Search Results (3,073)

Accurate registration of multi-temporal LiDAR point clouds is essential for reliable monitoring of mining subsidence. Systematic errors in point clouds acquired at different times can arise from GNSS/INS positioning drift, sensor calibration bias, and differences in observation geometry. These errors typically manifest as global reference shifts or gradual distortions. When such errors are superimposed on real terrain changes, they can mask subsidence signals and introduce observational pseudo-differences, thereby increasing the difficulty of separating actual subsidence from artifacts. To address this issue, this study proposes Robust-Registration-Based Systematic Error Correction for Time-Series Point Clouds (RR-SEC), which establishes a consistent reference framework across epochs. The method does not assume that stable areas remain strictly unchanged. Instead, it identifies regions whose local change patterns are more temporally consistent using an information entropy analysis of multi-temporal differences. Under complex terrain, the method selects points with lower difference entropy as stable control points and uses them to constrain the registration process. It then performs Generalized Iterative Closest Point (GICP) rigid registration under these constraints to estimate the overall three-dimensional translation and rotation between point clouds from different periods. The estimated transformation is applied to the entire point cloud to correct inter-epoch reference mismatches and unify the coordinate reference across all epochs. Comprehensive validation using simulated complex terrain data containing rigid reference biases and non-rigid deformations, as well as UAV LiDAR data collected from the MuduChaideng Coal Mine, shows that, compared with the baseline GICP method, RR-SEC reduces alignment errors. It decreases the mean residual in stable areas by approximately 85%. The subsidence values computed from the corrected point clouds are more consistent with measured values, and the spatial deformation patterns are easier to interpret. RR-SEC demonstrates robust performance and can serve as a practical approach to improve the accuracy of deformation monitoring in mining areas and potentially other geoscientific applications. Full article

Anilido-oxazoline iron(II) chloride complexes were synthesized and evaluated for their catalytic performance in the ring-opening polymerization (ROP) of cyclic esters. Complexes 1–5 were obtained via transmetalation of FeCl₂(THF)_1.5 and pyridine derivatives with in situ generated anilido-oxazoline lithium. They exhibited excellent controllability and high initiating efficiency in the ROP of ε-caprolactone (CL). In the presence of benzyl alcohol as the initiator, these iron complexes efficiently catalyzed the ROP of CL, reaching a TOF of 3.2 × 10³ h⁻¹. High molecular weight polycaprolactone was obtained with a number-average molecular weight of 161.38 kg/mol. The chain initiation and propagation processes were investigated using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and kinetic analyses. Kinetic studies confirmed a pseudo-first-order dependence of the polymerization rate on catalyst concentration. Furthermore, the iron(II) complexes were also found to be efficient catalysts for the ROP of δ-valerolactone. Full article

Predicting the future impact of technical economic documents at release time is challenging due to delayed supervision signals, long-tailed label distributions, and time- and domain-dependent shifts in language and topics. Moreover, similarity graphs derived from text embeddings can be noisy due to boilerplate and evolve under temporal drift, making robustness and leakage-free evaluation essential. We formulate grant-time patent impact prediction as a node classification and within-domain ranking problem on a large-scale semantic similarity document graph built from patent text embeddings, avoiding any future citation leakage. The document graph is constructed via ANN Top-K retrieval and similarity thresholding, enabling scalable and reproducible sparsification on hundreds of thousands of nodes. We propose GraphGPT-Patent, which adapts a reversible graph-to-sequence foundation backbone to local subgraphs extracted from the similarity network. The model incorporates time- and domain-conditioned edge reliability to suppress drift-induced and template-driven pseudo-similarity, and optimizes a joint objective coupling high-impact classification with ranking consistency within comparable groups. Experiments on USPTO granted patents (2000–2022) across three high-volume CPC domains and three evaluation horizons show consistent gains over text-only and GNN baselines, achieving up to 0.94 recall for the positive class and improved macro-average recall across nine settings. Temporal shift analyses further quantify the effect of training-data freshness, while explanation subgraphs provide auditable structural evidence of model decisions. The proposed framework offers an effective graph-based learning pipeline for scalable impact prediction and downstream triage under strict information constraints. Full article

Drought stress severely restricts the growth of pear trees. As a widely used drought-tolerant rootstock, Pyrus betulifolia exhibits stable growth performance; however, the molecular mechanisms underlying its drought tolerance remain to be elucidated. In this study, we investigated the molecular responses of P. betulifolia leaves to osmotic stress induced by 20% PEG-4000 using time-series RNA-seq technology. A total of 3745 differentially expressed genes were identified, with transcriptional changes peaking at 6 h, indicating a critical phase of transcriptional reprogramming during drought response. Genes associated with osmotic adjustment (e.g., P5CS) and oxidative stress responses (e.g., SOD and POD) were significantly upregulated between 6 and 12 h. Weighted gene co-expression network analysis (WGCNA) identified three distinct temporal modules and screened out NF-Y, RVE1, COL9, COL6, C2C2 zinc finger proteins, and Pseudo ARR-B as putative key regulators, whose expression patterns were validated using qRT-PCR. Collectively, these results provide a comprehensive view of the temporal transcriptional dynamics of drought response in P. betulifolia and offer valuable candidate gene resources for further functional studies and drought tolerance breeding. Full article

This study investigates orange peel valorisation through KOH pre-treatment and high-temperature pyrolysis (800 °C) to develop a highly porous activated char for the efficient removal of phenolic compounds, specifically 2,4-dinitrophenol (DNP) and aminophenol (AP), from water. The main objective of the study is to synthesise high-surface area activated char from orange peel and investigate its performance for the adsorption of DNP and AP from water. The synthesised adsorbent exhibited a Brunauer–Emmett–Teller (BET) specific surface area of 965 m²/g, contributing to its excellent phenol adsorption efficiency. Batch adsorption experiments were performed, and a maximum removal efficiency of 99% and 92% was observed at pH 4 and 7 with initial concentration 50 mg/L, contact time 60 min, and adsorbent dosage 0.6 g/L, for DNP and AP, respectively. The adsorption process was described by the Langmuir isotherm model (R² = 0.99), indicating monolayer adsorption and followed pseudo-second-order kinetics, achieving a maximum adsorption capacity of 366 mg/g for DNP and 341 mg/g for AP. Furthermore, DFT analysis revealed that DNP possesses a lower HOMO-LUMO energy gap (−0.54 eV), favouring a stronger adsorption interaction, whereas AP exhibited a relatively higher energy gap (−0.27 eV), corresponding to its comparatively lower adsorption capacity. Overall, the findings demonstrates that a single step chemical-thermal conversion of orange peel into biochar-based adsorbent offers a sustainable pathway for the removal of phenolic compounds from water. Full article

Remote sensing scene classification is one of the crucial techniques for high-resolution remote sensing image interpretation and has received widespread attention in recent years. However, acquiring high-quality labeled data is both costly and time-consuming, making unsupervised domain adaptation (UDA) an important research focus in scene classification. Existing UDA methods focus primarily on aligning the overall feature distributions across domains but neglect class feature alignment, resulting in the loss of critical class information. To address this issue, a cross-layer feature fusion and attention-based class feature alignment network (CFACA-NET) is proposed for unsupervised cross-domain remote sensing scene classification. Specifically, a multi-layer feature extraction module (MFEM) consisting of a cross-layer feature fusion module (CFFM), a multi-scale dynamic attention module (MSDAM), and a fused feature optimization module (FFOM) is designed to enhance the representation ability of scene features. A high-confidence sample selection module is further introduced, which utilizes evidence theory and information entropy to obtain reliable pseudo-labels. Finally, a class feature alignment module is proposed, incorporating a two-stage training strategy to achieve effective class feature alignment. Experimental results on three remote sensing scene classification datasets demonstrate that CFACA-NET outperforms existing state-of-the-art methods in cross-domain classification performance, effectively enhancing cross-domain adaptation capability. Full article

Background: Voluntary activation testing quantifies the ability of the motor nervous system to produce maximal force. Laboratory assessment of ankle plantar flexor voluntary activation is common, but field testing in practical settings is limited by equipment portability. We aimed to compare plantar flexor voluntary activation and maximal voluntary contraction (MVC) using a portable device with a standardised laboratory method and evaluate the test–retest reliability of the portable protocol. Methods: We performed a pseudo-randomised, crossover design. Participants completed two protocols: (1) portable force plate testing and (2) a laboratory-based isokinetic dynamometer. Voluntary activation was assessed using twitch interpolation via tibial nerve stimulation. Differences between protocols were analysed using generalised estimating equations. Reliability was assessed with the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and coefficient of variation (CV). Results: Twenty healthy participants (8 females, 12 males; median age 28.5 years) were included. No difference between protocols was detected for voluntary activation (β = 0.6, p = 0.68). The portable protocol demonstrated good reliability (ICC = 0.85) and low measurement error (SEM = 2.56%, CV = 2.79%). Conclusions: We demonstrated that the portable protocol is a valid and reliable method for assessing plantar flexor voluntary activation. It is suitable for assessing within-subject changes over time and can reduce participant attendance burden for neurophysiological muscle testing. Full article

We forecast monthly Canadian real GDP growth using machine learning models trained on Official macroeconomic indicators and Google Trends (GT) data. Predictors are selected dynamically in each rolling window using PDC-SIS, with cross-validation-based tuning to support real-time forecasting and avoid data leakage. The evaluation is conducted on the latest-available (final-vintage) series and should be interpreted as a pseudo out-of-sample forecasting exercise rather than real-time vintage nowcasting. We evaluate GBM, XGBoost, LightGBM, CatBoost, and Random Forest against an ARIMA baseline. Official data deliver the strongest performance at short and medium horizons, while combining Official and GT data yields the clearest improvement at the longest horizon. With GT data alone, LightGBM is the only ML model maintaining positive out-of-sample R² across all horizons. Diebold–Mariano tests corroborate these patterns: LightGBM dominates other ML models under GT-only predictors, whereas with Official and combined data, the horizon-specific best models significantly outperform ARIMA, with smaller differences among leading tree-based methods. Full article

The glioma tumor microenvironment (TME) exhibits profound heterogeneity that drives tumor progression and therapy resistance. By integrating single-cell RNA sequencing (eleven samples) and spatial transcriptomics (two samples), the cellular components of the glioma microenvironment were deconvoluted, revealing tumor-associated foam cells (TAFCs) as the most abundant and centrally connected subtype. The high expression of two prognostic candidate genes, growth differentiation factor 8 (GDF-8, also known as myostatin, MSTN) and transcription factor 12 (TCF12), in TAFCs was found to be correlated with poor overall survival. These two genes were associated with M2 macrophage infiltration, altered cholesterol homeostasis, and immunosuppressive signaling. Regulatory network and pathway analyses, based on computational motif enrichment and co-expression analysis, linked them to ribosome, Notch signaling, DNA repair, and cell-cycle pathways. Pseudotime trajectories revealed dynamic expression during differentiation. Additionally, drug sensitivity prediction analysis demonstrated that MSTN expression was significantly associated with sensitivity to paclitaxel and VE-822, while TCF12 expression showed potential associations with sensitivity to cytarabine, olaparib, Wee1 inhibitor, paclitaxel, and VE-822. Logistic regression analysis combining clinical parameters with MSTN and TCF12 expression effectively achieved risk stratification for glioma, with higher composite scores predicting worse 2- and 3-year survival outcomes. Calibration curves demonstrated high consistency between nomogram-predicted overall survival probabilities and actual observed outcomes. Immunofluorescence confirmed upregulated expression of MSTN and TCF12 in glioma tissues and their co-localization with macrophages. In conclusion, this study identified TAFCs as the central cells in the glioma microenvironment, with their signature genes MSTN and TCF12 representing candidate immunometabolic signatures associated with macrophage-mediated immunosuppression and metabolic reprogramming in glioma, suggesting their potential as biomarkers for patient stratification and as targets for immunometabolic therapies. Full article

3D printing (3DP) of cement-based systems (CBSs) is a highly demanded technology in the construction field. Material requirements include specific rheological conditions for proper extrusion, followed by fast stiffening and strength gain to allow the construction process to continue, taking into account variable environmental conditions if the construction is on-site. To guarantee quality control of the process, it is essential to define field-oriented testing methodologies that allow real-time monitoring of mechanical properties’ evolution of the printed material, which will govern construction speed. This study evaluates the cone penetration test (CPT) method as a field-oriented test method to estimate the mechanical properties of 3DP CBSs over time. CPT penetration depth measurements were used to calculate shear yield stress and fresh compressive strength over time for 90 min. The experimental results were compared to two widely used laboratory tests: the fresh compressive strength test (squeeze test—SQT) and DSR test (vane test—VT). CBS pastes with and without fly ash and with three inorganic modifiers (nanoclays) and two types of organic rheology-modifying admixtures were considered. The results showed that CPT is highly conditioned by the stiffness of the paste, measured by the compressive Young Modulus (E), overestimating CBSs’ strength. The increase in E over time showed an inflection point at 130 kPa, corresponding to the evolution from plastic to pseudo-rigid behavior in the pastes. The corresponding time was used to define a linear adjustment for the average strength calculated using the CPT regarding both the fresh compressive SQT and shear yield stress VT. Full article

The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and web-based framework designed to execute and analyze the results of statistical tests defined in the NIST SP 800-22 standard, enabling systematic assessment of entropy sources and random numbers generators in IoT devices and environments. The proposed system integrates a Python-based backend built upon an optimized implementation of the original NIST suite, along with an intuitive web interface that facilitates configuration, monitoring, and parallel execution of tests through Representational State Transfer (REST) endpoints. Session management based on Redis ensures reliable and concurrent operation of multiple users or devices while maintaining isolation and data integrity. To demonstrate its applicability, an emulated IoT ecosystem was implemented in which multiple virtual devices periodically and asynchronously request real-time validation of their local random numbers generators. The obtained results confirm the system’s capability to detect deficiencies in pseudo random generators and validate true random number sources, highlighting its potential as a diagnostic and verification tool for distributed IoT security systems. The tool developed in this work is fully accessible to the public, allowing researchers, engineers, and practitioners to evaluate random number generators without requiring specialized hardware or proprietary software. Full article

Background/Objectives: HPV status is a key prognostic determinant in head and neck squamous cell carcinoma (HNSCC), yet the immunological mechanisms underlying the survival advantage of HPV-positive (HPV⁺) over HPV-negative (HPV⁻) disease remain poorly defined. This study aimed to characterize the tumor-infiltrating natural killer (NK) cell landscape in HPV-stratified HNSCC and identify novel therapeutic targets. Methods: We performed an NK-cell-centric re-analysis of published scRNA-seq data from 28 HNSCC patients (10 HPV⁺, 18 HPV⁻; GEO: GSE139324, GSE164690), encompassing NK subset identification, pseudotime trajectory inference, and cell–cell interaction analysis. Key findings were validated by immunohistochemistry (IHC) in an independent cohort of 10 FFPE tissue sections, and prognostic associations were assessed using TCGA-HNSC data. Results: Four transcriptionally distinct NK cell subsets were identified: adaptive, cell-killing, CD56^bright, and virus-responsive. A cytotoxic CX3CR1⁺KLRB1^dim NK subset was specifically enriched in HPV⁺ tumors and independently associated with favorable survival. Conversely, HPV⁻ tumors upregulated CLEC2C and CLEC2D ligands on tumor cell surfaces, engaging the inhibitory receptor KLRB1 on NK cells; this CLEC2–KLRB1 axis correlated with suppressed NK activity and poorer prognosis, and was confirmed at the protein level by IHC. Conclusions: NK cell function in HNSCC is dichotomously regulated by HPV status. The CX3CR1⁺KLRB1^dim subset represents a candidate prognostic biomarker in HPV⁺ disease, and the CLEC2–KLRB1 axis is a targetable immune evasion mechanism in HPV⁻ HNSCC. These insights support the development of HPV-stratified immunotherapies; however, clinical translation requires validation in large, prospectively designed, subsite-matched cohorts to disentangle HPV-specific effects from anatomical site-dependent immune contextures. Full article

Mangrove extent has increased in many regions under strengthened conservation policies and large-scale restoration programs. Nevertheless, mangrove ecosystems continue to face multiple pressures, including limited total area, habitat degradation, biodiversity decline, and biological invasion, and localized deterioration in ecosystem structure and function has been increasingly reported. Despite extensive mapping efforts, the spatiotemporal dynamics of mangrove degradation—particularly in tidally influenced environments—remain insufficiently understood. Focusing on the Nanliu River Delta, the largest deltaic mangrove system in the Northern Beibu Gulf of China, this study integrates long-term Landsat time-series imagery (1990–2025) with deep learning to quantify both mangrove extent change and canopy degradation. To mitigate tidal inundation effects, a NDVI Pseudo-P75 compositing strategy was applied using Google Earth Engine (GEE), enabling consistent observation of mangrove canopies across tidal stages. Global Mangrove Watch v4 (GMW-V4) and HGMF2020 mangrove dataset for China were used as reference labels to train a ResNet34–UNet segmentation framework incorporating Digital Elevation Model (DEM) constraints. The model achieved high classification performance, with an IoU of 0.822 for mangroves and 0.981 for background, yielding a mean IoU of 0.902. The resulting maps, following manual verification, provided a robust basis for spatiotemporal and degradation analyses. Canopy condition was further assessed using the Enhanced Vegetation Index (EVI), which is less prone to saturation in high-biomass mangrove stands. Results show that mangrove area in the Nanliu River Delta expanded from 266 ha in 1990 to 1414 ha in 2025, with the annual expansion rate after 2005 being nearly seven times higher than that before 2005. Despite this net gain, a cumulative loss of 347.45 ha was recorded, primarily during 1990–2000, with approximately 70% converted to aquaculture and coastal engineering. Spatial analysis revealed that mangrove expansion occurred predominantly seaward, whereas both mangrove loss and canopy degradation exhibited an inverse J-shaped relationship with seawall proximity. More than 80% of mangrove loss occurred within 200 m of seawalls, indicating concentrated anthropogenic encroachment, while 75.6% of canopy degradation was observed within 350 m, potentially reflecting landward forest senescence. These results indicate a transition in dominant threats from permanent land conversion in the late 20th century to more subtle, internal functional degradation in recent decades, underscoring the need to complement extent-based assessments with canopy condition monitoring in mangrove conservation and management. Full article

Periodic supercell lattice structures with elements of random polydispersity disorder were created to simulate the effect of randomization on photonic crystals using finite-difference time domain (FDTD) methods. As a key exemplar system, a three-dimensional “inverse opal” structure of a face-centered cubic lattice with air spheres in a silicon dielectric was simulated, with sphere radii within supercells following a randomized Gaussian distribution, with characteristic standard deviation and mean. A corresponding ordered lattice with a bandgap with magnitude 3.5% of the normalized frequency range was used as a direct control, with sphere radius 0.34 times the lattice constant a. For a range of standard deviations, up to 5.9% of the 0.34a mean, a Monte Carlo-style approach was adopted, with photonic band properties analyzed over a large number of repeat simulations to ensure statistical significance. The corresponding Gaussian distribution in the resultant photonic bandgap magnitudes is broadened with increasing polydispersity such that an evolving fraction of simulations no longer exhibits a non-zero bandgap. A characteristic pseudo-transition occurs at a standard deviation of approximately 4.1% of the 0.34a mean, above where the frequency of simulations still returning a finite bandgap rapidly diminishes. Some isolated configurations, with a high degree of uniqueness, can exhibit enhanced bandgap properties (greater than the 3.5% benchmark) despite considerable polydisperse disordering; we envisage that these findings point towards the use of engineered randomness in supercell systems to create desired photonic crystal properties and functionality, such as localization and photonic bandgaps. Full article

The use of adsorbents based on naturally occurring materials to eliminate antibiotics from industrial effluents has attracted remarkable interest owing to the abundance of raw materials and the sustainability of this method. The ciprofloxacin (CIP) removal capacity of a previously synthesized antimicrobial hydrogel based on carboxymethyl chitosan (CMCs)/xanthan gum (XG) was investigated for the first time in this study. CMCs and XG were blended in an equivalent-weight ratio and crosslinked using trimellitic anhydride isothiocyanate (TAI) to synthesize an eco-friendly, low-cost hydrogel, which was characterized using FTIR, SEM, and XRD analyses. The pseudo-second-order model fitted the experimental data well: the experimental q_e (49.59 mg g⁻¹) was close to the theoretical value (51.81 mg g⁻¹). The Langmuir isotherm best fitted the adsorption results (R² = 0.999), with a maximum adsorption capacity of 147.06 mg g⁻¹. The thermodynamic results indicate that adsorption is spontaneous, favorable, and exothermic in nature. The percentages of desorption obtained were 95.72, 94.34, 89.52, 88, and 86.28% after five consecutive cycles. Thus, this hydrogel possesses potential for further testing and application in wastewater remediation. Full article