Search

Visual servo grasping technology has garnered significant attention in intelligent manufacturing for its potential to enhance both the flexibility and precision of robotic operations. However, traditional approaches frequently encounter challenges such as task failure when visual features move outside the camera’s field of view (FoV) and system instability due to interaction matrix singularities, limiting the technology’s effectiveness in complex environments. This study introduces a novel control strategy that leverages an asymmetric time-varying performance function to address the issue of visual feature escape. By strictly limiting the range of feature error, our approach ensures that visual features consistently remain within the camera’s FoV, thereby enhancing both transient and steady-state system performance. Furthermore, we have developed an adaptive damped least squares controller that dynamically adjusts the damping term to mitigate numerical instability resulting from interaction matrix singularities. The effectiveness of our method has been validated through grasping experiments involving significant rotations around the camera’s optical axis and other complex movements.

To meet real-time grasping demands in complex environments, this paper proposes a lightweight yet high-performance robotic grasping model. The model integrates large kernel convolution and residual connections to generate grasping information for unknown objects from RGB and depth images, enabling real-time generation of stable grasping plans from the images. The proposed model achieved favorable accuracy on both the Cornell and Jacquard standard grasping datasets. Compared to other methods, the proposed model significantly reduces the number of parameters while achieving comparable performance, making it a lightweight model. Additionally, real-world experiments were conducted using a six-axis collaborative robot on a set of previously unseen household objects with diverse and adversarial shapes, achieving a comprehensive grasping success rate of 93.7%. Experimental results demonstrate that the proposed model not only improves grasping accuracy but also has strong potential for practical applications, particularly in resource-constrained robotic systems.

The acquisition of side-scan sonar (SSS) images is complex, expensive, and time-consuming, making it difficult and sometimes impossible to obtain rich image data. Therefore, we propose a novel image generation algorithm to solve the problem of insufficient training datasets for SSS-based target detection. For zero-sample detection, we proposed a two-step style transfer approach. The ray tracing method was first used to obtain an optically rendered image of the target. Subsequently, UA-CycleGAN, which combines U-net, soft attention, and HSV loss, was proposed for generating high-quality SSS images. A one-stage image-generation approach was proposed for few-sample detection. The proposed ADA-StyleGAN3 incorporates an adaptive discriminator augmentation strategy into StyleGAN3 to solve the overfitting problem of the generative adversarial network caused by insufficient training data. ADA-StyleGAN3 generated high-quality and diverse SSS images. In simulation experiments, the proposed image-generation algorithm was evaluated subjectively and objectively. We also compared the proposed algorithm with other classical methods to demonstrate its advantages. In addition, we applied the generated images to a downstream target detection task, and the detection results further demonstrated the effectiveness of the image generation algorithm. Finally, the generalizability of the proposed algorithm was verified using a public dataset.

Pitaya (Hylocereus undatus) fruit is an attractive, nutrient-rich tropical fruit with commercial value. However, low-temperature stress severely affects the yield and quality of pitaya. The relevant mechanisms involved in the response of pitaya to low-temperature stress remain unclear. To study whether the SWEET gene family mediates the response of H. undatus to low-temperature stress and the related mechanisms, we performed genome-wide identification of the SWEET gene family in pitaya, and we used ‘Baiyulong’ tissue-cultured plantlets as material in the present study. We identified 28 members of the SWEET gene family from the H. undatus genome and divided these family members into four groups. Members of this gene family presented some differences in the sequences of introns and exons, but the gene structure, especially the motifs, presented relatively conserved characteristics. The promoter regions of most HuSWEETs have multiple stress- or hormone-related cis-elements. Three duplicated gene pairs were identified, including one tandem duplication gene and two fragment duplication gene pairs. The results revealed that the SWEET genes may regulate the transport and distribution of soluble sugars in plants; indirectly regulate the enzyme activities of CAT, POD, and T-SOD through its expression products; and are involved in the response of pitaya to low-temperature stress and play vital roles in this process. After ABA and MeJA treatment, the expression of HuSWEETs changed significantly, and the cold stress was also alleviated. This study elucidated the molecular mechanism and physiological changes in the SWEET gene in sugar metabolism and distribution of pitaya when it experiences low-temperature stress and provided a theoretical basis for cold-resistant pitaya variety breeding.

Developing an efficient power system is an important way for icebreakers to respond to high maneuverability and strong fluctuation loads under icebreaking conditions. The performance of power systems under short-period, regularly fluctuating load-sea conditions has been intensively studied. However, the performance of the power system in the face of a long-period, stochastic multi-frequency fluctuation icebreaking process has not been fully explored, especially the parameter uncertainty and battery cycle life. In this study, an integrated electric propulsion system with an optimal control strategy is suggested for improving the power system’s dynamic performance and battery cycle life. First, an energy flow model with a diesel–electric unit as the main body and coupled energy storage system/hybrid energy storage system has been constructed. A comparative analysis of rule-based and optimization-based energy management strategies has been performed, and an optimized strategy with dynamic programming as global regulation at the upper level and model predictive control at the lower level is suggested to integrate the slow and fast dynamic powers and achieve adaptability to strong fluctuation loads. In this control strategy, the uncertainties of energy storage system/hybrid energy storage system parameters have been introduced to eliminate their impact on the system performance. Then, the icebreaking process with multi-frequency fluctuation has been simulated, and the hybrid energy storage system with battery and supercapacitor is recommended to reach multi-objective with the lowest power fluctuation of diesel–electric unit, highest efficiency, and the minimum battery degradation. Finally, the fuel oil consumption and emissions of the hybrid energy storage system have been discussed, and the optimized strategy can save fuel oil by up to 5.33% and reduce the CO2 emission by 22% during the icebreaking process, exhibiting great potential in the environmental friendliness and significant advantages in terms of low fuel oil consumption.

Background: CPT is a pentacyclic monoterpene alkaloid with a wide spectrum of antitumor activity. Its clinical application is restricted due to poor water solubility, instability, and high toxicity. We developed a new kind of multifunctional micelles to improve its solubility, reduce the side effecs, and obtain enhanced antitumor effects. Methods: We constructed HA-CPT nano-self-assembly prodrug micelles, which combined the advantages of pH-sensitivity, redox-sensitivity, and active targeting ability to CD44 receptor-overexpressing cancer cells. To synthesize dual sensitive HA-CPT conjugates, CPT was conjugated with HA by pH-sensitive histidine (His) and redox-sensitive 3,3′-dithiodipropionic acid (DTPA). In vitro, we studied the cellular uptake and antitumor effect for tumor cell lines. In vivo, we explored the bio-distribution and antitumor effects of the micelles in HCT 116 tumor bearing nude mice. Results: The dual-sensitive and active targeting HA-His-ss-CPT micelles was proved to be highly efficient in CPT delivery by the in vitro cellular uptake study. The HA-His-ss-CPT micelles escaped from endosomes of tumor cells within 4 h after cellular uptake due to the proton sponge effect of the conjugating His and then quickly released CPT in the cytosol by glutathione (GSH). In mice, HA-His-ss-CPT micelles displayed efficient tumor accumulation and conspicuous inhibition of tumor growth. Conclusions: The novel, dual-sensitive, active targeting nano-prodrug micelles exhibited high efficiency in drug delivery and cancer therapy. This “all in one” drug delivery system can be realized in an ingenious structure and avoid intricate synthesis. This construction strategy can illume the design of nanocarriers responding to endogenous stimuli in tumors.

Spectrum-narrowed and -locked broad-area diode lasers operating at 780 nm are essential for rubidium laser development. With the help of Littrow-type transmission gratings, we demonstrated a simple scheme with a narrow linewidth and the diode laser’s center wavelength locked without thermal drift, in contrast to volume Bragg gratings. By carefully collimating the diode laser beam, we realized a linewidth narrower than 0.17 nm and a side-mode suppression ratio over 20 dB. Furthermore, broad-spectrum tuning at 9 nm was demonstrated by grating angle tuning. This method could easily be adapted to other wavelength diode lasers.

Recent advances in 3D scanning have enabled the digital recording of complex objects as large-scale point clouds, which require clear visualization to convey their 3D shapes effectively. Edge-highlighting visualization is used to improve the comprehensibility of complex 3D structures by enhancing the 3D edges and high-curvature regions of the scanned objects. However, traditional methods often struggle with real-world objects due to inadequate representation of soft edges (i.e., rounded edges) and excessive line clutter, impairing resolution and depth perception. To address these challenges, we propose a novel visualization method for 3D scanned point clouds based on dual 3D edge extraction and opacity–color gradation. Dual 3D edge extraction separately identifies sharp and soft edges, integrating both into the visualization. Opacity–color gradation enhances the clarity of fine structures within soft edges through variations in color and opacity, while also creating a halo effect that improves both resolution and depth perception of the visualized edges. Computation times required for dual 3D edge extraction are comparable to conventional binary statistical edge-extraction methods. Visualizations with opacity–color gradation are executable at interactive rendering speeds. The effectiveness of the proposed method is demonstrated using 3D scanned point cloud data from high-value cultural heritage objects.

The recently introduced Euro 7 emissions standard regulation foresees the addition of abrasion limits for tyres sold in the European Union. The measurement procedures for tyre abrasion are described in the newly introduced Annex 10 of the United Nations (UN) Regulation 117. However, the limits are not yet defined as there is no data available regarding the new procedure. For this reason, a market assessment campaign is ongoing under the auspices of the UN Task Force on Tyre Abrasion (TFTA). Recent reviews on the topic also concluded that there is a lack of studies measuring the abrasion rates of tyres. In this study, we measured the abrasion rate of one tyre model at two different locations (Spain and China) with the aim of deep diving into possible influencing factors. Additionally, wear rates were studied separately for urban, rural, and motorway routes to get more insight into the impact of the route characteristics. The abrasion rates varied from 22 mg/km to 123 mg/km per vehicle, depending on the route (urban, rural, motorway) and ambient temperature. The overall average trip abrasion rates were 75 mg/km and 45 mg/km per vehicle at the two locations, respectively. However, when corrected for the different ambient temperatures, the rates were 63 mg/km and 60 mg/km per vehicle, respectively. The impacts of other parameters, such as driving dynamics and road surface, on the final results are also discussed. The average tread depth reduction was estimated to be 0.8–1.4 mm every 10,000 km.

Wax apple (Syzygium samarangense) is a characteristic tropical fruit with great potential value, but the cold sensitivity and male sterility limit its cultivation and breeding. Despite the important role in JA-mediated cold-stress resistance and flower development, jasmonate ZIM-domain (JAZ) proteins have not been identified in wax apple. This study employed sequence blast, phylogenetic analysis, and RT-PCR to identify SsJAZs in wax apple systematically. First, 14 SsJAZs family members with TIFY and Jas domains were identified and named according to the distribution on ten chromosomes. Low-temperature responsiveness elements and TGACG-motif (response to MeJA) were abundant in the promoters of most SsJAZs. The transcription factors ERF and MYB were predicted to be involved in the regulation of SsJAZs. RT-PCR analyses showed that SsJAZs were mainly expressed in mature leaves and flowers. Further analysis revealed differences in the expression patterns of SsJAZs under cold treatment, as well as in different anther stages. SsJAZ proteins were predicted to interact with the N terminus of SsMYC2 protein via the Jas motif at the C-terminal domain. This study characterized the SsJAZs family members and examined the expression patterns in different samples. The results will advance our understanding of the role of SsJAZs in wax apple.

Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (Cc), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e0), and specific gravity (Gs), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the Cc values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, Gs, and e0 as the inputs, with Cc as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R2 of 0.925 and a testing R2 of 0.930 with the input combination [w, PL, LL, PI, e0, Gs]. The RFR model follows closely, with a training R2 of 0.970 and a testing R2 of 0.926 using the same input combination. The ABR model records a training R2 of 0.847 and a testing R2 of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the Cc value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing.

Our review paper evaluates the impact of plant-based products, primarily derived from plants from Serbia, on P-glycoprotein (P-gp) activity and their potential in modulating drug resistance in cancer therapy. We focus on the role and regulation of P-gp in cellular physiology and its significance in addressing multidrug resistance in cancer therapy. Additionally, we discuss the modulation of P-gp activity by 55 natural product drugs, including derivatives for some of them, based on our team’s research findings since 2011. Specifically, we prospect into sesquiterpenoids from the genera Artemisia, Curcuma, Ferula, Inula, Petasites, and Celastrus; diterpenoids from the genera Salvia and Euphorbia; chalcones from the genera Piper, Glycyrrhiza, Cullen, Artemisia, and Humulus; riccardins from the genera Lunularia, Monoclea, Dumortiera, Plagiochila, and Primula; and diarylheptanoids from the genera Alnus and Curcuma. Through comprehensive analysis, we aim to highlight the potential of natural products mainly identified in plants from Serbia in influencing P-gp activity and overcoming drug resistance in cancer therapy, while also providing insights into future perspectives in this field.

The restoration of fine motor function in the hand is crucial for stroke survivors with hemiplegia to reintegrate into daily life and presents a significant challenge in post-stroke rehabilitation. Current mirror rehabilitation systems based on wearable devices require medical professionals or caregivers to assist patients in donning sensor gloves on the healthy side, thus hindering autonomous training, increasing labor costs, and imposing psychological burdens on patients. This study developed a low-load wearable hand function mirror rehabilitation robotic system based on visual gesture recognition. The system incorporates an active visual apparatus capable of adjusting its position and viewpoint autonomously, enabling the subtle monitoring of the healthy side’s gesture throughout the rehabilitation process. Consequently, patients only need to wear the device on their impaired hand to complete the mirror training, facilitating independent rehabilitation exercises. An algorithm based on hand key point gesture recognition was developed, which is capable of automatically identifying eight distinct gestures. Additionally, the system supports remote audio–video interaction during training sessions, addressing the lack of professional guidance in independent rehabilitation. A prototype of the system was constructed, a dataset for hand gesture recognition was collected, and the system’s performance as well as functionality were rigorously tested. The results indicate that the gesture recognition accuracy exceeds 90% under ten-fold cross-validation. The system enables operators to independently complete hand rehabilitation training, while the active visual system accommodates a patient’s rehabilitation needs across different postures. This study explores methods for autonomous hand function rehabilitation training, thereby offering valuable insights for future research on hand function recovery.

Aiming at the problems of the incomplete recommendation and sparsity of session data in session recommendation, a new multi-granularity and multi-interest contrast-enhanced hypergraph convolutional network (MGMI-CEHCN) model for session recommendation is proposed. The session data are modeled as a heterogeneous hypergraph, the information is embedded by the hypergraph using two granularities of item price and category, and then the information between different granularities is fused, while the final item embedding is obtained through multi-layer convolution. Finally, an interest perceptron is used to detect multiple potential interests for each item, and a decentralized interest extraction network based on a gated recurrent unit (GRU) is used to integrate the user’s final interests and obtain a global session representation through a soft attention mechanism; a local session representation is generated with the help of a weighted line graph convolutional network. A further joint contrast enhancement strategy is used to maximize the mutual information between the global session representation and local session representation, to improve the recommendation performance. Experiments on several real datasets showed that the recommendation performance of the MGMI-CEHCN model outperformed the current mainstream models. On Cosmetics, the P@20 reached up to 55.25% and M@20 reached up to 38.26%, improvements of 3.06% and 3.09%, respectively; on Diginetica-buy, the P@20 reached up to 65.60% and M@20 reached up to 27.47%, improvements of 2.47% and 6.64%, respectively, which proved the validity of the model.

Anthracyclines represent a highly efficacious class of chemotherapeutic agents employed extensively in antitumor therapy. They are universally recognized for their potency in treating diverse malignancies, encompassing breast cancer, gastrointestinal tumors, and lymphomas. Nevertheless, the accumulation of anthracyclines within the body can lead to significant cardiac toxicity, adversely impacting both the survival rates and quality of life for tumor patients. This limitation somewhat restricts their clinical utilization. Determining how to monitor and mitigate their cardiotoxicity at an early stage has become an urgent clinical problem to be solved. Therefore, this paper reviews the mechanism of action, early monitoring, and strategies for the prevention of anthracycline-induced cardiotoxicity for clinical reference.

The vehicle load on a bridge is a critical and dynamic variable. It adversely affects bridges, especially when overloading occurs. Bridges are prone to fatigue damage or collapse. Therefore, identifying the size and type of dynamic vehicle loads on bridges is critical for theoretical studies and practical applications, such as health monitoring, daily maintenance, safety assessment, and traffic planning. The paper proposes a method for identifying the dynamic load parameters based on a convolutional neural network (CNN) and dynamic strain data. The model is implemented in MATLAB. An initial finite-element model of a three-span precast beam bridge is established in the software ABAQUS and modified by combining the modal and experimental data derived from a segmental girder bridge. The dynamic strain response of the bridge under a moving vehicle load is simulated under different working conditions. The results are used as the training data of the CNN to identify the vehicle’s position, speed, and load on the bridge. The high prediction accuracy indicates the proposed model’s suitability for identifying the dynamic load parameters.

In this paper, the generation of inorganic carbon (mainly HCO3−) in a karst system (soil-limestone system) under karst soil and non-karst soil conditions was investigated using two Zn2+ concentrations and water flow rates. The results showed that (1) the dynamic equilibrium state of the chemical weathering of limestone is altered by Zn2+, which is the primary cause of the change in HCO3− in soil-limestone systems; (2) ion exchange and adsorption are the primary characteristics of Zn2+ depletion under 1 mg/L ZnCl2 settings, whereas Zn2+ under 50 mg/L ZnCl2 conditions created two new solid phases (Zn5(OH)6(CO3)2, ZnCO3) in the soil-limestone system; (3) the dissolution rate of limestone increases with the water flow rate, which facilitates the dissolution process; (4) the notable difference in ion release between non-karst and karst soil conditions could potentially be attributed to variations in the mineral composition, specific surface area, and particle size of the two soil types; (5) the combination of SEM, XPS, FT-IR, and XRD microstructure observation methods reveals that when limestone is exposed to a high flow rate (1.23 mL/min) and a high concentration (50 mg/L) of ZnCl2, it experiences obvious dissolution and surface precipitation phenomena, as well as a significant change in HCO3− content.

Eu/Tb metal–organic frameworks (Eu/Tb-MOFs), exhibiting Eu3+ and Tb3+ emissions, stand out as some of the most fascinating luminescent thermometers. As the relative thermal sensitivity model is limited to its lack of precision for fitting ratio of Eu3+ and Tb3+ emissions, accurately predicting the sensing performance of Eu/Tb-MOFs remains a significant challenge. Herein, we report a series of luminescent Eu/Tb-MOF thermometers, EuxTb1−xL, with excellent thermal sensitivity around physiological levels, achieved through the tuning energy transfer from ligands to Eu3+ and Tb3+ and between the Ln ions. It was found that the singlet lowest-energy excited state (S1) of the ligand and the higher triplet energy level (Tn) are crucial in the energy transfer processes of ligand→Tb3+ and ligand→Eu3+. This enables EuxTb1−xL to serve as an effective platform for exploring the impact of these energy transfer processes on the temperature-sensing properties of luminescent Eu/Tb-MOF thermometers. The relative thermal sensitivity is comparable to that of dual-center MOF-based luminescent thermometers operating at physiological levels. This study provides valuable insights into the design of new Eu/Tb thermometers and the accurate prediction of their sensing performance.

The Gannan yak, a superior livestock breed found on the Tibetan Plateau, exhibits significantly enhanced body size, weight, and growth performance in comparison to the Tianzhu white yak. MiRNAs play a pivotal role in regulating muscle growth by negatively modulating target genes. In this study, we found the average diameter, area, and length of myofibers in Gannan yaks were significantly higher than those of Tianzhu white yaks. Further, we focused on analyzing the longissimus dorsi muscle from both Gannan yaks and Tianzhu white yaks through transcriptome sequencing to identify differentially expressed (DE)miRNAs that influence skeletal muscle development. A total of 254 DE miRNAs were identified, of which 126 miRNAs were up-regulated and 128 miRNAs were down-regulated. GO and KEGG enrichment analysis showed that the target genes of these DE miRNAs were significantly enriched in signaling pathways associated with muscle growth and development. By constructing a DE miRNA- DE mRNA interaction network, we screened 18 key miRNAs, and notably, four of the candidates (novel-m0143-3p, novel-m0024-3p, novel-m0128-5p, and novel-m0026-3p) targeted six genes associated with muscle growth and development (DDIT4, ADAMTS1, CRY2, AKIRIN2, SIX1, and FOXO1). These findings may provide theoretical references for further studies on the role of miRNAs in muscle growth and development in Gannan yaks.

Xuanwei ham is a traditional fermented meat product in China with a unique production process and excellent-quality reputation at home and abroad. To reveal the microbial community succession of Xuanwei ham at different post-ripening times (W1-4) and its relationship with flavour formation, the microbial community, free amino acids, and volatile flavour compounds (VOCs) were analysed by high-throughput sequencing, liquid chromatography (LC), and gas chromatography–mass spectrometry (GC-MS), respectively. A total of 25 free amino acids were detected, among which W3 contained the fewest, and most were generally lower than in hams in the other three years. Fifty-nine VOCs were detected, among which 17 were esters, and the highest ester content was found in W4. Analysis of the bacterial community composition revealed that the bacterial community composition of ham samples from W3 and other years differed greatly, and at the gate level, the dominant bacterial group of Xuanwei ham from different years was Pseudomonadota. At the genus level, the most abundant genera in W1, W2, and W4 were all dominated by Sarocladium, Klebsiella, and Vibrio, with Klebsiella being the most abundant in W1. The most abundant genus in W3 was Vibrio, and the second most dominant genera were Sarocladium and Gammaretrovirus. In short, this study provides a theoretical basis for the storage, quality, and improvement of Xuanwei ham.

The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing regions, are vital for restoring the integrity of cultural heritage structures, enhancing restoration accuracy and efficiency. In this paper, for challenges in processing large-scale cultural heritage point clouds, particularly the slow processing speed and visualization impairments from uneven point density during completion, we propose a point cloud completion employing centroid-based voxel feature extraction, which significantly accelerates feature extraction for massive point clouds. Coupled with an efficient upsampling module, it achieves a uniform point distribution. Experimental results show that the proposed method matches SOTA performance in completion accuracy while surpassing in point density uniformity, demonstrating capability in handling larger-scale point cloud data, and accelerating the processing of voluminous point clouds. In general, the proposed method markedly enhances the efficiency and quality of large-scale point cloud completion, holding significant value for the digital preservation and restoration of cultural heritage.

To determine the influence of pig type (Landrace, Wujin, or Tibetan fragrant) on the quality of Xuanwei ham, we measured the ham pH, color, fat content, and moisture content; used an E-nose (a device intended to detect odors or flavors); and analyzed flavoring substances using headspace solid-phase microextraction–gas chromatography, free amino acids using high-performance liquid chromatography, and microbial diversity using high-throughput sequencing. The ham types differed from each other in these attributes. The moisture and fat contents of Landrace pig ham were significantly lower than those of the other pig types, the brightness values of the Tibetan fragrant pig ham were significantly lower than those of the other pig types, and the redness values of the Landrace and Wujin pig hams were significantly higher than those of the Tibetan fragrant pig ham. The essential amino acid contents, e-wind odor response values, and volatile flavor substances of Wujin pig hams were significantly higher than those of the Tibetan fragrant pig ham, and the relative aldehyde contents of Wujin pig ham were significantly higher than those of the other pig types. The dominant microbial phyla in each ham type were assessed based on the species commonness, composition, and diversity and included taxa such as Actinobacteria and Ascomycetes and thick-walled bacteria such as Orphanomyces, Grass Spirochaetes, and Pseudoalteromonas. The microbial diversity and richness were the greatest in the Wujin pig ham. Of the three pigs, we conclude that the Wujin pig produces the best Xuanwei ham.

Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder–decoder paradigm (UIDF-Net). This network leverages the Image Fusion Module (MDL-IFM) to fuse the features of dehazed images, producing clearer results. Additionally, to better extract haze information, we introduce a haze encoder (Mist-Encode) that effectively processes different frequency features of images, improving the model’s performance in image dehazing tasks. Experimental results demonstrate that the proposed model achieves superior dehazing performance compared to existing algorithms on outdoor datasets.

Clostridium perfringens (C. perfringens), a Gram-positive bacterium, produces a variety of toxins and extracellular enzymes that can lead to disease in both humans and animals. Common symptoms include abdominal swelling, diarrhea, and intestinal inflammation. Severe cases can result in complications like intestinal hemorrhage, edema, and even death. The primary toxins contributing to morbidity in C. perfringens-infected intestines are CPA, CPB, CPB2, CPE, and PFO. Amongst these, CPB, CPB2, and CPE are implicated in apoptosis development, while CPA is associated with cell death, increased intracellular ROS levels, and the release of the inflammatory factor IL-18. However, the exact mechanism by which PFO toxins exert their effects in the infected gut is still unidentified. This study demonstrates that a C. perfringens PFO toxin infection disrupts the intestinal epithelial barrier function through in vitro and in vivo models. This study emphasizes the notable influence of PFO toxins on intestinal barrier integrity in the context of C. perfringens infections. It reveals that PFO toxins increase ROS production by causing mitochondrial damage, triggering pyroptosis in IPEC-J2 cells, and consequently resulting in compromised intestinal barrier function. These results offer a scientific foundation for developing preventive and therapeutic approaches against C. perfringens infections.

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios.

When spacecraft execute missions in space, their solar panels—crucial components—often need to be folded, unfolded, and adjusted at an angle. These operations can induce numerous detrimental nonlinear vibrations. This paper addresses the issues of nonlinear and thermal-coupled vibration control within the context of space-based flexible solar panel systems. Utilizing piezoelectric smart hybrid vibration control technology, this study focuses on a flexible plate augmented with an active constrained layer damping. The solar panel, under thermal field conditions, is modeled and simulated using the commercial finite element simulation software ABAQUS. The research examines variations in the modal frequencies and damping properties of the model in response to changes in the coverage location of the piezoelectric patches, their coverage rate, rotational angular velocity, and the thickness of the damping layer. Simulation results indicate that structural damping is more effective when the patches are closer to the rotation axis, the coverage area of the patches is larger, the rotational speed is lower, and the damping layer is thicker. Additionally, the effectiveness of vibration suppression is influenced by the interplay between the material shear modulus, loss factor, and specific working temperature ranges. The selection of appropriate parameters can significantly alter the system’s vibrational characteristics. This work provides necessary technical references for the analysis of thermally induced vibrations in flexible solar sails under complex space conditions.

To reveal the deformation patterns of the surrounding rock in deeply buried straight-wall arch-shaped roadways under seepage action, this study, based on an FLAC3D numerical simulation and classic elastoplastic theory, investigates the influences of surrounding rock classification, roadway burial depth, pore water pressure, and roadway cross-sectional dimensions on the deformation of surrounding rock. A multivariate regression prediction model for rock deformation was established based on the numerical simulation conclusions, and the correctness of the conclusions was verified through comparative analysis. Correlation analysis of various factors with rock deformation was conducted, ranking their impact as follows: pore water pressure > roadway burial depth > surrounding rock classification > roadway height > roadway width. The research results can provide guidance for the construction and support of deeply buried roadways under seepage action.

Post-activation performance enhancement (PAPE) can significantly improve athletic performance. This study investigated the effects of two different velocity loss (10% VL and 20% VL) protocols on PAPE in 20 m sprint performance among sprint athletes. Twenty-four male sprint athletes (100 m sprint time: 10.96 ± 0.15 s) participated in the study. A randomized crossover experimental design was used to compare the traditional group (TG) and 10% VL and 20% VL interventions. Sprint tests were conducted at 4, 8, 12, and 16 min post-intervention. A two-way repeated measures ANOVA revealed a significant interaction effect between group and time on 20 m sprint performance (F = 2.817, p = 0.037, partial η2 = 0.585). Simple main effects analysis revealed significant improvements at 4 min for the 20% VL group (p < 0.05). Cohen’s d values indicated improvements in 10 m sprint times at 8 min for all groups (TG: effect size (ES) = −0.270, 10% VL: ES = −0.038, 20% VL: ES = −0.279). Improvements in 20 m sprint times were observed at 4 min for the 20% VL group (ES = −0.296) and at 16 min for the 10% VL group (ES = −0.276). In conclusion, the velocity loss-based PAPE protocol (20% VL) demonstrated a superior induction of PAPE effects in sprint athletes at 4 min compared to traditional 1RM-based PAPE protocols. However, no significant differences were observed between the two protocols at 8, 12, and 16 min.

The goal of vehicular automation is to enhance driver comfort by reducing the necessity for active engagement in driving. This allows for the performance of non-driving-related tasks (NDRTs), with attention shifted away from the driving process. Despite this, there exists a discrepancy between current in-vehicle display configurations and the escalating demands of NDRTs. This study investigates drivers’ preferences for in-vehicle display configurations within highly automated driving contexts. Utilizing virtual reality head-mounted displays (VR-HMDs) to simulate autonomous driving scenarios, this research employs Unity 3D Shape for developing sophisticated head movement tracking software. This setup facilitates the creation of virtual driving environments and the gathering of data on visual attention distribution. Employing an orthogonal experiment, this experiment methodically analyses and categorizes the primary components of in-vehicle display configurations to determine their correlation with visual immersion metrics. Additionally, this study incorporates subjective questionnaires to ascertain the most immersive display configurations and to identify key factors impacting user experience. Statistical analysis reveals that a combination of Portrait displays with Windshield Head-Up Displays (W-HUDs) is favored under highly automated driving conditions, providing increased immersion during NDRTs. This finding underscores the importance of tailoring in-vehicle display configurations to individual needs to avoid distractions and enhance user engagement.

Studying the hydro-liquefaction kinetics of lignite contributes to optimizing the mild liquefaction process for lignite. In this paper, the direct liquefaction performance of Shengli lignite (SL) was investigated using a H2/THN system with 4 MPa of initial pressure, and reaction kinetic models were established for the heating-up stage and the isothermal stage. The result showed that the liquefaction performance of the SL was excellent, with a conversion of 62.18% and an oil and gas (O + G) yield of 29.88% at 698.15 K. After one hour of reaction, the conversion and O + G yield were 94.61% and 76.78%, respectively. During the heating-up stage, the easily reactive part of the SL was 50.07%, and it was converted directly into oil, gas, asphaltene (AS), and preasphaltene (PA) simultaneously. There was no significant secondary hydrogenation conversion of the AS and PA products. During the isothermal stage, the hard-to-react part was predominantly converted into AS and PA, while the remaining easily reactive part continue to react completely. The conversion of AS and PA into oil and gas was a rate-controlling step during this stage. The amount of unreacted coal estimated using the model calculated in the isothermal stage was 2.98%, which was significantly consistent with the experimental value of 2.81%.

The flexible mode of operation of coal-fired units can accommodate large-scale renewable power integration into the grid, providing more grid capacity. The flexibility transformation of coal-fired units in thermal power plants can be achieved through main steam extraction and reheated steam extraction. A 300 MW subcritical unit, 600 MW subcritical unit and 660 MW ultra-supercritical unit with six flexible operation modes were chosen as the research model to investigate the thermal adaptability for flexible operation. The results show that from the perspective of the source of steam extraction, the main steam extraction scheme is suitable for the flexible adjustment of peak load capacity, and the reheated extraction scheme is suitable for the flexible operation of low load and high thermal efficiency. Moreover, from the perspective of thermal performance adaptability, the 600 MW unit has a wider load regulation capacity than the 300 MW and 660 MW units, and is suitable as the peak shaving unit. This work can provide theoretical guidance for different types of coal-fired units in choosing flexible operation schemes.

RNA is an important information and functional molecule. It can respond to the regulation of life processes and is also a key molecule in gene expression and regulation. Therefore, RNA detection technology has been widely used in many fields, especially in disease diagnosis, medical research, genetic engineering and other fields. However, the current RT-qPCR for RNA detection is complex, costly and requires the support of professional technicians, resulting in it not having great potential for rapid application in the field. PCR-free techniques are the most attractive alternative. They are a low-cost, simple operation method and do not require the support of large instruments, providing a new concept for the development of new RNA detection methods. This article reviews current PCR-free methods, overviews reported RNA biosensors based on electrochemistry, SPR, microfluidics, nanomaterials and CRISPR, and discusses their challenges and future research prospects in RNA detection.

The association between newborn DNA methylation (DNAm) and asthma acquisition (AA) during adolescence has been suggested. Lung function (LF) has been shown to be associated with asthma risk and its severity. However, the role of LF in the associations between DNAm and AA is unclear, and it is also unknown whether the association between DNAm and AA is consistent with that between DNAm and LF. We address this question through assessing newborn epigenetic features of preadolescence LF and of AA during adolescence, along with their biological pathways and processes. Our study’s primary medical significance lies in advancing the understanding of asthma’s early life origins. By investigating epigenetic markers in newborns and their association with lung function in preadolescence, we aim to uncover potential early biomarkers of asthma risk. This could facilitate earlier detection and intervention strategies. Additionally, exploring biological pathways linking early lung function to later asthma development can offer insights into the disease’s pathogenesis, potentially leading to novel therapeutic targets. Methods: The study was based on the Isle of Wight Birth cohort (IOWBC). Female subjects with DNAm data at birth and with no asthma at age 10 years were included (n = 249). The R package ttScreening was applied to identify CpGs potentially associated with AA from 10 to 18 years and with LF at age 10 (FEV1, FVC, and FEV1/FVC), respectively. Agreement in identified CpGs between AA and LF was examined, along with their biological pathways and processes via the R function gometh. We tested the findings in an independent cohort, the Avon Longitudinal Study of Parents and Children (ALSPAC), to examine overall replicability. Results: In IOWBC, 292 CpGs were detected with DNAm associated with AA and 1517 unique CpGs for LF (514 for FEV1, 436 for FVC, 408 for FEV1/FVC), with one overlapping CpG, cg23642632 (NCKAP1) between AA and LF. Among the IOWBC-identified CpGs, we further tested in ALSPAC and observed the highest agreement between the two cohorts in FVC with respect to the direction of association and statistical significance. Epigenetic enrichment analyses indicated non-specific connections in the biological pathways and processes between AA and LF. Conclusions: The present study suggests that FEV1, FVC, and FEV1/FVC (as objective measures of LF) and AA (incidence of asthma) are likely to have their own specific epigenetic features and biological pathways at birth. More replications are desirable to fully understand the complexity between DNAm, lung function, and asthma acquisition.

The current article focuses on the examination of nonlinear instability and dynamic transitions in a double-diffusive rotating couple-stress fluid layer. The analysis was based on the newly developed dynamic transition theory by T. Ma and S. Wang. Through a comprehensive linear spectrum analysis and investigation of the principle of exchange of stability (PES) as the thermal Rayleigh number crosses a threshold, the nonlinear orbital changes during the transition were rigorously elucidated utilizing reduction methods. For both single real and complex eigenvalue crossings, local pitch-fork and Hopf bifurcations were discovered, and directions of these bifurcations were identified along with transition types. Furthermore, nondimensional transition numbers that signify crucial factors during the transition were calculated and the orbital structures were illustrated. Numerical studies were performed to validate the theoretical results, revealing the relations between key parameters in the system and the types of transition. The findings indicated that the presence of couple stress and a slow diffusion rate of solvent and temperature led to smoother nonlinear transitions during convection.

The trans-media aircraft water entry process generates strong slamming loads that will seriously affect the stability and safety of the aircraft. To address this problem, we design a fixed-wing aircraft configuration and employ numerical simulations with the volume of fluid (VOF) multiphase flow model, standard k-epsilon turbulence model, and dynamic mesh technique. We explore the characteristics of aircraft subjected to bang loads under different conditions. The results show the following: the pressure load on the aircraft surface increases with higher water entry velocity; larger entry angles lead to more drastic changes in the aircraft’s drag coefficient, demonstrating strong nonlinear characteristics; the greater the angle of attack into the water, the greater the pressure load on the root underneath the wing, with little effect on the pressure load on the head; and the water entry drag coefficient and average pressure load follow an increasing order of conical head, hemispherical head, and flat head. These findings provide theoretical references for studying the load characteristics during trans-media water entry of various flying bodies and optimizing fuselage structural strength.

Accurately assessing ice loads is a fundamental issue in the field of structural design for ships in ice-covered regions. In this paper, we conducted research on extreme ice load estimation for icebreaking ships, combining stochastic theory with numerical simulation. Firstly, using sea ice data from the Arctic region of the United States National Snow and Ice Data Center, a stochastic ice field model was established under Arctic sea ice conditions using non-parametric estimation and the rejection sampling method, and ice field data were generated stochastically. Then, based on the stochastic ice field data, a three-dimensional numerical model of the interaction between the ice field and the ship hull was established, and the reliability of the numerical model was verified by experimental results. Finally, based on the numerical model of the interaction between the ice field and the ship hull, asymptotic methods were used to study the extreme ice load estimation in different parts of the ship hull, revealing the variation law of the extreme ice load in different parts of the ship hull. This study provides basic theory and technical support for the structural design of ships in polar regions and has engineering application value.

Micro-vibrations significantly influence the imaging quality and pointing accuracy of high-precision space-borne payloads. To mitigate this issue, vibration isolation technology must be employed to reduce the transmission of micro-vibrations to payloads. In this paper, a novel active–passive hybrid isolation (APHI) system based on a strain sensor is proposed for high-precision space payloads, and corresponding theoretical and experimental studies are implemented. First, a theoretical analysis model of the APHI system is established using a two-degrees-of-freedom system, and an integral control method based on strain sensing is presented. Then, an electromagnetic damper, active piezoelectric actuator, and strain sensor are designed and manufactured. Finally, an APHI experimental system is implemented to validate the effectiveness of electromagnetic damping and strain-sensing active control. Additionally, the control effects of acceleration, displacement, and strain sensors are compared. The results demonstrate that strain sensors can achieve effective active damping control, and the control method based on strain sensors can effectively suppress the payload response while maintaining stability. Both displacement and strain sensors exhibit superior suppression effects compared with the acceleration sensor, with the strain sensor showing greater potential for practical engineering applications than the displacement sensor.

Swine enteric coronaviruses (SECoVs), including porcine deltacoronavirus (PDCoV), transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), and swine acute diarrhea syndrome coronavirus (SADS-CoV), have caused high mortality in piglets and, therefore, pose serious threats to the pork industry. Coronaviruses exhibit a trend of interspecies transmission, and understanding the host range of SECoVs is crucial for improving our ability to predict and control future epidemics. Here, the replication of PDCoV, TGEV, and PEDV in cells from different host species was compared by measuring viral genomic RNA transcription and protein synthesis. We demonstrated that PDCoV had a higher efficiency in infecting human lung adenocarcinoma cells (A549), Madin–Darby bovine kidney cells (MDBK), Madin–Darby canine kidney cells (MDCK), and chicken embryonic fibroblast cells (DF-1) than PEDV and TGEV. Moreover, trypsin can enhance the infectivity of PDCoV to MDCK cells that are nonsusceptible to TGEV. Additionally, structural analyses of the receptor ectodomain indicate that PDCoV S1 engages Aminopeptidase N (APN) via domain II, which is highly conserved among animal species of different vertebrates. Our findings provide a basis for understanding the interspecies transmission potential of these three porcine coronaviruses.

Respirometric microbial assays are gaining popularity, but their uptake is limited by the availability of optimal O2 sensing materials and the challenge of validating assays with complex real samples. We conducted a comparative evaluation of four different O2-sensing probes based on Pt-porphyrin phosphors in respirometric bacterial assays performed on standard time-resolved fluorescence reader. The macromolecular MitoXpress, nanoparticle NanO2 and small molecule PtGlc4 and PtPEG4 probes were assessed with E. coli cells in five growth media: nutrient broth (NB), McConkey (MC), Rapid Coliform ChromoSelect (RCC), M-Lauryl lauryl sulfate (MLS), and Minerals-Modified Glutamate (MMG) media. Respiration profiles of the cells were recorded and analyzed, along with densitometry profiles and quenching studies of individual media components. This revealed several limiting factors and interferences impacting assay performance, which include probe quenched lifetime, instrument temporal resolution, inner filter effects (mainly by indicator dyes), probe binding to lipophilic components, and dynamic and static quenching by media components. The study allowed for the ranking of the probes based on their ruggedness, resilience to interferences and overall performance in respirometric bacterial assays. The ‘shielded’ probe NanO2 outperformed the established MitoXpress probe and the small molecule probes PtGlc4 and PtPEG4.

Dairy products are susceptible to modifications in protein oxidation during heat processing, which can lead to changes in protein function, subsequently affecting intestinal health. Despite being a unique nutritional source, yak milk has not been thoroughly examined for the effects of its oxidized proteins on intestinal microbiota and metabolism. Hence, this study employed different heat treatment methods (low-temperature pasteurization, high-temperature pasteurization, and high-temperature sterilization) to induce oxidation in yak milk proteins. The study then assessed the degree of oxidation in these proteins and utilized mice as research subjects. Using metagenomics and metabolomics methods, this study examined the structure of intestinal microbial communities and metabolic products in mice consuming oxidized yak milk. The results showed a decrease in carbonyl and total thiol contents of yak milk proteins after different heat treatments, indicating that heat treatment causes oxidation in yak milk proteins. Metagenomic analysis of mouse intestinal microbiota revealed significant changes in 66 genera. In the high-temperature sterilization group (H), key differential genera included Verrucomicrobiales, Verrucomicrobiae, Akkermansiaceae, and 28 others. The high-temperature pasteurization group (M) mainly consisted of Latilactobacillus, Bacillus, and Romboutsia. The low-temperature pasteurization group (L) primarily comprised of Faecalibacterium, Chaetomium, Paenibacillaceae, Eggerthella, Sordariales, and 33 others. Functionally, compared to the control group (C), the H group upregulated translation and energy metabolism functions, the L group the M group significantly upregulated metabolism of other amino acids, translation, and cell replication and repair functions. Based on metabolomic analysis, differential changes in mouse metabolites could affect multiple metabolic pathways in the body. The most significantly affected metabolic pathways were phenylalanine metabolism, vitamin B6 metabolism, steroid hormone biosynthesis, and pantothenate and CoA biosynthesis. The changes were similar to the functional pathway analysis of mouse metagenomics, affecting amino acid and energy metabolism in mice. In summary, moderate oxidation of yak milk proteins exhibits a positive effect on mouse intestinal microbiota and metabolism. In conclusion, yak milk has a positive effect on mouse intestinal microflora and metabolism, and this study provides a scientific basis for optimizing dairy processing technology and further developing and applying yak milk.

Accurate integrity risk evaluation is of significance in ensuring that aviation navigation applications satisfy the predefined integrity requirement. The integrity risk evaluation method over a specified exposure interval has been proposed in previous works for the development of advanced receiver autonomous integrity monitoring (ARAIM) (ARAIM technical subgroup reference airborne algorithm description document v4.1, 2022). However, this method typically relies on an underlying optimistic assumption that the satellite geometry remains constant throughout the exposure interval. The variation in satellite geometry due to potential satellite outages undermines the widely-used geometry constant assumption. Thus, we investigate the influence of satellite geometry variations throughout the exposure interval on the integrity performance by introducing a geometry-sensitive risk-evaluation model. The findings demonstrate that, under the nominal situation, the region where the ARAIM fails to meet predefined integrity requirement could expand by a maximum of 2.93% when accounting for satellite geometry variations. Furthermore, in the situation of a single satellite outage, this hazardous region has significantly expanded from 13.12% of the global coverage to 66.82%. Based on these findings, we recommend that the ARAIM should consider satellite outages as a critical factor in real-time integrity risk evaluation.

In this paper, we investigate the orbit-adjustment problem of satellite systems in the presence of nonlinear uncertainties in kinematics and dynamics. We propose a novel direct adaptive fuzzy control scheme with prescribed tracking accuracy to address uncertain nonlinear dynamics by employing advanced fuzzy logic systems and integrating a class of sophisticated smooth functions, thereby ensuring convergence of the tracking error within a precisely defined interval. The ingeniously designed control scheme guarantees negative semi-definiteness of the Lyapunov function, ensuring boundedness for all variables. Moreover, our groundbreaking control approach requires only one adaptive law, completely eliminating any direct correlation with the number of nonlinear functions. Simulation results unequivocally validate the remarkable effectiveness and superiority of our innovative control approach.

This article proposes a hierarchical control strategy to address semi-ABS control as well as the precise clamping force control problems for an integrated electric parking brake (iEPB) system. To this end, a detailed system model, including modeling of the motor, transmission mechanism, friction and braking torque, is constructed for controller and observer design, and a sliding-mode-based observer (SMO) is proposed to estimate the load torque by using the motor rotational speed without installing a force sensor. In addition, a stable and reliable tire–road friction coefficient (TRFC) estimation method is adopted, and the desired slip ratio (DSR) is observed as the target that the rear wheels cycle around. At the upper level of the hierarchical control structure, the desired clamping forces of the rear wheels are generated using a sliding mode control (SMC) technique, and the control objective is to track the DSR to make full use of the road condition. At the lower level, the motor is controlled to track the desired clamping force generated from the upper controller. The hardware-in-the-loop (HIL) experimental results demonstrate the effectiveness and high tracking precision of the proposed strategy under different road conditions, and the estimation parameters based on the proposed observers are timely and accurate to satisfy the control requirements.

Metformin is the first-line oral medication for treating type 2 diabetes mellitus (T2DM). In the current study, an untargeted lipidomic analytical approach was used to investigate the alterations in the serum lipidome of a cohort of 89 participants, including healthy lean controls and obese diabetic patients, and to examine the alterations associated with metformin administration. A total of 115 lipid molecules were significantly dysregulated (64 up-regulated and 51 down-regulated) in the obese compared to lean controls. However, the levels of 224 lipid molecules were significantly dysregulated (125 up-regulated and 99 down-regulated) in obese diabetic patients compared to the obese group. Metformin administration in obese diabetic patients was associated with significant dysregulation of 54 lipid molecule levels (20 up-regulated and 34 down-regulated). Levels of six molecules belonging to five lipid subclasses were simultaneously dysregulated by the effects of obesity, T2DM, and metformin. These include two putatively annotated triacylglycerols (TGs), one plasmenyl phosphatidylcholine (PC), one phosphatidylglycerol (PGs), one sterol lipid (ST), and one Mannosyl-phosphoinositol ceramide (MIPC). This study provides new insights into our understanding of the lipidomics alterations associated with obesity, T2DM, and metformin and offers a new platform for potential biomarkers for the progression of diabetes and treatment response in obese patients.

In modern advanced genetics and breeding programs, the study of genes related to pigmentation in ducks is gaining much attention and popularity. Genes and DNA mutation cause variations in the plumage color traits of ducks. Therefore, discovering related genes responsible for different color traits and pigment patterns on each side of the single feathers in Chinese ducks is important for genetic studies. In this study, we collected feather images from 340 ducks and transported them into Image Pro Plus (IPP) 6.0 software to quantify the melanin content in the feathers. Thereafter, a genome-wide association study was conducted to reveal the genes responsible for variations in the feather color trait. The results from this study revealed that the pigmented region was larger in the male ducks as compared to the female ducks. In addition, the pigmented region was larger on the right side of the feather vane than on the left side in both dorsal and ventral feathers, and a positive correlation was observed among the feather color traits. Further, among the annotated genes, WNT3A, DOCK1, RAB1A, and ALDH1A3 were identified to play important roles in the variation in pigmented regions of the various feathers. This study also revealed that five candidate genes, including DPP8, HACD3, INTS14, SLC24A1, and DENND4A, were associated with the color pigment on the dorsal feathers of the ducks. Genes such as PRKG1, SETD6, RALYL, and ZNF704 reportedly play important roles in ventral feather color traits. This study revealed that genes such as WNT3A, DOCK1, RAB1A, and ALDH1A3 were associated with different pigmentation patterns, thereby providing new insights into the genetic mechanisms of single-feather pigmentation patterns in ducks.

The temperature and moisture content inside a grain pile are two important indicators for judging the safety of grain storage. To accurately predict the temperature and moisture content inside a grain pile, a numerical simulation was carried out of the drying process of a mesoscale wheat grain soil based on a thin-layer drying method, considering non-uniform porosity. The effectiveness of this method for wheat piles was verified through a comparison with the experimental data. The influence of different ventilation cage heights and ventilation temperatures on heat and moisture transfer in the wheat grain pile were also studied. The results show the following points. The numerical simulation method in this paper can effectively predict the temperature and moisture content of a wheat grain pile. The non-uniform porosity distribution model can better reproduce the state of ventilation during storage of wheat grain piles than the uniform porosity distribution model. The distribution patterns of flow lines in silos with different ventilation cage heights have certain similarities, but the high-speed airflow area will decrease as the height of the ventilation cage increases. Different ventilation temperatures will significantly affect the areas of high temperature and the rewetting inside a wheat grain pile.

This paper uses high-order approximate coupling (HOAC) dynamics equations for the hub–beam system with segmented active constrained layer damping treatment (SACLD). To improve the damping characteristics of traditional active constrained layer damping (ACLD), the viscoelastic damping layer, and the piezoelectric constraining layer are cut at the same position. The damping characteristics of the structure are enhanced by increasing the shear strain of the viscoelastic damping layer. The finite element method is used to discretize the SACLD beam. The discontinuity of the SACLD beam element-to-element displacement achieves the notch. Based on the theory of rigid–flexible coupling dynamics, the dynamic responses of the SACLD rotating beam under different cases are studied. The results show that the segmentation method is not always effective. A SACLD beam provides better vibration suppression than an ACLD beam only when appropriate material and dimensional parameters are used. The influences of base-layer thickness, piezoelectric constraining layer thickness, viscoelastic damping-layer thickness, angular velocity, the viscoelastic damping-layer loss factor, and control gains on the vibration of the rotating flexible beam with SACLD treatment are also discussed.

Quantum correlation is a key resource for a variety of quantum information processing and communication tasks, the efficient utilization of which has been a longstanding concern, and it is also one of the main challenges in the application of quantum technology. In this review, we focus on the interaction between quantum measurements and quantum correlations by designing appropriate measurement strategies, specifically exploring the trade-off between information gain and disturbance degree in weak measurements to ensure that quantum correlations from the same source can be shared among multiple independent observers. We introduce the basic knowledge and classification of quantum measurements, investigate the weak measurement scenario, and show the theoretical model construction of quantum correlation recycling in the original works. We summarize the theoretical and experimental development process and the latest progress in this field. Finally, we provide an outlook for more quantum resource applications that can profit from the optimization of quantum measurement strategies.

(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm−1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm−1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs.

In this work, we propose a deep-learning-based mapping model for simulating and predicting the flow field of Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) of propellers. The model employs image processing and computer vision methods to process the two-dimensional propeller RANS and LES simulation data. First, images are obtained by simulating the flow fields with the location data used to acquire a set of features specific to the corresponding positions. Second, the regression models for the flow fields and the mapping between the two different flow fields are established to predict the LES flow field at that position. Specifically, we utilize a deep convolutional neural network (CNN) for feature extraction from the flow field, which is then integrated with a nonlinear module for the purposes of regression and mapping. The effectiveness and accuracy of the proposed model in flow field prediction are demonstrated by its application to propeller RANS and LES simulations. It is shown that the overall error rate between the LES flow field predictions generated using this method and actual flow field data is 7.92%. Additionally, we also evaluate the model’s generalization ability, stability, and robustness by testing it on the data of propeller flow fields at different Reynolds numbers. The results verify the applicability of the proposed model in various problems of flow field simulation and prediction.