Clinical value of precise rehabilitation nursing in management of cerebral infarction

BACKGROUND Cerebral infarction,previously referred to as cerebral infarction or ischemic stroke,refers to the localized brain tissue experiencing ischemic necrosis or softening due to disorders in brain blood supply,ischemia,and hypoxia.The precision rehabilitation nursing model for chronic disease management is a continuous,fixed,orderly,and efficient nursing model aimed at standardizing the clinical nursing process,reducing the wastage of medical resources,and improving the quality of medical services.AIM To analyze the value of a precise rehabilitation nursing model for chronic disease management in patients with cerebral infarction.METHODS Patients(n=124)admitted to our hospital with cerebral infarction between November 2019 and November 2021 were enrolled as the study subjects.The random number table method was used to divide them into a conventional nursing intervention group(n=61)and a model nursing intervention group(n=63).Changes in the nursing index for the two groups were compared after conventional nursing intervention and precise rehabilitation intervention nursing for chronic disease management.RESULTS Compared with the conventional intervention group,the model intervention group had a shorter time to clinical symptom relief(P<0.05),lower Hamilton Anxiety Scale and Hamilton Depression Scale scores,a lower incidence of total complications(P<0.05),a higher disease knowledge mastery rate,higher safety and quality,and a higher overall nursing satisfaction rate(P<0.05).CONCLUSION The precision rehabilitation nursing model for chronic disease management improves the clinical symptoms of patients with cerebral infarction,reducing the incidence of total complications and improving the clinical outcome of patients,and is worthy of application in clinical practice.

Optimal waveband identification for estimation of leaf area index of paddy rice

The objectives of the study were to select suitable wavebands for rice leaf area index （LAI） estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance （350-2500 nm） and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation： correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350-2500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1633 rim. They were followed by 444, 524, 576, 594, 804, 849, 974, 1074, 1219, 1510, and 2194 rim. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.

Cross electro-nape-acupuncture ameliorates cerebral hemorrhageinduced brain damage by inhibiting necroptosis

BACKGROUND Receptor interacting protein kinase 1(RIPK1)-mediated cell death,including apoptosis and necroptosis,belongs to programmed cell death.It has been reported that RIPK1-mediated necroptosis exists in lesions of cerebral hemorrhage(CH).Electroacupuncture,a treatment derived from traditional Chinese medicine,could improve neurological impairment in patients with brain injury.AIM To investigate the protective role of cross electro-nape acupuncture(CENA)in CH,and clarify the potential mechanism.METHODS CH rat models were established,and CENA was applied to the experimental rats.Neurological functions and encephaledema were then measured.Necrotic cells in the brain of rats with CH were evaluated by propidium iodide staining.Necroptosis was assessed by immunofluorescence.Activation of the necroptosisrelated pathway was detected by western blot.Extraction of brain tissue,cerebrospinal fluid and serum samples was conducted to measure the expression and secretion of inflammatory cytokines by quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay.RESULTS The necroptotic marker p-MLKL was detectable in the brains of rats with CH.Next,we found that CENA could ameliorate neurological functions in rat models of CH.Moreover,the upregulation of RIPK1-mediated necroptosis-related molecules in the brains of rats with CH were inhibited by CENA.Further investigation revealed that CENA partially blocked the interaction between RIPK1 and RIPK3.Finally,in vivo assays showed that CENA decreased the expression of the inflammatory cytokines tumor necrosis factor-α,interleukin-6 and interleukin-8 in CH rat models.CONCLUSION These findings revealed that CENA exerts a protective role in CH models by inhibiting RIPK1-mediated necroptosis.

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

To further develop the methods to remotely sense the biochemical content of plant canopies,we report the results of an experiment to estimate the concentrations of three biochemical variables of corn,i.e.,nitrogen(N),crude fat(EE) and crude fiber(CF) concentrations,by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed,and a set of estimation models were established using curve-fitting analyses. Coefficient of determination(R2),root mean square error(RMSE) and relative error of prediction(REP) of estimation models were calculated for the model quality evaluations,and the possible opti-mum estimation models of three biochemical variables were proposed,with R2 being 0.891,0.698 and 0.480 for the estimation models of N,EE and CF concentrations,respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation,and that the first derivative reflectances at 759 nm,1954 nm and 2370 nm were most suitable to develop the estimation models of N,EE and CF concentrations,respectively. In addition,the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained,especially for nitrogen(r=0.948).

Analysis model of internal residence time distribution for fluid flow in a multi-strand continuous casting tundish

The external residence time distribution(RTD)curve is extensively used to characterise fluid flow within the single-strand continuous casting tundish.Dead volume fraction determination typically relies on the external RTD curve to reveal macroscopic fluid flow behaviour.Based on the external RTD to effectively assess dead volume fractions and other fluid characteristics under conditions of internal non-uniform flow,an internal RTD was introduced.In a smooth pipe under laminar flow conditions,the dead region occupies 25%of the total volume,which is defined as the space between the pipe wall and a radius of 0.866 R0(where R0 is the radius of pipe).Under turbulent flow conditions,the dead region only occupies 0.38%of the reactor’s internal volume,spanning from the pipe wall to a radius of 0.00189 R0.The results obtained using the external RTD method are consistent with the theoretical analysis.Experimental trials involving water were conducted to examine the flow of molten steel within a five-strand tundish.Subsequently,an analysis approach employing internal RTD was employed to evaluate fluid mixing within a multi-flow continuous casting tundish.Using the internal RTD method,the analysis revealed that the whole dead zone volume fraction of the intermediate package decreased from 26.9%to 18.9%after the addition of the flow control device.The dead volume fraction can be accurately depicted by utilising the internal mean RTD function.The association between the internal RTD function and the external average RTD can be effectively employed to scrutinise the response curve of the tracer within a system exhibiting uneven flow distribution.

Optimizing FeCoNiCrTi high-entropy alloy with hydrogen pumping effect to boost de/hydrogenation performance of magnesium hydride

The exploration of efficient,long-lived and cost-effective transition metal catalysts is highly desirable for the practical hydrogen storage of magnesium hydride(MgH_(2)) in sustainable energy devices.Herein,FeCoNiCrTi high-entropy alloy(HEA) nanosheets were prepared via a facile wet chemical ball milling strategy and they were introduced into MgH_(2) to boost the hydrogen storage performance.The refined HEA exhibited superior catalytic activity on MgH_(2).In contrast to additive-free MgH_(2),the initial desorption temperature of the constructed MgH_(2)-HEA composite was reduced from 330.0 to 198.5℃ and a remarkable 51% reduction in the dehydrogenation activation energy was achieved.Besides,the MgH_(2)-HEA composite only required one-twentieth time of that consumed by pure MgH_(2) to absorb 5.0 wt% of H_(2) at 225℃.The synergy between the "hydrogen pumping" effect of Mg_2Ni/Mg_2NiH_4 and Mg_2Co/Mg_2CoH_5 couples,as well as the good dispersion of Fe,Cr and Ti on the surface of MgH_(2) contributed to the enhanced de/hydrogenation performance of the MgH_(2)-HEA composites.This study furnishes important steering for the design and fabrication of multiple transition metal catalysts and may push the commercial application of magnesium-based hydrides one step forward.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data

The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.

Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images

Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of H J-1 CCD and Landsat-8 operational land imager （OLI） by using the ordinary least-squares （OLS） and construct higher temporal resolution vegetation indices （VIs） time-series data to extract the phenological param- eters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index （NDVI） and 2-band enhanced vegetation index （EVI2）, were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay （S-G） filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error （RMSE）, and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.

Identification of Optimal Hyperspectral Bands for Estimation of Rice Biophysical Parameters

The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground-level hyperspectral reflectance measurements for five rice species at three levels of nitrogen fertilization during the growing period. Reflectance was measured in discrete narrow bands between 350 and 2 500 nm. Observed rice biophysical parameters included leaf area index （LAI）, wet biomass and dry biomass. The stepwise regression method was applied to identify the optimal bands for rice biophysical parameter estimation. This research indicated that combinations of four narrow bands in stepwise regression models explained 69% to 83% variability for LAI, 56% to 73% for aboveground wet biomass and 70% to 83% for leaf wet biomass. An overwhelming proportion of rice information was in a particular portion of near infrared （NIR） （1 100-1 150 nm）, red-edge （700-750 nm）, and a longer portion of green （550-600 nm）. These were followed by the moisture-sensitive NIR （950-1 000 nm）, the intermediate portion of shortwave infrared （SWlR） （1 650-1 700 nm）, and another portion of NIR （1 000-1 050 nm）.