Evaluation indices of sour flavor for apple fruit and grading standards

In order to establish grading standards of evaluation indices for sour flavor of apples, 10 indices of samples from 106 apple cultivars were tested, including： malic acid（Mal）, oxalic acid（Oxa）, citric acid（Cit）, lactic acid（Lac）, succinic acid（Suc）, fumaric acid（Fum）, total organic acids（To A, the sum of the six organic acids tested）, titratable acid（TiA）, acidity value（AcV）, and pH value. For most of the cultivars studied（85.8%）, the order of the organic acid contents in apples was Mal〉Oxa〉Cit〉Lac〉Suc〉Fum. Mal was the dominant organic acid, on average, accounting for 94.5% of To A. Among the 10 indices, the dispersion of pH value was the smallest with a coefficient of variation of only 8.2%, while the coefficients of variation of the other nine indices were larger, ranging between 31 and 66%. There were significant linear relationships between Mal and two indices（ToA and AcV） as well as between ToA and AcV. There were significant logarithmic relationships between pH value and four indices： Mal, TiA, ToA, and AcV. All the equations had very high fitting accuracy and can be used to accurately predict related indices. According to this study, Mal, ToA, and AcV of apple were normally distributed, TiA was close to normally distributed, whereas pH value had a skewed distribution. Using the fitted normal distribution curves, the grading standards of Mal, TiA, ToA, and AcV were established. The grading node values of pH value were obtained using the logarithmic relationship between pH value and Mal. The grading standards of these five indices can be used to evaluate the sour flavor of apple. This study provides a scientific basis for evaluating apple flavor and selecting apple cultivars.

Limits and grading evaluation criteria of tight oil reservoirs in typical continental basins of China

Based on the microscopic pore-throat characterization of typical continental tight reservoirs in China,such as sandstone of Cretaceous Qingshankou and Quantou formations in Songliao Basin,NE China sandy conglomerate of Baikouquan Formation in Mahu area and hybrid rock of Lucaogou Formation in Jimusaer sag of Junggar Basin,NE China the theoretical lower limit,oil accumulation lower limit,effective flow lower limit and the upper limit of tight oil reservoirs were defined by water film thickness method,oil bearing occurrence method,oil testing productivity method and mechanical balance method,respectively.Cluster analysis method was used to compare the differences in pore-throat structure of different tight reservoirs,determine the grading criterion of tight reservoirs,and analyze its correlation with the limit of reservoir formation.The results show that the boundary between tight reservoir and conventional reservoir corresponds to the upper limit of physical properties,the boundary of classⅡand classⅢtight reservoirs corresponds to the lower limit of effective flow,the boundary of classⅢand classⅣtight reservoirs corresponds to the lower limit of reservoir forming,and the theoretical lower limit of tight reservoir corresponds to the boundary between tight reservoir and non-reservoir.Finally,the application results of the grading evaluation criterion show that the tight oil productivity is highly controlled by the type of tight reservoir,and classⅠand classⅡtight reservoirs are the favorable sections for high production of tight oil.

Handwashing quality assessment via deep learning:a modelling study for monitoring compliance and standards in hospitals and communities

Background Hand hygiene can be a simple,inexpensive,and effective method for preventing the spread of infectious diseases.However,a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging.The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.Methods We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks(3D CNNs)and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation.An assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps,measure the exact duration,and grade the standard level of recognized steps.Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.Results Using a deep learning algorithm and an assessment scheme,combined with a probability smoothing method,each handwashing step was recognized(ACC ranged from 90.64%to 98.87%in the hospital and from 87.39%to 96.71%in the community).An assessment scheme measured each step’s exact duration,and the intraclass correlation coefficients were 0.98(95%CI:0.97-0.98)and 0.91(95%CI:0.88-0.93)for the total video duration in the hospital and community,respectively.Furthermore,the system assessed the quality of handwashing,similar to the expert panel(kappa=0.79 in the hospital;kappa=0.65 in the community).Conclusions This work developed an algorithm to directly assess handwashing compliance and quality from videos,which is promising for application in healthcare settings and communities to reduce pathogen transmis-sion.