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Background Red flesh is a desired fruit trait, but the regulation of red flesh formation in grape is not well understood. ‘Mio Red’ is a seedless table grape variety with light-red flesh and blue-purple skin. The skin color develops at veraison whereas the flesh color develops at a later stage of berry development. The flesh and skin flavonoid metabolomes and transcriptomes were analyzed. Results A total of 161 flavonoids were identified, including 16 anthocyanins. A total of 66 flavonoids were found at significantly different levels in the flesh and skin (fold change ≥ 2 or ≤ 0.5, variable importance in projection (VIP) ≥ 1). The main anthocyanins in the flesh were pelargonidin and peonidin, and in the skin were peonidin, delphinidin, and petunidin. Transcriptome comparison revealed 57 differentially expressed structural genes of the flavonoid-metabolism pathway (log 2 fold change  ≥  1, FDR < 0.05, FPKM ≥ 1). Two differentially expressed anthocyanin synthase (ANS) genes were annotated, ANS2 ( Vitvi02g00435 ) with high expression in flesh and ANS1 ( Vitvi11g00565 ) in skin, respectively. One dihydro flavonol 4-reductase ( DFR, Vitvi18g00988 ) gene was differentially expressed although high in both skin and flesh. Screened and correlation analysis of 12 ERF, 9 MYB and 3 bHLH genes. The Y1H and dual luciferase assays showed that MYBA1 highly activates the ANS2 promoter in flesh and that ERFCBF6 was an inhibitory, EFR23 and bHLH93 may activate the DFR gene. These genes may be involved in the regulation of berry flesh color. Conclusions Our study revealed that anthocyanin biosynthesis in grape flesh is independent of that in the skin. Differentially expressed ANS , MYB and ERF transcription factors provide new clues for the future breeding of table grapes that will provide the health benefits as red wine.

Structural health monitoring of highway bridges is a vital but currently challenging aspect of infrastructure engineering due to the number of sensors required, power requirements, and harsh environmental conditions. The purpose of this study is to develop a structural health monitoring system using fiber optic sensors based on fiber Bragg gratings that addresses these issues and is field deployable. Prototype systems were installed on two steel girder bridges. The first bridge used sensors adhered to the web and flange. The second bridge used a flange-only array of mechanically mounted sensors. The results demonstrated the accuracy of the fiber Bragg grating sensors and indicated that fewer multiplexed fiber optic cables and loosely routed cables were needed to maintain signal integrity. Adhered sensors were prone to lose their bond due to the curing conditions in the field. The findings suggest that the proposed system may be best used in a hybrid deployment, where a diagnostic field test with conventional sensors is used to determine the baseline bridge response and fiber optic sensors are periodically installed for short-term monitoring.

This paper describes a case study of a novel teaching method where the “Teaching with Industry” (TWI) model–industry practitioners incorporated as co-instructors in a semester-long classroom setting–is enhanced by using new videoconferencing technologies such as Zoom and Meeting Owl Pro, and innovative classroom setups. This enhanced model was developed with the intent to bridge the gap between information acquired in the classroom and the skills and competencies required in the industry. The different teaching platforms not only facilitated the teaching when industry practitioners were/are not able to be physically present in the classroom, but also led to efficient organization of the different activities carried out in class. Results obtained from end-course surveys showed that students had a positive experience using Zoom and Meeting Owl Pro welcoming the opportunity to engage with industry practitioners and gain better understanding of the practical usefulness of the course.

This paper conducts an in-depth study on the on-board energy storage system for electric vehicles. We analyze the advantages and disadvantages of domestic and foreign energy storage systems including battery, flywheel, superconductor and supercapacitor. We propose a hybrid energy storage system composed of battery and supercapacitor as the on-board power supply system. It adopts a two-phase staggered parallel bidirectional DC-DC converter as the main control circuit of the storage system. And we adopt the power outer loop and current inner loop control strategy. Then the rate limiter prevents the sudden change of battery power and realizes that the instantaneous power is provided by the supercapacitor individually. The purpose is to extend the battery life and achieve the purpose of reasonable and efficient utilization of energy. Finally, the simulation verification is carried out by Matlab/Simulink. And the results show that the energy management strategy is more efficient for both power and energy management, which meets the design expectation and achieves better experimental results.

Based on the analysis of foreign classic maturity models and the actual situation of electrical engineering projects, this paper constructs a suitable three-dimensional project risk management maturity model and establishes a project risk management evaluation index system. Through fuzzy comprehensive evaluation method and analytic hierarchy process, the key element indicators are evaluated and weighted, and the maturity score is obtained, which finally reflects the maturity of project risk management. Finally, through empirical analysis, we found out the shortcomings of enterprise project risk management and proposed the direction for improvement.

In view of the time-series characteristics of the grid load data, this paper proposes a method to predict electricity demand by optimizing a long-and short-term memory (LSTM) neural network model using the grey wolf optimization algorithm, taking into account the effects of time, weather conditions and holiday conditions on electricity loads. The model overcomes the disadvantage that the backpropagation through the time algorithm tends to converge to a local optimum. The experimental results show that the prediction results outperform those of the traditional LSTM for short-term electricity loads, providing a reference direction for future electricity forecasting models.

Bearing component is the most important component of wind turbine, the harsh operating environment makes the bearing prone to failure, and the maintenance cost is very expensive when a failure occurs. Complex environmental noise brings serious noise pollution to fault samples. In addition, the serious sample imbalance between fault samples and normal samples brings great challenges to bearing fault diagnosis. In view of the above mentioned problems, this paper uses a self-attention mechanism optimization and Wasserstein distance improvement deep convolutional adversarial network model based on self-attention mechanism optimization and Wasserstein distance improvement deep Bearing fault diagnosis method based on the convolution generative Adversarial Network Model (SAW-DCGAN). The experimental results show that W-DCGAN has excellent generating ability and can generate samples like real samples to achieve the balance of samples. The addition of self-attention makes the classification features more expressive, and can accelerate the training speed of the model while having a higher fault diagnosis rate, which verifies the practicability of the method.

The kernel function parameter g and penalty factor c in Support Vector Machine (SVM) will have an important impact on the fault classification and performance of the support vector machine. Based on this, a fault analysis and diagnosis model using ant colony algorithm to optimize support vector machine is proposed to improve the accuracy of gearbox fault diagnosis. First, the collected original vibration signal is decomposed by EEMD to obtain the modal function component IMF, and then the energy entropy of the IMF component is calculated as the feature vector of the original vibration signal. Finally, the feature vector is input to the support vector optimized by the ant colony algorithm identify and classify in the machine, and finally get the diagnosis result. Comparing ACO-SVM with SVM, the experimental results prove that the ACO-SVM model has a higher fault diagnosis rate, stronger optimization ability, and faster convergence speed.

This article uses the Novy model improved by value-added trade data to measure the cost of inter-provincial trade in the Yangtze River Delta and, on this basis, uses the differential decomposition method to explore the promoting effect of inter-provincial trade costs on the development of inter-provincial trade in the Yangtze River Delta. The results show that the inter-provincial trade costs of the provinces/municipalities in the Yangtze River Delta have increased and decreased, but the changes are small, and there are significant differences in sectoral and bilateral trade costs; the results of the differential decomposition show that the contribution of inter-provincial trade costs to inter-provincial trade development in the Yangtze River Delta is much lower than that of economic growth. Therefore, the Yangtze River Delta should deepen the division of labor and cooperation, give full play to the radiation and leading role of Shanghai as an economic center, and accelerate the digital innovation transformation of the service industry to reduce inter-provincial trade costs and achieve a higher level of integrated development of the Yangtze River Delta.

Skewed highway bridges are commonplace, but the behavior of a skewed bridge can be significantly more complex compared to a straight bridge. As a consequence, the theoretical equations currently used to determine the load rating of a skewed bridge can be inaccurate for a large skew angle because the location of the girder and the moment (positive or negative) under consideration, the arrangement and position of cross-frames and parapets, and the location of loading applied on the bridge are neglected. In this study, an accurate load rating method is proposed that accounts for these effects. In the proposed method, a field test is used to determine the safe load-carrying capacity of the bridge, and a two-dimensional grid with longitudinal and transverse beam elements rigidly connected at intersecting nodes and vertical loads applied at the nodes (“grillage”) model is used to calculate the contribution due to skew. The field test is conducted using a calibrated truck driven over the bridge in successive runs that traverse the entire width of the roadway, and the measured strain response is used to calculate internal moments and experimental live load effects. To compare the behaviors between the actual bridge and the analytical estimation, the ratio of the bridge experimental and analytical load ratings is obtained. Additionally, contributors to the bridge live load are decomposed and compared to the analytical load rating to obtain the bridge deaggregated ratio of load ratings. The effect of critical span adjustment, longitudinal and lateral load distribution, unintended composite action between the slab and the girders, slab flexure, and additional stiffness in the system on the ratio of load ratings are qualified and quantified. The contribution of additional stiffness in the system to the actual load rating in comparison to the analytical is further discretized into the contribution to stiffness due to skew and the contribution to stiffness due to curbs and railings. The contribution to stiffness due to skew is calculated as the ratio of the statical moment in the skewed bridge grillage model to the corresponding statical moment in the equivalent straight bridge. The proposed method is illustrated using a case study of a steel girder highway bridge with a 43-degree skew angle. The tests indicate that the experimental load rating is 156% greater than the analytical load rating at the positive moment location, and 11% greater at the negative moment location. The increased load rating is primarily due to unintended composite action. Since unintended composite action is not reliable for load-levels above the linear-elastic region, the effect of unintended composite action is removed, resulting in a reliable experimental rating load rating that is 46% greater than the analytical load rating at the positive moment location. The grillage model indicates that additional stiffness due to skew contributes to a 19% increase in the positive moment load rating. The case study demonstrates that the proposed method provides a more accurate determination of the skew effect, compared to the theoretical equations currently in use, because the location of the girder and moment under consideration, the arrangement and position of cross-frames and parapets, and the location of loading applied on the bridge are explicitly incorporated. Therefore, field testing coupled with a grillage model has the potential to effectively load rate skewed highway bridges.