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Pseudomonas fragi (P. fragi) is usually detected in low-temperature meat products, and seriously threatens food safety and human health. Therefore, the study investigated the antibacterial mechanism of linalool against P. fragi from membrane damage and metabolic disruption. Results from field-emission transmission electron microscopy (FETEM) and atomic force microscopy (AFM) showed that linalool damage membrane integrity increases surface shrinkage and roughness. According to Fourier transform infrared (FTIR) spectra results, the components in the membrane underwent significant changes, including nucleic acid leakage, carbohydrate production, protein denaturation and modification, and fatty acid content reduction. The data obtained from amino acid metabolomics indicated that linalool caused excessive synthesis and metabolism of specific amino acids, particularly tryptophan metabolism and arginine biosynthesis. The reduced activities of glucose 6-phosphate dehydrogenase (G6PDH), malate dehydrogenase (MDH), and phosphofructokinase (PFK) suggested that linalool impair the respiratory chain and energy metabolism. Meanwhile, genes encoding the above enzymes were differentially expressed, with pfkB overexpression and zwf and mqo downregulation. Furthermore, molecular docking revealed that linalool can interact with the amino acid residues of G6DPH, MDH and PFK through hydrogen bonds. Therefore, it is hypothesized that the mechanism of linalool against P. fragi may involve cell membrane damage (structure and morphology), disturbance of energy metabolism (TCA cycle, EMP and HMP pathway) and amino acid metabolism (cysteine, glutamic acid and citrulline). These findings contribute to the development of linalool as a promising antibacterial agent in response to the food security challenge.

The significant deposition of tail fat in sheep has a profound impact on the economic benefits of animal husbandry. Furthermore, increasing the litter size is a crucial means of enhancing economic benefits. The BMPR1B and T/Brachyury genes are considered major functional genes that could affect sheep litter size and tail bone number, respectively. In this study, we employed direct sequencing to identify specific mutations of the BMPR1B gene in Gobi short tail sheep and carried out genotyping using MassARRAY technology for each variant of both the BMPR1B and T genes. Significant associations were demonstrated between the c.687G>A mutation of BMPR1B and the litter size in both the Gobi short tail sheep and Ujimqin sheep breeds. Meanwhile, the g.30058882_30058873GCAGATTAAAIndel mutation was significantly associated with the litter size in Gobi short tail sheep. These findings may provide valuable genetic markers for expanding sheep litter size. In addition, we also confirmed that the frequency of tail-bone-number-related T alleles was significantly higher in Gobi short tail sheep than in longer-tailed Ujimqin sheep.

This paper proposes a method of enhancing public filler (PF) with nano-SiO2 (NS) to prepare modified recycled aggregate mortar (RAM). The improvement effect of NS solution at different concentrations and immersion times on the macroscopic physical properties of recycled public fine aggregates (PFA) was investigated. Moreover, the effect of NS on the basic physical properties and durability of recycled mortar (RM) and the reinforcement mechanism of NS on recycled mortar was analyzed through various techniques. Results indicated that the modification effect of NS could remove loose cement mortar from the surface of PFA. It reacted with calcium hydroxide and calcite to generate nano-particles that could fill pores in PFA. The water absorption rate of PFA decreased to 9.3% when immersed in 2% NS solution for 72 h. There was no significant improvement in the mechanical properties of RM when the solution concentration and immersion time were increased. However, the compressive strength of RM prepared by modifying PFA with 2% NS was increased by about 21.9%, and the capillary water absorption and electric flux were reduced by 56.3% and 15.1%, respectively. Micro-analysis results showed that the volcanic ash effect of NS enabled it to react with Ca(OH)2 adhered to the surface of PFA, generating C-S-H and improving the interfacial bonding of PFA. Moreover, NS adsorbed on the surface of PFA dispersed into the freshly mixed cement slurry, which further enhanced the internal structure of PFA.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi-Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial, and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed the ADown down-sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the Complete Intersection over Union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieved a mAP50 of 94.3% and a mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3%, respectively, compared to the original model. The model size was reduced by 0.53 MB to just 5.45 MB, and the GFLOPs were reduced by 0.3 to only 7.8. YOLOv8-RFMD has displayed great potential for application in real-world mulberry leaf disease detection systems and automatic spraying operations.

Direct barrier discharge (DBD) plasma is a potential antibacterial strategy for controlling Fusarium oxysporum (F. oxysporum) in the food industry. The aim of this study was to investigate the inhibitory effect and mechanism of action of DBD plasma on F. oxysporum. The result of the antibacterial effect curve shows that DBD plasma has a good inactivation effect on F. oxysporum. The DBD plasma treatment severely disrupted the cell membrane structure and resulted in the leakage of intracellular components. In addition, flow cytometry was used to observe intracellular reactive oxygen species (ROS) levels and mitochondrial membrane potential, and it was found that, after plasma treatment, intracellular ROS accumulation and mitochondrial damage were accompanied by a decrease in antioxidant enzyme activity. The results of free fatty acid metabolism indicate that the saturated fatty acid content increased and unsaturated fatty acid content decreased. Overall, the DBD plasma treatment led to the oxidation of unsaturated fatty acids, which altered the cell membrane fatty acid content, thereby inducing cell membrane damage. Meanwhile, DBD plasma-induced ROS penetrated the cell membrane and accumulated intracellularly, leading to the collapse of the antioxidant system and ultimately causing cell death. This study reveals the bactericidal effect and mechanism of the DBD treatment on F. oxysporum, which provides a possible strategy for the control of F. oxysporum.

With increasing environmental pollution from ship exhaust emissions and increasingly stringent International Maritime Organization carbon regulations, there is a growing demand for cleaner and lower-carbon fuels and near-zero-emission marine engines worldwide. Liquefied natural gas is a low-carbon fuel, and when liquefied natural gas (LNG) is used on ships, dual-fuel methods are often used. The pre-chamber plays a key role in the working process of dual-fuel engines. In this paper, an effective three-dimensional simulation model based on the actual operating conditions and structural characteristics of a marine low-pressure dual-fuel engine is established. In addition, the effects of changing the Precombustion chamber (PCC) volume ratio and the PCC orifice diameter ratio on the mixture composition, engine combustion performance, and pollutant generation were thoroughly investigated. It was found that a small PPC volume ratio resulted in a higher flame jet velocity, a shorter stagnation period, and an acceleration of the combustion process in the main combustion chamber. When the PCC volume was large, the Nitrogen oxygen (NOx) ratio emission was elevated. Moreover, the angle of the PCC orifice affected the flame propagation direction of the pilot fuel. Optimizing the angle of the PCC orifice can improve combustion efficiency and reduce the generation of NOx. Furthermore, reasonable arrangement of the PCC structure can improve the stability of ignition performance and accelerate the flame jet velocity.

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes.

This paper comprehensively summarizes moisture transport, ion transport, and mechanical damage models applied to concrete under sulfate attack and drying–wetting cycles. It highlights the essential aspects and principles of each model, emphasizing their significance in understanding the movement of moisture and ions, as well as the resulting mechanical damage within the concrete during these degradation processes. The paper critically analyzes the assumptions made in each model, shedding light on their limitations and implications for prediction accuracy. Two primary challenges faced by current models under sulfate attack and drying–wetting cycles are identified: the limited consideration of the coupled effects of chemical and physical attacks from sulfate, and the unclear mechanism of the sulfate attacks. Future research directions are proposed, focusing on exploring the transport mechanism of sulfate ions under various driving forces and further clarifying the crystallization process and expansion damage mechanism in concrete pores. Addressing these research directions will advance our understanding of sulfate attack under drying–wetting cycles, leading to improved models and mitigation strategies for enhancing the durability and performance of concrete structures.

Background: The treatment options for third-trimester musculoskeletal issues are limited. This study aims to examine how heel height affects gait biomechanics and provides heel height recommendations for various musculoskeletal problems. Methods: Five third-trimester gravidas were recruited wearing uniform footwear with four heel heights (0 mm, 15 mm, 30 mm, and 45 mm). Lower-limb muscle forces, joint angles, joint torques, joint contact forces, and ground reaction forces (GRF) at specific moments (the first peak, valley, and second peak of GRF) were collected for one-way analysis of variance with repeated measures. Results: The soleus, gastrocnemius, tibialis posterior, plantaris, obturator externus, gluteus maximus, gemellus superior, and obturator internus were the smallest at heel heights of 45 mm and 15 mm at the valley of GRF. Hip extension and knee flexion displayed the smallest joint angle and joint torques at a height of 15 mm. Ankle joint contact force decreased with increased heel height. Conclusions: The height of the heel significantly impacts muscle force, joint angles, joint torques, and joint contact force. A heel of 15 mm might be the most suitable heel height to potentially avoid or alleviate musculoskeletal problems during the third trimester.

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5–0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs.

The Changzhou ship lock is approaching its capacity limit. In order to quantitatively analyze the influencing factors that restrict the capacity of the Changzhou ship lock, this study proposes an influencing factor analysis method based on principal component analysis (PCA). This method estimates the confidence interval of ship passing time by fitting a lognormal distribution curve, eliminates redundancy in navigability data by combining the hydrological data and cargo load data, and quantitatively analyzes the influencing factors of ship lock capacity under saturated operating conditions. The results show that the influencing factors of Changzhou ship lock capacity are classified according to their influence contribution rate as minimum water depth above the lock sill, operation direction, ship dimensions, draft, loading capacity, and actual load. The research results can provide a theoretical basis for improving the ship lock capacity and have application value for lock scheduling management.

Despite several decades of research, the beneficial effect of flavonoids on health is still enigmatic. Here, we focus on the antioxidant effect of flavonoids, which is elementary to their biological activity. A relatively new strategy for obtaining a more accurate understanding of this effect is to leverage computational chemistry. This review systematically presents various computational chemistry indicators employed over the past five years to investigate the antioxidant activity of flavonoids. We categorize these strategies into five aspects: electronic structure analysis, thermodynamic analysis, kinetic analysis, interaction analysis, and bioavailability analysis. The principles, characteristics, and limitations of these methods are discussed, along with current trends.

Volatile active ingredients in biopolymer nanofibers are prone to burst and uncontrolled release. In this study, we used electrospinning and crosslinking to design a new sustained-release active packaging containing zein and eugenol (EU). Vapor-phase glutaraldehyde (GTA) was used as the crosslinker. Characterization of the crosslinked zein nanofibers was conducted via scanning electron microscopy (SEM), mechanical properties, water resistance, and Fourier transform infrared (FT-IR) spectroscopy. It was observed that crosslinked zein nanofibers did not lose their fiber shape, but the diameter of the fibers increased. By increasing the crosslink time, the mechanical properties and water resistance of the crosslinked zein nanofibers were greatly improved. The FT-IR results demonstrated the formation of chemical bonds between free amino groups in zein molecules and aldehyde groups in GTA molecules. EU was added to the zein nanofibers, and the corresponding release behavior in PBS was investigated using the dialysis membrane method. With an increase in crosslink time, the release rate of EU from crosslinked zein nanofibers decreased. This study demonstrates the potential of crosslinking by GTA vapors on the controlled release of the zein encapsulation structure containing EU. Such sustainable-release nanofibers have promising potential for the design of fortified foods or as active and smart food packaging.

Human defense against infection remains a global topic. In addition to developing novel anti-infection drugs, therapeutic drug delivery strategies are also crucial to achieving a higher efficacy and lower toxicity of these drugs for treatment. The application of hydrogels has been proven to be an effective localized drug delivery approach to treating infections without generating significant systemic adverse effects. The recent emerging dynamic hydrogels further show power as injectable formulations, giving new tools for clinical treatments. In this review, we delve into the potential applications of dynamic hydrogels in antibacterial and antiviral treatments and elaborate on their molecular designs and practical implementations. By outlining the chemical designs underlying these hydrogels, we discuss how the choice of dynamic chemical bonds affects their stimulus responsiveness, self-healing capabilities, and mechanical properties. Afterwards, we focus on how to endow dynamic hydrogels with anti-infection properties. By comparing different drug-loading methods, we highlight the advantages of dynamic chemical bonds in achieving sustained and controlled drug release. Moreover, we also include the design principles and uses of hydrogels that possess inherent anti-infective properties. Furthermore, we explore the design principles and applications of hydrogels with inherent anti-infective properties. Finally, we briefly summarize the current challenges faced by dynamic hydrogels and present a forward-looking vision for their future development. Through this review, we expect to draw more attention to these therapeutic strategies among scientists working with chemistry, materials, as well as pharmaceutics.

Predicting urban morphology based on local attributes is an important issue in urban science research. The deep generative models represented by generative adversarial network (GAN) models have achieved impressive results in this area. However, in such methods, the urban morphology is assumed to follow a specific probability distribution and be able to directly approximate the distribution via GAN models, which is not a realistic strategy. As demonstrated by the score-based model, a better strategy is to learn the gradient of the probability distribution and implicitly approximate the distribution. Therefore, in this paper, an urban morphology prediction method based on the conditional diffusion model is proposed. Implementing this approach results in the decomposition of the attribute-based urban morphology prediction task into two subproblems: estimating the gradient of the conditional distribution, and gradient-based sampling. During the training stage, the gradient of the conditional distribution is approximated by using a conditional diffusion model to predict the noise added to the original urban morphology. In the generation stage, the corresponding conditional distribution is parameterized based on the noise predicted by the conditional diffusion model, and the final prediction result is generated through iterative sampling. The experimental results showed that compared with GAN-based methods, our method demonstrated improvements of 5.5%, 5.9%, and 13.2% in the metrics of low-level pixel features, shallow structural features, and deep structural features, respectively.

In order to address the challenges of resource utilization posed by construction waste, the substitution of natural aggregate (NA) with public fill (PF) contents was investigated for load reclamation and road grassroots applications. A comprehensive assessment of road performance for the recycled mixture was conducted, focusing on parameters such as unconfined compressive strength, splitting strength, compressive resilience modulus, dry shrinkage, and frost resistance. Additionally, the impact of incorporating PF at various types and replacement ratios on the microstructure of cement-stabilized aggregate (CSA) was analyzed. The results indicated that the unconfined compressive strength of cement-stabilized recycled mixture with varying PF contents meets the base strength requirements for heavy, medium, and light traffic pavement on secondary and sub-secondary roads in China. Notably, the unconfined compressive strength and resilience modulus follow a similar pattern, reaching their peak at a 25% PF content. Microscopic examination reveals that an appropriate PF content leads to the predominant formation of C(N)-A-S-H, hydrotalcite, Ca(OH)2, and CaCO3 as paste reaction products. As the replacement of public fill increases from 0% to 25%, there is a gradual stacking of gel products, which enhances the compactness of the microstructure by cementing together unreacted particles. Consequently, this process reduces dry shrinkage strain and effectively mitigates the formation of reflection cracks. Applying large quantities of public fill to road construction can effectively deal with various waste accumulation problems and produce a novel road material with significant social, economic, and environmental benefits.

During the landing and taxiing process of aircraft, the frame-type landing gear (FTLG) usually generates large pitch vibrations under external excitation. Excessive vibration increases localized loads on the landing gear, which may lead to localized failure of the structure. To minimize this undesirable vibration, a passive oil–pneumatic pitch-stabilizing buffer (PSB) is designed in this paper to provide pitch damping. This paper applies the basic principles of dynamics to establish a dynamic model of FTLG considering the influence of PSB. And based on the design of experiments (DOE) method, by changing the filling parameters and structural parameters of PSB, the results of the changes of the frame vibration angle, angular velocity, and landing gear load are obtained, so as to analyze the effects of different parameters on the dynamic performance of the landing gear in the landing and taxiing process. The results demonstrate that increasing the oil damping coefficient of PSB and decreasing the installation angle of PSB on the main strut during the landing period resulted in less frame vibration and lower wheelset load ratios, but increased the landing overloads of landing gears. In the taxiing phase, increasing the PSB air spring stiffness can effectively reduce the frame vibration caused by the uneven road surface. The PSB structural parameters have little effect on the dynamic performance of FTLG.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson’s correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

The properties of xanthan gum protein gels composed of quinoa protein (XG-QPG) and ultrasound-treated quinoa protein (XG-UQPG) were compared for the preparation of high-quality quinoa protein gels. The gel qualities at different pH values were compared. The gels were used to produce eggless bread. Microscopically, the secondary structure of the proteins in XG-QPG (pH 7.0) was mainly α-helix, followed by random coiling. In contrast, the content of β-sheet in XG-UQPG was higher, relative to the viscoelastic properties of the gel. Moreover, the free sulfhydryl groups and disulfide bonds of XG-QPG (pH 7.0) were 48.30 and 38.17 µmol/g, while XG-UQPG (pH 7.0) was 31.95 and 61.58 µmol/g, respectively. A high disulfide bond content was related to the formation of gel networks. From a macroscopic perspective, XG-QPG (pH 7.0) exhibited different pore sizes, XG-UQPG (pH 7.0) displayed a loose structure with uniform pores, and XG-UQPG (pH 4.5) exhibited a dense structure with small pores. These findings suggest that ultrasound can promote the formation of a gel by XG-UQPG (pH 7.0) that has a loose structure and high water-holding capacity and that XG-UQPG (pH 4.5) forms a gel with a dense structure and pronounced hardness. Furthermore, the addition of the disulfide bond-rich XG-UQPG (pH 7.0) to bread promoted the formation of gel networks, resulting in elastic, soft bread. In contrast, XG-UQPG (pH 4.5) resulted in firm bread. These findings broaden the applications of quinoa in food and provide a good egg substitute for quinoa protein gels.

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests.

The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset plays an ever-shrinking role due to RSIs’ diverse distribution shifts. Instead, SSL promotes a ‘global-to-local’ transfer paradigm, in which generalized models pre-trained on arbitrarily large unlabeled datasets are fine-tuned to the target dataset to overcome data distribution shifts. However, the SSL pre-trained models may contain both useful and useless features for the downstream semantic segmentation task, due to the gap between the SSL tasks and the downstream task. To adapt such pre-trained models to semantic segmentation tasks, traditional supervised fine-tuning methods that use only a small number of labeled samples may drop out useful features due to overfitting. The main reason behind this is that supervised fine-tuning aims to map a few training samples from the high-dimensional, sparse image space to the low-dimensional, compact semantic space defined by the downstream labels, resulting in a degradation of the distinguishability. To address the above issues, we propose a class distinguishability-enhanced self-training (CDEST) method to support global-to-local transfer. First, the self-training module in CDEST introduces a semi-supervised learning mechanism to fully utilize the large amount of unlabeled data in the downstream task to increase the size and diversity of the training data, thus alleviating the problem of biased overfitting of the model. Second, the supervised and semi-supervised contrastive learning modules of CDEST can explicitly enhance the class distinguishability of features, helping to preserve the useful features learned from pre-training while adapting to downstream tasks. We evaluate the proposed CDEST method on four RSI semantic segmentation datasets, and our method achieves optimal experimental results on all four datasets compared to supervised fine-tuning as well as three semi-supervised fine-tuning methods.

The full Poincaré (FP) beam, encompassing all possible polarization states in its beam cross-section, has demonstrated advantages in various applications. However, conventional FP beams are typically considered as spatially fully coherent, rendering them sensitive to disturbances in the propagation path and susceptible to speckle effects. In this work, we propose an alternative approach to synthesize the optical beam with a FP polarization state through the spatial coherence engineering of a partially coherent beam. In this process, the FP polarization state is initially encoded into the spatial coherence structure of the beam source. We demonstrate that during the encoding process, the vector nature of the beam transitions from the FP polarization state to the spatial coherence structure of the source. However, during the propagation of the partially coherent beam, the vectorness reverts to the polarization state, resulting in the re-emergence of the encoded FP polarization in the output plane. We illustrate that the synthesized FP polarization state, achieved through spatial coherence engineering, is highly robust against obstructions in the propagation path. Furthermore, we examine the effect of the spatial coherence area of the beam on the quality of the recovered FP polarization state. The findings of this work can have valuable applications in optical trapping and optical imaging in complex environments.

A variety of neurological and psychiatric disorders have recently been shown to be highly associated with the abnormal development and function of oligodendrocytes (OLs) and interneurons. OLs are the myelin-forming cells in the central nervous system (CNS), while interneurons are important neural types gating the function of excitatory neurons. These two types of cells are of great significance for the establishment and function of neural circuits, and they share similar developmental origins and transcriptional architectures, and interact with each other in multiple ways during development. In this review, we compare the similarities and differences in these two cell types, providing an important reference and further revealing the pathogenesis of related brain disorders.

With the increasingly strict limitations on emission standards of vehicles, deep desulfurization in fuel is indispensable for social development worldwide. In this study, a series of hybrid materials based on SiO2-supported polyoxometalate ionic liquid were successfully prepared via a facile ball milling method and employed as catalysts in the aerobic oxidative desulfurization process. The composition and structure of prepared samples were studied by various techniques, including FT-IR, UV-vis DRS, wide-angle XRD, BET, XPS, and SEM images. The experimental results indicated that the synthesized polyoxometalate ionic liquids were successfully loaded on SiO2 with a highly uniform dispersion. The prepared catalyst (C16PMoV/10SiO2) exhibited good desulfurization activity on different sulfur compounds. Moreover, the oxidation product and active species in the ODS process were respectively investigated via GC-MS and ESR analysis, indicating that the catalyst can activate oxygen to superoxide radicals during the reaction to convert DBT to its corresponding sulfone in the fuel.

Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch’s method complemented by time–frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.

The adsorption and diffusion of oxygen at the B2(110)[1¯11]||O(001)[11¯0] interface in Ti2AlNb alloys were investigated via first-principles calculations. Only a 2.6% interfacial mismatch indicates that B2(110)–O(001) is basically a stable coherent interface. The calculated adsorption energies and diffusion energy barriers show that oxygen prefers to occupy the Ti-rich interstitial sites, and once trapped, it hardly diffuses to other interstitial sites, thus promoting the preferential formation of Ti oxides. Under the premise of a Ti-rich environment, a Nb-rich environment is more favorable for oxygen adsorption than an Al-rich environment. The electronic structures suggest that O 2p orbitals mainly occupy the energy region below −5 eV, bonding with its coordinated atoms of Ti, Al, and Nb. However, Al 3p and Nb 4d orbitals near the Fermi level couple with sparsely distributed O 2p orbitals, forming anti-bonding, which is not conducive to oxygen adsorption. Because Nb 4d electrons are more localized than Al 3p electrons are, Nb–O anti-bonding is weaker. O–Ti has almost no contribution to anti-bonding, suggesting good bonding between them. This is consistent with the experimental observations that TiO2 is the main oxidation product.

To analyze the effect of static eccentricity on the air ingestion distribution and vibration damping properties of the SFD, a numerical simulation study of SFDs considering two-phase flow was carried out based on CFD using a transient solution method and dynamic mesh technique. The results show that the angle between the static eccentricity direction and the circumferential direction of the oil supply hole increases and the air ingestion area in the oil film expands. In contrast, the oil film damping decreases, and the larger the static eccentricity distance, the greater its effect on the air ingestion area in the oil film. When the circumferential angle is small, the oil film damping increases with the increase of static eccentricity distance, and when the circumferential angle is large, the oil film damping decreases with the increase of static eccentricity distance and then increases. With the increase of static eccentricity distance, the air ingestion area at both ends of the oil film increases. At the same time, studying the effect of dynamic eccentricity shows that as the dynamic eccentricity increases, the oil film damping first decreases and then increases, and the air ingestion area increases. Comparing the 1 hole, the 2 hole, and the 3 hole oil supplies, the air ingestion area is significantly larger in the 1 hole oil supply than in the 2 hole or the 3 hole oil supplies, and the oil film damping of the 1 hole oil supply is smaller than the oil film damping of the 2 hole or the 3 hole oil supplies. It can be seen from the present study that in the actual installation of the SFD, when the circumferential angle is less than 60°, the static eccentricity can be increased appropriately. When the circumferential angle is greater than 60°, the static eccentricity can be appropriately reduced.

In our research on naturally occurring sesquiterpenes, eight shizukaol-type dimers, one chlorahololide-type dimer, and one sarcanolide-type dimer were isolated from the roots of Chloranthus fortunei. As the project was implemented, we accidentally discovered that shizukaol-type dimers can be converted into peroxidized chlorahololide-type dimers. This potential change was discovered after simulations of the changes in corresponding shizukaols showed that three peroxide products were generated (1–3), indicating that peroxidation reactions occurred. HPLC-HR-MS analysis results obtained for the shizukaol derivatives further demonstrate that the reaction occurred, and the type of substituent of small organic ester moieties at positions C-15’ and C-13’ of unit B were not decisively related to the reaction. Quantum chemical calculations of the mode dimer further demonstrated this phenomenon. The highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) energy of the precursor and production revealed the advantageous yield of 4β-hydroperoxyl production. Additionally, the potential reaction mechanism was speculated and validated using the free energy in the reaction which successfully explained the feasibility of the reaction. Finally, the anti-inflammatory activity of the precursors and products was evaluated, and the products of peroxidation showed better anti-inflammatory activity.

Hydnum is a well-characterized genus in the family Hydnaceae of Cantharellales and is characterized by spinose hymenophores. In this study, an ITS phylogenetic overview and a multilocus (ITS-nrLSU-tef1) phylogenetic tree of Hydnum were carried out. On the basis of morphological characteristics and phylogenetic results, seven species from China were confirmed, described, illustrated, and compared with similar species, including three new species, i.e., H. longipes, H. microcarpum, and H. sinorepandum, and four known species, i.e., H. cremeoalbum, H. melitosarxm, H. orientalbidum, and H. pinicola were recorded for the first time in China. A key to the species of Hydnum in China was provided.

V-4Cr-4Ti alloy is one of the candidate structural materials for future fusion reactors due to its desirable characteristics. In our previous research, MAX-phase-dispersion-strengthened vanadium alloy (V-4Cr-4Ti-1.5Y-0.3Ti3SiC2), prepared through mechanical alloying, showed excellent thermal stability and creep resistance and was expected to have good radiation resistance. This study investigates the effects of 2.5 MeV V2+ ion irradiation on V-4Cr-4Ti-1.5Y-0.3Ti3SiC2 and V-4Cr-4Ti alloys at 500 °C, with peak damage of 0.8, 3.5, and 6.1 dpa. Transmission electron microscopy and nanoindentation were used to examine the changes in microstructure and hardness before and after irradiation. The microscopic analysis reveals that dispersed nanoparticles maintained good stability under irradiation. Defect clusters grow with increasing irradiation doses in both materials. The nanoindentation results show that V-4Cr-4Ti-1.5Y-0.3Ti3SiC2 has higher initial hardness and lower irradiation hardening, indicating better resistance to radiation hardening than V-4Cr-4Ti. This research serves as a valuable reference for the assessment of the irradiation resistance of Ti3SiC2-dispersion-strengthened V-4Cr-4Ti alloy.

A dynamic model is established to investigate the shimmy instability of a landing gear system, considering the influence of nonlinear damping. The stability criterion is utilized to determine the critical speed at which the landing gear system becomes unstable. The central manifold theorem and canonical method are employed to simplify the dynamic model of the landing gear. The first Lyapunov coefficient of the system is theoretically derived and verified using numerical simulation. Further investigation on the Hopf bifurcation characteristics and stability of the shimmy in the landing gear system is conducted. The results indicate that above a certain threshold speed, with a tire stability distance greater than half the tire length in contact with the ground plus the slack length, the aircraft remains stable during taxiing. At critical speeds, a shimmy system with higher-order nonlinear damping will undergo supercritical Hopf bifurcation. Quantitative analysis suggests an increase in the linear damping coefficient within a range that ensures a stability margin to mitigate undesired oscillation, while the nonlinear damping coefficient should be designed within a reasonable range to decrease the amplitude of the limit cycle.

Hydrogen sulfide (H2S) is a significant actor in the virulence and pathogenicity of fungi. The analysis of endogenous H2S in fungi benefits the prevention and treatment of pathogenic infections. Herein, a H2S-activated turn-on fluorescent probe named DDX-DNP was developed for the sensitive and selective detection of H2S. Using DDX-DNP, the ability of several oral fungi strains to produce H2S was identified, which was also validated using a typical chromogenic medium. In addition, DDX-DNP was successfully used for the visual sensing of endogenous H2S in fungal cells via microscope, flow cytometry, and colony imaging, along with a specific validation with the co-incubation of H2S production inhibitors in living cells. Above all, DDX-DNP could be used for H2S detection, the fluorescent imaging of fungi, and even the identification of related fungi.

Project sustainability has become a research hotspot in the construction industry and a crucial driving force for the successful delivery of projects. How enterprises can improve project sustainability performance and realize sustainable development by applying BIM has become an important research topic. In this study, based on the resource-based view and institutional theory, a relationship model of BIM application affecting project sustainability performance is constructed, and data from 449 questionnaires with electric power construction industry practitioners obtained by the two-stage data collection method are used to explore the relationship between BIM application and project sustainability performance, and to investigate the mediating role of green innovation and the moderating role of institutional pressures. The study found that: (1) BIM application has a significant positive impact on project sustainability performance; (2) BIM application has a significant positive predictive effect on green innovation, and green innovation plays a mediating role in the relationship between BIM application and project sustainability performance; and (3) under a high degree of institutional pressures, the positive relationship between BIM application and green innovation is strengthened, and, in this case, the mediating role of green innovation is enhanced. The study results help to expand the theoretical analysis of the relationship between BIM application and project sustainability performance and provide practical guidance for improving project sustainability. Finally, the data in this study only come from the power construction industry and do not differentiate between the types of green innovations, and further research could be conducted on these two aspects in the future.

The genus Laccaria is a type of cosmopolitan and ecologically important fungal group. Members can form ectomycorrhizal associations with numerous trees, and some species are common edible fungi in local markets. Although some new species from China are recently published, the species diversity of Laccaria is still unclear in China. In this study, some samples of Laccaria were collected from southern China, and morphological characteristics and phylogenetic analyses based on the multilocus dataset of ITS-LSU-tef1-rpb2 confirmed five new species. Laccaria miniata, L. nanlingensis and L. neovinaceoavellanea were collected from subtropical broad-leaved forests, and L. rufobrunnea and L. umbilicata were collected from subtropical mixed forests of southwest China. Full descriptions, illustrations, comparisons with similar species and phylogenetic analysis are provided.

This study is the first to research the microstructure and mechanical properties of the workpiece after additive friction stir deposition (AFSD) of the feedstock at different heat treatment stages. AA2219 aluminum alloys with three different heat treatment stages were selected as the feedstock, and alloys with dense structure were successfully prepared by the additive friction stir deposition AFSD process. Experimental results show that AFSD exhibits an excellent ability to refine grains and improve the uniform distribution of precipitates in the second phase, thereby improving the plasticity of AA2219 alloy after the AFSD process. Because of the continuous dynamic recrystallization (CDRX) in the AA2219 alloy during AFSD, the grain size after the AFSD process is independent of the initial feedstock grain size for three samples. The equilibrium phase (θ) size is genetically related to the initial size of the second-phase particles in the feedstock. Due to grain refinement and dislocation strengthening, the yield strength of AA2219-casting increased significantly from 79.8 MPa to 124.1 MPa after AFSD. The yield strength of the AA2219-T4 decreases slightly from 151.8 MPa to 140.4 MPa after AFSD. The precipitation of the second phase leads to a decrease in solid solution strengthening and dislocation strengthening. However, grain refinement strengthening partially offsets this reduction. The yield strength of AA2219-T87 decreased from 398.5 MPa to 147.2 MPa after AFSD. As such, grain refinement strengthening and solid solution strengthening by the AFSD process are much smaller than the yield strength lost by precipitation strengthening and dislocation strengthening.

In recent years, the ecosystem has been seriously affected by sewage discharge and oil spill accidents. A series of issues (such as the continuous pollution of the ecological environment and the imminent exhaustion of freshwater resources) are becoming more and more unmanageable, resulting in a crisis of water quality and quantity. Therefore, studies on industrial wastewater purification and solar-driven seawater desalination based on wood composites have been widely considered as an important development direction. This paper comprehensively analyzes and summarizes the applications of wood composites in the fields of solar-driven seawater desalination and polluted water purification. In particular, the present situation of industrial wastewater containing heavy metal ions, microorganisms, aromatic dyes and oil stains and related problems of solar-driven seawater desalination are comprehensively analyzed and summarized. Generally, functional nanomaterials are loaded into the wood cell wall, from which lignin and hemicellulose are selectively removed. Alternatively, functional groups are modified on the basis of the molecular structure of the wood microchannels. Due to its three-dimensional (3D) pore structure and low thermal conductivity, wood is an ideal substrate material for industrial wastewater purification and solar-driven seawater desalination. Based on the study of objective conditions such as the preparation process, modification method and selection of photothermal conversion materials, the performances of the wood composites in filtration, adsorption and seawater desalination are analyzed in detail. In addition, this work points out the problems and possible solutions in applying wood composites to industrial wastewater purification and solar-driven seawater desalination.

The presence of antibiotics in water environments increases the resistance of bacterial and can also cause irreversible damage to ecosystems and the human body. In this study, disposable bamboo chopsticks were used as raw material to prepare bamboo biochar (BB) via oxygen-limited pyrolysis to remove norfloxacin (NOR) from aqueous solutions. The properties of the BB were explained through the characterization of its SBET, morphology, structure, and functional groups. The effects of the dosage, pH, ionic strength, and water type on the removal of NOR using BB were investigated. The maximum theoretical adsorption capacities (Qmax) of NOR removed by BB at 25, 35, and 45 °C obtained using the Langmuir model were 76.17, 77.22, and 105.19 mg/g, respectively. To facilitate a comparison with other types of biochar, this study also prepared biochar of rice straw, wheat straw, soybean straw, corn straw, rape straw, peanut shell, Eichhornia crassipes, and other biomass raw materials under the same preparation conditions as the BB. The results demonstrated that the removal rate of NOR using BB was the highest under the same adsorption conditions, reaching 99.71%. Biochar from waste disposable bamboo chopsticks can be used for the treatment of new types of pollutants in water bodies, such as antibiotics and other organic contaminants, which will help to achieve sustainable solid waste management.

The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles.

Obesity is a risk factor for many chronic diseases related to the overexpansion of adipose tissue during obesity, leading to metabolic dysfunction and ectopic lipids. Previous studies reported a close relationship between succinate and obesity and its co-morbidities, and studies have also reported on its anti-obesity potential. To confirm its efficacy in obesity interventions, we supplemented mice with obesity induced by a high-fat diet with succinate (1.5% m/v in drinking water) for 11 weeks without changing the diet. After succinate supplementation, the changes in body weight, adipose tissue deposition, glucose tolerance, energy expenditure and lipid metabolism were evaluated. It was found that succinate supplementation significantly decreased subcutaneous adipose tissue (HFD: 4239.3 ± 211.2 mg; HFD-SA: 3268.9 ± 265.7 mg. p < 0.05), triglyceride contents (decreased by 1.53 mmol/g and 0.39 mmol/g in eWAT and ingWAT, respectively, p < 0.05) and NEFA (decreased by 1.41 μmol/g and 1.31 μmol/g in eWAT and ingWAT, respectively, p < 0.05). The adipocytes’ sizes all significantly decreased in both subcutaneous and visceral adipose tissue (the proportion of adipocytes with diameters larger than 100 μm in eWAT and ingWAT decreased by 16.83% and 11.96%, respectively. p < 0.05). Succinate significantly enhanced lipolysis in adipose tissue (eWAT: Adrb3, Hsl and Plin1; ingWAT: Hsl and CPT1a; p < 0.05), whereas the expression of lipogenesis-related genes remained unchanged (p > 0.05). Succinate supplementation also enhanced the activity of BAT by stimulating the expression of Ucp1 and Cidea (p < 0.05). Our results reported that succinate has a potential beneficial effect on obesity pathogenesis but cannot efficiently decrease bodyweight.

To investigate the hysteretic behavior of an X-shaped metal damper (XMD) with an oblique angle, cyclic loading tests were carried out on nine specimens, including two XMDs without buckling-restrained devices, four XMDs with stiffening ribs, and three XMDs with cover plates as references. The test results showed that the oblique angle could effectively increase the stiffness, strength, and energy dissipation of the XMD. When the oblique angle of an XMD with stiffening ribs increased from 0° to 30° at the applied displacement of 8.4 mm, the mean strengths and cumulative energy dissipation of specimens increased by about 80.77% and 80.57%, respectively. Although asymmetric hysteretic loops were also observed in specimens with an oblique angle and buckling-restrained devices, stable hysteretic curves were obtained. This indicated that the stiffening ribs and cover plates can effectively constrain the buckling behavior of XMDs. Additionally, the mean strengths of specimens with stiffening ribs were a little higher than those of specimens with cover plates. Subsequently, the finite element analysis models of the XMDs were proposed, in which the metal plasticity model considering isotropic and kinematical hardening was used to model the material properties of steel, and the simulation results matched well with the test results. Finally, the theoretical calculation method was proposed to predict the elastic stiffness of specimens, and the theoretical elastic stiffness matched well with the test results.

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions.

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a “decomposition” style (in the abstract of the well-known “Transformer” article “Attention is All You Need”, they use “attention” instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or “gathered” for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two “where to go” strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation.

The ocean is the largest reservoir on Earth. With the scarcity of water resources, the destruction of the benign cycle of the marine ecosystem would seriously impact people’s quality of life and health. Oyster reefs, the world’s most endangered marine ecosystems, have been recognized as a global issue due to their numerous essential ecological functions and provision of various ecosystem services. As a result, interest in oyster reef research has been steadily increasing worldwide in recent decades. The goal of this study is to assess the knowledge structure, development trends, research hotspots, and frontier predictions of the global oyster reef research field. Based on 1051 articles selected from the Web of Science Core Collection from 1981 to 2022, this paper conducted a visual analysis of oyster reef ecosystems conservation, restoration, and management. Specifically, it examined research output characteristics, research cooperation networks, highly cited papers and core journals, and keywords. Results indicate a steady rise in research interest in oyster reefs over the past 40 years, with notable acceleration after 2014. Authoritative experts and high-impact organizations were also identified. This paper outlines habitat conservation and restoration, ecosystem services, and the impacts of climate change as the primary research hotspots and frontiers. This paper provides valuable guidance for scholars and regulators concerned about oyster reef conservation to conduct research on oyster reefs.

Hainan, a well-known center of tropical agricultural production in south China, has received little attention regarding groundwater fluoride contamination. This study investigates the occurrence of fluoride in the western coastal area of Hainan Island and discusses factors affecting groundwater fluoride contamination in various aquifers and areas with different land-use types using hydrochemistry and multivariate statistical analysis. A total of 100 groundwater samples were collected from the western coastal area of Hainan Island. The results show that the groundwater fluoride concentration is as high as 4.18 mg/L and that F−-high (>1 mg/L) groundwater accounts for 9% of total groundwater. The proportion of F−-high fissure water is about two times that of F−-high pore water. Among the different land-use types, the proportion of F−-high groundwater from highest to lowest is as follows: bare land > cultivated land > woodland > construction land > grassland. The main factor affecting fluoride in pore water is the leaching of fluorine/aluminum-containing minerals such as phlogopite and calcite in the vadose zone, which is characterized by the co-enrichment of fluoride and aluminum in pore water. The leading cause of fluoride in fissure water is the leaching of fluorine-containing fertilizers, and continuous irrigation promotes the cation exchange of sodium, strontium, and calcium, which is characterized by the co-enrichment of fluoride with sodium and strontium in fissure water. Consequently, it is advised to minimize the excessive use of fluoride fertilizers and increase groundwater quality monitoring in order to decrease the emergence of F−-high groundwater in the western coastal area of Hainan Island.

In the context of difficulty in detection problems and the limited computing resources of various fault scales in aerial images of transmission line UAV inspections, this paper proposes a TD-YOLO algorithm (YOLO for transmission detection). Firstly, the Ghost module is used to lighten the model’s feature extraction network and prediction network, significantly reducing the number of parameters and the computational effort of the model. Secondly, the spatial and channel attention mechanism scSE (concurrent spatial and channel squeeze and channel excitation) is embedded into the feature fusion network, with PA-Net (path aggregation network) to construct a feature-balanced network, using channel weights and spatial weights as guides to achieving the balancing of multi-level and multi-scale features in the network, significantly improving the detection capability under the coexistence of multiple targets of different categories. Thirdly, a loss function, NWD (normalized Wasserstein distance), is introduced to enhance the detection of small targets, and the fusion ratio of NWD and CIoU is optimized to further compensate for the loss of accuracy caused by the lightweightedness of the model. Finally, a typical fault dataset of transmission lines is built using UAV inspection images for training and testing. The experimental results show that the TD-YOLO algorithm proposed in this article compresses 74.79% of the number of parameters and 66.92% of the calculation amount compared to YOLOv7-Tiny and increases the mAP (mean average precision) by 0.71%. The TD-YOLO was deployed into Jetson Xavier NX to simulate the UAV inspection process and was run at 23.5 FPS with good results. This study offers a reference for power line inspection and provides a possible way to deploy edge computing devices on unmanned aerial vehicles.

Ammonia (NH3) is considered a promising zero-carbon fuel and was extensively studied recently. Mixing high-reactivity oxygenated fuels such as dimethyl ether (DME) or dimethoxymethane (DMM) with ammonia is a realistic approach to overcome the low reactivity of NH3. To study the combustion characteristics of NH3/DMM and NH3/DME mixtures, we constructed a NH3/DMM chemical mechanism and tested its accuracy using measured laminar burning velocity (LBV) and ignition delay time (IDT) of both NH3/DMM and NH3/DME mixtures from the literature. The kinetic analysis of NH3/DMM flames using this mechanism reveals that the CH3 radicals generated from the oxidation of DMM substantially affects the oxidation pathway of NH3 at an early stage of flame propagation. We investigated the formation of nitrogen oxides (NOx) in NH3/DMM and NH3/DME flames and little difference can be found in the NOx emissions. Using NH3/DMM flames as an example, the peak NOx emissions are located at an equivalence ratio (φ) of 0.9 and a DMM fraction of 40% in the conditions studied. Kinetic analysis shows that NOx emission is dominated by NO, which primarily comes from fuel nitrogen of NH3. The addition of DMM at 40% significantly promotes the reactive radical pool (e.g., H, O, and OH) while the maintaining a high concentration of NO precursors (e.g., HNO, NO2, and N2O), which results in a high reaction rate of NO formation reaction and subsequently generates the highest NO emissions.

Inactivation is a crucial step in the production of postbiotics, with thermal inactivation being the prevailing method employed. Nevertheless, the impact of thermal treatment on bioactivity and chemical composition remains unexplored. The objective of this study was to assess the influence of heating temperature on the antioxidant, anti-inflammatory properties and the chemical composition of ET-22 and BL-99 postbiotics. The findings revealed that subjecting ET-22 and BL-99 to thermal treatment ranging from 70 °C to 121 °C for a duration of 10 min effectively deactivated them, leading to the disruption of cellular structure and release of intracellular contents. The antioxidant and anti-inflammatory activity of ET-22 and BL-99 postbiotics remained unaffected by mild heating temperatures (below 100 °C). However, excessive heating at 121 °C diminished the antioxidant activity of the postbiotic. To further investigate the impact of thermal treatments on chemical composition, non-targeted metabolomics was conducted to analyze the cell-free supernatants derived from ET-22 and BL-99. The results revealed that compared to mild inactivation at temperatures below 100 °C, the excessive temperature of 121 °C significantly altered the chemical profile of the postbiotic. Several bioactive components with antioxidant and anti-inflammatory properties, including zomepirac, flumethasone, 6-hydroxyhexanoic acid, and phenyllactic acid, exhibited a significant reduction in their levels following exposure to a temperature of 121 °C. This decline in their abundance may be associated with a corresponding decrease in their antioxidant and anti-inflammatory activities. The cumulative evidence gathered strongly indicates that heating temperatures exert a discernible influence on the properties of postbiotics, whereby excessive heating leads to the degradation of heat-sensitive active constituents and subsequent diminishment of their biological efficacy.

This study delves into the effects of sample height on consolidation behaviour, utilising the automatic air pressure consolidometer. Extensive tests were conducted on three varieties of dredged silt samples of varying heights from Qianwan, Shenzhen, China. The salient findings can be summarised as follows: (1) Compression curves for samples of different dimensions transitioned through three distinct phases: minimal load disturbance, elastic deformation, and plastic deformation. Notably, the void ratio during the latter two phases diminished as sample height increased. (2) A rising sample height corresponded to a reduced stable strain and compression index. Furthermore, the consolidation coefficient notably diminished with an escalation in the sample height, whereas the structural yield stress remained largely unaffected. (3) Given the disparate formation processes, stress histories, and material compositions between dredged and marine silts, the permeability coefficient of dredged silt was found to be superior to that of marine silt. Within the typical preloading pressure scope (50~300 kPa), the consolidation coefficient of dredged silt was lower compared to marine silt. However, as the consolidation pressure significantly surpassed this threshold, the coefficient disparity between the two silts narrowed.

Myocardial ischemia/reperfusion (I/R) elicits an acute inflammatory response involving complement factors. Recently, we reported that myocardial necrosis was decreased in complement C3−/− mice after heart I/R. The current study used the same heart model to test the effect of C3 on myocardial apoptosis and investigated if C3 regulation of apoptosis occurred in human cardiomyocytes. Comparative proteomics analyses found that cytochrome c was present in the myocardial C3 complex of WT mice following I/R. Incubation of exogenous human C3 reduced apoptosis in a cell culture system of human cardiomyocytes that did not inherently express C3. In addition, human C3 inhibited the intrinsic apoptosis pathway in a cell-free apoptosis system. Finally, human pro-C3 was found to bind with an apoptotic factor, pro-caspase 3, in a cell-free system. Thus, we present firsthand evidence showing that C3 readily reduces myocardial apoptosis via interaction with the intrinsic apoptotic pathway.