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Fresh milk with added hyaluronic acid (HA) was fermented by inoculating Streptococcus thermophilus and Lactobacillus bulgaricus. By measuring the pH, water holding capacity, viable bacteria count, rheological properties, volatile flavor substances, stability of yoghurt, the effects of HA with various molecular masses and various amounts of HA on the quality of yoghurt were evaluated. The results showed that various molecular masses of HA had different effects on the quality of yoghurt, but all could significantly improve its water holding capacity, smoothness and fineness (P < 0.05). The addition of high molecular mass (1 280 kDa) HA improved the elastic modulus (G’) and viscosity modulus (G”) of yoghurt, enhanced its stability index, promoted the growth and reproduction of L. bulgaricus and S. thermophilus, but caused obvious whey syneresis at the end of fermentation. Medium molecular mass (350 kDa) HA inhibited the growth and reproduction of L. bulgaricus, increased the pH, reduced the titratable acidity, and optimized the sensory quality of yoghurt. Low molecular mass (180 kDa) HA reduced the stability of yoghurt. To sum up, medium molecular mass HA can improve the quality of fermented milk.

Vacuum preloading combined with prefabricated vertical drains (PVDs) has been widely used to improve soft clayey soil deposits. However, the consolidation deformation of high-water-content slurry under vacuum pressure is still not fully understood. In this study, the displacement field of the slurry during vacuum preloading was directly observed using the particle image velocimetry (PIV) technique. The test results showed that the displacement field of the slurry could be divided into three different zones that reflect distinct consolidation patterns. In zones I and III, the slurry was predominantly compressed along the horizontal and vertical directions, respectively, whereas in zone II, compression occurred in both vertical and horizontal directions. In particular, the slurry elements in zones I and II underwent horizontal extension prior to horizontal compression. The study also visualized the boundary of the soil column and provided an equation for the relation between soil column thickness and consolidation time, which could provide a reference for the theoretical consolidation calculations related to soil columns. Key words: dredged slurry, vacuum preloading, particle image velocimetry, PIV, deformation, soil column. Le prechargement par le vide combine a des drains verticaux prefabriques (PVD) a ete largement utilise pour ameliorer les depots de sols argileux mous. Cependant, la deformation de consolidation d'une boue a forte teneur en eau sous pression sous vide n'est pas encore totalement comprise. Dans cette etude, le champ de deplacement de la boue pendant le prechargement sous vide a ete directement observe en utilisant la technique de velocimetrie par image de particules (PIV). Les resultats des essais ont montre que le champ de deplacement de la boue pouvait etre divise en trois zones differentes qui refletent des modeles de consolidation distincts. Dans les zones I et III, la boue a ete principalement comprimee dans les directions horizontale et verticale, respectivement, tandis que dans la zone II, la compression a eu lieu dans les deux directions, verticale et horizontale. En particulier, les elements de boue des zones I et II ont subi une extension horizontale avant la compression horizontale. L'etude a egalement permis de visualiser la limite de la colonne de sol et de fournir une equation pour la relation entre l'epaisseur de la colonne de sol et le temps de consolidation, qui pourrait servir de reference pour les calculs theoriques de consolidation lies aux colonnes de sol. [Traduit par la Redaction] Mots-cles : boue de dragage, prechargement sous vide, velocimetrie par image de particules, PIV, deformation, colonne de sol., Introduction Land reclamation is an effective measure to address land shortages in coastal counties. The raw material used for land reclamation is usually dredged slurry with a high water content [...]

Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To improve the efficiency and accuracy of monitoring chlorophyll content in maize canopies, this study collected RGB, multispectral (MS), and SPAD data from maize canopies at the jointing, tasseling, and grouting stages, constructing a dataset with fused features. We developed maize canopy chlorophyll content monitoring models based on four machine learning algorithms: BP neural network (BP), multilayer perceptron (MLP), support vector regression (SVR), and gradient boosting decision tree (GBDT). The results showed that, compared to single-feature methods, the MS and RGB fused feature method achieved higher monitoring accuracy, with R² values ranging from 0.808 to 0.896, RMSE values between 2.699 and 3.092, and NRMSE values between 10.36% and 12.26%. The SVR model combined with MS–RGB fused feature data outperformed the BP, MLP, and GBDT models in monitoring maize canopy chlorophyll content, achieving an R² of 0.896, an RMSE of 2.746, and an NRMSE of 10.36%. In summary, this study demonstrates that by using the MS–RGB fused feature method and the SVR model, the accuracy of chlorophyll content monitoring can be effectively improved. This approach reduces the need for traditional methods of measuring chlorophyll content in maize canopies and facilitates real-time management of maize crop nutrition. [ABSTRACT FROM AUTHOR]

In the field of Natural Language Processing (NLP), the lack of support for minority languages, especially Uyghur, the scarcity of Uyghur language corpora in the agricultural domain, and the lightweight nature of large language models remain prominent issues. This study proposes a method for constructing a bilingual (Uyghur and Chinese) lightweight specialized large language model for the agricultural domain. By utilizing a mixed training approach of Uyghur and Chinese, we extracted Chinese corpus text from agricultural-themed books in PDF format using OCR (Optical Character Recognition) technology, converted the Chinese text corpus into a Uyghur corpus using a rapid translation API, and constructed a bilingual mixed vocabulary. We applied the parameterized Transformer model algorithm to train the model for the agricultural domain in both Chinese and Uyghur. Furthermore, we introduced a context detection and fail-safe mechanism for the generated text. The constructed model possesses the ability to support bilingual reasoning in Uyghur and Chinese in the agricultural domain, with higher accuracy and a smaller size that requires less hardware. It (our work) addresses issues such as the scarcity of Uyghur corpora in the agricultural domain, mixed word segmentation and word vector modeling in Uyghur for widespread agricultural languages, model lightweighting and deployment, and the fragmentation of non-relevant texts during knowledge extraction from small-scale corpora. The lightweight design of the model reduces hardware requirements, facilitating deployment in resource-constrained environments. This advancement promotes agricultural intelligence, aids in the development of specific applications and minority languages (such as agriculture and Uyghur), and contributes to rural revitalization. [ABSTRACT FROM AUTHOR]

This study focuses on the enhancement of back‐impact resistance in flexible OLED modules, a critical aspect for the durability of modern slim‐profile electronic devices. Under rear compressive forces, flexible OLED modules are prone to bending towards the glass cover, placing the outermost touch layer under significant tensile strain. The brittle nature of the inorganic film within the touch layer, coupled with its low fracture elongation, predisposes it to crack initiation under stress. This mechanical vulnerability can lead to catastrophic failure of the OLED functionality due to moisture and oxygen infiltration, manifesting as black spots on the display. Addressing this, we propose a dual‐faceted approach: optimizing stress distribution by the module design and enhancing the tensile strength of the touch layer's inorganic film, achieving a sixfold increase in the module's resistance to back compression. This paper outlines the experimental setup, finite element method (FEM) simulations, and the resultant implications for flexible OLED module design and durability. [ABSTRACT FROM AUTHOR]

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