Search Results (1,438)

This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. The proposed approach explicitly exploits the inherent symmetry of the undirected communication topology among the agents, which ensures reciprocal information exchange, balanced cooperative interactions, and facilitates the rigorous analysis of consensus under asynchrony. By extending the generalized discrete-time control Lyapunov function (g-dclf) framework to the perturbed setting, we introduce a robust g-dclf together with a robust average decrease constraint that explicitly accounts for the worst-case effect of disturbances. A coordinated self-triggering mechanism, built upon the cost prediction mismatch index and the flexible-step execution strategy, is developed to simultaneously determine the inter-execution times and the number of control steps to be applied in each iteration. In addition, an adaptive shrinking prediction horizon strategy is incorporated to further reduce the computational complexity of the local optimization control problems (OCPs) as the agents approach consensus. The resulting robust flexible-step terminal-free DMPC (RFSTDMPC) algorithm is fully distributed, handles asynchronous communication, and operates without any stability-related terminal constraint. Recursive feasibility of each local OCP and input-to-state stability (ISS) of the overall closed-loop MAS are rigorously established under the symmetric network structure. Simulation results on the consensus problem of three perturbed nonholonomic vehicles demonstrate the effectiveness of the proposed scheme in achieving practical full-state stabilization while significantly alleviating the online computational burden. Full article

Lithium-ion batteries are widely used in various energy sectors, and accurately predicting their early remaining useful life (RUL) is crucial for shortening battery evaluation time and accelerating battery commercialization. However, information on degradation during the early cycling stages of batteries is limited, and it is difficult to fully characterize their lifespan. This study proposes a CNN–Transformer–BiGRU-based method for predicting the early RUL of lithium-ion batteries using Grey Wolf Optimization (GWO). First, using only the first 100 cycles of each battery in the MIT dataset, early degradation features are extracted from the dimensions of capacity and internal resistance, and then standardized. Second, a CNN is employed to extract local degradation features, while the Transformer’s self-attention mechanism is used to capture global correlations, and BiGRU is utilized to further extract bidirectional temporal dependency information. Building on this foundation, GWO is introduced to perform joint optimization of the model’s key hyperparameters to obtain optimal network parameters. Finally, the effectiveness of the proposed method is validated through ablation and comparison experiments. The experimental results show that the proposed model achieved an R² of 0.9633, with RMSE, MAE, and MAPE values of 80.5608 cycles, 63.2524 cycles, and 7.29%, respectively, demonstrating overall prediction performance superior to that of the comparison models. This method can effectively mine degradation information related to battery life from limited early-cycle data, providing an effective approach for the accurate prediction of the early RUL of lithium-ion batteries. Full article

In the domain of rotating machinery fault diagnosis, challenges such as multi-operating condition distribution heterogeneity and the difficulty of distinguishing fault features within multi-scale temporal signals persist. To address these issues, this paper introduces the Time-Gated Multi-Expert Generative Adversarial Network (TGME-GAN), a fault diagnosis approach that integrates a multi-expert gated conditional generative adversarial network with a clustering structure-aware feature enhancement. This method combines unsupervised K-means clustering with supervised discriminative learning. The optimal number of clusters is selected adaptively using the silhouette coefficient, and the distance vector from each sample to the cluster centers serves as a topological prior feature. A spatial–temporal joint representation matrix is then formed by concatenating PCA principal components, differential features, cumulative statistical features, and standardized change rates, which together capture both abrupt mutations and progressive degradation in fault signals. In the model, the discriminator incorporates a multi-expert gated network. Each expert learns a feature subspace corresponding to a distinct operating condition, and the gated network dynamically assigns fusion weights, allowing the discriminator to capture heterogeneous distributions across industrial conditions. The generator extracts multi-scale local patterns with a three-layer one-dimensional convolutional network and models sequential dependencies with a two-layer LSTM, producing high-quality fault samples that preserve intrinsic consistency. At the engineering level, TGME-GAN is deployed for gearbox fault diagnosis in uneven, small-sample industrial settings. In two gearbox fault experiments, this method substantially outperforms current mainstream models. Full article

Deliberate practice plays a central role in high-quality music learning, yet the motivational and technology-related factors associated with sustained deliberate music practice remain insufficiently understood. Drawing on social cognitive theory, achievement goal theory, and the literature on artificial intelligence (AI) literacy, this study examined the relationships among music learning self-efficacy, achievement goal orientation, AI literacy, and deliberate music practice. A total of 458 music students from a teacher-training university in Liaoning Province, China, completed measures of the four constructs. Music learning self-efficacy was positively associated with deliberate music practice. Achievement goal orientation also showed a significant indirect association between music learning self-efficacy and deliberate music practice, such that students with higher self-efficacy reported stronger achievement goal orientations, which in turn were associated with greater engagement in deliberate music practice. AI literacy further moderated the association between achievement goal orientation and deliberate music practice, with this positive relationship being stronger among students reporting higher levels of AI literacy. These findings suggest that deliberate music practice is associated with both learners’ motivational beliefs and their self-reported AI literacy. Full article

Background: Wampee (Clausena lansium (Lour.) Skeels) is an understudied Rutaceae crop native to southern China, whose fruit features a complex aroma profile with simultaneous sour, sweet, bitter and astringent notes. Although bioactive compounds including flavonoids, alkaloids and volatile oils in wampee fruit have been partially characterized, the tissue-specific metabolic and transcriptional basis underlying its distinctive aroma formation remains largely unclear. Methods: We performed an integrated volatile metabolomic and transcriptomic analysis on the pericarp (peel) and flesh of wampee fruit across three cultivars (Shanyellowpi, Heijingang, Bingtangxin). Volatile metabolites were profiled via headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and transcriptome profiles were generated by RNA-Seq. Multi-omics integration was conducted using Procrustes analysis, gene–metabolite correlation network construction and weighted gene co-expression network analysis (WGCNA). Results: A total of 288 volatile metabolites were identified, representing the most comprehensive volatile inventory for C. lansium reported to date. Principal component analysis and partial least squares discriminant analysis revealed distinct volatile profiles between pericarp and flesh; terpenoids were the dominant chemical class, accounting for 71.56–91.48% of total volatiles in pericarp and 61.88–67.22% in flesh. Notably, organoheterocyclic compounds were significantly enriched in Shanyellowpi flesh (40.45%), forming a cultivar-specific metabolic signature absent in the other two cultivars. Transcriptomic analysis showed that phenylpropanoid biosynthesis was the most significantly enriched pathway among differentially expressed genes, followed by monoterpene biosynthesis and sesquiterpenoid biosynthesis. Procrustes analysis demonstrated a strong global concordance between the two omics layers (M² = 0.535, p < 0.001). Gene–metabolite correlation networks identified terpene synthase (TPS) genes (HP075360, HP217350) and oxidoreductase genes (SOD1, GST, 10HGO) as candidate co-regulators of terpenoid biosynthesis. WGCNA further prioritized TPS genes, cytochrome P450 genes and MYB transcription factor genes as key regulators driving volatile metabolic divergence between tissues. Conclusion: This study provides a comprehensive volatile and transcriptomic atlas of wampee fruit, and identifies tissue-specific and cultivar-specific metabolic signatures as well as their candidate regulatory genes. These findings advance our understanding of quality differentiation in Rutaceae fruits and lay a foundation for molecular breeding and flavor improvement of wampee. Full article

Cashmere goats are widely distributed in the cold, arid and semi-arid remote regions of northern China. Their main economic value is the production of precious cashmere fibers. The differentiation of second hair follicle (SHF) stem cells into hair follicle lineages plays a crucial role in SHF regeneration as well as in the morphogenesis and growth of cashmere fibers; however, its precise molecular mechanism is still unclear. In this study, we found that N⁶-methyladenosine (m⁶A)-circHECA recruiting FUS promoted the differentiation of SHF stem cells into hair follicle lineages through stabilizing FOXM1 mRNA in SHF stem cells, thereby activating the NOTCH pathway in cashmere goats. Furthermore, we confirmed that the m⁶A modification of circHECA is required for the FUS/FOXM1-mediated NOTCH signaling pathway to facilitate the differentiation of SHF stem cells into hair follicle lineages via transfecting circHECA m⁶A-deficient mutants. Our results contribute to elucidating the functional mechanism of m⁶A-circHECA in the differentiation of SHF stem cells into hair follicle lineages in cashmere goats. Full article

Respiratory metabolism reflects energy demand and physiological variation in bivalves, but interpretation of among-population differences requires careful consideration of developmental stage, diet, breeding background, and previous rearing history. In this study, oxygen consumption rate (OR), ammonia excretion rate (NR), O:N ratio, and key metabolic enzyme activities were compared among three Manila clam (Ruditapes philippinarum) populations: Laizhou wild-origin stock (LZ), Fujian-origin stock cultured in Liaoning (FJ), and Zebra shell-color selected line (ZS). These populations were selected to represent three common germplasm categories used in Manila clam production: a wild-origin stock, a translocated aquaculture stock, and a Zebra shell-color selected line. Clams with nominal shell lengths of 10, 15, and 20 mm were used for respiratory measurements, and enzyme activities were determined in 15 mm individuals. Two-way ANOVA revealed significant effects of population on OR, NR, and O:N ratio (p < 0.05), whereas shell length significantly affected NR and O:N ratio (p < 0.05). OR decreased with increasing shell length in LZ and FJ, while ZS showed a different pattern. NR generally declined with shell length across all populations. The O:N ratio increased markedly in LZ but remained relatively low in FJ and ZS. Among enzyme activities, SDH was significantly higher in LZ (p < 0.01), whereas AKP activity was highest in ZS (p < 0.05). LDH activity did not differ significantly among populations. These results demonstrate that Manila clam populations with different source backgrounds exhibit distinct physiological profiles under standardized laboratory conditions. The observed variation represents population-associated physiological characteristics rather than direct evidence of genetic differentiation, providing baseline information for future germplasm characterization integrating physiological, genetic, and environmental approaches. Full article

The Scaly-sided Merganser (Mergus squamatus) is an endangered duck endemic to East Asia. It usually gathers in single-species flocks through winter. Existing field records focus almost entirely on southern winter flocks. No prior work has described lone individuals spending winter in northern China. We conducted standardized three-year winter surveys (2019–2022) across five isolated ice-free river patches along the Taizi River, Liaoning Province, northern China, to document habitat use and flocking behavior of one solitary male. Despite the availability of five discrete overwintering sites, the focal bird only occupied two patches characterized by dense riparian concealment and low human disturbance, avoiding open, highly disturbed sites. Resource Selection Function (RSF) modeling indicated habitat attributes (concealment and human disturbance) strongly predicted site use (AICc weight = 0.79), whereas the abundance of heterospecific flocking partners (Common Mergansers Mergus merganser) exerted negligible influence (AICc weight < 0.01). This solitary male associated only with Common Mergansers, the species with the closest phylogenetic relatedness (mitochondrial genetic distance = 0.04365), and never aggregated with other sympatric waterbirds. Model comparison confirmed phylogenetic relatedness outperformed dietary similarity as a predictor of heterospecific flocking (AICc weight = 0.76). Our three-year observations of this single individual demonstrate that, when conspecifics are absent and suitable winter habitat is limited, this male prioritized high-quality, low-disturbance ice-free patches over social aggregation opportunities, and selectively formed mixed flocks with its closest phylogenetic congener. This multi-year case study provides rare empirical baseline data for understanding winter behavioral trade-offs of endangered cavity-nesting waterfowl under severe frozen river conditions, though all conclusions herein apply only to this focal male and cannot be generalized to the entire species. Full article

Coal–rock cutting state recognition is essential for adaptive cutting and intelligent speed regulation in shearers. To address the limited representational capability of individual signals, the confusion between adjacent gangue-bearing cutting conditions, and the domain discrepancy between simulation and experimental data, a vibration–current multimodal fusion method based on CORRECT-Net is proposed. First, an EDEM–RecurDyn–MATLAB/Simulink co-simulation system was developed to generate cutting records for four coal–rock states. After screening for physical equivalence and label conflicts, 158 valid records were retained and grouped into 150 physical-condition groups, which were partitioned at the group level into training, validation, and test sets. Subsequently, SincNet was employed to extract frequency-band-constrained features, a Transformer was used to model long-range temporal dependencies, and a residual importance-guided GATv2 module was introduced to perform cross-modal fusion of vibration-impact and current-load features. On 2500 test windows, CORRECT-Net achieved an accuracy of 96.20% ± 0.11%, a macro-F1 score of 95.14% ± 0.21%, and a hazardous-condition miss rate of 0.58% ± 0.13%. Compared with the multimodal 1D-CNN, TCN, and Bi-LSTM models, CORRECT-Net improved the accuracy by 8.80, 4.00, and 2.08 percentage points, respectively. In the progressive ablation study, the accuracy increased from 87.40% ± 0.26% to 96.20% ± 0.11%, while the macro-F1 score increased from 84.57% ± 0.34% to 95.14% ± 0.21%. Under Gaussian noise with a standard deviation of 0.05, the model retained an accuracy of 92.76% ± 0.24%. When the vibration and current modalities were separately unavailable, the corresponding accuracies were 86.56% ± 0.37% and 92.44% ± 0.25%, respectively. A five-fold simulation-to-experiment transfer evaluation was further conducted at the independent-run level using five experimental records per class. Without adaptation using experimental samples, the model achieved an accuracy of 91.33% ± 5.19%. When 20% and 50% of the experimental windows were used for adaptation, the accuracy increased to 96.33% ± 0.75% and 98.67% ± 1.39%, respectively. These results demonstrate that CORRECT-Net effectively integrates mechanical vibration responses and motor-load information and, under the present simulation and experimental conditions, achieves high recognition accuracy, a low hazardous-condition miss rate, and effective adaptability to the experimental domain. Full article

Phyllosphere microorganisms interact with host plants to regulate growth, promote nutrient uptake and enhance stress tolerance with host specificity, while arbuscular mycorrhizal fungi facilitate plant nutrient absorption and stress adaptation. Current poplar microbial studies mostly focus on hermaphroditic species, with limited research on dioecious poplars. This study selected four poplar species commonly hybridized with Populuscathayana and Populus deltoides in the Xinmin area of Liaoning Province as research subjects: two female plants, DM-9-18 and DX-08-01, and two male plants, 2111 and Qingshan poplar. We performed MiSeq high-throughput sequencing targeting bacterial 16S rRNA, fungal ITS, and arbuscular mycorrhizal fungal (AMF) marker genes from poplar phyllosphere, coupled with chemical quantification of leaf, root and rhizosphere soil, to disentangle clone- and sex-associated divergence in microbial assemblages and their core environmental drivers. No significant gender differences were observed in leaf and rhizosphere nutrient levels and microbial α diversity, whereas male poplars had higher rhizosphere soil nutrients. Male and female poplars genotypes harbored distinct microbial ASVs. The dominant phyllosphere microbes and arbuscular mycorrhizal fungi exhibited gender-specific abundance variations, and nutrient content was the key factor shaping microbial communities. This study clarifies microbial community differences among the four selected hybrid poplar clones. While the experimental design confounds sex with host genotype, the observed patterns provide insights into potential sex-related variations. Our results advance the mechanistic understanding of how dioecious poplar genotype and sexual phenotype jointly filter leaf and root-associated microbial symbionts, with applied implications for hybrid poplar breeding and shelterbelt microbial regulation. Full article

ROVs are essential for marine exploration and underwater operations, yet conventional teleoperation relies heavily on skilled human operators, and many autonomous methods stop at high-level planning rather than low-level actuation, limiting robustness in disturbed and cluttered environments. This paper proposes ODARRL, an obstacle- and disturbance-aware sensor-to-thruster (ST) end-to-end residual reinforcement learning framework for safe trajectory execution of underwater robots. Using a three-stage curriculum, ODARRL first acquires a basic policy from MPC demonstrations in a static obstacle-free environment, then improves disturbance-robust tracking under random currents, and finally extends to scenarios involving both currents and obstacles. A Dual-Horizon Attention Disturbance Encoder is further designed to capture current-related features from long- and short-term histories, which are fused with robot states and reference information as the input to the ST end-to-end policy. Experiments in Marine Gym with BlueROV2 Heavy demonstrate that ODARRL achieves more stable and robust trajectory tracking under random currents, reducing the mean total tracking error by 69.3%, 31.9%, 45.8%, 73.0% and 25.8% relative to the MPC-imitation policy, PPO, SAC, A2C and VNRS-SAC, respectively. With obstacles introduced, curriculum-initialized policies also exhibit higher path progress and more stable task completion during obstacle-avoidance training. Full article

Phosphorus (P) deficiency is a major factor governing peanut (Arachis hypogaea L.) productivity, and the physiological mechanisms by which different genotypes (with contrasting photosynthetic capacities) coordinate carbon assimilation and photoprotection remain elusive. This study elucidated the strategic divergence among different peanut genotypes in their foliar photosystems to perform physiological homeostasis under low-phosphorus (LP) conditions. Based on a peanut mini-core collection, six representative accessions with contrasting photosynthetic capacities were selected and categorized into high- and low-photosynthetic functional groups. We integrated leaf gas exchange, chlorophyll fluorescence, the trans-thylakoid proton gradient (ΔpH), and antioxidant enzyme assays to evaluate their adaptive responses to low-P stress relative to the high-P (HP) control. Our results demonstrated that LP stress induced widespread photosynthetic inhibition across all accessions; this suppression was primarily driven by non-stomatal limitations. Under LP stress, high-Pn accessions exhibited superior cyclic electron flow (CEF) plasticity synergized with highly plastic guaiacol peroxidase (POD) activity, suppressing the leaf-level ROS burst and maintaining a substantial ΔpH for ATP synthesis and PSI stability. Conversely, low-Pn accessions suffered from severe oxidative overload and relied heavily on passive thermal dissipation, characterized by elevated non-photochemical quenching (NPQ) values and restricted CEF engagement. Principal component analysis (PCA) confirmed that while baseline biochemical impairments were universal, the capacity to dynamically modulate this ΔpH-dependent regulatory network—which integrates CEF, cytochrome b6f photosynthetic control, and antenna-level NPQ—served as the decisive determinant underlying genotypic variations in photosystem resilience under P deficiency. This study demonstrated that peanut genotypes deploy divergent, ΔpH-centered strategies to balance light energy distribution under P-limited conditions. These findings provide a novel and plausible mechanistic framework for selecting and breeding P-efficient peanut cultivars in poor soils with enhanced photosystem resilience. Full article

Symmetry power integrity is a core issue for power system data acquisition. However, industrial load data integrity is affected by missing values, abnormal disturbances, and low-reliability observations. A reliability-aware cross-scene industrial load symmetry power data reconstruction method is proposed based on adversarial learning. Firstly, an industrial electricity scene classification is proposed. Temporal and frequency-domain features are jointly encoded by a multilayer perceptron. The scene affiliation of the data is identified by cosine similarity to improve the cross-scene generalization capability. Secondly, a diffusion denoising generative adversarial reconstruction framework is proposed. For missing data, a conditional diffusion model is constructed with historical temporal distributions. Data structures are recovered by forward diffusion and reverse denoising processes. For low-reliability observations, original observations, first-order differences, and second-order differences are adopted to construct local shape constraints. In addition, the residual-correction guidance mechanism is introduced to estimate and correct observation deviations to improve the data reconstruction accuracy. Finally, simulations are conducted with industrial load datasets in Liaoning Province. The results validated the effectiveness of the proposed method. The average accuracies of data correction and data reconstruction reach 97.21% and 97.17%, respectively. Moreover, reconstruction accuracies exceeding 90% are maintained in cross-scene conditions involving different seasons and regions. Full article

This study investigated the population dynamics of Cynoglossus gracilis in the offshore waters of Liaoning Province, China, to support sustainable fisheries management. Biological samples were collected from five major fishing ports between March and November 2024. Population dynamics were assessed based on growth, mortality, and exploitation parameters, and four seasonal fishing closure scenarios were evaluated using egg production per recruit (EPR) and spawning biomass per recruit (SBR) models. The estimated exploitation rate (E = 0.53) indicated that the stock was experiencing relatively high fishing pressure but remained close to the sustainable exploitation reference level. Yield-per-recruit analysis suggested that increasing the minimum catchable size could improve resource utilization efficiency. Among the evaluated closure scenarios, the April–September closure provided the greatest conservation benefit by enhancing reproductive potential and protecting spawning stock biomass. These findings provide valuable insights for optimizing harvest regulations and seasonal closure strategies, contributing to the sustainable management of Cynoglossus gracilis resources in the offshore waters of Liaoning Province. Full article

Background: Contemporary data on the clinical detection rate and profile of phenotypical hypertrophic cardiomyopathy (HCM) in major Chinese healthcare settings are limited. This multicenter study aimed to determine the detection rate and echocardiographic features of the HCM phenotype in a large Chinese cohort. Methods: This cross-sectional study analyzed echocardiography databases from nine medical centers across China, including adult patients examined during 2023. HCM phenotype was defined as end-diastolic wall thickness ≥15 mm in the left ventricle. Patients with moderate to severe aortic stenosis were excluded. Subcategories included phenotypes of obstructive HCM and apical hypertrophy. Results: Among 655,383 examinations, 2610 patients met the criteria of the HCM phenotype, yielding a detection rate of 0.40% (≈1 in 250). The mean age was 60.2 years with male predominance (70.3%). Asymmetric septal hypertrophy was present in 53.3% of patients. The most commonly involved site with maximal wall thickness was the interventricular septum (57.6%), followed by the apex (20.9%) and the basal septum (17.7%). The overall intra-left ventricular obstruction rate was 16.2%; left ventricular outflow tract obstruction (LVOTO) accounted for 12.1%. LVOTO patients had greater septal thickness, smaller left ventricular diastolic dimensions, and more mitral regurgitation. Female sex was associated with a significantly higher LVOTO rate than males (18.2% vs. 9.6%, p < 0.001). Pure apical hypertrophy was identified in 4.5% of patients, with an increasing detection rate in older age groups. Conclusions: This large-scale, multicenter study confirms a high clinical detection rate for the HCM phenotype (≈1 in 250) in major Chinese centers. Substantial phenotypic heterogeneity across age and sex, coupled with the identification of key factors associated with LVOTO, highlights the need for sex- and age-specific diagnostic strategies and therapeutic planning in clinical practice. Full article