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This paper addresses the problem of the robust stabilization control for a quadrotor slung-load system using sliding mode control. The hybrid model of the slung-load system is presented as two separate systems, one fully actuated, and another under actuated. To achieve the robust stabilization control with the unexpected external disturbance, a sliding mode controller is proposed to enforce the slung-load system tracking of the desired trajectory with a slight swing of the suspended load. The Lyapunov function is defined as the system energy function that is always dissipated over time until nullity, when the system reaches the point of equilibrium. The stability of the controller is analyzed by the Lyapunov function and the stability criteria are given. Finally, the simulations are performed to evaluate the effectiveness of the proposed control scheme.

Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This paper proposes a hybrid optimization method based on priority strategy and an improved sparrow search algorithm for DMFB online test path planning. At the algorithmic level, the improved sparrow search algorithm incorporates three main components: tent chaotic mapping for population initialization, cosine adaptive weights together with Elite Opposition-based Learning (EOBL) to balance global exploration and local exploitation, and a Gaussian perturbation mechanism for fine-grained refinement of promising solutions. Concurrently, this paper proposes an intelligent rescue strategy that integrates global graph-theoretic pathfinding, local greedy heuristics, and space–time constraint verification to establish a closed-loop decision-making system. The experimental results show that the proposed algorithm is efficient. On the standard 7 × 7–15 × 15 DMFB benchmark chips, the shortest offline test path length obtained by the algorithm is equal to the length of the Euler path, indicating that, for these regular layouts, the shortest test path has reached the known optimal value. In both offline and online testing, the shortest paths found by the proposed method are better than or equal to those of existing mainstream algorithms. In particular, for the 15 × 15 chip under online testing, the proposed method reduces the path length from 543 and 471 to 446 compared with the IPSO and IACA algorithms, respectively, and reduces the standard deviation by 53.14% and 39.4% compared with IGWO in offline and online testing.

γδ T cells are a unique lymphocyte population that have been reported to have either anti- or pro-tumoral functions in several cancer types, but the mechanisms are underinvestigated. Exosomes, initially considered to be cellular “garbage dumpsters,” are now implicated in mediating interactions with the cellular environment. Hypoxia is a common feature of solid tumors and is believed to alter tumor-derived exosomes (TEXs), which mediate the hypoxic evolution of the tumor microenvironment in return. This study sought to investigate whether TEXs mediate the anti- and pro-tumoral equilibrium of γδ T cells under different oxygen pressures in the tumor microenvironment. We show that TEXs can alter the expansion and cytotoxicity of γδ T cells in an HSP70-dependent but dendritic cell-independent manner. The stimulating effects of normoxic TEXs on γδ T-cell activity were absent from hypoxic TEXs, which enhanced the suppressive effect of myeloid-derived suppressor cells (MDSCs) on γδ T cells through a miR-21/PTEN/PD-L1 regulation axis. Finally, the therapeutic outcome benefited from combined miR-21 and PD-L1 targeting in oral squamous cell carcinoma (OSCC)-bearing immunocompetent mice. We conclude that oxygen pressure in the tumor microenvironment orchestrates an anti- and pro-tumoral γδ T-cell equilibrium by altering TEX content, which subsequently regulates MDSC function in a miR-21/PTEN/PD-L1-axis-dependent manner. Our results should prompt further investigation into integrated exosomal miRNA inhibition and immune checkpoint inhibitor therapeutic modalities for patients with OSCC.

The growth of aquaculture poses significant challenges for disease management, impacting economic sustainability and global food security. Traditional diagnostics are slow and require expertise, while current deep learning models, including CNNs and Transformers, face a trade-off between capturing global symptom context and maintaining computational efficiency. This paper introduces FishMambaNet, a novel framework that integrates selective state space models (SSMs) with convolutional networks for accurate and efficient fish disease diagnosis. FishMambaNet features two core components: the Fish Disease Detection State Space block (FSBlock), which models long-range symptom dependencies via SSMs while preserving local details with gated convolutions, and the Multi-Scale Convolutional Attention (MSCA) mechanism, which enriches multi-scale feature representation with low computational cost. Experiments demonstrate state-of-the-art performance, with FishMambaNet achieving a mean Average Precision at 50% Intersection over Union (mAP@50) of 86.7% using only 4.3 M parameters and 10.7 GFLOPs, significantly surpassing models like YOLOv8-m and RT-DETR. This work establishes a new paradigm for lightweight, powerful disease detection in aquaculture, offering a practical solution for real-time deployment in resource-constrained environments.

The sustainable cultivation of agarwood, a high-value tree species, is significantly threatened by foliar pests, requiring efficient and accurate monitoring solutions. While deep learning is widely used, mainstream models face inherent limitations: Convolutional Neural Networks have restricted receptive fields and Transformers incur high computational complexity, complicating the balance of accuracy and efficiency for tiny pest detection in complex environments. To address these challenges, a novel Adaptive State-space Convolutional Fusion Network (ASCNet) is proposed. Its core component, the Adaptive State-space Convolutional Fusion Block (ASBlock), integrates the global context modeling of state-space models—which have linear complexity—with the local feature extraction of convolutional networks through a dual-path adaptive fusion mechanism. A Grouped Spatial Shuffle Downsampling (GSD) module replaces standard strided convolutions to preserve fine-grained spatial details during downsampling. For small object detection, a Normalized Wasserstein Distance (NWD)-based loss function mitigates the sensitivity of traditional IoU to minor localization errors. Evaluations on a new agarwood pest dataset show that ASCNet outperforms state-of-the-art detectors (including the YOLO series, RT-DETR, and Gold-YOLO), achieving a maximum mAP@50 of 93.0 ± 0.2% and mAP@50:95 of 71.2 ± 0.3% with high computational efficiency. The results confirm ASCNet as a robust and effective solution for intelligent pest monitoring in high-value crops like agarwood.

A kind of full-function two-sided optical bench interferometer (OBI) is designed to meet the practical requirements of the Taiji Program for space gravitational wave detection. The main optical paths are arranged on the A-side for transmission and interference, and other optical paths and electronic devices are placed on the B-side. According to the design scheme, we successfully constructed two OBIs by using hydrogen–oxygen catalytic stress-free bonding technology. When the OBI is installed and adjusted, the position and Angle error of the interference beam are controlled within 30 μm and 50 μrad through the self-designed precision mechanical clamping mechanism and beam position measuring device. The built OBI was placed on the vibration isolation platform in the vacuum tank for the stability test. The test results show that the noise of the OBI is less than 10 pm/√Hz in the frequency band of 0.1 Hz to 1 Hz, which meets the noise budget requirements of the Taiji Pathfinder in the middle- and high-frequency band.

Nitrogen (N) is an essential yield-limiting factor in maize, and identifying genes that improve nitrogen use efficiency (NUE) is critical for sustainable agriculture and environmental protection. However, the genetic basis of NUE in maize remains poorly understood. In this study, we performed a genome-wide association study (GWAS) using a mixed linear model (MLM) controlling for population structure and kinship, based on an association panel of 282 maize inbred lines genotyped via the Maize 50K GBTS array (53,162 SNPs). Ten NUE-related traits (grain yield, hundred-kernel weight, ear length, ear diameter, kernel row number, kernel number per row, SPAD value, ASI, plant height, ear height) were evaluated under two N levels during the 2024–2025 growing seasons. The GWAS analysis detected 122 significant SNPs in gene regions linked to low N tolerance under the studied conditions. Linkage disequilibrium analysis and functional annotation narrowed down 26 candidate genes, whose GO and KEGG enrichment analyses (Fisher’s exact test) identified three core genes (Zm00001d027880, Zm00001d034047, Zm00001d010574). Furthermore, several inbred lines (H1710, 23N272, and 23N41) demonstrating superior low-nitrogen tolerance were identified. The primary subsequent focus in future research for these genetic materials will be their utilization to breed new cultivars with enhanced nitrogen use efficiency.

Breast cancer (BC) is the second leading cause of cancer-related death in females, followed by lung cancer. Disadvantages exist in conventional diagnostic techniques of BC, such as radiation risk. The present study integrated bioinformatics analysis with machine learning to elucidate potential key candidate genes associated with the tumorigenesis of BC. Eleven datasets were downloaded from the Gene Expression Omnibus (GEO) database and were consolidated into two independent cohorts (training cohort and validation cohort) after batch-effect removal. We employed “limma” package to screen differentially expressed genes (DEGs) between BC and adjacent normal breast samples. Subsequently, the most reliable diagnostic indicators were identified utilizing LASSO-Logistic regression, SVM-RFE and multivariate stepwise Logistic regression analysis. Logistic model and nomogram were created based on these hub genes and applied in external validation cohort to verify the robustness of the model. As a result, a total of six hub genes connected with BC pathogenesis were identified, including CD300LG, IGSF10, FAM83D, MAMDC2, COMP and SEMA3G. Then, a diagnostic model of BC on the basis of these genes was established. ROC analysis of the diagnostic model illustrated that AUC of the training cohort was 0.978 (0.962, 0.995). In the validation cohort, AUC of training set and validation set were 0.936 (0.910, 0.961) and 0.921 (0.870, 0.972), respectively. This indicated that the model was reliable in separating BC patients from healthy individuals. The model may assist in early diagnosis of BC with implications for improving the prognosis of BC patients.

The precise movement of the test mass along a geodesic is crucial for gravitational wave detection in space. To maintain this motion, the core payload-inertial sensor incorporates multiple functional units designed to mitigate various sources of stray force noise affecting the test mass. Understanding the limits of these noise sources is essential for enhancing the inertial sensor system design. Additionally, thorough ground-based verification of these functional units is necessary to ensure their reliability for space missions. To address these challenges, we developed a low-frequency torsion pendulum apparatus that utilizes a commercial autocollimator as the optical readout element for testing this type of space inertial sensor. This paper provides a comprehensive overview of the apparatus’s operating principle, structural characteristics, and the results of laboratory tests of its background noise. Experimental data demonstrate that the torsion pendulum achieves a sensitivity of 1 × 10−11 Nm/Hz1/2 within the measurement band from 1 mHz to 0.1 Hz, confirming its suitability for various inertial sensor tests. Furthermore, the insights gained from constructing the torsion pendulum will inform future system upgrades.

Fusarium stalk rot, which is a common disease caused by species, can seriously decrease maize grain yield and quality. Hence, the genetic mechanism mediating maize resistance to Fusarium stalk rot must be elucidated and the associated resistance genes useful for breeding disease-resistant cultivars should be identified. In this study, the highly resistant maize inbred line H1710 and highly susceptible inbred line Huangzaosi were used to construct segregating populations through hybridization, backcrossing, and other methods. A resistance/susceptibility pool was constructed from the F population. A BSA-seq analysis revealed one candidate region associated with stalk rot resistance on chromosome 6; this region (3.98 Mb) contains 38 genes. Furthermore, KASP molecular markers designed for the candidate region precisely localized two candidate genes, and , which were considered to be the most likely genes mediating stalk rot resistance. The study findings lay a theoretical foundation for analyzing the molecular basis of maize resistance to Fusarium stalk rot and cloning the relevant resistance genes.