This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (SSC), Acidity and pulp percentage (PP) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the SSC, Acidity and PP in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the SSC (R² = 0.9240, RMSE = 0.2595) and the PP (R² = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting Acidity (R² = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit.

In pig farming, multi-object tracking (MOT) algorithms are effective tools for identifying individual pigs and monitoring their health, which enhances management efficiency and intelligence. However, due to the considerable variation in breeding environments across different pig farms, existing models often struggle to perform well in unfamiliar settings. To enhance the model’s generalization in diverse tracking scenarios, we have innovatively proposed the SDGTrack method. This method improves tracking performance across various farming environments by enhancing the model’s adaptability to different domains and integrating an optimized tracking strategy, significantly increasing the generalization of group pig tracking technology across different scenarios. To comprehensively evaluate the potential of the SDGTrack method, we constructed a multi-scenario dataset that includes both public and private data, spanning ten distinct pig farming environments. We only used a portion of the daytime scenes as the training set, while the remaining daytime and nighttime scenes were used as the validation set for evaluation. The experimental results demonstrate that SDGTrack achieved a MOTA score of 80.9%, an IDSW of 24, and an IDF1 score of 85.1% across various scenarios. Compared to the original CSTrack method, SDGTrack improved the MOTA and IDF1 scores by 16.7% and 33.3%, respectively, while significantly reducing the number of ID switches by 94.6%. These findings indicate that SDGTrack offers robust tracking capabilities in previously unseen farming environments, providing a strong technical foundation for monitoring pigs in different settings.

Compression and shearing force on kernel rupture in shelling fresh lotus seeds

We have studied the magnetization, dielectric permittivity, thermal expansion, and magnetostriction of ${\mathrm{Ni}}_2{\mathrm{NbBO}}_6$ single crystals. Different from that of the $ab$ plane, the $c$ axis magnetization approaches zero at low magnetic fields and a spin-flop transition happens around $H_{\mathrm{SF}}=3.65\mathrm{T}$. The results indicate that the $c$ axis is the hard axis and the $ab$ plane is the easy plane. The high-field isothermal magnetization measured at $T=4.2\mathrm{K}$ reveals that the saturation magnetic field is $H_s=17\mathrm{T}$, above which the magnetic structure changes from canted-antiferromagnetic order to ferromagnetic order with the saturation magnetic moment ${\mu }_{\mathrm{eff}}=2.40 {\mu }_{\mathrm{B}}/{\mathrm{Ni}}^{2+}$ Below antiferromagnetic transition temperature $T_N$, an unusual negative thermal expansion phenomenon is detected along the $b$ axis and it can be interpreted by the magnetic structural variation. Based on the magnetization and the magnetostriction data, the magnetic field–temperature phase diagram of ${\mathrm{Ni}}_2{\mathrm{NbBO}}_6$ is constructed. The magnetodielectric effect confirms the existence of magnetoelectric coupling in ${\mathrm{Ni}}_2{\mathrm{NbBO}}_6$.

Pollution generated by livestock and poultry rearing is an important environmental issue, and gas emissions during animal production are continuously increasing. A digital rearing chamber inspection system was designed in the present study in order to examine the waste tea litter’s impact on the growth performance and harmful gas emissions, such as ammonia (NH₃) and carbon dioxide (CO₂), during broiler rearing. Broilers were raised without litter and with waste tea litter. According to the results, broiler growth showed little difference between the two groups during the experimental period, and it was concluded that waste tea litter had no impact on broiler growth. Meanwhile, the gas emissions of the waste tea-litter group were lower than the non-bedding-materials group. In detail, the average concentrations of NH₃ and CO₂ of the non-bedding-materials group were 9.33 ± 3.65 ppm and 797 ± 107 ppm, respectively; while these concentrations in the waste-tea-litter group were 1.01 ± 0.35 ppm and 713 ± 69 ppm, respectively. According to the analysis of the litter properties, it was suggested that waste tea litter can reduce the moisture content in litter, and affect microbial and urease activity due to its low carbon nitrogen ratio (C/N), weak acid, and porous structure characteristics. In conclusion, this study showed the potential of waste tea litter in NH₃ and CO₂ emission reduction during broiler rearing.