treatment images and the Post-treatment images, respectively. In addition, an ensemble learning model based on the Pre-model and Post-models was established. The three established models were evaluated by receiver operating characteristic curve (ROC), confusion matrix, and Harrell's concordance indices (C-index). High-risk-related gradient-weighted class activation mapping (Grad-CAM) images were developed according to the DL models.
The Pre-model, Post-model, and ensemble model displayed a C-index of 0.717 (95% CI: 0.639 to 0.795), 0.811 (95% CI: 0.745–0.877), 0.830 (95% CI: 0.767–0.893), and AUC of 0.741 (95% CI: 0.584–0.900), 0.806 (95% CI: 0.670–0.942), and 0.842 (95% CI: 0.718–0.967) for the test cohort, respectively. In comparison with the models, the performance of Post-model was better than the performance of Pre-model, which indicated the importance of Post-treatment images for prognosis prediction. All three DL models performed better than the TNM staging system (0.723, 95% CI: 0.567–0.879). The captured features presented on Grad-CAM images suggested that the areas around the tumor and lymph nodes were related to the prognosis of the tumor.
The three established DL models based on Pre- and Post-treatment MR images have a better performance than TNM staging. Post-treatment MR images are of great significance for prognosis prediction and could contribute to clinical decision-making.
Hand, foot, and mouth disease (HFMD) is a global infectious disease; particularly, it has a high disease burden in China. This study was aimed to explore the temporal and spatial distribution of the ...disease by analyzing its epidemiological characteristics, and to calculate the early warning signals of HFMD by using a logistic differential equation (LDE) model.
This study included datasets of HFMD cases reported in seven regions in Mainland China. The early warning time (week) was calculated using the LDE model with the key parameters estimated by fitting with the data. Two key time points, "epidemic acceleration week (EAW)" and "recommended warning week (RWW)", were calculated to show the early warning time.
The mean annual incidence of HFMD cases per 100,000 per year was 218, 360, 223, 124, and 359 in Hunan Province, Shenzhen City, Xiamen City, Chuxiong Prefecture, Yunxiao County across the southern regions, respectively and 60 and 34 in Jilin Province and Longde County across the northern regions, respectively. The LDE model fitted well with the reported data (R2 > 0.65, P < 0.001). Distinct temporal patterns were found across geographical regions: two early warning signals emerged in spring and autumn every year across southern regions while one early warning signals in summer every year across northern regions.
The disease burden of HFMD in China is still high, with more cases occurring in the southern regions. The early warning of HFMD across the seven regions is heterogeneous. In the northern regions, it has a high incidence during summer and peaks in June every year; in the southern regions, it has two waves every year with the first wave during spring spreading faster than the second wave during autumn. Our findings can help predict and prepare for active periods of HFMD.
Along with rapid urbanization, the growth and persistence of slums is a global challenge. While remote sensing imagery is increasingly used for producing slum maps, only a few studies have analyzed ...their temporal dynamics. This study explores the potential of fully convolutional networks (FCNs) to analyze the temporal dynamics of small clusters of temporary slums using very high resolution (VHR) imagery in Bangalore, India. The study develops two approaches based on FCNs. The first approach uses a post-classification change detection, and the second trains FCNs to directly classify the dynamics of slums. For both approaches, the performances of 3 × 3 kernels and 5 × 5 kernels of the networks were compared. While classification results of individual years exhibit a relatively high F1-score (3 × 3 kernel) of 88.4% on average, the change accuracies are lower. The post-classification results obtained an F1-score of 53.8% and the change-detection networks obtained an F1-score of 53.7%. According to the trajectory error matrix (TEM), the post-classification results scored higher for the overall accuracy but lower for the accuracy difference of change trajectories than the change-detection networks. Although the two methods did not have significant differences in terms of accuracy, the change-detection network was less noisy. Within our study area, the areas of slums show a small overall decrease; the annual growth of slums (between 2012 and 2016) was 7173 m2, in contrast to an annual decline of 8390 m2. However, these numbers hid the spatial dynamics, which were much larger. Interestingly, areas where slums disappeared commonly changed into green areas, not into built-up areas. The proposed change-detection network provides a robust map of the locations of changes with lower confidence about the exact boundaries. This shows the potential of FCNs for detecting the dynamics of slums in VHR imagery.
Recently, supervised deep learning has achieved a great success in remote sensing image (RSI) semantic segmentation. However, supervised learning for semantic segmentation requires a large number of ...labeled samples, which is difficult to obtain in the field of remote sensing. A new learning paradigm, self-supervised learning (SSL), can be used to solve such problems by pretraining a general model with a large number of unlabeled images and then fine-tuning it on a downstream task with very few labeled samples. Contrastive learning is a typical method of SSL that can learn general invariant features. However, most existing contrastive learning methods are designed for classification tasks to obtain an image-level representation, which may be suboptimal for semantic segmentation tasks requiring pixel-level discrimination. Therefore, we propose a global style and local matching contrastive learning network (GLCNet) for RSI semantic segmentation. Specifically, first, the global style contrastive learning module is used to better learn an image-level representation, as we consider that style features can better represent the overall image features. Next, the local features matching the contrastive learning module is designed to learn the representations of local regions, which is beneficial for semantic segmentation. We evaluate four RSI semantic segmentation datasets, and the experimental results show that our method mostly outperforms the state-of-the-art self-supervised methods and the ImageNet pretraining method. Specifically, with 1% annotation from the original dataset, our approach improves Kappa by 6% on the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset relative to the existing baseline. Moreover, our method outperforms supervised learning methods when there are some differences between the datasets of upstream tasks and downstream tasks. Our study promotes the development of SSL in the field of RSI semantic segmentation. Since SSL could directly learn the essential characteristics of data from unlabeled data, which is easy to obtain in the remote sensing field, this may be of great significance for tasks such as global mapping. The source code is available at https://github.com/GeoX-Lab/G-RSIM .
Sleep quality and depression are two reciprocal causation socioemotional factors and their roles in the relationship between physical exercise and cognition are still unclear.
A face-to-face survey ...of 3230 older adults aged 60+ was conducted in Xiamen, China, in 2016. Frequency of exercise (FOE) referred to the number of days of exercise per week. Quality of sleep (QOS) was categorized into five levels: very poor/poor/fair/good/excellent. The 15-item Geriatric Depression Scale (GDS-15) and the Montreal Cognitive Assessment (MoCA) were used to measure depression (DEP) and cognitive function (CF), respectively. Serial multiple mediator models were used. All mediation analyses were analyzed using the SPSS PROCESS macro.
2469 respondents had valid data with mean scores for GDS-15 and MoCA being 1.87 and 21.61, respectively. The direct path from FOE to CF was significant (c'= 0.20,
< 0.001). A higher FOE was associated with better QOS (B = 0.04,
< 0.01), which in turn was associated with fewer symptoms of DEP (B = -0.40,
< 0.001), and further contributed to better CF (B = -0.24,
< 0.001). Similarly, a higher FOE was associated with lower GDS-15 scores (B = -0.17,
< 0.001) which then resulted in higher MoCA scores (B = -0.24, p < 0.001). However, QOS alone did not alter the relationship between FOE and CF.
FOE is a protective factor of CF in older adults. Moreover, CF is influenced by QOS through DEP, without which the working path may disappear.
A series of hyperbranched polyurethane elastomers (PEO‐HBPUEs) as polymer electrolyte substrate materials was developed for anodic bonding with aluminum (Al) foil in micro‐electro‐mechanical system ...(MEMS) devices. The PEO‐HBPUEs were prepared by pre‐polymerization method with toluene‐2,4‐diisocyanate(TDI), polypropylene glycol (PPG), 1,4‐butanediol(BDO), trimethylolpropane(TMP), lithium bis(trifluoromethanesulphonyl)imide (LiTFSI), and polyethylene oxide (PEO)‐based electrolyte in varying proportions via solution casting technique at room temperature. All prepared PEO‐HBPUEs exhibited low glass transition temperatures, good thermal stabilities, and suitable mechanical properties. The XRD results showed that PEO‐HBPUEs are amorphous, and LiTFSI was well dissolved in the polymer matrix. The component of PEO‐based electrolyte in PEO‐HBPUEs contributed to increase the ionic conductivity, of which the highest value reached 1.23 × 10−3 S/cm at 75°C for PEO‐HBPUE4. The anodic bonding of PEO‐HBPUE substrate with Al foil was conducted by the coupling action of electric field, temperature field, and pressure field. A clear intermediate bonding layer between the substrate and Al foil was observed and the elements diffusion around bonding layer can be detected by SEM, indicating PEO‐HBPUEs and Al foil have been jointed together successfully. The highest tensile strength of the bonding interface of PEO‐HBPUE4/Al reached 1.88 MPa. All results demonstrated that the prepared PEO‐HBPUEs materials would be promising substrates for flexible MEMS device that can be applied to flexible packaging by anodic bonding technology.
The prepared polymer electrolyte substrate (PEO‐HBPUEs) and aluminum foil (Al) are “welded” together by anodic bonding instead of traditional adhesive, which extends the materials available for anodic bonding to polymers. What it means is really that it provides a possibility for packaging of flexible MEMS device to use anodic bonding technology.
Microfinance institutions (MFIs) are critical in providing financial services to low-income individuals in developing countries, but challenges such as inadequate infrastructure, limited resources, ...and low financial literacy have affected effective service delivery. In Ghana, MFIs have adopted information and communication technology (ICT) and mobile banking/money solutions to address these challenges. This study uses qualitative and quantitative research methods to explore the impact of integrating ICT and mobile banking/money adoption on the efficiency, growth, and outreach of MFIs in Ghana. The findings reveal that the integration of ICT and mobile banking/money adoption positively impacts the efficiency, growth, and outreach of MFIs, loan repayment rates, and financial inclusion. However, effective utilization and adoption of these technologies face barriers such as limited ICT infrastructure, a lack of technical expertise, and insufficient awareness of the benefits of these technologies. Furthermore, the study shows that integrating ICT solutions into microfinance operations has a significant positive relationship with MFIs’ growth and customer satisfaction rates. The study contributes to the literature on the adoption of ICT solutions in microfinance operations and provides insights into how MFIs can integrate ICT solutions into their operations to enhance their performance.
PDF
