Inter-well Connectivity Mapping System Based on Production Dynamic Data

In the process of oilfield development, interwell connectivity is an important index for reservoir evaluation, but after most of the oilfields enter into the medium-high water content development stage, the evaluation of interwell connectivity becomes the key to improve the efficiency of injection and recovery development. In order to solve the problems of time-consuming and costly of the traditional dynamic connectivity research methods, a water-driven injection and recovery inter-well connectivity calculation model was established through the study of Liu76-60 well group. In the process of the study, the inter-well model was established by processing the data of the well group, and the inter-well connectivity between injection and extraction wells was calculated on the basis of this model, and the calculated results were verified by multivariate linear regression equations. The final results show that the model established in the study is more accurate in evaluating the inter-well connectivity, and the comparison can be confirmed to be consistent with the results of the production dynamics, so the model can be used to evaluate the inter-well connectivity, which has a positive effect on the effective improvement of the efficiency of injection and recovery development in the reservoir

A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion

In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring

A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves

The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management

Exploring disease interrelationships in older inpatients: a single-centre, retrospective study

BackgroundComorbidity is a common phenomenon in the older population; it causes a heavy burden on societies and individuals. However, the relevant evidence, especially in the southwestern region of China, is insufficient.ObjectivesWe aimed to examine current comorbidity characteristics as well as correlations among diseases in individuals aged >60 years.DesignRetrospective study.MethodsWe included records of 2,995 inpatients treated at the Gerontological Department of Sichuan Geriatric Hospital from January 2018 to February 2022. The patients were divided into groups according to sex and age. Diseases were categorised based on the International Classification of Diseases and their Chinese names. We calculated the age-adjusted Charlson Comorbidity Index (ACCI), categorised diseases using the China Health and Retirement Longitudinal Study questionnaire, and visualised comorbidity using web graphs and the Apriori algorithm.ResultsThe ACCI was generally high, and it increased with age. There were significant differences in the frequency of all diseases across age groups, especially in individuals aged ≥90 years. The most common comorbid diseases were liver diseases, stomach or other digestive diseases, and hypertension. Strong correlations between the most common digestive diseases and hypertension were observed.ConclusionOur findings provide insights into the current situation regarding comorbidity and the correlations among diseases in the older population. We expect our findings to inform future research directions as well as policies regarding general clinical practice and public health, especially for medical consortiums

Management of singlet and triplet excitons for efficient white organic light-emitting devices

Lighting accounts for approximately 22 per cent of the electricity consumed in buildings in the United States, with 40 per cent of that amount consumed by inefficient (similar to 15 lm W-1) incandescent lamps(1,2). This has generated increased interest in the use of white electroluminescent organic light-emitting devices, owing to their potential for significantly improved efficiency over incandescent sources combined with low-cost, high-throughput manufacturability. The most impressive characteristics of such devices reported to date have been achieved in all-phosphor-doped devices, which have the potential for 100 per cent internal quantum efficiency(2): the phosphorescent molecules harness the triplet excitons that constitute three-quarters of the bound electron-hole pairs that form during charge injection, and which (unlike the remaining singlet excitons) would otherwise recombine non-radiatively. Here we introduce a different device concept that exploits a blue fluorescent molecule in exchange for a phosphorescent dopant, in combination with green and red phosphor dopants, to yield high power efficiency and stable colour balance, while maintaining the potential for unity internal quantum efficiency. Two distinct modes of energy transfer within this device serve to channel nearly all of the triplet energy to the phosphorescent dopants, retaining the singlet energy exclusively on the blue fluorescent dopant. Additionally, eliminating the exchange energy loss to the blue fluorophore allows for roughly 20 per cent increased power efficiency compared to a fully phosphorescent device. Our device challenges incandescent sources by exhibiting total external quantum and power efficiencies that peak at 18.7 +/- 0.5 per cent and 37.6 +/- 0.6 lm W-1, respectively, decreasing to 18.4 +/- 0.5 per cent and 23.8 +/- 0.5 lm W-1 at a high luminance of 500 cd m(-2).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62889/1/nature04645.pd

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content

Modelling Future Coronary Heart Disease Mortality to 2030 in the British Isles.

OBJECTIVE: Despite rapid declines over the last two decades, coronary heart disease (CHD) mortality rates in the British Isles are still amongst the highest in Europe. This study uses a modelling approach to compare the potential impact of future risk factor scenarios relating to smoking and physical activity levels, dietary salt and saturated fat intakes on future CHD mortality in three countries: Northern Ireland (NI), Republic of Ireland (RoI) and Scotland. METHODS: CHD mortality models previously developed and validated in each country were extended to predict potential reductions in CHD mortality from 2010 (baseline year) to 2030. Risk factor trends data from recent surveys at baseline were used to model alternative future risk factor scenarios: Absolute decreases in (i) smoking prevalence and (ii) physical inactivity rates of up to 15% by 2030; relative decreases in (iii) dietary salt intake of up to 30% by 2030 and (iv) dietary saturated fat of up to 6% by 2030. Probabilistic sensitivity analyses were then conducted. RESULTS: Projected populations in 2030 were 1.3, 3.4 and 3.9 million in NI, RoI and Scotland respectively (adults aged 25-84). In 2030: assuming recent declining mortality trends continue: 15% absolute reductions in smoking could decrease CHD deaths by 5.8-7.2%. 15% absolute reductions in physical inactivity levels could decrease CHD deaths by 3.1-3.6%. Relative reductions in salt intake of 30% could decrease CHD deaths by 5.2-5.6% and a 6% reduction in saturated fat intake might decrease CHD deaths by some 7.8-9.0%. These projections remained stable under a wide range of sensitivity analyses. CONCLUSIONS: Feasible reductions in four cardiovascular risk factors (already achieved elsewhere) could substantially reduce future coronary deaths. More aggressive polices are therefore needed in the British Isles to control tobacco, promote healthy food and increase physical activity

Blood-Based Gene Expression Profiles Models for Classification of Subsyndromal Symptomatic Depression and Major Depressive Disorder

Subsyndromal symptomatic depression (SSD) is a subtype of subthreshold depressive and also lead to significant psychosocial functional impairment as same as major depressive disorder (MDD). Several studies have suggested that SSD is a transitory phenomena in the depression spectrum and is thus considered a subtype of depression. However, the pathophysioloy of depression remain largely obscure and studies on SSD are limited. The present study compared the expression profile and made the classification with the leukocytes by using whole-genome cRNA microarrays among drug-free first-episode subjects with SSD, MDD, and matched controls (8 subjects in each group). Support vector machines (SVMs) were utilized for training and testing on candidate signature expression profiles from signature selection step. Firstly, we identified 63 differentially expressed SSD signatures in contrast to control (P< = 5.0E-4) and 30 differentially expressed MDD signatures in contrast to control, respectively. Then, 123 gene signatures were identified with significantly differential expression level between SSD and MDD. Secondly, in order to conduct priority selection for biomarkers for SSD and MDD together, we selected top gene signatures from each group of pair-wise comparison results, and merged the signatures together to generate better profiles used for clearly classify SSD and MDD sets in the same time. In details, we tried different combination of signatures from the three pair-wise compartmental results and finally determined 48 gene expression signatures with 100% accuracy. Our finding suggested that SSD and MDD did not exhibit the same expressed genome signature with peripheral blood leukocyte, and blood cell–derived RNA of these 48 gene models may have significant value for performing diagnostic functions and classifying SSD, MDD, and healthy controls