Frying food can provide an attractive flavor relatively quickly; however, it inevitably produces some safety risks during high-temperature processing, with potentially adverse human health effects. Prolonged exposure to high temperatures during frying might raise the concentration of some harmful compounds that accumulate from the complex chemical reactions taking place inside the food matrix. This review elaborates on the development of food risk factors during frying, adding acrylamide (AA) and advanced glycation end products (AGEs), which are involved in various health problems, including chronic illnesses and carcinogenesis. The two commonly recognized pathways for acrylamide formation include the Maillard reaction pathway and the acrylic acid pathway, with the Maillard reaction considered to be the primary pathway for AGE formation. The processing conditions and food components that affect the formation of these toxic compounds are then specified, demonstrating the importance of factors including type of oil, composition of food (such as moisture and fat content), frying temperature, and duration. Finally, the corresponding health hazards posed by the risk factors are summarized, with an emphasis on the long-term effects of acrylamide and AGE exposure on human health. Increased risks of neurotoxicity, cancer, inflammation, and metabolic diseases have been associated with both compounds. The aim is to clarify the formation pathways, influencing factors, and health impacts of risk factors in frying food and to provide a reference for the prevention of food safety problems caused by acrylamide and advanced glycation end products.

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R² = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed.

The global shortage of high-quality forage has significantly constrained the development of animal husbandry. Leveraging the complementary effects of forage rapeseed and hairy vetch intercropping can enhance forage yield and quality; however, the underlying mechanisms of overyielding in forage rapeseed–hairy vetch intercropping systems remain unclear. Over two years of field experiments, three cropping systems—rapeseed sole cropping, hairy vetch sole cropping, and rapeseed–hairy vetch intercropping—were investigated to assess the effects of intercropping on root and stem morphology, canopy light distribution, leaf photosynthetic physiology, and nitrogen metabolism. Our results demonstrated that intercropping increased forage biomass and crude protein yield by 14.3–20.0% and 30.7–92.8%, respectively, compared to sole cropping. Intercropping significantly enhanced root biomass, increasing lateral root biomass by 81% compared to rapeseed sole cropping. It also improved stem anatomical traits, including the cortex area (58.8–80.7%), cortex thickness (25.1–38.3%), number of vascular bundles (18.0–37.3%), vascular bundle length (17.8–18.4%), vascular bundle perimeter (6.7–18.7%), vascular bundle area (34.6–63.9%), and stem breaking strength (25.7–76.6%). Additionally, intercropping optimized vertical canopy light interception, reduced the activity of antioxidant enzymes (CAT, POD, SOD) and reactive oxygen species (ROS) accumulation, and enhanced the activities of glutamine synthetase and nitrate reductase, stomatal traits, and photosynthetic rates in the leaves of both crops. Structural equation modeling revealed that, in the intercropping system, improved population lodging resistance directly promoted nitrogen metabolism and leaf photosynthetic rates, ultimately increasing population biomass. In summary, rapeseed–hairy vetch intercropping improved canopy light distribution, strengthened rapeseed stem anatomy and root penetration, and enhanced population lodging resistance, leaf photosynthetic physiology, and nitrogen metabolism, thereby boosting forage biomass and quality. The supportive role of rapeseed in the intercropping system elucidates the overyielding mechanisms of rapeseed–hairy vetch intercropping, offering a theoretical framework for optimizing forage production systems worldwide.

Low temperature during germination of late-seeded rapeseed disrupts multiple physiological and biochemical processes and thus limits growth and yield. Accordingly, methods to improve cold tolerance in late-sown rapeseed are needed. In this study, Zhongshuang 11 seeds were primed for 10 h with different concentrations of erucic acid (EA) or glucosinolates (GSLs). After drying, seeds were germinated at low temperature (15 °C/10 °C, 16 h/8 h light/dark) for 14 days. Compared with the control (distilled water priming), the optimal treatments—500 mg/L EA and 300 mg/L GSLs—increased germination rates by 2.9% and 15.6%, respectively, and raised total seedling biomass by 14–24%. Physiological assays on day 14 showed that EA priming increased peroxidase (POD) activity by 28.3%, while GSL priming enhanced superoxide dismutase (SOD) and POD activities by 12.6% and 36.2%, respectively. EA seed priming increased auxin (IAA), brassinolide (BR), cytokinin (CTK), and gibberellin (GA) contents in underground tissues by 37.2%, 18.7%, 53.9%, and 46.7%, respectively, while GSL priming raised IAA, BR, and GA levels in aerial tissues by 74.0%, 59.0%, and 26.6%. Moreover, EA seed priming significantly increased the activities of long-chain acyl-CoA synthetase (LACS) and carnitine acyltransferase (CPT) in rapeseed seedlings, whereas GSL priming elevated glutathione S-transferase (GST) and thioredoxin reductase (TrxR) activities. Field experiments confirmed that EA and GSL priming enhanced seedling biomass accumulation, producing 31.4% and 23.8% increases in total dry weight, respectively, and increased silique number per plant by 15.6% and 17.3%, ultimately raising grain yield by 12.9% and 20.0%. These results indicate that EA or GSL seed priming can improve cold tolerance and yield of late-seeded rapeseed, although further multi-environment and mechanistic studies are required.