Resilience governance provides a new governance model for emergency management in higher education institutions facing major public crises. Higher education resilience governance, while achieving short-term objectives such as mitigating external impacts and maintaining core functions, enhances the enduring adaptability of higher education institutions to the increasingly changing natural and social environments. This transformation shifts the emergency management mode in higher education from reactive responses to proactive governance. In terms of value logic, resilience governance upholds the mission of higher education as the primary nurturing ground, ensuring fairness, quality, and sustainability in education. In terms of organizational logic, it emphasizes collaborative cooperation among higher education institutions, government, society, and the market, as well as the integration of functions within higher education institutions and relatively close connections among their personnel. In terms of institutional logic, it relies on both top-down institutional supply and bottom-up institutional demand to support effective participation, resulting in authoritative and complementary behavioral rules. In terms of action logic, it combines vertical coordination mechanisms and horizontal community collaboration to enhance the effectiveness of cooperative governance. Therefore, the highest value should be placed on the safety of faculty and students, and education planning should be imbued with resilience. It starts with organizational adaptability to develop resilient institutions, focuses on orderly transitions to enhance the emergency management system in higher education, and centers on proactivity and collaboration to build a community of action for emergency governance in higher education.

This study presents a research plan that utilizes data obtained from wearable devices to identify human activities and gain insights into human behavior. We developed a model capable of classifying activities similar to human behavior and evaluated the effectiveness and generalization capabilities of this model. The data underwent initial preprocessing, including standardization and normalization. Additionally, recognizing the inherent similarities between human activity behaviors, we introduced a multi-layer classifier model. The first layer is a random forest model based on stepwise regression, which may encounter reduced accuracy for similar activities. The second layer employs a Support Vector Machine (SVM) model based on Kernel Fisher Discriminant Analysis (KFDA). KFDA is used to reduce the dimensionality of data points with potential confusion, followed by SVM for classification. The model was experimentally evaluated and applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that our approach achieved recognition accuracies of 99.71%, 98.71%, 99.12%, and 97.6% on these datasets, indicating excellent recognition performance. Furthermore, to assess the model's generalization ability, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

The traditional bored pile technology has some arduous problems, such as the sediment at the pile tip, the mud skin along the pile shaft, and the stress release due to borehole construction. The post-grouted technology at the pile tip of bored pile has emerged because of demand. The grouting migration height (GMH) is of great significance to the strengthen and reinforcement of the pile foundation. This paper derives the calculation formula of the GMH based on the theory of the column hole expansion and Herschel-Bulkley model. The influence of relevant parameters on the GMH is discussed. Aiming at the problem of the grouting migration along the pile shaft in layered soils, the iterative calculation method of the GMH is proposed. The correctness of the GMH is verified by an engineering case, which can guide the engineering practice. The result shows that the GMH increases with the increase of the grouting pressure, the pile diameter and the thickness of the mud skin, and the grouting pressure is positively correlated with the GMH. The GMH decreases with the increase of the buried depth, the consistency coefficient and the rheological index. On this basis, the correctness of the GMH is verified by an engineering case.

The development of new diabetes drugs continues to be explored. Loureirin B, a flavonoid, extracted from Dracaena cochinchinensis, has been confirmed to increase insulin secretion and decrease blood glucose levels. For understanding the mechanism, a series of experiments had been employed based on computational methods and cell experiments. The insulin secretion significantly increased with the incubation of 0.01μM loureirin B for 4 hours. The viability of Ins-1 cells showed no significant difference with the treatment of loureirin B. Through computational methods, we hypothesized that loureirin B could interacts with K_ATP channels to promote insulin secretion. In cell experiments, it could be found that the current of K_ATP channel of Ins-1 cells was inhibited by the effect of loureirin B. After then, the voltage-dependent calcium channels were activated, the increase of Cx43 protein expression might mediate the Ca²⁺ to the intracellular. In summary, it could be concluded that loureirin B promoted insulin secretion mainly through inhibiting K_ATP current, the influx of Ca²⁺ to the Intracellular and the expression of Cx43.

Hyperspectral anomaly detection is to recognize anomalies from complex scene in an unsupervised way. Currently, many spectral-spatial detection methods have been proposed with a cascaded manner. However, they often neglect complementary characteristics between spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral-spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets prove that the proposed SSIF produces superior detection result over other state-of-the-art detection techniques.

Glyphosate　(GLYP)　is a broad-spectrum, non-selective, organic phosphine post emergence herbicide registered for use on many food and non-food field. Herein, we developed a biosensor (Mbs@dsDNA) based on carboxylated modified magnetic beads incubated with NH2-polyA and then hybridized with polyT-glyphosate aptamer and complementary DNA. Afterward, a quantitative detection method based on qPCR was established.　When the glyphosate aptamer on Mbs@dsDNA specifically recognized glyphosate, a complementary DNA is released and then enters the qPCR signal amplification process. The linear range of the method was 0.1-5 μg/mL, and the detection limit was set at 0.1 μg/mL. The recoveries in tap water were ranged from 103.4 ~ 104.9%, and the relative standard deviations (RSDs) were < 1%. The aptamer proposed in this study has a good potential for recognizing glyphosate. The detection method combined with qPCR might have a good application prospect in detecting and supervising other pesticide residues.

Alfalfa, an important legume forage, is an ideal crop for sustainable agriculture and a potential bioenergy plant. Drought, one of the most common environmental stresses, substantially affects plants’ growth, development and productivity. MicroRNAs (miRNAs) are newly discovered gene expression regulators that have been linked to several plant stress responses. To elucidate the role of miRNAs in drought stress regulation of alfalfa, a high-throughput sequencing approach was used to analyze 12 small RNA libraries comprising of 4 samples, each with 3 biological replicates. We identified 348 known miRNAs, belonging to 80 miRNA families, from the 12 libraries and 281 novel miRNAs using Mireap software. 18 known miRNAs in roots and 12 known miRNAs in leaves were screened out as drought-responsive miRNAs. Except for miR319d and miR157a which were upregulated under drought stress, the expression pattern of drought-responsive miRNAs were different between roots and leaves in alfalfa. This is the first study discovering miR157a, miR1507, miR3512, miR3630, miR5213, miR5294, miR5368 and miR6173 are drought-responsive miRNAs. Target transcripts of drought-responsive miRNAs were computationally predicted. All 447 target genes for the known miRNAs were predicted using an online tool. This study provides a significant insight on understanding drought-responsive mechanisms of alfalfa.