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To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change. The difference features before and after the change are then fused, and the fused difference features are used to generate a change map, which reduces the false-positive problem to a certain extent. Experiments were conducted on two publicly available building change detection datasets, LEVIR-CD and WHU-CD. The F1 scores for LEVIR-CD and WHU-CD reached 89.58% and 84.51%, respectively. The experimental results demonstrate that when utilized for building change detection in remote sensing images, the proposed method exhibits improved robustness and detection performance. Additionally, this method serves as a valuable technical reference for the identification of building damage in remote sensing images.

Silicon photonics has emerged as a critical enabling technology for a diverse range of applications, from high-speed data communication and computing to advanced sensing and quantum information processing. This paper provides a comprehensive review of recent progress in the foundational passive devices that underpin this technological revolution. We survey the state of the art in fundamental building blocks, including strip, rib, and silicon nitride waveguides, with a focus on achieving ultra-low propagation loss. The review details essential components for light coupling and splitting, such as grating couplers, edge couplers, multimode interference couplers, and directional couplers, citing their typical performance metrics. Key wavelength filtering and routing components, including high-Q ring resonators, Mach–Zehnder interferometers, and arrayed waveguide gratings, are analyzed. Furthermore, we provide a comparative overview of the capabilities of major photonic foundries operating on a multi-project wafer model. The paper concludes by discussing persistent challenges in packaging and polarization management, and explores future trends driven by co-packaged optics, inverse design methodologies, and the expansion of silicon photonics into new application domains.

Bridge structural health monitoring (SHM) systems often suffer from large-scale data missing due to sensor faults, communication interruptions and other reasons during long-term operation, which seriously restricts the reliability of structural state assessment and maintenance decision-making. Compared with conventional single-channel independent modeling strategies commonly used for data imputation, their inherent neglect of spatial correlations and cross-modal causal associations among multi-source heterogeneous monitoring data such as displacement, wind speed, and temperature constrain the imputation capability, particularly when the target channel suffers from long-term continuous data loss. To address the above problems, this paper proposes a collaborative imputation framework integrating a graph attention network (GAT), a modal-aware cross-attention (MACA) mechanism and temporal encoder–decoder architecture (ITimeGAN). Firstly, the sensor feature topological graph is constructed based on the Pearson correlation coefficient, and the spatial dependency among multi-source features is adaptively learned through GAT. Then, the MACA module is introduced, which takes the target displacement as Query and environmental loads as Key/Value, and dynamically aggregates cross-modal driving information through multi-head attention. Finally, a bidirectional LSTM encoder and a unidirectional LSTM decoder are adopted to capture long-range temporal dependencies, so as to realize the accurate reconstruction of missing displacement data. Validated on the 9-dimensional real-world monitoring data from the GeoSHM system of the Forth Road Bridge (UK) under both random missing (10–50%) and continuous long-term missing (1–10 days) scenarios, ITimeGAN achieves an R2 of 0.9950 (MAE = 4.25 mm) for longitudinal displacement and 0.9759 (MAE = 6.70 mm) for vertical displacement even under 10 consecutive days of complete data absence. Ablation analysis further reveals that the incorporation of graph attention and cross-modal attention modules reduces the longitudinal displacement MAE by 57% over the baseline, with the imputation performance ranking across three displacement directions being fully consistent with the underlying physical correlation strengths, thereby confirming the effectiveness of the proposed cross-modal collaborative strategy.

This study proposes a framework that leverages natural language processing and unsupervised machine learning techniques to measure, identify, and classify examinees’ writing strategies. The framework integrates three categories of writing strategies (text complexity, evidence use, and argument structure) to identify the characteristics of examinees’ writing. Additionally, a measurement model is used to calibrate examinees’ writing proficiency. An empirical example is presented to demonstrate the performance of the framework. The data comprise 430 Grade 8 examinees’ responses to English Language Arts (ELA) assessments in the United States. Using K-means clustering, distinct patterns were identified in each category. The one-parameter logistic measurement model was applied to estimate examinees’ writing proficiency. Analyses revealed significant effects of text complexity and evidence use on writing proficiency, while argument structure was not significant. This study has implications for writing instruction and assessment design that highlight the point that effective writing is not simply a matter of isolated skill acquisition, but rather the coordinated implementation of complementary strategies, a finding that supports cognitive developmental theories of writing.

Constructed-response items offer rich evidence of writing proficiency, but the linguistic signals they contain vary with grade level. This study presents a cross-sectional analysis of 5638 English Language Arts essays from Grades 6–12 to identify which linguistic features predict proficiency and to characterize how their importance shifts across grade levels. We extracted a suite of lexical, syntactic, and semantic-cohesion features, and evaluated their predictive power using an interpretive dual-model framework combining LASSO and XGBoost algorithms. Feature importance was assessed through LASSO coefficients, XGBoost Gain scores, and SHAP values, and interpreted by isolating both consensus and divergences of the three metrics. Results show moderate, generalizable predictive signals in Grades 6–8, but no generalizable predictive power was found in the Grades 9–12 cohort. Across the middle grades, three findings achieved strong consensus. Essay length, syntactic density, and global semantic organization served as strong predictors of writing proficiency. Lexical diversity emerged as a key divergent feature, it was a top predictor for XGBoost but ignored by LASSO, suggesting its contribution depends on interactions with other features. These findings inform actionable, grade-sensitive feedback, highlighting stable, diagnostic targets for middle school while cautioning that discourse-level features are necessary to model high-school writing.

Introduction Retinal ganglion cells (RGCs) are susceptible to degenerative conditions such as glaucoma and traumatic optic neuropathies, which lead to vision loss. MicroRNA-21-5p has demonstrated potential neuroprotective effects, but its mechanisms in optic nerve injury remain underexplored. This study evaluates the neuroprotective role of microRNA-21-5p derived from induced pluripotent stem cells (iPSCs) in an optic nerve crush (ONC) model. Materials and methods In vitro qPCR demonstrated that the expression of microRNA-21-5p was increased in the co-culture medium of RGCs and iPSCs. Subsequently, in the in vivo experiments, we used a microRNA-21-5p agonist to assess its protective effects on RGCs. RNA sequencing was then performed in a mouse ONC model after treatment with a microRNA-21-5p agonist to explore the mechanisms underlying its neuroprotective effects on RGCs. Results As demonstrated in our previous experiments, the RGCs-iPSCs co-culture group led to a higher survival rate of RGCs, as indicated by live/dead cell staining, compared to the RGCs-only group. Quantitative PCR (qPCR) results revealed a significant increase in the expression of microRNA-21-5p in the medium of the RGCs-iPSCs co-culture group. Furthermore, the survival rate of mouse retinal RGCs treated with a microRNA-21-5p agonist was significantly greater than that of the control group. Lastly, RNA sequencing of the retina from microRNA-21-5p agonist-treated mice indicated that microRNA-21-5p plays a protective role in RGCs by downregulating the expression of several genes, including Irf1, Ccl4, Itk, Cxcr2, Dclre1c, Traf1, Traf2, Rbl1, Cxcl5, Cxcl3, Cxcl1, Cxcl9, Il2rg, Cd3e, Cd3d, Cxcl10, Ccl5, Ccl12, Tap1, and Cxcr4. Conclusion MicroRNA-21-5p derived from iPSCs can enhance the survival rate of RGCs in the ONC model. This suggests that microRNA-21-5p may represent a novel and effective strategy for repairing RGC damage. Such a strategy could potentially be realized through the modulation of apoptosis, T-cell regulatory pathways, or TNF-α signaling.

Soil salinization poses a serious threat to crop growth. The selection of appropriate soil modifiers and water-soluble fertilizers for saline soils represents a crucial method for enhancing crop yields. The modifiers and medium-element water-soluble fertilizers were prepared using hydrochar derived from rice straw. Two distinct experiments were designed to study the effect of modifiers and water-soluble fertilizers on saline soils. The first experiment, designated as the “Soil Cultivation Experiment” , sought to investigate the impact of various modifiers on soil quality. The second experiment, designated as the “Method of Field Micro-Area Experiment”, aimed to assess the influence of water-soluble fertilizers on saline soils. The results showed that the application of modifiers and water-soluble fertilizers significantly enhanced comprehensive soil physical and chemical properties, crop growth, soil enzyme activity, and other key indicators in saline and alkaline soils. The optimal dosage of the modifier was 20 g/kg, which reduced the pH value from 8.62 to 8.21 and the decreased alkalinity by 8.26%. Furthermore, their application effectively boosted nutrient levels, including organic matter, and increased soil enzyme activity. The biomass of alfalfa showed enhancements of 63.01% and 20.87% and the biomass of leymus chinensis increased by 29.39% and 9.02% for the two batches, respectively. Notably, the application of water-soluble fertilizer yielded achieved superior results. This study also provided a theoretical basis for their future application in soda saline–alkali soil.

Plant hormone brassinosteroids (BRs) play key roles in plant adaptation to biotic stresses, including various pathogen infections. As a core factor in BR signaling, the transcription factor BRI1-EMS-SUPPRESSOR 1 (BES1) activates BR responses via regulating the expression of target genes. However, the molecular mechanism of BRs in regulating plant immunity is unclear, and the key components are not identified. In this study, we found that BR biosynthesis and signaling transduction are essential for plant resistance to pathogen infection, and BR biosynthesis or BR signaling-deficient mutants displayed susceptibility to Pseudomonas syringae pv. tomato DC3000 ( Pst DC3000) infection [including more serious symptoms and more photosystem II (PSII) photochemistry damage]. We identified a callose synthase gene GLUCAN SYNTHASE-LIKE 8 ( GSL8 ) as a direct target of BES1, and its expression was induced by BRs/BES1. Meanwhile, BRs induced callose accumulation after Pst DC3000 infection. Moreover, BES1 gain-of-function mutant bes1-D showed promoted Pst DC3000 resistance. GSL8 T-DNA insertion mutant gsl8-1 was susceptible to DC3000, while brassinolide (BL) treatment partially rescued gsl8-1 susceptible phenotypes. Our study suggests that BR-induced pathogen resistance partly depends on the BR-induced BES1-GSL8 cascade to mediate callose accumulation.

Acupuncture is a Traditional Chinese Medicine (TCM) method that achieves therapeutic effects through the interaction of neurotransmitters and neural regulation. It is generally carried out manually, making the related process expert-biased. Meanwhile, the neural stimulation effect of acupuncture is difficult to track objectively. In recent years, virtual reality (VR) in medicine has been on the fast lane to widespread use, especially in therapeutic stimulation. However, the use of related technologies in acupuncture has not been reported. In this work, a novel acupuncture stimulation technique using VR is proposed. To track the stimulation effect, the electroencephalogram (EEG) is used as the marker to validate brain activities under acupuncture. After statistically analyzing the data of 24 subjects during acupuncture at the \"Zusanli (ST36)\" acupoint, it has been determined that Virtual Acupuncture (VA) has at least a 63.54% probability of inducing similar EEG activities as in Manual Acupuncture (MA). This work may provide a new solution for researchers and clinical practitioners using Brain-Computer Interface (BCI) in acupuncture.

This paper proposes a dynamic phase comparison algorithm for planar direction finding on a high-speed moving satellite radio receiver, treating the moving antenna as equivalent to single-baseline array antennas. Based on a phase interferometer algorithm, this algorithm adjusts the baseline length according to the frequency measurement module and the satellite’s high-speed motion to avoid phase ambiguity indirectly. By integrating the traditional amplitude comparison algorithm based on orthogonal dipole antennas, a dynamic fusion direction-finding method is proposed. Simulations demonstrate that this approach method not only covers a broader range of direction finding but also achieves higher accuracy, providing valuable insights for acquiring three-dimensional plasmagrams with space-borne plasma imagers.