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Accurate traffic volume prediction is crucial for intelligent transportation systems to control traffic conditions and improve travel efficiency. Traditional traffic volume prediction models focus on the similarity of passenger volume patterns in historical data. However, they ignore the continuous and periodic features of traffic volume data and the deviation in traffic volume caused by external factors such as holidays and weather. This paper proposes a multifeature fusion convolutional long-short-term memory (CLSTM) model. The model is based on a convolutional neural network (CNN) and a long-short-term memory (LSTM) neural network. The CLSTM model considers time continuity as a short-term feature, daily periodicity as a long-term feature, spatial correlation between roads as a spatial feature, and environmental factors as external features. The CNN model is applied to represent the temporal and spatial features as a two-dimensional spatial-temporal matrix, and two sets of high-level features are proposed. The fully connected neural network model is used to fuse the predictions from the feature matrix and LSTM neural networks. The effectiveness of feature extraction, model design, and model sensitivity are tested using the London M25 motorway as the research object. The results illustrate that the CLSTM model enhances both prediction accuracy and model adaptability, achieving a balance between prediction efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste. [ABSTRACT FROM AUTHOR]

High-pressure processing (HPP) technology can significantly improve the texture and flavor of Mercenaria mercenaria. This study aimed to investigate the effect of HPP treatment with varying levels of pressure (100, 200, 300, 400, 500, and 600 MPa) and a holding time of 8 min at 20 °C on the physicochemical properties and volatile flavors of M. mercenaria. The significant changes in hardness, resilience, and water holding capacity occurred with increasing pressure (p < 0.05), resulting in improved meat quality. Scanning electron microscopy (SEM) was utilized to observe the decomposition of muscle fibers in M. mercenaria due to varying pressures, which explains the differences in texture of M. mercenaria. Different pressure treatments also had an influence on the volatile flavor of M. mercenaria, and the quantities of low-molecular-weight aldehydes (hexanal, heptanal, and nonanal) with a fishy taste decreased dramatically following 400 and 500 MPa HPP treatments. Furthermore, the level of 2-Methylbutyraldehyde, which is related to sweetness, increased significantly following 400 MPa HPP treatment. The study found that 400 MPa HPP treatment resulted in minor nutrient losses and enhanced sensory quality. The results of this study provide a theoretical basis for the application of HPP treatment to M. mercenaria. [ABSTRACT FROM AUTHOR]

There is significant demand for high-purity DHA in the pharmaceutical industry. Traditionally, this high-purity DHA is extracted from raw materials with relatively low DHA content (10–20%), such as fish oil. Recently, through electroporation-induced mutation, a high-DHA-content strain of Schizochytrium sp. GCD2032 was isolated. To further enhance its DHA production, optimizations were conducted on the culture medium and fermentation conditions (in shaking flasks), as well as different nitrogen source concentrations (in a 5 L fermenter) for biomass, fatty acid content, and DHA content (as a percentage of total fatty acids). In a 5 L fermenter, Schizochytrium sp. GCD2032 achieved a biomass of 50 g/L, with fatty acid content of 55.71% and DHA content of 61.29%. Notably, the DHA content reached an impressive 341.45 mg/g of dry weight. This strain consistently produces high levels of fatty acids and DHA, demonstrating significant potential for pharmaceutical applications. [ABSTRACT FROM AUTHOR]

In order to develop and utilize Clarias fuscus skin, conventional nutrient analysis methods, gas chromatogra-phy-mass spectrometry (GC-MS), gas chromatography-ion mobility spectroscopy (GC-IMS) technology were used to analyze the nutrients and volatile substances in fish skin. The results showed that the water content, crude protein, crude fat, crude ash and total sugar content in fish skin were 62.85%, 27.50%, 5.28%, 0.15%, 3.84%, respectively. The essential amino acid content in fish skin was 16.60 g/100 g, the umami amino acid content was 45.72 g/100 g. The content of essential free amino acid and fresh free amino acid were 52.6 mg/100 g and 30.54 mg/100 g, respectively. Taurine had the highest free amino acid content, which was 28.34 mg/100 g. The content of unsaturated fatty acids in the skin was relatively rich and the oleic acid was the highest fatty acid content that was 1.18 mg/mL. Many mineral elements were contained in fish skin. Trans-2-octenal, benzaldehyde, n-hexanal and n-hexanol could be obtained by GC-MS and GC-IMS analysis methods. Therefore, the combination of GC-MS and GC-IMS analysis methods could better study the flavor substances of catfish skin. [ABSTRACT FROM AUTHOR]