36 research outputs found

    Channel Spectral Separation Narrowing for Spectral Beam Combining by Apodisation of the Reflecting Volume Bragg Grating

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    The sidelobe in diffraction efficiency of reflecting volume Bragg grating (RVBG) limits the wavelength channel spectral separation, which determines the combining power output in spectral beam combining (SBC) systems. A novel SBC system based on the apodised RVBG has been proposed to suppress the sidelobe. Several apodised RVBGs have been compared and the optimal apodised RVBG is obtained by using the chain-matrix approach. Numerical results show that the sidelobe could be suppressed excellently with Blackman apodised RVBG. In the numerical example, the minimal channel spectral separation was 1.0 nm for SBC system based on the RVBG and it decreased to 0.6 nm for the novel SBC system based on the Blackman apodised RVBG when the spectral combining efficiency of both systems achieves the same maximum value.Defence Science Journal, 2011, 61(3), pp.201-205, DOI:http://dx.doi.org/10.14429/dsj.61.52

    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

    Correlation Analysis for Fiber Characteristics and Strength Properties of Softwood Kraft Pulps from Different Stages of a Bleaching Fiber Line

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    During sequential bleaching operations, pulp fiber properties are gradually changed due to mechanical and chemical treatments. In this study, the correlations between pulp or fiber properties such as kappa number, viscosity, total charge, fiber length, and zero-span tensile strength as well as Scott bond of elemental chlorine free (ECF) bleached softwood kraft pulps was investigated. The influence of zero-span tensile strength and Scott bond on tensile and tear strength was also discussed. The Scott bond and zero-span tensile strength showed a strong logarithmic correlation with pulp kappa number and pulp viscosity, while the regression coefficient for Scott bond was negative. An overall deterioration of paper tensile and tear strength from pulps whether beaten or not were observed along the multi-stage ECF bleaching operations. Changing contributions to sheet tensile or tear strength could be mostly attributed to changes in zero-span tensile strength rather than Scott bond during ECF bleaching

    Physical-Based Spatial-Spectral Deep Fusion Network for Chlorophyll-a Estimation Using MODIS and Sentinel-2 MSI Data

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    Satellite-derived Chlorophyll-a (Chl-a) is an important environmental evaluation indicator for monitoring water environments. However, the available satellite images either have a coarse spatial or low spectral resolution, which restricts the applicability of Chl-a retrieval in coastal water (e.g., less than 1 km from the shoreline) for large- and medium-sized lakes/oceans. Considering Lake Chaohu as the study area, this paper proposes a physical-based spatial-spectral deep fusion network (PSSDFN) for Chl-a retrieval using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 Multispectral Instrument (MSI) reflectance data. The PSSDFN combines residual connectivity and attention mechanisms to extract effective features, and introduces physical constraints, including spectral response functions and the physical degradation model, to reconcile spatial and spectral information. The fused and MSI data were used as input variables for collaborative retrieval, while only the MSI data were used as input variables for MSI retrieval. Combined with the Chl-a field data, a comparison between MSI and collaborative retrieval was conducted using four machine learning models. The results showed that collaborative retrieval can greatly improve the accuracy compared with MSI retrieval. This research illustrates that the PSSDFN can improve the estimated accuracy of Chl-a for coastal water (less than 1 km from the shoreline) in large- and medium-sized lakes/oceans

    A Heterogeneity-Enhancement and Homogeneity-Restraint Network (HEHRNet) for Change Detection from Very High-Resolution Remote Sensing Imagery

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    Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance efficiency in monitoring tasks such as changes in national resources, it is crucial to promote the practical implementation of automatic change detection technology. Therefore, we propose a deep learning approach utilizing heterogeneity enhancement and homogeneity restraint for CD. In addition to comprehensively extracting multilevel features from multitemporal images, we introduce a cosine similarity-based module and a module for progressive fusion enhancement of multilevel features to enhance deep feature extraction and the change information utilization within feature associations. This ensures that the change target completeness and the independence between change targets can be further improved. Comparative experiments with six CD models on two benchmark datasets demonstrate that the proposed approach outperforms conventional CD models in various metrics, including recall (0.6868, 0.6756), precision (0.7050, 0.7570), F1 score (0.6958, 0.7140), and MIoU (0.7013, 0.7000), on the SECOND and the HRSCD datasets, respectively. According to the core principles of change detection, the proposed deep learning network effectively enhances the completeness of target vectors and the separation of individual targets in change detection with VHR remote sensing images, which has significant research and practical value

    Photo-Electrical Properties of Trilayer MoSe 2

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    Ward renovation and PPE use procedures to protect medical staff from COVID-19 infection

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    In the early stages of the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, many cross-infections occurred due to the limited number of wards and insufficient medical staff, which could not cope with the large number of patients visiting the hospital. A series of new infection control measures were implemented in our institution and a Wuhan hospital supported by our medical team, mainly including temporarily transforming the general ward into a passage for the staff to enter the infectious ward and standardizing the procedure for the wearing and removal of personal protection equipment (PPE). These measures significantly improved the situation, and no member of our medical staff was infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the middle and late stages of the disease epidemic. We hope that these experiences can provide references for medical institutions that may face an outbreak of COVID-19, especially those in underdeveloped countries and regions
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