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Current real-time direction judgment systems are inaccurate and insensitive, as well as limited by the sampling rate of analog-to-digital converters. To address this problem, we propose a dynamic real-time direction judgment system based on an integral dual-frequency laser interferometer and field-programmable gate array technology. The optoelectronic signals resulting from the introduction of a phase subdivision method based on the amplitude resolution of the laser interferometer when measuring displacement are analyzed. The proposed system integrates the optoelectronic signals to increase the accuracy of its direction judgments and ensures these direction judgments are made in real time by dynamically controlling the integration time. Several experiments were conducted to verify the performance of the proposed system. The results show that, compared with current real-time direction judgment systems, the proposed system makes accurate judgements during low-speed motions and can update directions within 0.125 cycles of the phase difference change at different speeds. Moreover, a sweep frequency experiment confirmed the system’s ability to effectively judge dynamic directions. The proposed system is capable of accurate and real-time directional judgment during low-speed movements of a table in motion.

Enzyme-assisted ultrasonic extraction (EAUE) was utilized and optimized for extracting polysaccharides from Schizochytrium limacinum meal (SLMPs) via the response surface methodology. The optimal EAUE conditions were determined as follows: enzyme concentration at 5.18%, ultrasonic temperature at 53 °C, ultrasonic duration of 40 min, ultrasonic power at 60 W, and a liquid-to-material ratio of 34 mL/g, achieving a polysaccharide extraction yield of 11.86 ± 0.61%. The purified polysaccharide component, SLMP1-1, isolated using DEAE Sepharose Fast Flow and Sephadex G-100 columns, exhibited potent antioxidant activity. SLMP1-1, with a molecular weight of 25.5 kDa, comprises glucose, mannose, arabinose, and galactose in a molar ratio of 16.39:14.75:1:693.03. 1H NMR analysis revealed the α configuration of SLMP1-1. Antioxidant assessments, including DPPH, ABTS, and ferric ion reduction assays, were detected with inhibitory values at 21.82–82.98%, 38.21–98.46%, and 3.30–20.30% at 0.2–1.0 mg/mL. This confirmed the effective antioxidant capacity of SLMP1-1, which is notably enhanced post oral and gastric digestion. The findings suggest that polysaccharides extracted from Schizochytrium limacinum meal hold significant promise as natural antioxidants.

Inhibiting α-amylase can lower postprandial blood glucose levels and delay glucose absorption, offering an effective approach for the development of antidiabetic diets. In this study, an active constituent with inhibitory activity against α-amylase was isolated and purified by bioassay-guided fractionation from Carya cathayensis Sarg. peel (CCSP). The active constituent was identified by NMR and Q-Exactive Orbitrap Mass Spectrometry as 5-O-p-coumaroylquinic acid (5-CQA). 5-CQA possessed strong inhibitory activity against α-amylase, with an IC50 value of 69.39 µM. In addition, the results of the kinetic study indicated that 5-CQA was a potent, reversible, noncompetitive inhibitor against α-amylase. The findings indicate that 5-CQA derived from CCSP has potential as a novel inhibitor against α-amylase, which can help mitigate postprandial blood sugar spikes, making it suitable for inclusion in antidiabetic diets.

The Internet of Vehicles (IoV) enables vehicular data services and applications through vehicle-to-everything (V2X) communications. One of the key services provided by IoV is popular content distribution (PCD), which aims to quickly deliver popular content that most vehicles request. However, it is challenging for vehicles to receive the complete popular content from roadside units (RSUs) due to their mobility and the RSUs’ constrained coverage. The collaboration of vehicles via vehicle-to-vehicle (V2V) communications is an effective solution to assist more vehicles to obtain the entire popular content at a lower time cost. To this end, we propose a multi-agent deep reinforcement learning (MADRL)-based popular content distribution scheme in vehicular networks, where each vehicle deploys an MADRL agent that learns to choose the appropriate data transmission policy. To reduce the complexity of the MADRL-based algorithm, a vehicle clustering algorithm based on spectral clustering is provided to divide all vehicles in the V2V phase into groups, so that only vehicles within the same group exchange data. Then the multi-agent proximal policy optimization (MAPPO) algorithm is used to train the agent. We introduce the self-attention mechanism when constructing the neural network for the MADRL to help the agent accurately represent the environment and make decisions. Furthermore, the invalid action masking technique is utilized to prevent the agent from taking invalid actions, accelerating the training process of the agent. Finally, experimental results are shown and a comprehensive comparison is provided, which demonstrates that our MADRL-PCD scheme outperforms both the coalition game-based scheme and the greedy strategy-based scheme, achieving a higher PCD efficiency and a lower transmission delay.