47 research outputs found
High-throughput profiling of sweet potato vine biomass for cellulosic ethanol production using near-infrared spectroscopy and chemometrics
No full textHigh-throughput near-infrared spectroscopy analysis of nutritional composition in sweet potato stem tips
No full texthttp://dx.doi.org/10.13039/501100011798 Ministry of Agriculture and Rural Affairs of the People's Republic of Chin
Mobile Process in Unifying Theories
No full textThis report presents the initial work in the development of a theory of mobile processes in Circus, a language for describing state-based reactive systems. The mathematical basis for the work is Hoare and He's Unifying Theories of Programming (UTP), where the alphabetised relational calculus is used to provide a common framework for the semantics and refinement calculus of different programming paradigms. As our first step, we study the denotational semantics of mobile processes in UTP. Process mobility is interpreted as the assignment or communication of higher-order variables, whose values are process constants or parameterised processes, in which the target variables update their values and the source variables lose their values. We then present a set of algebraic and refinement to be used for the development of mobile systems. The correctness of these laws can be ensured by the UTP semantics of mobile processes. We illustrate our theory through a simple example that can be implemented in both a centralised and a distributed way. First, we present the pi-calculus specification for both systems and demonstrate that they are observationally equivalent. Next, we show how the centralised system may be derived into the distributed one using our proposed laws. By formalising mobile processes and studying their refinement laws in the same semantic framework of Circus, they can be included in Circus' refinement calculus to enhance Circus with the ability to develop networks of mobile processes
Total Phenolics and Anthocyanins Contents and Antioxidant Activity in Four Different Aerial Parts of Leafy Sweet Potato (Ipomoea batatas L.)
No full textLeafy sweet potato (Ipomoea batatas L.) is an excellent source of nutritious greens and natural antioxidants, but reports on antioxidants content and activity at buds, leaves, petioles, and stems are scarce. Therefore, the total phenolics content (TPC), total anthocyanins content (TAC), and antioxidant activity (assessed by DPPH and ABTS radical scavenging activities and ferric reducing antioxidant power (FRAP)) were investigated in four aerial parts of 11 leafy sweet potato varieties. The results showed that varieties with pure green aerial parts, independently of the part analyzed, had higher TPC, FRAP, and ABTS radical scavenging activities. The green-purple varieties had a significantly higher TAC, while variety GS-17-22 had the highest TAC in apical buds and leaves, and variety Ziyang in petioles and stems. Among all parts, apical buds presented the highest TPC and antioxidant capacity, followed by leaves, petioles, and stems, while the highest TAC level was detected in leaves. The TPC was positively correlated with ABTS radical scavenging activity and FRAP in all parts studied, whereas the TAC was negatively correlated with DPPH radical scavenging activity. Collectively, the apical buds and leaves of sweet potato had the higher levels of nutritional values. These results would provide reference values for further breeding of leafy sweet potatoes
Characterization of volatile compounds profiles and identification of key volatile and odor-active compounds in 40 sweetpotato (Ipomoea Batatas L.) varieties
No full textSweetpotato with different flesh colors exhibits significant differences in flavor. Nevertheless, research on the identification of the key aromatic compounds in sweetpotato is scarce. Therefore, 40 primary sweetpotato varieties with different flesh colors were analyzed by HS-SPME/GC–MS to characterize the volatile compounds. A total of 121 volatile compounds were detected, with aldehydes, furans and terpenes being the most abundant components. Additionally, 35 compounds were identified as the key aromatic compounds by OAV > 1, of which 17 were found in all varieties and hence considered as the major components of sweetpotato aroma. Further analysis demonstrated that the yellow-fleshed sweetpotato had the strongest aroma, which was presumed due to the aldehydes like (E,E)-2,4-heptadienal, nonanal, (E,E)-2,4-decadienal,etc. that were produced during the degradation of unsaturated fatty acids. The unique sweety and floral aroma of orange-fleshed sweetpotato might be attributed to apocarotenoid volatiles (trans-beta-ionone, β-ionone, geranylacetone, etc.), the derivates of carotenoids. The purple-fleshed sweetpotato exhibited weak aroma with a significantly high terpenoids concentration. Overall, these findings may be the main reason for the different aromas of different colored sweetpotatoes and provides insights into sweetpotato aroma and a theoretical basis for improving sweetpotato aroma
Machine learning-enhanced near-infrared spectroscopy for high-throughput phenotyping of sweetpotato sugars across raw and cooked states
No full textSweetpotato is a major root crop with high yield and nutritional benefits. However, existing methods for evaluating sugars level are inefficient, limiting the breeding and processing of high-quality varieties. This study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algorithms to develop a high-throughput assay for fructose, glucose, sucrose, and maltose in sweetpotatoes across their raw, steamed, and baked states. Leveraging representative samples, characteristic spectral variables, and advanced learning algorithms, twelve optimal models were established for the four sugar indicators under three processing states. These models exhibited outstanding performance in calibration (R2C: 0.941–0.984), cross-validation (R2CV: 0.926–0.976), external validation (R2V: 0.898–0.971), and the ratio of prediction to deviation (RPD: 5.83–10.3), confirming their robust predictive capacity. The findings suggest that these machine learning-enhanced NIRS models enable rapid, high-throughput analysis of sweetpotato sugars, significantly benefiting both breeding programs and food processing applications
Short-Term Response of Switchgrass to Nitrogen, Phosphorus, and Potassium on Semiarid Sandy Wasteland Managed for Biofuel Feedstock
No full textSorghum biomass and quality and soil nitrogen balance response to nitrogen rate on semiarid marginal land
No full textEnlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
No full textImage restoration is a low-level visual task, and most CNN methods are
designed as black boxes, lacking transparency and intrinsic aesthetics. Many
unsupervised approaches ignore the degradation of visible information in
low-light scenes, which will seriously affect the aggregation of complementary
information and also make the fusion algorithm unable to produce satisfactory
fusion results under extreme conditions. In this paper, we propose
Enlighten-anything, which is able to enhance and fuse the semantic intent of
SAM segmentation with low-light images to obtain fused images with good visual
perception. The generalization ability of unsupervised learning is greatly
improved, and experiments on LOL dataset are conducted to show that our method
improves 3db in PSNR over baseline and 8 in SSIM. Zero-shot learning of SAM
introduces a powerful aid for unsupervised low-light enhancement. The source
code of Enlighten Anything can be obtained from
https://github.com/zhangbaijin/enlighten-anythingComment: it will be revise
