Hyperspectral Nondestructive Detection of Maturity of Preserved Eggs Using Deep Learning Combined with Two-Dimensional Correction Spectral Image

In this study, hyperspectral imaging was used for nondestructive detection of preserved eggs at different maturity levels during the pickling period. First, the optimal waveband was determined based on the one-dimensional spectra and two-dimensional correlation spectra in the time-series mode, separately. Then, the modeling effects of traditional machine learning and the improved ResNet20_SE model in the optimal waveband were compared, and the results showed that the improved ResNet20_SE model was better; the overall recognition accuracy was 97.29% for the synchronous spectral dataset, and the average detection speed for a single image was 24.62 ms. Finally, the better synchronous spectral dataset ResNet20_SE model was applied to the hyperspectral pixel spectral image to calculate the value of each pixel point, and a pseudo-color technique was used for the visual detection of the spatial distribution of preserved egg maturity during the pickling process. The results of this study showed that hyperspectral imaging combined with deep learning is useful for nondestructive detection of preserved egg maturity during curing, which can lay a theoretical foundation for high-throughput online sorting of preserved egg maturity in the future

Synaptic Neurotransmission Depression in Ventral Tegmental Dopamine Neurons and Cannabinoid-Associated Addictive Learning

Drug addiction is an association of compulsive drug use with long-term associative learning/memory. Multiple forms of learning/memory are primarily subserved by activity- or experience-dependent synaptic long-term potentiation (LTP) and long-term depression (LTD). Recent studies suggest LTP expression in locally activated glutamate synapses onto dopamine neurons (local Glu-DA synapses) of the midbrain ventral tegmental area (VTA) following a single or chronic exposure to many drugs of abuse, whereas a single exposure to cannabinoid did not significantly affect synaptic plasticity at these synapses. It is unknown whether chronic exposure of cannabis (marijuana or cannabinoids), the most commonly used illicit drug worldwide, induce LTP or LTD at these synapses. More importantly, whether such alterations in VTA synaptic plasticity causatively contribute to drug addictive behavior has not previously been addressed. Here we show in rats that chronic cannabinoid exposure activates VTA cannabinoid CB1 receptors to induce transient neurotransmission depression at VTA local Glu-DA synapses through activation of NMDA receptors and subsequent endocytosis of AMPA receptor GluR2 subunits. A GluR2-derived peptide blocks cannabinoid-induced VTA synaptic depression and conditioned place preference, i.e., learning to associate drug exposure with environmental cues. These data not only provide the first evidence, to our knowledge, that NMDA receptor-dependent synaptic depression at VTA dopamine circuitry requires GluR2 endocytosis, but also suggest an essential contribution of such synaptic depression to cannabinoid-associated addictive learning, in addition to pointing to novel pharmacological strategies for the treatment of cannabis addiction

A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency

The remaining discharge energy prediction of the battery pack is an important function of a battery management system. One of the key factors contributing to the inaccuracy of battery pack remaining discharge energy prediction is the inconsistency of the state and model parameters. For a batch of lithium-ion batteries with nickel cobalt aluminum oxide cathode material, after analyzing the characteristics of battery model parameter inconsistency, a “specific and difference” model considering state of charge and R0 inconsistency is established. The dual time-scale dual extended Kalman filter algorithm is proposed to estimate the state of charge and R0 of each cell in the battery pack, and the remaining discharge energy prediction algorithm of the battery pack is established. The effectiveness of the state estimation and remaining discharge energy prediction algorithm is verified. The results show that the state of charge estimation error of each cell is less than 1%, and the remaining discharge energy prediction error of the battery pack is less than 1% over the entire discharge cycle. The main reason which causes the difference between the “specific and difference” and “mean and difference” models is the nonlinearity of the battery’s state of charge - open circuit voltage curve. When the nonlinearity is serious, the “specific and difference” model has higher precision

In Situ Synthesis of WSe₂/CMK‑5 Nanocomposite for Rechargeable Lithium-Ion Batteries with a Long-Term Cycling Stability

Transition metal dichalcogenides (TMDs) have received intensive interests in lithium-ion batteries owing to their unique lithium-ion storage ability when evaluated as anode materials. In the present work, a nanocomposite of WSe₂/CMK-5 was successfully fabricated via a nanocasting route, introducing the unique structure of mesoporous carbon (CMK-5) as a nanorecator. Benefiting from a synergetic effect of WSe₂ nanosheets and mesoporous carbon, the WSe₂/CMK-5 hybrid electrode exhibited large reversible capacity, high rate performance, and excellent long-term cycling stability. For instance, a specific capacity of 490 mA h g^–1 can remain even after 600 cycles at a current density of 0.5 A g^–1