Search Results (30,142)

Accurate change detection (CD) in very high-resolution (VHR, <1 m) optical remote sensing images remains challenging, as it requires effective modeling of long-range bi-temporal dependencies and robustness against label noise in complex urban environments. Existing deep learning-based CD methods either rely on convolutional operations with limited receptive fields or employ global attention mechanisms with high computational cost, making it difficult to simultaneously achieve efficient global context modeling and fine-grained structural sensitivity. To address these challenges, we propose a Mamba-based self-refinement framework for remote sensing change detection (MSRNet). Specifically, we introduce an attention-enhanced oblique state space module (AOSS) to model spatio-temporal dependencies with linear complexity while preserving fine-grained structural information. The four-branch attention fusion module (FBAM) further enhances cross-dimensional feature interaction to improve the discriminative capability of differential representations. In addition, a self-refinement module (SRM) incorporates a momentum encoder to generate high-quality pseudo-labels, mitigating annotation noise and enabling learning from latent changes. Extensive experiments on two benchmark VHR datasets, LEVIR-CD and WHU-CD, demonstrate that MSRNet achieves state-of-the-art performance in both accuracy and computational efficiency. Full article

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

Ammopiptanthus nanus (Fabaceae) is a Class II nationally protected endangered evergreen shrub in China and is endemic to the arid regions of Central Asia. To assess how contrasting ex situ management histories are associated with sequence-variant retention at an ecologically relevant gene, we analyzed a 594 bp coding fragment of the antifreeze protein gene (AnAFP) in one wild population and two ex situ collections maintained under active versus passive management contexts. Only two variable sites were detected across 75 individuals, both represented by single-base indels near the 5′ end of the coding region. The wild population contained both rare variants, the actively managed ex situ collection retained one of them at low frequency, and the passively maintained collection was monomorphic across the analyzed fragment. Rarefaction analysis indicated that the absence of variation in the passive collection is unlikely to be explained by sample-size disparity alone at this targeted locus. Because only one locus was analyzed, these results are interpreted as locus-specific patterns rather than evidence of genome-wide diversity change. Nevertheless, the observed pattern is consistent with reduced retention of rare sequence variants in the passive ex situ collection and with the possibility that a narrow founder base, together with the absence of subsequent genetic supplementation, contributed to this outcome. These results support the view that ex situ conservation of A. nanus may benefit from maximizing founder representation, maintaining sufficiently large managed collections, and combining neutral marker approaches with targeted monitoring of ecologically relevant loci. Targeted loci such as AnAFP should, however, be regarded as complementary indicators rather than stand-alone proxies for broader genetic diversity or adaptive potential. Full article

Background/Objectives: The incidence of pediatric pathological scars (PPS) has been gradually increasing due to various causes, highlighting the need for accurate scar assessment to monitor disease progression and therapeutic efficacy. Vancouver Scar Scale (VSS) and other scar evaluation systems are relatively subjective evaluation methods that rely on physicians’ or patients’ own judgment. By contrast, when comparing different scar scale evaluation methods, a three-dimensional (3D) camera and dermoscopy may provide relatively objective measurable parameters to avoid possible subjective bias created by the observers. This study aimed to compare the utility of traditional VSS evaluation with that of 3D cameras and dermoscopy in PPS evaluation. Methods: A total of 35 pediatric patients (aged 0–18 years) with PPS were involved, and their scars were assessed via the VSS, dermoscopy, and the Antera 3D^® system. In addition, a subset of 18 patients (36 scar regions) was also evaluated for therapeutic efficacy after 3–6 months of treatment. Briefly, VSS scores were blindly evaluated by two independent dermatologists under standardized conditions. Quantitative assessment was also performed using dermoscopy and the Antera 3D^® system. The former quantified chromatic parameters (pigmentation: L*, vascularity: a*, green value); the latter captured multispectral 3D images to analyze volume, pigmentation, and erythema. Data are presented as means ± standard deviation and analyzed using paired-sample t tests (one-tailed), the Wilcoxon signed-rank test, and standardized response means (SRMs) to assess therapeutic sensitivity, while baseline variability was evaluated using the standard deviation and coefficient of variation (CV). Results: The results showed that Antera 3D^® detected significant reductions in pigmentation (p < 0.01, SRM = −0.46), vascularity (p < 0.001, SRM = −0.59), and volume (p < 0.0001, SRM = −0.83), while dermoscopy indicated similar moderate improvements in vascularity (Green value: p < 0.001, SRM = 0.57; a*: p < 0.0001, SRM = −0.68) and pigmentation (L*: p < 0.0001, SRM = 0.48) after treatments. VSS showed significant gains in pliability (p < 0.0001, SRM = −1.13), height (p < 0.01, SRM = −0.54), and overall impression (p < 0.0001, SRM = −0.86), but minimal changes in pigmentation (p > 0.05, SRM = 0) or vascularity (p > 0.05, SRM = −0.21). At baseline, Antera 3D^® showed the greatest variability in pigmentation (CV 43.41%) and volume (CV 91.21%), followed by VSS in vascularity (CV 52.95%), pliability (CV 34.05%), and overall impression (CV 31.76%). Dermoscopy presented the lowest variability, indicating limited discriminative power. Conclusions: In conclusion, Antera 3D^® offers an objective, sensitive, and spatially precise approach for PPS assessment and may provide additional quantitative information for evaluating subtle and early changes alongside traditional scar assessment scales. Its integration into clinical practice will enhance treatment monitoring and support more accurate timing of therapeutic interventions. Full article

Background: Dental professionals are routinely exposed to occupational noise from high-speed handpieces and ultrasonic scalers, with levels that can reach up to 90 dB(A). While such exposure is suspected to affect cochlear function, objective assessments in this population remain limited. This study investigated short-term changes in distortion product otoacoustic emissions (DPOAEs) as a biomarker of outer hair cell (OHC) function following routine clinical dental procedures. Methods: DPOAEs were recorded at frequencies from 1000 to 6000 Hz in young dental professionals with clinically normal hearing. Measurements were obtained at three time points: prior to dental procedures (baseline), immediately after exposure (3–5 min post-procedure), and at a 48-h (follow-up). Participants were stratified into two groups based on exposure profile: those exposed to occupational dental noise alone (Group 1) and those with concurrent use of personal listening devices (PLDs) in addition to occupational exposure (Group 2). Results: A significant reduction in DPOAE amplitudes was observed immediately following dental procedures in both groups, indicating an acute effect on OHC function. This reduction was more pronounced in Group 1 (PLD users) compared to Group 2 (occupational noise only). Amplitudes returned to baseline levels at the 48-h follow-up in both groups, confirming the transient nature of the effect. The absence of significant Frequency × Time interactions indicates that the observed amplitude reductions were broadly distributed across the tested frequency range rather than confined to a specific spectral region. Conclusions: Routine clinical dental procedures can induce transient, measurable changes in cochlear outer hair cell function, detectable by DPOAEs in young professionals with normal audiometric thresholds. Although these changes appear reversible within 48 h, the greater acute response observed in individuals with concurrent personal listening device use suggests that cumulative acoustic exposure may increase cochlear susceptibility. These findings support the integration of objective cochlear monitoring into occupational health surveillance for dental personnel. Full article

Background: Parkinson’s disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta, accompanied by neuromelanin loss. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) enables in vivo visualization of these changes; however, its diagnostic and clinical utility remains incompletely defined. This study evaluated the feasibility, reliability, and biological sensitivity of semi-automated NM-MRI–based substantia nigra volumetry in PD. Methods: In this prospective case–control study, 50 participants (25 PD patients and 25 healthy controls) underwent 3T NM-sensitive MRI using a high-resolution T1-weighted spin-echo sequence. Semi-automated segmentation of hyperintense substantia nigra regions was performed using Mango v3.5.1, with intracranial volume normalization derived from FreeSurfer v7.3. Four participants were excluded due to motion artifacts, yielding a final cohort of 46 subjects. Clinical assessment included the Unified Parkinson’s Disease Rating Scale (UPDRS) Part III and Hoehn and Yahr (H&Y) staging. Group comparisons, receiver operating characteristic (ROC) analysis, and reliability testing using intraclass correlation coefficients (ICC) were performed. Results: Corrected substantia nigra volume was significantly reduced in PD patients compared with controls (18% reduction; p = 0.039, Mann–Whitney U test). Semi-automated measurements demonstrated excellent agreement with manual segmentation (ICC = 0.945). ROC analysis showed moderate discriminative performance for corrected volume (AUC = 0.700; sensitivity 68.4%, specificity 74.1%). No significant correlation was observed between corrected substantia nigra volume and UPDRS-III motor scores, while a trend toward lower SNc volume was observed with advancing H&Y stage. Conclusions: Semi-automated NM-MRI volumetry detects biologically meaningful substantia nigra volume loss in early-stage Parkinson’s disease with high measurement reliability. However, diagnostic performance was moderate and insufficient for standalone clinical diagnosis or motor severity prediction. These findings support the role of NM-MRI as a complementary imaging marker within multimodal diagnostic and research frameworks rather than as an independent diagnostic tool. Full article

Background: Understanding the temporal relationship between neurological and functional recovery after cervical spinal cord injury (SCI) is crucial for optimizing rehabilitation timing and clinical interpretation. This prospective longitudinal study aimed to investigate the temporal dynamics of clinically meaningful neurological and functional recovery in individuals with subacute SCI during inpatient rehabilitation. Methods: We enrolled 21 individuals with incomplete cSCI (AIS C and D). Evaluations were performed every 15 days, from admission up to 120 days. Recovery was defined using the Time to First Improvement based on thresholds exceeding the Minimal Detectable Change or Minimal Important Difference) for neurological scales (Upper Extremities Motor Score—UEMS, Graded Redefined Assessment of Strength, Sensation and Prehension—GRASSP subtests for Strength and Sensation) and the Minimal Clinically Important Difference for functional scales (Spinal Cord Independence Measure, GRASSP Ability and Prehension Performance). Survival analysis (Kaplan–Meier) and pairwise comparisons were used to analyze the temporal sequence of recovery. Results: Neurological and functional recovery showed a parallel macro-evolution. However, granular analysis revealed that motor strength improved significantly earlier than sensory recovery and fine motor dexterity. No significant differences were found between dominant and non-dominant limbs. Conclusions: Upper limb recovery follows a phase-specific evolution where motor strength provides the substrate for functional gains supporting a phase-specific approach to rehabilitation. Full article

The radial growth of the tree stem reflects tree responses to climate change. This study examines the response of Fagus orientalis to more than half a century of climate dynamics in Armenia using a dendrochronological approach. Two forest stands were analyzed: one geographically isolated stand in the arid southern part of the country and one stand in the mesic northern mountainous region, where the main beech forests are distributed. The study period was divided into two phases (1965–1993 and 1994–2023). Climate dynamics were assessed by the months of the biological year, from October of the previous year to the end of September of the current growing season. Substantial warming trends were detected at both stands, except in November, December, and April, and in July in the northern part of Armenia. Between periods, the mean ring width increased from 1.67 mm to 2.14 mm at the northern stand, while decreasing from 1.95 mm to 0.89 mm at the southern stand. Despite climate warming and declining precipitation, some study trees exhibited increased (northern) or stable (both stands) radial growth. Comparison of the two periods revealed pronounced ecological and tree-specific variability in climate–growth relationships, including shifts in correlation strength and sign reversals. These patterns were primarily driven by climate sensitivity rather than age-related effects. The results provide valuable insights for conserving the southern stand and may support assisted migration strategies for F. orientalis toward the southern margin of the F. sylvatica distribution range. Full article

A neural network simulation–regression method was developed to assess the impact of sanctions on the level of administrative offences under fragmented, noisy, and short administrative time series. The study addresses the problem of quantifying and predicting changes at the offence level as a sanction size function, using detection probability, prior violation level, compliance costs, and auxiliary contextual factors. The proposed framework combines a hybrid MLP–LSTM neural network, double machine learning-based orthogonal causal estimation, the simulation-based generation of counterfactual scenarios through domain randomization, multiple imputation for missing data, debiasing procedures, and ensemble uncertainty estimation. The contribution to administrative law consists of a quantitative tool creation for substantiating and optimising sanction policy, assessing heterogeneous effects, and supporting evidence-based rulemaking and law enforcement decisions. In comparative experiments, the developed method achieved an RMSE of 8…12%, a prediction accuracy of 93…96%, an overall accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 93.5%, thereby outperforming contemporary econometric, simulation, causal machine learning, and predictive machine learning approaches used for sanction effect modelling. Additional verification through nonparametric statistical testing cponfirmed that the proposed model’s superiority over the compared algorithms is statistically significant across the main quality metrics, which strengthens the evidence for its robustness and practical value in sanction policy analysis under fragmented administrative data conditions. Full article

Masson Pine (Pinus massoniana), an important afforestation species in southern China, is severely threatened by pine wilt disease caused by pine wood nematode (Bursaphelenchus xylophilus, PWN). To differentiate mortality induced by B. xylophilus from that caused by abiotic environmental factors, hyperspectral imaging and needle chlorophyll content were measured and analyzed for the early detection physiological changes in Masson pine seedlings under various environmental stressors. Four-year-old Masson pine seedlings were subjected to PWN inoculation, mechanical injury, drought, and waterlogging treatments. Hyperspectral reflectance and needle chlorophyll content of Masson pine were measured concurrently at 7-day intervals. The results showed that hyperspectral imaging responses varied among the stressors. Both PWN and waterlogging stress induced rapid mortality, with spectral changes observed as early as the 3rd week and reaching statistical significance by the 5th week. Under PWN infection, hyperspectral reflectance increased markedly in the 405–580 nm range, accompanied by a pronounced blue-shift of the red edge position (680–750 nm), while needle chlorophyll content declined sharply from approximately 0.8 mg g⁻¹ to 0.48 mg g⁻¹. Waterlogging stress produced a uniform increase in reflectance within the 500–580 nm range, with the hyperspectral curve gradually flattening, and needle chlorophyll content decreasing from 0.75 mg g⁻¹ to 0.6 mg g⁻¹. Conversely, drought-stressed seedlings exhibited only minor hyperspectral changes and maintained relatively stable chlorophyll levels, demonstrating the inherent drought tolerance of Masson pine. The RF and XGBoost models performed best in fitting the entire process of pine wood nematode infection and waterlogging stress, with all R² values greater than 0.69. The distinct hyperspectral imaging patterns under nematode infection and water-related stresses provide a reliable basis for early diagnosis and monitoring pine wilt disease in Masson pine stands. Full article

Tree ensembles—Random Forests (RFs) and Gradient Boosting Machines (GBMs)—often stabilize before all trees are evaluated. We study early stopping as a nonparametric change-point problem on prediction increments. The P²-STOP method family monitors a robust interquartile-range (IQR) scale of prediction increments online and stops when a relative-scale criterion is met. The default variant uses a rolling-window exact-quantile estimator ($O\left(w\right)$ memory), which provides a clean finite-sample stopping guarantee; a full-prefix P² streaming approximation ($O\left(1\right)$ memory) is available as a memory-light alternative. The stopping rule applies to both RFs and GBMs without model-specific distributional assumptions. On four RF benchmarks (MNIST, Covertype, HIGGS, and Credit Card Fraud), P²-STOP achieves 44.8% mean work reduction (range: 0.7–71.7%) with an accuracy change from $-0.53$ to $+0.02$ percentage points versus full-ensemble inference. On XGBoost ($T=500$), work reduction is dataset-dependent (41.4% on Covertype up to 89.0% on Credit Card), with corresponding accuracy trade-offs. Under random-tree contamination conditions (5%, 15%, and 25%), performance remains stable, whereas IQR-versus-standard-deviation baseline differences are mixed rather than uniformly dominant. Designed for compiled inference engines (e.g., C++/Numba), P²-STOP translates theoretical work reduction into consistent wall-clock speedups ($4.14\times$–$4.82\times$ versus compiled full RF on MNIST/Covertype/HIGGS for $T=500$). Native Python implementations serve purely as logical baselines due to loop overhead, while Credit Card exhibits the expected slowdown when work reduction is near zero. All comparisons use five seeds with 95% confidence intervals and seed-level paired tests. With only five seeds, inferential power is limited, and p-values should be interpreted cautiously. Relative to the Dirichlet RF baseline, our contribution is not larger RF-specific work reduction; it is a robust nonparametric IQR-scale stopping criterion, cast as a change-point/sequential-inference problem, that works as a post hoc wrapper across RF and GBM settings. Full article

As key headwater regions of the upper Yangtze River, the Yalong and Dadu River basins are expected to experience highly uncertain hydrological responses under climate warming. However, the nonlinear and spatially heterogeneous evolution of streamflow across multiple time-frequency scales remains insufficiently understood. In this study, a SWAT model driven by CMIP6 climate projections under four shared socioeconomic pathways (SSP1-2.6 to SSP5-8.5) was coupled with multivariate wavelet coherence, spatial wavelet transform, and change-point detection methods to investigate the spatiotemporal evolution of streamflow and extreme risks during 2017–2100. Results indicate that precipitation is the primary driver of streamflow variability, with streamflow responding rapidly, while air temperature mainly regulates seasonal intensity via snowmelt. Streamflow seasonal intensity exhibits a northwest-southeast gradient, with low variability upstream and high sensitivity downstream, reflecting precipitation-concentrated, forested canyons where rapid lateral flow and dry-season evapotranspiration amplify flow contrasts. Moreover, hydrological nonstationarity and extreme risks are projected to intensify, with structural regime shifts emerging in the 2040s–2050s and extreme high-flow magnitudes doubling under SSP5-8.5, accompanied by more frequent drought-flood alternations. These findings highlight an upstream buffering-downstream sensitivity pattern, emphasizing the need for spatially differentiated water resources management under nonstationary climate conditions. Full article

Background and Objectives: To assess the reliability and construct validity of the Functional Rating Index (FRI) in Italian-speaking individuals with chronic non-specific low back pain (CLBP), in order to improve assessment and clinical management in this population. Materials and Methods: This cross-sectional study consecutively enrolled 75 individuals with CLBP (52 females; mean age 48.71 ± 19.18 years; mean pain duration 298.64 ± 427.52 weeks). Internal consistency and test–retest reliability were evaluated using Cronbach’s α and the intraclass correlation coefficient [ICC_2,1], respectively, while measurement error was estimated through the minimum detectable change (MDC). Construct validity was examined by testing a priori hypotheses through correlations (Pearson’s r) between the FRI and disability measures (Roland–Morris Disability Questionnaire, RMQ; Oswestry Disability Index, ODI), pain intensity (Numerical Rating Scale, NRS), and quality of life (Short-Form Health Survey, SF-36). Results: Cronbach’s α was 0.88, and test–retest reliability showed an ICC_2,1 of 0.86 (95%CI: 0.82–0.93). The MDC was 18.05, corresponding to approximately 20% of the total score. The Italian FRI demonstrated strong correlations with the RMQ (r = 0.70) and ODI (r = 0.77), and a moderate correlation with the NRS (r = 0.60). The physical and social domains of the SF-36 showed stronger negative correlations with the FRI than the mental and emotional domains. Conclusions: The Italian version of the FRI is a reliable and valid instrument for individuals with CLBP and is recommended for both clinical practice and research applications. Full article

Objectives: This study aimed to investigate whether COVID-19 infection or COVID-19 mRNA vaccination affects ovarian reserve and reproductive hormone profiles in reproductive-aged women. Methods: This retrospective longitudinal (before–after observational) single-center study included women aged 16–44 years who presented to a tertiary center between January 2021 and September 2023. Participants either had a confirmed COVID-19 infection by a positive polymerase chain reaction (PCR) test or had received two doses of a COVID-19 mRNA vaccine without prior infection. Women with available ovarian reserve parameters within six months of infection or vaccination were included. Anti-Müllerian hormone (AMH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E₂), prolactin (PRL), thyroid-stimulating hormone (TSH), total testosterone, and free testosterone levels were evaluated at baseline and reassessed six months later. Menstrual cycle characteristics were recorded. Parametric and non-parametric statistical tests were applied as appropriate. Results: No statistically significant differences were observed in AMH, FSH, LH, E2, PRL, TSH, total testosterone, or free testosterone levels before and after COVID-19 infection or vaccination (all p > 0.05). Comparisons between infection and vaccination groups across age subgroups (<25, 25–35, ≥35 years) revealed no significant differences in ovarian reserve parameters. Menstrual irregularities were reported in 38.0% of women following infection and 18.6% following vaccination. All reported menstrual changes were transient and resolved within six months. Conclusions: COVID-19 infection and mRNA vaccination were not associated with detrimental effects on ovarian reserve or reproductive hormone profiles. Although transient menstrual irregularities were observed, no long-term adverse reproductive impact was detected. Larger prospective studies are warranted to confirm these findings. Full article

Unsupervised change detection (CD) in heterogeneous remote sensing images is intrinsically difficult due to severe sensor-specific discrepancies. In the absence of ground truth, these discrepancies result in ambiguous optimization objectives that make it difficult for models to distinguish true land-cover changes from modality-driven pseudo-changes. To address these challenges, we propose MaskUCD, a novel unsupervised framework that reformulates heterogeneous CD as a dynamic mask-driven constraint scheduling problem. Fundamentally distinct from conventional strategies that enforce selective feature alignment, MaskUCD employs a spatially adaptive optimization mechanism. Specifically, the iteratively refined mask serves as a geometric reference to guide optimization. It enforces strict feature alignment in mask-unchanged regions to suppress modality-induced discrepancies, while simultaneously promoting feature divergence in mask-changed regions to emphasize semantic inconsistencies. In this way, explicit optimization objectives are established, together with an intrinsic interpretability constraint that guides the CD process. This strategy treats the mask as a structural guide for representation learning rather than a ground-truth reference, thereby avoiding error accumulation caused by directly using inaccurate masks as supervisory signals. To facilitate this optimization, we design a specialized asymmetric autoencoder with a hybrid encoder architecture, utilizing multi-scale frequency analysis and global context modeling to enhance feature representation capabilities. Consequently, this design enables the generation of refined and semantically consistent masks, which provide increasingly precise structural guidance, yielding converged and discriminative difference maps. Extensive experiments demonstrate that MaskUCD achieves state-of-the-art performance and superior robustness compared to existing advanced methods. Full article