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In order to establish an effective mechanism for predicting spoilage of chilled goose breast meat, the microfloral succession was investigated by high-throughput sequencing and the dominant spoilage bacteria of chilled goose breast meat were determined. Meanwhile, the effects of three dominant spoilage bacteria on the volatile flavor substances of chilled goose breast meat were characterized by using gas chromatography-ion mobility spectrometry (GC-IMS) and solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). The results showed that the diversity of microflora was richer during the early and late storage periods. The dominant phylum was Amoeba, and the dominant spoilage bacterium was Pseudomona. Pseudomona began to grow significantly starting from day 3 and reached a peak with a relative abundance of 96.79% on day 9. Contamination with the dominant spoilage bacteria significantly affected the composition of volatile components in goose breast at the end of storage, resulting in higher proportions and contents of esters and ketones than fresh goose breast. Orthogonal partial least squares discrimination analysis (OPLS-DA) showed that methyl palmitoleate, methyl palmitate, ethyl oleate, methyl linoleate, ethyl linoleate, ethyl palmitoleate, 3-hydroxy-2-butanone, methyl trans-oleate, ethyl trans-oleate and neopentyl hexanoate were the major contributors to the volatile flavor of spoiled goose breast.

The traditional hulling of Camellia oleifera fruit is mainly manual, with high work intensity, high labor cost and low production efficiency. Mechanized hulling is the inevitable trend of modernization of Camellia oleifera industry. The existing hulling device of Camellia oleifera fruit has poor universality, low hulling rate and high seed kernel breakage rate, which affects the popularization of mechanized hulling technology of Camellia oleifera. By comparing the existing hulling methods of Camellia oleifera, the structure and working principle of each hulling device were analyzed, their advantages and disadvantages were summarized, and their development direction was prospected.

Regarding the difficulty of extracting the acquired fault signal features of bearings from a strong background noise vibration signal, coupled with the fact that one-dimensional (1D) signals provide limited fault information, an optimal time frequency fusion symmetric dot pattern (SDP) bearing fault feature enhancement and diagnosis method is proposed. Firstly, the vibration signals are transformed into two-dimensional (2D) features by the time frequency fusion algorithm SDP, which can multi-scale analyze the fluctuations of signals at minor scales, as well as enhance bearing fault features. Secondly, the bat algorithm is employed to optimize the SDP parameters adaptively. It can effectively improve the distinctions between various types of faults. Finally, the fault diagnosis model can be constructed by a deep convolutional neural network (DCNN). To validate the effectiveness of the proposed method, Case Western Reserve University's (CWRU) bearing fault dataset and bearing fault dataset laboratory experimental platform were used. The experimental results illustrate that the fault diagnosis accuracy of the proposed method is 100%, which proves the feasibility and effectiveness of the proposed method. By comparing with other 2D transformer methods, the experimental results illustrate that the proposed method achieves the highest accuracy in bearing fault diagnosis. It validated the superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings. [ABSTRACT FROM AUTHOR]

In this paper, a 2D off-grid small compact model of vertical axis wind turbine was established. The sliding grid technology, the RNG turbulence model and the Coupld algorithm was applied to simulate the unsteady value of the model's aerodynamic performance. Through the analysis on the flow field at difference moments, the rules about velocity fields, vortices distributions and the wind turbine's total torque were obtained. The results show that: the speed around wind turbine blades have obvious gradient, and the velocity distribution at different times show large differences in the computional domain. In the rotating domain vorticity is large. With away from the rotation domain, vorticity reduced quickly. In the process of rotating for vertical axis wind turbine, the wind turbine's total torque showed alternating positive and negative changes.

Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted Lu3Fe5O12 with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance. [ABSTRACT FROM AUTHOR]

Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/Y3Fe5O12 bilayer structure. We demonstrated that the CoFeB/Y3Fe5O12 bilayer structure can create a sharp rejection band in its spin-wave transmission spectra. The lowest point of this strong rejection band allows the detection of a small frequency shift owing to the external magnetic field variation. Experimental observations revealed that such a bilayer magnetic sensor exhibits 20 MHz frequency shifts upon the application of an external magnetic field of 0.5 mT. Considering the lowest full width half maximum, which is about 2 MHz, a sensitivity of 10–2 mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10–6 T (µT) has been demonstrated using the sharp edge of the rejection band of the CoFeB/Y3Fe5O12 bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y3Fe5O12 and Y3Fe5O12 has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of 10 - 9 T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe. [ABSTRACT FROM AUTHOR]