An Evaluation of the Corrosion Inhibition Performance of Chitosan Modified by Quaternary Ammonium Salt for Carbon Steel in Stone Processing Wastewater.

Chitosan was used as the raw material. A quaternization reaction was carried out between 2,3-epoxypropyltrimethylammonium chloride and water-soluble chitosan to prepare quaternary ammonium salt water-soluble chitosan (QWSC), and its corrosion inhibition performance against the corrosion of carbon steel in stone processing wastewater was evaluated. The corrosion inhibition efficiencies of QWSC on carbon steel in stone processing wastewater were investigated through weight loss, as well as electrochemical and surface morphology characterization techniques. The results show that QWSC has superior corrosion inhibition performance for A3 carbon steel. When an amount of 60 mL·L-1 is added, the corrosion inhibition efficiency can reach 59.51%. Electrochemical research has shown that a QWSC inhibitor is a mixed-type corrosion inhibitor. The inhibition mechanisms of the QWSC inhibitor revealed that the positive charge on the surface of carbon steel in stone wastewater was conducive to the adsorption of Cl- in the medium, which produced an excessive negative charge on the metal's surface. At the same time, the quaternary ammonium cation and amino cation formed in QWSC in stone processing wastewater can be physically absorbed on the surface of A3 carbon steel, forming a thin-film inhibitor to prevent metal corrosion.

Sustainable Remediation of Soil and Water Utilizing Arbuscular Mycorrhizal Fungi: A Review.

Phytoremediation is recognized as an environmentally friendly technique. However, the low biomass production, high time consumption, and exposure to combined toxic stress from contaminated media weaken the potential of phytoremediation. As a class of plant-beneficial microorganisms, arbuscular mycorrhizal fungi (AMF) can promote plant nutrient uptake, improve plant habitats, and regulate abiotic stresses, and the utilization of AMF to enhance phytoremediation is considered to be an effective way to enhance the remediation efficiency. In this paper, we searched 520 papers published during the period 2000-2023 on the topic of AMF-assisted phytoremediation from the Web of Science core collection database. We analyzed the author co-authorship, country, and keyword co-occurrence clustering by VOSviewer. We summarized the advances in research and proposed prospective studies on AMF-assisted phytoremediation. The bibliometric analyses showed that heavy metal, soil, stress tolerance, and growth promotion were the research hotspots. AMF-plant symbiosis has been used in water and soil in different scenarios for the remediation of heavy metal pollution and organic pollution, among others. The potential mechanisms of pollutant removal in which AMF are directly involved through hyphal exudate binding and stabilization, accumulation in their structures, and nutrient exchange with the host plant are highlighted. In addition, the tolerance strategies of AMF through influencing the subcellular distribution of contaminants as well as chemical form shifts, activation of plant defenses, and induction of differential gene expression in plants are presented. We proposed that future research should screen anaerobic-tolerant AMF strains, examine bacterial interactions with AMF, and utilize AMF for combined pollutant removal to accelerate practical applications.

Effects of PEF on Cell and Transcriptomic of Escherichia coli.

Pulsed electric field (PEF) is an up-to-date non-thermal processing technology with a wide range of applications in the food industry. The inactivation effect of PEF on Escherichia coli was different under different conditions. The E. coli inactivated number was 1.13 ± 0.01 lg CFU/mL when PEF was treated for 60 min and treated with 0.24 kV/cm. The treatment times were found to be positively correlated with the inactivation effect of PEF, and the number of E. coli was reduced by 3.09 ± 0.01 lg CFU/mL after 100 min of treatment. The inactivation assays showed that E. coli was inactivated at electrical intensity (0.24 kV/cm) within 100 min, providing an effective inactivating outcome for Gram-negative bacteria. The purpose of this work was to investigate the cellular level (morphological destruction, intracellular macromolecule damage, intracellular enzyme inactivation) as well as the molecular level via transcriptome analysis. Field Emission Scanning Electron Microscopy (TFESEM) and Transmission Electron Microscope (TEM) results demonstrated that cell permeability was disrupted after PEF treatment. Entocytes, including proteins and DNA, were markedly reduced after PEF treatment. In addition, the activities of Pyruvate Kinase (PK), Succinate Dehydrogenase (SDH), and Adenosine Triphosphatase (ATPase) were inhibited remarkably for PEF-treated samples. Transcriptome sequencing results showed that differentially expressed genes (DEGs) related to the biosynthesis of the cell membrane, DNA replication and repair, energy metabolism, and mobility were significantly affected. In conclusion, membrane damage, energy metabolism disruption, and other pathways are important mechanisms of PEF's inhibitory effect on E. coli.

Adsorption of Cd2+ by Lactobacillus plantarum Immobilized on Distiller's Grains Biochar: Mechanism and Action.

Immobilized microbial technology has recently emerged as a prominent research focus for the remediation of heavy metal pollution because of its superior treatment efficiency, ease of operation, environmental friendliness, and cost-effectiveness. This study investigated the adsorption characteristics and mechanisms of Cd2+ solutions by Lactobacillus plantarum adsorbed immobilized on distiller's grains biochar (XIM) and Lactobacillus plantarum-encapsulated immobilized on distiller's grains biochar (BIM). The findings reveal that the maximum adsorption capacity and efficiency were achieved at a pH solution of 6.0. Specifically, at an adsorption equilibrium concentration of cadmium at 60 mg/L, XIM and BIM had adsorption capacities of 8.40 ± 0.30 mg/g and 12.23 ± 0.05 mg/g, respectively. BIM demonstrated noticeably greater adsorption capacities than XIM at various cadmium solution concentrations. A combination of isothermal adsorption modeling, kinetic modeling, scanning electron microscopy-energy dispersive X-ray spectroscopy, X-ray diffractometer (XRD), and Fourier-transform infrared spectroscopy (FTIR) analyses showed that cadmium adsorption by XIM primarily involved physical adsorption and pore retention. In contrast, the adsorption mechanism of BIM was mainly attributed to the formation of Cd(CN)2 crystals.

Metabolic Response of the Lycium barbarum Variety 'Ningqi No. 7' to Drought Stress.

Lycium barbarum has been widely planted in arid and semi-arid areas due to its drought-resistant ability, which is of great economic value as a medicinal and edible homology plant. In this study, the metabolome of the L. barbarum variety "Ningqi 7" under different drought stress conditions was compared and analyzed by the non-targeted UPLC-MS (ultra-high performance liquid chromatography with mass spectrometry) technique. The results showed that drought stress significantly decreased the water content of leaves, increased the activity of antioxidant enzymes in plants, and up-regulated the metabolites and pathways involved in osmoregulation, antioxidant stress, energy metabolism, and signal transduction. Under moderate drought (40-45% FC), L. barbarum accumulated osmoregulatory substances mainly through the up-regulation of the arginine metabolism pathway. At the same time, phenylalanine metabolism and cutin, suberine, and wax biosynthesis were enhanced to improve the antioxidant capacity and reduce water loss. However, in severe drought (10-15% FC), L. barbarum shifted to up-regulate purine metabolism and lysine degradation and redistributed energy and nitrogen resources. In addition, vitamin B6 metabolism was significantly upregulated in both groups of stress levels, playing a key role in antioxidant and growth regulation. These observations delineate the metabolic adaptations of L. barbarum "Ningqi 7" in response to drought stress.

Progress on Salt Tolerance in Brassica napus.

In China, saline-alkali lands constitute 5.01% of the total land area, having a significant impact on both domestic and international food production. Rapeseed (Brassica napus L.), as one of the most important oilseed crops in China, has garnered considerable attention due to its potential adaptability to saline conditions. Breeding and improving salt-tolerant varieties is a key strategy for the effective utilization of saline lands. Hence, it is important to conduct comprehensive research into the adaptability and salt tolerance mechanisms of Brassica napus in saline environments as well as to breed novel salt-tolerant varieties. This review summarizes the molecular mechanism of salt tolerance, physiological and phenotypic indexes, research strategies for the screening of salt-tolerant germplasm resources, and genetic engineering tools for salt stress in Brassica napus. It also introduces various agronomic strategies for applying exogenous substances to alleviate salt stress and provide technological tools and research directions for future research on salt tolerance in Brassica napus.

Influence of Light Irradiation on the Degradation of Dezocine in Injections.

Dezocine, which is well-known as an analgesic, had about 45% share of the Chinese opioid analgesic market. Since drug products containing impurities could bring serious health consequences, it was important to control the generation of impurities and degradation products in the dezocine product. In this study, two kinds of photodegradation products (i.e., degradation product 1 and degradation product 2) in the dezocine injection were isolated using high-performance liquid chromatography. The possible structures of the photodegradation products were identified using both high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy. In addition, the possible generation mechanism showed that degradation product 1 was the oxidation product of dezocine, and degradation product 2 was the coupled dimer of dezocine. Finally, we found that the degradation rate of dezocine increased with the increase in light intensity. Moreover, the degradation of dezocine easily occurred under ultraviolet light in comparison with visible light. A deeper insight into the generation of the photodegradation products in the dezocine injection would directly contribute to the safety of drug therapy based on the dezocine injection by minimizing the degradant/impurity-related adverse effects of drug preparations.

Predicting Drugs Suspected of Causing Adverse Drug Reactions Using Graph Features and Attention Mechanisms.

BACKGROUND: Adverse drug reactions (ADRs) refer to an unintended harmful reaction that occurs after the administration of a medication for therapeutic purposes, which is unrelated to the intended pharmacological action of the drug. In the United States, ADRs account for 6% of all hospital admissions annually. The cost of ADR-related illnesses in 2016 was estimated at USD 528.4 billion. Increasing the awareness of ADRs is an effective measure to prevent them. Assessing suspected drugs in adverse events helps to enhance the awareness of ADRs. METHODS: In this study, a suspect drug assisted judgment model (SDAJM) is designed to identify suspected drugs in adverse events. This framework utilizes the graph isomorphism network (GIN) and an attention mechanism to extract features based on patients' demographic information, drug information, and ADR information. RESULTS: By comparing it with other models, the results of various tests show that this model performs well in predicting the suspected drugs in adverse reaction events. ADR signal detection was conducted on a group of cardiovascular system drugs, and case analyses were performed on two classic drugs, Mexiletine and Captopril, as well as on two classic antithyroid drugs. The results indicate that the model can accomplish the task of predicting drug ADRs. Validation using benchmark datasets from ten drug discovery domains shows that the model is applicable to classification tasks on the Tox21 and SIDER datasets. CONCLUSIONS: This study applies deep learning methods to construct the SDAJM model for three purposes: (1) identifying drugs suspected to cause adverse drug events (ADEs), (2) predicting the ADRs of drugs, and (3) other drug discovery tasks. The results indicate that this method can offer new directions for research in the field of ADRs.

Research on the Mechanism and Material Basis of Corn (Zea mays L.) Waste Regulating Dyslipidemia.

Corn (Zea mays L.) is an essential gramineous food crop. Traditionally, corn wastes have primarily been used in feed, harmless processing, and industrial applications. Except for corn silk, these wastes have had limited medicinal uses. However, in recent years, scholars have increasingly studied the medicinal value of corn wastes, including corn silk, bracts, husks, stalks, leaves, and cobs. Hyperlipidemia, characterized by abnormal lipid and/or lipoprotein levels in the blood, is the most common form of dyslipidemia today. It is a significant risk factor for atherosclerosis and can lead to cardiovascular and cerebrovascular diseases if severe. According to the authors' literature survey, corn wastes play a promising role in regulating glucose and lipid metabolism. This article reviews the mechanisms and material basis of six different corn wastes in regulating dyslipidemia, aiming to provide a foundation for the research and development of these substances.

Performance-Degradation Analysis of the Planetary Roller Screw Mechanism under Multi-Factor Coupling Effects.

The performance-degradation pattern of the planetary roller screw mechanism (PRSM) is difficult to predict and evaluate due to a variety of factors. Load-carrying capacity, transmission accuracy, and efficiency are the main indicators for evaluating the performance of the PRSM. In this paper, a testing device for the comprehensive performance of the PRSM is designed by taking into account the coupling relationships among temperature rise, vibration, speed, and load. First, the functional design and error calibration of the testing device were conducted. Secondly, the PRSM designed in the supported project was taken as the research object to conduct degradation tests on its load-bearing capacity and transmission accuracy and analyze the changes in transmission efficiency. Third, the thread profile and wear condition were scanned and inspected using a universal tool microscope and an optical microscope. Finally, based on the monitoring module of the testing device, the vibration status during the PRSM testing process was collected in real time, laying a foundation for the subsequent assessment of the changes in the performance state of the PRSM. The test results reveal the law of performance degradation of the PRSM under the coupled effects of temperature, vibration, speed, and load.

Research Progress on Ammonia Sensors Based on Ti3C2Tx MXene at Room Temperature: A Review.

Ammonia (NH3) potentially harms human health, the ecosystem, industrial and agricultural production, and other fields. Therefore, the detection of NH3 has broad prospects and important significance. Ti3C2Tx is a common MXene material that is great for detecting NH3 at room temperature because it has a two-dimensional layered structure, a large specific surface area, is easy to functionalize on the surface, is sensitive to gases at room temperature, and is very selective for NH3. This review provides a detailed description of the preparation process as well as recent advances in the development of gas-sensing materials based on Ti3C2Tx MXene for room-temperature NH3 detection. It also analyzes the advantages and disadvantages of various preparation and synthesis methods for Ti3C2Tx MXene's performance. Since the gas-sensitive performance of pure Ti3C2Tx MXene regarding NH3 can be further improved, this review discusses additional composite materials, including metal oxides, conductive polymers, and two-dimensional materials that can be used to improve the sensitivity of pure Ti3C2Tx MXene to NH3. Furthermore, the present state of research on the NH3 sensitivity mechanism of Ti3C2Tx MXene-based sensors is summarized in this study. Finally, this paper analyzes the challenges and future prospects of Ti3C2Tx MXene-based gas-sensitive materials for room-temperature NH3 detection.

Research on the Method of Foreign Object Detection for Railway Tracks Based on Deep Learning.

Addressing the limitations of current railway track foreign object detection techniques, which suffer from inadequate real-time performance and diminished accuracy in detecting small objects, this paper introduces an innovative vision-based perception methodology harnessing the power of deep learning. Central to this approach is the construction of a railway boundary model utilizing a sophisticated track detection method, along with an enhanced UNet semantic segmentation network to achieve autonomous segmentation of diverse track categories. By employing equal interval division and row-by-row traversal, critical track feature points are precisely extracted, and the track linear equation is derived through the least squares method, thus establishing an accurate railway boundary model. We optimized the YOLOv5s detection model in four aspects: incorporating the SE attention mechanism into the Neck network layer to enhance the model's feature extraction capabilities, adding a prediction layer to improve the detection performance for small objects, proposing a linear size scaling method to obtain suitable anchor boxes, and utilizing Inner-IoU to refine the boundary regression loss function, thereby increasing the positioning accuracy of the bounding boxes. We conducted a detection accuracy validation for railway track foreign object intrusion using a self-constructed image dataset. The results indicate that the proposed semantic segmentation model achieved an MIoU of 91.8%, representing a 3.9% improvement over the previous model, effectively segmenting railway tracks. Additionally, the optimized detection model could effectively detect foreign object intrusions on the tracks, reducing missed and false alarms and achieving a 7.4% increase in the mean average precision (IoU = 0.5) compared to the original YOLOv5s model. The model exhibits strong generalization capabilities in scenarios involving small objects. This proposed approach represents an effective exploration of deep learning techniques for railway track foreign object intrusion detection, suitable for use in complex environments to ensure the operational safety of rail lines.

Physics Constrained High-Precision Data-Driven Modeling for Multi-Path Ultrasonic Flow Meter in Natural Gas Measurement.

Ultrasonic flow meters are crucial measuring instruments in natural gas transportation pipeline scenarios. The collected flow velocity data, along with the operational conditions data, are vital for the analysis of the metering performance of ultrasonic flow meters and analysis of the flow process. In practical applications, high requirements are placed on the modeling accuracy of ultrasonic flow meters. In response, this paper proposes an ultrasonic flow meter modeling method based on a combination of data learning and industrial physics knowledge. This paper builds ultrasonic flow meter flow velocity prediction models under different working conditions, combining pipeline flow field velocity distribution knowledge for data preprocessing and loss function design. By making full use of the characteristics of the physics and data learning, the prediction results are close to the real acoustic path flow velocity distribution; thus, the model has high accuracy and interpretability. Experiments are conducted to prove that the prediction error of the proposed method can be controlled within 1%, which can meet the needs of ultrasonic flow meter modeling and subsequent performance analysis in actual production.

Convolutional Block Attention Module-Multimodal Feature-Fusion Action Recognition: Enabling Miner Unsafe Action Recognition.

The unsafe action of miners is one of the main causes of mine accidents. Research on underground miner unsafe action recognition based on computer vision enables relatively accurate real-time recognition of unsafe action among underground miners. A dataset called unsafe actions of underground miners (UAUM) was constructed and included ten categories of such actions. Underground images were enhanced using spatial- and frequency-domain enhancement algorithms. A combination of the YOLOX object detection algorithm and the Lite-HRNet human key-point detection algorithm was utilized to obtain skeleton modal data. The CBAM-PoseC3D model, a skeleton modal action-recognition model incorporating the CBAM attention module, was proposed and combined with the RGB modal feature-extraction model CBAM-SlowOnly. Ultimately, this formed the Convolutional Block Attention Module-Multimodal Feature-Fusion Action Recognition (CBAM-MFFAR) model for recognizing unsafe actions of underground miners. The improved CBAM-MFFAR model achieved a recognition accuracy of 95.8% on the NTU60 RGB+D public dataset under the X-Sub benchmark. Compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, the recognition accuracy was improved by 2%, 2.7%, 7.3%, and 14.3%, respectively. On the UAUM dataset, the CBAM-MFFAR model achieved a recognition accuracy of 94.6%, with improvements of 2.6%, 4%, 12%, and 17.3% compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, respectively. In field validation at mining sites, the CBAM-MFFAR model accurately recognized similar and multiple unsafe actions among underground miners.

FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks.

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network's ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model's attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance.

An Underwater Image Denoising Method Based on High-Frequency Abrupt Signal Separation and Hybrid Attention Mechanism.

During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal.

MSA-Net: A Precise and Robust Model for Predicting the Carbon Content on an As-Received Basis of Coal.

The carbon content as received (Car) of coal is essential for the emission factor method in IPCC methodology. The traditional carbon measurement mechanism relies on detection equipment, resulting in significant detection costs. To reduce detection costs and provide precise predictions of Cars even in the absence of measurements, this paper proposes a neural network combining MLP with an attention mechanism (MSA-Net). In this model, the Attention Module is proposed to extract important and potential features. The Skip-Connections are utilized for feature reuse. The Huber loss is used to reduce the error between predicted Car values and actual values. The experimental results show that when the input includes eight measured parameters, the MAPE of MSA-Net is only 0.83%, which is better than the state-of-the-art Gaussian Process Regression (GPR) method. MSA-Net exhibits better predictive performance compared to MLP, RNN, LSTM, and Transformer. Moreover, this article provides two measurement solutions for thermal power enterprises to reduce detection costs.

IRBEVF-Q: Optimization of Image-Radar Fusion Algorithm Based on Bird's Eye View Features.

In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird's Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy.

Gearbox Fault Diagnosis Method in Noisy Environments Based on Deep Residual Shrinkage Networks.

Gearbox fault diagnosis is essential in the maintenance and preventive repair of industrial systems. However, in actual working environments, noise frequently interferes with fault signals, consequently reducing the accuracy of fault diagnosis. To effectively address this issue, this paper incorporates the noise attenuation of the DRSN-CW model. A compound fault detection method for gearboxes, integrated with a cross-attention module, is proposed to enhance fault diagnosis performance in noisy environments. First, frequency domain features are extracted from the public dataset by using the fast Fourier transform (FFT). Furthermore, the cross-attention mechanism model is inserted in the optimal position to improve the extraction and recognition rate of global and local fault features. Finally, noise-related features are filtered through soft thresholds within the network structure to efficiently mitigate noise interference. The experimental results show that, compared to existing network models, the proposed model exhibits superior noise immunity and high-precision fault diagnosis performance.

Flexible Sensors Based on Conductive Polymer Composites.

Elastic polymer-based conductive composites (EPCCs) are of great potential in the field of flexible sensors due to the advantages of designable functionality and thermal and chemical stability. As one of the popular choices for sensor electrodes and sensitive materials, considerable progress in EPCCs used in sensors has been made in recent years. In this review, we introduce the types and the conductive mechanisms of EPCCs. Furthermore, the recent advances in the application of EPCCs to sensors are also summarized. This review will provide guidance for the design and optimization of EPCCs and offer more possibilities for the development and application of flexible sensors.