Extraction Efficiency Research Articles

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

AbstractExisting studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

Development of a highly sensitive method based on QuEChERS and GC–MS/MS for the determination of polycyclic aromatic hydrocarbons in infant foods

Polycyclic aromatic hydrocarbons (PAHs) are environmental contaminants that can be found in various food products, including those intended for infants. Due to their potential health risks, it is crucial to develop sensitive analytical methods for the accurate determination of PAHs in infant foods. This study describes the development and validation of a highly sensitive method for the quantification of European PAH markers, namely benzo[a]pyrene, benzo[a]anthracene, chrysene, and benzo[b]fluoranthene, using gas chromatography–tandem mass spectrometry (GC–MS/MS), in baby food samples. The first step was the optimization of the sample preparation procedure, performed using different methods based on the QuEChERS approach, also testing different extraction solvents. Several factors such as extraction efficiency, selectivity, and recovery were evaluated to choose the most effective procedure for sample preparation. Furthermore, the GC–MS/MS method was optimized, evaluating parameters such as linearity, sensitivity, accuracy, and robustness using spiked infant food samples. The method demonstrated excellent linearities with a correlation coefficient higher than 0.999 over a wide concentration range, and limits of detection and limits of quantification in the range 0.019–0.036 μg/kg and 0.06–0.11 μg/kg, respectively. Extraction recoveries were between 73.1 and 110.7%, with relative standard deviations always lower than 8%. These findings are compliant with the indications of the European Commission (Reg. 836/2011). To assess the applicability of the method to official control activities, a survey was conducted on commercially available infant food products. Four markers were determined in commercial samples belonging to different food categories for infants and young children. The outcome of this monitoring showed that PAH contamination, in all samples, was below the quantification limits. In conclusion, the developed GC–MS/MS method provides a highly sensitive and reliable approach for the determination of PAHs in baby foods. The optimized sample preparation, instrumental parameters, and validation results ensure accurate quantification of 4 PAHs even at trace levels. This method could contribute to the assessment of PAH exposure in infants and it could support regulatory efforts to ensure the safety and quality of infant food products with regular monitoring.

Resources recovery-rubidium recovery from desalination brine through hydrometallurgy techniques

Because of the water scarcity in many regions, different methods have been implemented to address this problem. The desalination technique is known as a practical solution among them. However, brine from the desalination process, which contains high concentrations of salts, minerals, and chemicals, will cause environmental harm to the sea, soil, and groundwater if it is not properly treated. Therefore, recovering critical resources from brine is essential for reducing brine disposal. This study aims to apply two hydrometallurgy systems, namely ion exchange and ionic liquid extraction, to circulate rubidium resources from brine. Dowex G26 resin was employed in the ion exchange system, and the adsorption isotherm model and saturated adsorption capacity were explored initially. The optimal parameters such as pH value, L/S ratio (liquid/solid), adsorption period, and adsorption temperature will then be investigated. In the ionic liquid extraction process, the t-BAMBP/C2mimNTf2 system (4-tert-Butyl-2-(α-methylbenzyl) phenol/1-ethyl-3-methylimidazolium bis(trifluoromethyls​ulfonyl)​imide) was used, and the parameters including pH value, concentrations of t-BAMBP, (O + I)/A ratio (organic + ionic liquid/aqueous), extraction time, and extraction temperature will be optimized as well. The results reveal that adsorption capacity and extraction efficiencies were 14.3 mg g− 1 and 86%, respectively. Furthermore, suitable reagents, including HCl and HNO3, were applied to desorb and strip rubidium from the Dowex G26 and t-BAMBP/C2mimNTf2 systems. To sum up, environmental hazards of desalination brine and rubidium resources can be reduced and recovered through the two different extraction systems.

Deep Convolutional Network Based on Attention Mechanism for Matching Optical and SAR Images

Abstract. Complex geometric distortions and nonlinear radiation differences between optical and synthetic aperture radar (SAR) images present challenges for the matching of sufficient and evenly distributed corresponding points. To address this problem, this paper proposes a deep convolutional network based on an attention mechanism for matching optical and SAR images. In order to obtain robust feature points, we employ phase consistency instead of image intensity and gradient information for feature detection. A deep convolutional network (DCN) is designed to extract high-level semantic features between optical and SAR images, providing robustness to geometric distortion and nonlinear radiation changes. Notably, incorporating multiple inverted residual structures in the DCN facilitates efficient extraction of local and global features, promoting feature reuse, and reducing the loss of key features. Furthermore, a dense feature fusion module based on coordinate attention is designed, focusing on the spatial positional information of effective features, integrating key features into deep descriptors to enhance the robustness of deep descriptors to nonlinear radiometric differences. A coarse-to-fine strategy is then employed to enhance accuracy by eliminating mismatches. Experimental results demonstrate that the proposed network performs better than the manually designed descriptors-based methods and the stateof- the-art deep learning networks in both matching effectiveness and accuracy. Specifically, the number of matches achieved is approximately 2 times greater than that of other methods, with a 10% improvement in F-measure.

Advances in enhanced geothermal systems: Integrating laboratory, numerical and field insights

An enhanced geothermal system is a designed and engineered thermal reservoir that has been deliberately stimulated to extract economically viable amounts of heat from geothermal resources having no natural permeability. Enhanced geothermal system projects are typically initiated to meet the industrial production standards, usually via techniques such as chemical stimulation, hydraulic fracturing, or thermal processes to enhance the permeability of the reservoir. The main aim of this study is to evaluate the current advancements in enhanced geothermal system through a comprehensive examination of laboratory experiments, numerical simulation, and fieldwork investigations from numerous studies that are insufficient captures and combined by current open literature. This study highlighted the critical importance of precisely developing fracture systems, whether natural or artificially induced, in ensuring the effectiveness of such projects. Notably, hydraulic fracturing techniques have been proven instrumental in enhancing the performance of enhanced geothermal systems. On the contrary, research conducted through numerical simulations has demonstrated consistent findings that it is beneficial to enhance the permeability of low-permeability rock for the efficient use of enhanced geothermal systems. Additionally, this study has highlighted the intricate and expensive nature of designing enhanced geothermal systems due to uncertainties in subsurface conditions especially from projects that are in initial stages of development. This study enables researchers and operators to improve the efficiency and safety of geothermal energy extraction, paving the way for more sustainable enhanced geothermal system projects in the future.

A fast, reliable, low‐cost, and efficient xylan extraction for xylooligosaccharides production

AbstractA fast, reliable, low‐cost, and efficient method for the recovery of xylan from agro‐residue materials is needed to improve the use of xylan in industrial processes. Xylan was extracted from two agrowastes (corncobs and wheat bran) using a cost‐effective alkali extraction method. The characteristic band patterns and bending vibrations associated with hemicellulose and beta glycoside linkage could be seen in the corncob‐extracted xylan (CCEx) and wheatbran‐extracted xylan (WBEx). Scanning electron microscopy (SEM) analysis showed that the extracted xylans consisted of aggregated and nonaggregated particles with irregular morphology and rough surfaces. Particle‐size analysis displayed average sizes of 205.6, 210.8, and 794.2 nm for commercial beechwood xylan (Bx), CCEx, and WBEx, respectively. Alkali extraction was performed with different concentrations of NaOH, from 5% to 20%, with a solid–liquid ratio of 1:20. It was found that 82.90 and 76.77% of xylan were extracted from corncob and wheat bran, respectively, which represents a higher yield obtained in a shorter duration in comparison with studies in the literature. The enzymatic hydrolysis of wheat bran and corncob that used 250 IU/mL of Enterobacter hormaechie KS1 xylanase yielded 334.38 and 162.35 mg/g of xylooligosaccharides. Their properties were studied and were found to be very similar to those of commercial xylan. They could reduce costs if applied in industry.

Characterization of liquid-liquid two-phase flow patterns and mass transfer coefficients in Human-shaped microchannels

Based on the principle of splitting and recombining, this study designed and fabricated millimeter-scale Human-shaped microchannels. The research focused on investigating the flow patterns and mass transfer characteristics of liquid-liquid two-phase flow within the Human-shaped microchannels. The experiment utilized a water and n-butanol system to investigate the flow behavior of liquid-liquid two-phase in the channel with a flow rate ratio of 1. When the total volumetric flow rate increases from 120 µl/min to 2400 µl/min, the flow pattern experiences slug flow, slug-droplet flow, parallel flow and non-steady flow patterns respectively. The study investigated the impact of factors including total flow rate, feed method, angle, curvature, channel width, phase ration extraction efficiency and mass transfer coefficients. The Human-shaped channel exhibited superior mass transfer performance compared to conventional microchannels due to its advantageous configuration. The higher the included angle is, the stabler the flow pattern within the channel, resulting in a better mass transfer performance. Furthermore, the absence of curvature is more favorable for achieving higher mass transfer efficiency. In the experiments, the overall volume mass transfer coefficient ranged from 0.007 to 0.1742 s−1. This study can provide insights for the development and structural optimization of microchannels based on the split-recombine principle.

Improving the extraction efficiency of sugar cane mills using perforated rollers with longitudinal channels

Improving the extraction efficiency of sugar cane mills using perforated rollers with longitudinal channels

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs. In recent years, convolutional neural networks (CNNs) have become widely adopted in SSR research, primarily because of their exceptional ability to extract features. However, most current CNN-based algorithms are weak in terms of extracting the spectral features of HSIs. While certain algorithms can reconstruct HSIs through the fusion of spectral and spatial data, their practical effectiveness is hindered by their substantial computational complexity. In light of these challenges, we propose a lightweight network, Transformer with convolutional spectral self-attention (TCSSA), for SSR. TCSSA comprises a CNN-Transformer encoder and a CNN-Transformer decoder, in which the convolutional spectral self-attention blocks (CSSABs) are the basic modules. Multiple cascaded encoding and decoding modules within TCSSA facilitate the efficient extraction of spatial and spectral contextual information from HSIs. The convolutional spectral self-attention (CSSA) as the basic unit of CSSAB combines CNN with self-attention in the transformer, effectively extracting both spatial local features and global spectral features from HSIs. Experimental validation of TCSSA’s effectiveness is performed on three distinct datasets: GF5 for remote sensing images along with CAVE and NTIRE2022 for natural images. The experimental results demonstrate that the proposed method achieves a harmonious balance between reconstruction performance and computational complexity.

Bioactive Compound Extraction of Hemp (Cannabis sativa L.) Leaves through Response Surface Methodology Optimization

Hemp, commonly known as Cannabis sativa L., is a medicinal plant species of the Cannabaceae family. For the efficient extraction of C. sativa leaves using the conventional stirring process with water as the solvent, three crucial extraction parameters (i.e., extraction duration, liquid–solid ratio, and temperature) were investigated through the response surface methodology (RSM). The concentrations of the extracted bioactive compounds (polyphenols, ascorbic acid, and carotenoids) showed significant variations in the RSM design points, suggesting the importance of finding the optimal extraction conditions in which liquid–solid ratio and extraction temperature were found to have the highest impact. Further analysis was conducted on the optimal extract employing several assays to determine their polyphenol content, total carotenoid content, color evaluation, anti-inflammatory activity, and antioxidant capacity through FRAP, DPPH, and H2O2 assays. Α low extraction time (30 min) at 50 °C and a high liquid–solid ratio (50:1) were required for the highest possible yield of polyphenols. The total polyphenol content was determined to be 9.76 mg gallic acid equivalents/g under optimum conditions, with pelargonin being the most abundant polyphenol (1.51 mg/g) in C. sativa extracts. Ascorbic acid was measured at 282.23 μg/g and total carotenoids at 356.98 μg/g. Correlation analyses revealed that anti-inflammatory activity was negatively correlated with specific polyphenols. As determined by DPPH (27.43 μmol ascorbic acid equivalents (AAE)/g), FRAP (49.79 μmol AAE/g), and H2O2 (230.95 μmol AAE/g) assays, the optimized aqueous extract showed a high antioxidant capacity. Furthermore, it demonstrated considerable anti-inflammatory activity at 17.89%, with the potential to increase to 75.12% under particular extraction conditions. Given the high added-value of the aqueous extracts, the results of this study highlight the potential utility of C. sativa leaves as a source of health-improving antioxidant compounds in the pharmaceutical and food industries.

Crystallisation behaviour and enhanced separation of RE-phase in RE-bearing blast furnace slag

With the wide application and consumption of rare earths (REs), the secondary resources of RE-bearing blast furnace slag are being investigated as viable resources for the recovery of these critical elements. In this study, the crystallisation behaviour including nucleation and growth kinetics of the RE-phase in RE-bearing blast furnace slag was studied and the optimal precipitation conditions for the RE-phase during the cooling process was obtained, providing an important reference and guidance for the efficient extraction and subsequent functionalised reuse of REs. Subsequently, the supergravity enhanced separation of RE-phase from the slag system was conducted in the optimal precipitation conditions of cooling rate (2 °C/min), experiment temperature (1200 °C), and holding time (100 min). At G = 1000, the RE-phase was successfully separated from molten slag, and the mass fraction of CeO2 in the separated sample increased from the original 20.51% to 74.85%, and the recovery ratio reached 92.06%. This indicated that the supergravity separation technology is considered to be an effective technology for the enrichment and recovery of REs from RE-bearing blast furnace slag.

Box–Behnken Design-Based Optimization of Phytochemical Extraction from Diplazium esculentum (Retz.) Sw. Associated with Its Antioxidant and Anti-Alzheimer’s Properties

A previous study reported that the ethanolic extract of the edible fern, Diplazium esculentum (Retz.) Sw. (DE), obtained from a non-optimized extraction condition exhibited anti-Alzheimer’s disease (AD) properties through the inhibition of a rate-limiting enzyme in amyloid peptide formation, β-secretase-1 (BACE-1). Nevertheless, a non-optimized or suboptimal extraction may lead to several issues, such as a reduction in extraction efficiency and increased time and plant materials. In this study, extraction of the DE was optimized to obtain appropriate BACE-1 inhibition using a Box–Behnken design (BBD) and response surface methodology (RSM). Data revealed that the optimal extraction condition was 70% (v/v) aqueous ethanol, 50 min extraction time, 30 °C extraction temperature, and 1:30 g/mL solid/liquid ratio, giving BACE-1 inhibition at 56.33%. In addition, the extract also exhibited significant antioxidant activities compared to the non-optimized extraction. Metabolomic phytochemical profiles and targeted phytochemical analyses showed that kaempferol, quercetin, and their derivatives as well as rosmarinic acid were abundant in the extract. The optimized DE extract also acted synergistically with donepezil, an AD drug suppressing BACE-1 activities. Data received from Drosophila-expressing human amyloid precursor proteins (APPs) and BACE-1, representing the amyloid hypothesis, showed that the optimized DE extract penetrated the fly brains, suppressed BACE-1 activities, and improved locomotor functions. The extract quenched the expression of glutathione S transferase D1 (GSTD1), inositol-requiring enzyme (IRE-1), and molecular chaperone-binding immunoglobulin (Bip), while donepezil suppressed these genes and other genes involved in antioxidant and endoplasmic reticulum (ER) stress response, including superoxide dismutase type 1 (SOD1), activating transcription factor 6 (ATF-6), and protein kinase R-like endoplasmic reticulum kinase (PERK). To sum up, the optimized extraction condition reduced extraction time while resulting in higher phytochemicals, antioxidants, and BACE-1 inhibitors.

An Extensive Review on Gas Hydrates: Recent Patents, Properties, Formation, Detection, Production, Importance, and Challenges

Introduction:: Gas Hydrates, or Clathrate Hydrates, have been the subject of increasing scientific and industrial attention due to their potential as an alternative energy source, their role in climate change, and their association with geohazards. The growth of new indigenous gas supply sources could impart a significant positive ripple effect on a country's economy, ecological balance, and energy landscape. This burgeoning interest has led to a surge in research and development, resulting in numerous patents related to the extraction, processing, and utilization of gas Hydrates. Objectives:: This review paper aims to provide an up-to-date, comprehensive overview of the properties, formation, detection, production, importance, challenges, and patent landscape of Gas hydrates. The integration of patented technologies into the field underscores the importance of intellectual property in shaping the future of energy, environment, and economic development. Methods:: Patented technologies in this field are contributing to making this resource more accessible and commercially viable. Moreover, the development of gas hydrates as an energy source could act as a safeguard for manufacturing jobs that are sensitive to gas prices, with proprietary technologies enhancing the efficiency and sustainability of the production process. Results:: On the environmental front, an uptick in the consumption of natural gas, known for its cleaner combustion, could herald positive change. Patented innovations in clean and efficient extraction and utilization methods for Gas Hydrates are instrumental in reducing the environmental impact. From the standpoint of energy security, a larger domestic slice of the energy pie, complemented by an extensive array of gas supply alternatives, could equip the nation to better navigate the unpredictable terrain of future energy scenarios. Conclusion:: The strategic patenting of key technologies in the exploration, production, and application of Gas Hydrates ensures competitive advantage and fosters innovation, driving forward the energy industry's evolution.

Fabrication of Nitrogen Based Magnetic Conjugated Microporous Polymer for Efficient Extraction of Neonicotinoids in Water Samples

Facile and sensitive methods for detecting neonicotinoids (NEOs) in aquatic environments are crucial because they are found in extremely low concentrations in complex matrices. Herein, nitrogen-based magnetic conjugated microporous polymers (Fe3O4@N-CMP) with quaternary ammonium groups were synthesized for efficient magnetic solid-phase extraction (MSPE) of NEOs from tap water, rainwater, and lake water. Fe3O4@N-CMP possessed a suitable specific surface area, extended π-conjugated system, and numerous cationic groups. These properties endow Fe3O4@N-CMP with superior extraction efficiency toward NEOs. The excellent adsorption capacity of Fe3O4@N-CMP toward NEOs was attributed to its π–π stacking, Lewis acid–base, and electrostatic interactions. The proposed MSPE-HPLC-DAD approach based on Fe3O4@N-CMP exhibited a wide linear range (0.1–200 µg/L), low detection limits (0.3–0.5 µg/L), satisfactory precision, and acceptable reproducibility under optimal conditions. In addition, the established method was effectively utilized for the analysis of NEOs in tap water, rainwater, and lake water. Excellent recoveries of NEOs at three spiked levels were in the range of 70.4 to 122.7%, with RSDs less than 10%. This study provides a reliable pretreatment method for monitoring NEOs in environmental water samples.

Hollow multi-shelled structure engineering of organosilica for efficient and selective uranium extraction from seawater

Uranium extraction from seawater is critical for the development of nuclear energy, but the progress is still seriously limited by the performance and cost of current adsorbents. Herein, a convenient and general co-condensation process is designed to prepare amidoximated multi-shelled hollow mesoporous organosilica adsorbent (AO-HMO), which exhibits a high uranium extraction capacity of 862.07 mg·g−1 in uranium-spiked simulated seawater and 9.67 mg·g−1 in natural seawater. The run-through mesoporous structure and multi-shelled surface provide fast diffusion channels and more active sites for uranium adsorption. Moreover, AO-HMO exhibits a long service life due to its robust structure, maintaining an ideal adsorption capacity over 10 adsorption-desorption cycles. Coupled with the synthesis-cost advantage, AO-HMO will surely show great application potential in uranium extraction form seawater in future.

MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.

A magnetic imprinted polymer nano-adsorbent with embedded quantum dots and mesoporous carbon for the microextraction of triazine herbicides

A magnetic molecularly imprinted polymer (MMIP) adsorbent incorporating amino-functionalized magnetite nanoparticles, nitrogen-doped graphene quantum dots and mesoporous carbon (MIP@MPC@N-GQDs@Fe3O4NH2) was fabricated to extract triazine herbicides from fruit juice. The embedded magnetite nanoparticles simplified the isolation of the adsorbent from the sample solution. The N-GQDs and MPC enhanced adsorption by affinity binding with triazines. The MIP layer provided highly specific recognition sites for the selective adsorption of three target triazines. The extracted triazines were determined by high-performance liquid chromatography (HPLC) coupled with diode-array detection (DAD). The developed method exhibited linearity from 1.5 to 100.0 µg L−1 with a detection limit of 0.5 µg L−1. Recoveries from spiked fruit juice samples were in the range of 80.1– 108.4 %, with a relative standard deviation of less than 6.0 %. The developed MMIP adsorbent demonstrated good selectivity, high extraction efficiency, ease of fabrication and use, and good stability.

Magnetic solid-phase extraction of tetracyclines from milk using metal-organic framework MIL-101(Cr)-NH2 functionalised hydrophilic magnetic nanoparticles

Novel metal-organic framework MIL-101(Cr)-NH2 functionalised hydrophilic polydopamine-modified Fe3O4 magnetic nanoparticles (Fe3O4@PDA@MIL-101(Cr)-NH2) were synthesised and used as magnetic solid-phase extraction (MSPE) adsorbents for extracting tetracyclines (TCs) from milk samples. The integrated Fe3O4@PDA@MIL-101(Cr)-NH2 exhibited convenient magnetic separation and exceptional multi-target binding capabilities. Furthermore, the PDA coating significantly enhanced the hydrophilicity and extraction efficiency of the material, thereby facilitating the extraction of trace TCs. Various factors affecting MSPE, such as adsorbent dosage, extraction time, pH value, and desorption conditions, were optimised. The developed MSPE method coupled with high-performance liquid chromatography demonstrated good linearity (R2 ≥ 0.9989), acceptable accuracy (82.2%–106.1%), good repeatability (intra-day precision of 0.8%–4.7% and inter-day precision of 1.1%–4.5%), low limits of detection (2.18–6.25 μg L−1), and low limits of quantification (6.54–18.75 μg L−1) in TCs detection. The approach was successfully used for the quantification of trace TCs in real milk samples.

A bearing surface defect detection method based on multi-attention mechanism Yolov8

Surface defects in bearings not only affect the appearance but also impact the service life and performance. Therefore, it is imperative for bearing manufacturers to conduct quality inspections before bearings leave the factory. However, traditional visual inspection methods exhibit shortcomings such as high omission rates, insufficient feature fusion and oversized models when dealing with multiple target defects in bearings. To address these challenges, this paper proposes a surface defect detection method for bearings based on an improved Yolov8 algorithm (G-Yolov8). Firstly, a C3Ghost convolutional module based on the Ghost module is constructed in YOLOv8 to simplify model computational costs. Secondly, a global attention mechanism module is designed at the end of the backbone network to increase sensitivity to implicit small target area features and optimize feature extraction efficiency. Subsequently, a deep deformable convolution feature pyramid network is constructed by introducing the deformable convolutional networks version 2 (DCNv2) and the lightweight content-aware reassembly of features upsampling operator to reduce sampling information loss and improve the fusion of multi-scale target defects. Finally, different attention mechanisms are embedded in the detection network to construct a multi-attention detection head to replace the decoupled head, refining classification and localization tasks, reducing feature confusion, and improving the model’s detection accuracy. Experimental results demonstrate that the improved algorithm achieves a 3.5% increase in mean average precision on a self-made small-scale train bearing surface defect dataset, with a 17.3% reduction in model size. This improvement not only enhances accuracy but also addresses the requirement for lightweight deployment in subsequent stages.