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Abstract Background Teat number is one of the most important indicators to evaluate the lactation performance of sows, and increasing the teat number has become an important method to improve the economic efficiency of farms. Therefore, it is particularly important to deeply analyze the genetic mechanism of teat number traits in pigs. In this study, we detected Single Nucleotide Ploymorphism (SNP), Insertion-Deletion (InDel) and Structural variant (SV) by high-coverage whole-genome resequencing data, and selected teat number at birth and functional teat number as two types of teat number traits for genome-wide association study (GWAS) to reveal candidate genes associated with pig teat number traits. Results In this study, we used whole genome resequencing data from 560 Yorkshire sows to detect SNPs, InDels and SVs, and performed GWAS for the traits of born teat number and functional teat number, and detected a total of 85 significant variants and screened 214 candidate genes, including HEG1, XYLT1, SULF1, MUC13, VRTN, RAP1A and NPVF. Among them, HEG1 and XYLT1 were the new candidate genes in this study. The co-screening and population validation of multiple traits suggested that HEG1 may have a critical effect on the born teat number. Conclusion Our study shows that more candidate genes associated with pig teat number traits can be identified by GWAS with different variant types. Through large population validation, we found that HEG1 may be a new key candidate gene affecting pig teat number traits. In conclusion, the results of this study provide new information for exploring the genetic mechanisms affecting pig teat number traits and genetic improvement of pigs.

The growing diversity and number of industrial robots make energy consumption prediction and optimization increasingly essential. Current data-driven approaches, particularly those based on multi-layer perception (MLP), have shown feasibility but typically overlook the variability or unknown nature of load-related parameters in real-world applications. This paper presents a KAN-LSTM model designed to accurately predict energy consumption under unknown load conditions, alongside a particle swarm optimization (PSO) algorithm for minimizing energy use. First, an industrial robot dynamics and energy consumption model is established. Then, the KAN-LSTM model is trained on datasets from the AUBO-E5 robot, with its predictions compared to alternative network models. Finally, PSO is applied to optimize energy consumption. Experimental results indicate that the KAN-LSTM model achieves high prediction accuracy (95.7–97.1%) and offers substantial energy optimization potential (53.1–64.7%). Optimized industrial robots are particularly suitable for tasks such as picking and palletizing in the courier industry, saving operational costs and increasing the sustainability of automated systems in logistics environments.

Given the continual rise in mission diversity and environmental complexity, the adept integration of a robot’s aerial and terrestrial locomotion modes to address diverse application scenarios has evolved into a formidable challenge. In this paper, we design a reconfigurable airframe robot endowed with the dual functionalities of rolling and flying. This innovative design not only ensures a lightweight structure but also incorporates morphing capabilities facilitated by a slider-crank mechanism. Subsequently, a land-to-air transformation strategy for the robot is introduced, achieved through the coordinated movement of the robotic arm and the servo motor. To ensure stable control of the robot amid external wind disturbances, we leverage the collaboration between a Generative Adversarial Network (GAN)and a Nonlinear Model Predictive Control (NMPC) controller. After the wind force magnitude is predicted through the neural network, the robot’s adeptness in flexible trajectory tracking is verified. Under simulated wind conditions of 12.1 m/s, the trajectory error consistently remains within the range of 10–15 cm, affirming the effectiveness of this control method.

Empirical evidence indicates that NaCl can improve the quality of mung bean starch sheet jelly (MBSS) when properly incorporated. In this study, by comparison with a sample without NaCl, the influences of NaCl (1.5–8%, w/w) on the physicochemical and structural properties of mung bean starch (MBS) and the quality of MBSS were investigated. MBS with added NaCl had greater gelatinization temperature and pasting parameters but lower gelatinization enthalpy than native MBS. With the addition of NaCl, the drying rate of MBSS first accelerated and then declined in the oven-drying process. The addition of NaCl improved the cooking properties of MBSS but decreased the hardness of cooked MBSS. Rheological results implied that the linear viscoelastic region of cooked MBSS decreased with the NaCl addition, and the storage modulus and tan δ were more frequency-dependent than the loss modulus of cooked MBSS. The addition of NaCl gradually increased the toughness of dried MBSS and the overall acceptability of cooked MBSS. Furthermore, NaCl decreased the structure order degree of starch in MBSS. Correlation analysis demonstrated that the quality of MBSS had a significant correlation with the molecular and lamellar order of starch. Overall, NaCl could improve the quality of MBSS by regulating the thermal, gelatinizing, and structural properties of MBS.

In pig production, reducing production costs and improving immunity are important. Grape pomace, a good agricultural by-product, has been thrown away as food waste for a long time. Recently, we found that it could be used as a new source of pig feed. We investigated the effect of grape pomace on inflammation, gut barrier function, meat quality, and growth performance in finishing pigs. Our results indicated that treatment samples showed a significant decrease in water loss, IL-1β, DAO, ROS, and MDA content (p

Although face recognition has received a lot of attention and development in recent years, it is one of the research hotspots due to the low efficiency of Single Sample Per Person (SSPP) information in face recognition. In order to solve this problem, this article proposes a face recognition method based on virtual sample generation and multi-scale feature extraction. First, in order to increase the training sample information, a new NMF-MSB virtual sample generation method is proposed by combining the Non-negative Matrix Factorization (NMF) reconstruction strategy with Mirror transform(M), Sliding window(S), and Bit plane(B) sample extension methods. Second, a feature extraction method (named WPD-HOG-P) based on Wavelet Packet Decomposition, Histograms of Oriented Gradients, and image Pyramid is proposed. The proposed WPD-HOG-P method is beneficial to multi-scale facial image feature extraction. Finally, based on the extracted WPD-HOG-P features, the recognition model is established by using a grid search optimization support vector machine. Experimental results on ORL and FERET data sets show that the proposed method has higher recognition rates and lower computational complexity than the benchmark methods.

Resistant starch (RS) type 2-high-amylose corn starch (HACS) was subjected to simultaneous hydrothermal (25% moisture content, 90 °C for 12 h) and microwave (35% moisture content, 40 W/g microwaving for 4 min) treatment and zein (at a zein to treated starch ratio of 1:5, 50 °C for 1 h) to improve its resistance to enzymolysis. Scanning electron microscopy (SEM) highlighted the aggregation and adhesion of the composite. The average particle size of the composite (27.65 μm) was exceeded that of both the HACS (12.52 μm) and the hydrothermal and microwave treated HACS (hydro-micro-HACS) (12.68 μm). The X-ray diffraction results revealed that the hydro-micro-HACS and composite remained B-type, while their crystallinity significantly decreased to 16.98% and 12.11%, respectively. The viscosity of the hydro-micro-HACS and composite at 50 °C was 25.41% and 35.36% lower than that of HACS. The differential scanning calorimetry (DSC) results demonstrated that the composite displayed a new endothermic peak at 95.79 °C, while the weight loss rate and decomposition temperature were 7.61% and 2.39% lower than HACS, respectively. The RS content in HACS, the hydro-micro-HACS, and composite was 47.12%, 57.28%, and 62.74%, respectively. In conclusion, hydrothermal and microwave treatment combined with zein provide an efficient physical strategy to enhance the RS type 2-HACS.

Corn flour was prepared by wet-milling with the treatment of neutral protease and the gelatinization, thermal and rheological properties were analyzed. Tortilla was prepared with enzyme treated corn flour (ECF) and additives (xanthan gum and cassava starch) and the properties were analyzed. Compared with dry-milling corn flour (DCF) and wet-milling corn flour (WCF), the ECF had less average particle size (16.74 μm), higher peak viscosity and higher final viscosity of 2997 cP and 3300 cP, respectively. The thermal properties showed that ECF had higher ∆H and lower To, Tp and Tc. The G′ of ECF gel (6%, w/w) was higher than that of DCF gel and WCF gel. Dynamic viscoelastic measurement indicated that the tortillas made of ECF had lower G′ and G″ over the frequency range (0.1−100 rad/s) after adding xanthan gum and cassava starch. The gel structure of tortillas made of ECF was homogeneous in distribution of pores. The gelatinization, thermal and rheological properties of corn flour were improved by addition of neutral protease. The addition of xanthan gum and cassava starch helped to make the tortilla with porous structure and good sensory quality.