Kernel Nutrient Composition and Antioxidant Ability of Corylus spp. in China

Hazelnut (Corylus) is an important woody oil tree species in economic forests. China, as one of the original countries of native Corylus species, had 8 species and 2 varieties. However, little information is available on the hazelnut nutritional quality of these Chinese Corylus species. In this study, four main wild Corylus species (C. heterophylla Fisch., C. mandshurica Maxim., C. kweichowensis Hu., and C. yunnanensis Franch.) originating in China and one main cultivar of hybrid hazelnut (Corylus heterophylla Fisch. × C. avellana L.) cv. ‘Dawei’ from China were used to analyze the basic nutritional composition (content of oil, fatty acid, protein, saccharide, aminao acid, vitamin C, tocopherol, total phenols, and total flavonoids) and antioxidant ability. The results showed that oil content ranged from 52.97 to 60.88 g/100 g DW and highly unsaturated fatty acid (UFA) content was over 91%. Oleic was the most dominant UFA in these hazelnut kernels, and the relative content was ranging from 71.32 to 85.19%. Compared with other four hazelnut kernels, C. heterophylla Fisch. was the lowest oil content of hazelnut with lower oleic acid content and higher linoleic acid content, obviously. The total protein content ranged from 13.15 to 18.35 g/100 g DW, and all amino acids were detected as hydrate amino acids, but Tryptophan, an essential amino acid, was not detected as free amino acid in these hazelnut kernels. Kernel of C. heterophylla Fisch. was with the highest content of protein and amino acid. Saccharose was the most essential and abundant disaccharide in the hazelnut kernels. C. mandshurica Maxim. was the highest saccharide content among these hazelnut kernels. α-tocopherol was the main type of tocopherol found in the hazelnut kernels. Wild hazelnut kernels generally had higher bioactivity substance content (vitamin C, total tocopherol, total phenol and total flavonoid) and antioxidant capacity. Compared to the four wild hazelnut kernels, the hybrid hazelnut cv. ‘Dawei’ had higher content of oil, oleic acid, α-tocopherol and sugar. Overall, there were great differences in the nutritional composition of different hazelnut species. Wild species are a good source of breeding materials because of their own characteristics in nutrition composition, and the hybrid hazelnut cv. ‘Dawei’ with good quality has the value of commercial promotion

Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIM.2023.3291769Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on Convolutional and Joint Distribution Adaptation(CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), Marginal Distribution Adaptation(MDA) and Conditional Distribution Adaptation(CDA). The convolutional component can efficiently extract the customer’s electricity characteristics. The Marginal Distribution Adaptation can match marginal probability distributions and solve the discrepancies of residents from different regions while Conditional Distribution Adaptation can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial and State Grid Corporation of China and metrics including ACC, Recall, FPR, AUC and F1Score are used for evaluation. Compared with other methods including some machine learning methods such as DT, RF and XGBoost, some deep learning methods such as RNN, CNN and Wide & Deep CNN and some up-to-date methods such as BDA, WBDA, ROCKET and MiniROCKET, our proposed method has a better effect on identifying electricity theft from different regions.Peer reviewe

Opportunities and challenges of hole transport materials for high‐performance inverted hybrid‐perovskite solar cells

Abstract Inverted perovskite solar cells (inverted‐PSCs) have exhibited advantages of longer stability, less hysteresis, and lower fabrication temperature when compared to their regular counterparts, which are important for industry commercialization. Because of the great efforts that have been conducted in the past several years, the obtained efficiency of inverted‐PSCs has almost caught up with that of the regular ones, 25.0% versus 25.7%. In this perspective, the recent studies on the design of high‐performance inverted‐PSCs based on diverse hole transport materials, as well as device fabrication and characterization are first reviewed. After that, the authors moved on to the interface and additive engineering that were exploited to suppress the nonradiative recombination. Finally, the challenges and possible research pathways for facilitating the industrialization of inverted‐PSCs were envisaged

Analysis and trajectory planning for anti-swing on a one-stage cart pendulum

In order to achieve smooth and anti-swing positioning process, a smooth trajectory planning scheme, which is mainly the linear combination of trigonometric functions, is proposed for a one-stage cart pendulum. Based on the internal dynamics of the cart pendulum system, the planning trajectory of the cart is derived from the anticipated swing angle of the pendulum bar, which satisfies the initial and terminal state constraints. Experiments are conducted to demonstrate the performance of anti-swing with the introduced planned trajectory

Stabilizing the Ag Electrode and Reducing J-V Hysteresis through Suppression of Iodide Migration in Perovskite Solar Cells

Hysteresis and stability issues in perovskite solar cell (PSCs) hinder their commercialization. Here, we report an effective and reproducible approach for enhancing the stability of and suppressing the hysteresis in PSCs by incorporating a small quantity of two-dimensional (2D) PEA(2)PbI(4) [PEA = C6H5(CH2)(2)NH3] in three-dimensional (3D) MAPbI(3) [MA = CH3NH3] [denoted as (PEA(2)PbI(4)) (MAPbI(3))], where the perovskite films were fabricated by the Lewis acid base adduct method. A nanolaminate structure comprising layered MAPbI(3) nanobricks was created in the presence of 2D PEA(2)PbI(4). For x = 0.017, a power conversion efficiency (PCE) of as high as 19.8% was achieved, which was comparable to the 20.0% PCE of a MAPbI(3)-based cell. Density functional theory (DFT) calculations confirmed that iodide migration was suppressed in the presence of the 2D perovskite as a result of a higher activation energy, which was responsible for the significant reduction in hysteresis and the improved chemical stability against a Ag electrode as compared to the corresponding characteristics of its pristine MAPbI(3) counterpart. An unencapsulated MAPbI(3)-based device retained less than 55% of its initial PCE in a 35-day aging test, whereas a (PEA(2)PbI(4))(0.017)(MAPbI(3))-based device without encapsulation exhibited a promising long-term stability, retaining over 90% of its initial PCE after 42 days.117sciescopu

Two-degree-of-freedom based cross-coupled control for high-accuracy tracking systems

Recent work recognizes that cross-coupled control (CCC) can significantly improve the accuracy of contour tracking in biaxial systems. However, it's complicated to apply CCC to arbitrary contour because of extra requirements to calculation and switching the cross-coupled gains. In addition, since most of CCC are based on the PID controlled loops of the individual axes, performances are conserved in some sense. In this paper, we propose a structure for arbitrary regular contours by efficiently determining the cross-coupling gains for CCC with the two-degree-of-freedom (2DOF) controlled to the single axes. Furthermore, an approach for stability analysis of the CCC is posed. Experimental results for a two-axial motion system indicate that the proposed structure eliminates the contouring error significantly. © 2007 IEEE

An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis

Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675)