Search Results (7,723)

Weakly textured closed pipelines—including energy and buried drainage conduits—require timely inspection of cracks, corrosion, joints and related defects. Weak illumination, sparse texture and repetitive cylindrical geometry degrade visual odometry and RGB-D SLAM, especially along the pipe axis, while image-level detectors rarely provide the axial distance, circumferential angle and pipe-frame 3D coordinates needed for maintenance. This paper presents the Structure-Constrained Pipe Joint Estimator (SC-PipeJE), an RGB-D framework that jointly estimates robot poses and 3D damage locations under weak texture. SC-PipeJE improves YOLOv8-seg for structural landmarks and damage masks, fits cylinders and centerlines from local point clouds, and optimizes a sliding window that couples RGB-D odometry with continuous geometric residuals, discrete landmarks and multi-frame damage factors so that damage observations also refine pose. On a primary RGB-D corpus of 5468 annotated frames and multi-structure sequences, the detector reaches mAP@0.5:0.95 of 0.7194 and Mask AP of 0.6980. Absolute trajectory error is 0.0343 m (67.6% lower than RTAB-Map under the same RGB-D protocol), and multi-frame damage localization yields 32.88 mm mean 3D error (46.6% lower than single-frame back-projection) at about 20 FPS. Quantitative pose and 3D damage results are reported on the laboratory RGB-D Corpus A; a complementary CCTV subset (Corpus B) is used only for qualitative appearance stress checks and is not mixed into the quantitative protocol. Ablation and robustness studies show that continuous geometry, discrete landmarks and multi-frame damage factors provide complementary observability when appearance cues are unreliable. Full article

Bovine tuberculosis (bTB) remains endemic in Morocco, where a national control strategy is implemented through screening and culling of infected animals. In this study, seven Mycobacterium bovis (M. bovis) strains were analyzed. Three strains were isolated in 1987, two in 2015 and two between 2022 and 2023. Bacterial culture and isolation were performed according to the protocol recommended by the World Organization for Animal Health (WOAH). PCR confirmed isolates as M. bovis within the Mycobacterium tuberculosis complex (MTBC). Whole genome sequencing (WGS) was performed (short-read sequencing, Illumina platform), followed by phylogenetic tree construction, and determination of clonal complexes and sub-lineages. Six of the seven strains clustered within the Eu2 clonal complex (CC) and sub-lineage La1.7.1, whereas one strain belonged to the CC unknown 2 and sub-lineage La1.2. Single nucleotide polymorphism (SNP)-based analysis revealed genetic distances ranging from 0 to 204. Low SNP distances were observed between the pairs B2–EL2562 and B3–EL2562. High-impact coding SNPs showed variation, although lineage-specific patterns could not be reliably assessed due to the limited sample size. Comparison of M. bovis strains isolated in different years described the distribution of genetic lineages across time in Morocco, based on the available isolates. Within this limited set of isolates, the Eu2 CC (La1.7.1 sublineage) and the unknown2 CC (La1.2 sublineage) were identified across multiple time points; however, given the limited sample size, these findings should be considered descriptive and do not allow conclusions regarding persistence, evolutionary trends, or transmission dynamics. These findings may offer useful information to strengthen the national bTB control strategy and support One Health surveillance of this zoonotic pathogen in Morocco. Full article

The interplay between particle-specific hydrodynamics (settlement velocity, shape-specific drag, resuspension thresholds) and landscape-scale forcing (population-specific density, tidal asymmetry, sedimentological trapping, longshore drift) determines the fate of microplastics (MPs). The central challenge in the present study is the recognition that the existing literature suffers from a conceptual disconnect between the landscape-scale patterns of MPs and their physical dynamics at the particle scale: the former ignore the processes of vertical partitioning and coastal export, while the latter fail to acknowledge the heterogeneity of spatial clusters and source areas. First, we utilize published data on the abundance and composition of MPs in the Sebou Estuary and Atlantic Coast of Morocco to establish a novel conceptual–hydrodynamic–spatial model that simultaneously accounts for these processes. Published data include 18 stations with 10–298 (~300) particles kg⁻¹ in sediments and 10–168 particles m⁻³ in waters sampled on 16 December 2020. Second, we explore the relative roles of size and polymer-specific density, shape-specific drag, and tidal asymmetry in the observed patterns using a modified Stokes–Ganser settling solution, tidal pumping with Shields criterion, Getis–Ord Gi clustering analysis, ecological topology, non-metric multidimensional scaling, hierarchical clustering, and 2D predictive risk mapping. Fibers (PET/nylon, ρ ≈ 1380 kg/m³) settled 3.2–4.0× faster than fragments of the same size (PE/PP, ρ ≈ 920 kg/m³) according to the settling model, explaining the finding that fibers were abundant in Sebou sediments but rarely detected in water. Granules (industrial pellets, 1–5 mm) were not found in water as they rapidly settled to the bottom (at >38 mm/s) and accumulated on the 2 m deep bed within 10–52 s. Bed shear stresses (τ_b) in neap tides (τ_b ≈ 0.18 Pa) were lower than critical resuspension stresses (τ_c) of all sinking MPs, meaning that the bed retained them, while spring currents (τ_b ≈ 2.16 Pa) were sufficiently turbulent to erode particles > 0.1 mm and re-suspend them in the water column. Consequently, their export followed a fortnightly cycle determined by the critical shear-stress analysis with the Shields criterion. According to Getis–Ord Gi analysis, Kenitra Harbor (E5: Z = 11.85, p < 0.01; E6: Z = 8.94) and Mehdia Harbor (E1: Z = 6.42; E2: Z = 5.18) were associated with four statistically significant spatial clusters of MPs that showed a similar pattern of contamination, rather than being isolated anomalies. Non-metric multidimensional scaling (stress = 0.029) confirmed the presence of three distinct contamination regimes, namely background (reference), coastal transport corridor (B7–B10), and urban-sewage (E1, E2, E5, E6), as did hierarchical clustering. The correlation network analysis identified a diagnostic film–granule anti-correlation (r = −0.72) that could be used to distinguish domestic plastics from industrial pellets without spectral analysis. Consistent with the overall pattern, 1D advection–diffusion–decay modeling of the longshore current indicated that MPs would accumulate at Oulad Berjal Beach (B10) with a northward drift of ~0.3 km/day. According to the Monte Carlo simulation of n = 10,000 iterations, a 22% variation in settling velocity would account for a 35% variation in the transport distance. The Sebou Estuary is a local microplastic hotspot with a self-sustaining contamination cycle, while the most promising options for remediation target the identified clusters of anthropogenic input. Full article

Social isolation is known to cause a variety of behavioural abnormalities in many different species. Here we perform a detailed behavioural analysis of how chronic social deprivation alters behaviours in the fruit fly, Drosophila melanogaster, within an arena. We identify specific, quantifiable measures to capture behaviours of interest using the previously established cTrax and MATLAB BehavioralMicroarray toolkits. Socially isolated Drosophila exhibited behavioural changes in almost all measures assessed, identifying a surprisingly wide range of behavioural deficits. We observe that both male and female isolated flies demonstrate reduced locomotion (walk distance and speed), are more avoidant of other same-sex individuals (increased social distancing), and show reduced clustering into same-sex groups. Because these measures of sociality are assessed in same-sex groups, they are independent of sexual motivations. For the majority of measures taken, we observe the same behavioural changes in isolated male and female flies. In the brain, we observe that social isolation leads to a reduction in synaptic proteins specifically within dopamine neurons, as well as across the whole brain in both sexes, consistent with the varied behavioural abnormalities observed upon social isolation. Full article

The present national observational cohort study investigated in patients followed for long COVID—mostly hospitalized during acute infection—the presence, severity and evolution of dyspnea and its functional correlates. Dyspnea was assessed with the modified Medical Research Council (mMRC) dyspnea scale and its functional correlates via six-minutes walking distance (6MWD). A total of 217 individuals were evaluated. The mean difference in 6MWD between patients with and without dyspnea (mMRC score ≥ 1) was 87 m (95%CI 56–117, p < 0.001). Significant declines were observed between four and eight months from COVID-19 in prevalence of dyspnea (from 56.7% to 36.4%, p < 0.001), mean mMRC score (from 0.95 to 0.63, p < 0.001), and proportion of patients with 6MWD ˂ 65% of predicted value (from 15.5% to 9.4%, p = 0.035). Each additional point in mMRC score corresponded to −56.3 m walked at first assessment and to −47.7 m walked at second assessment. In the longitudinal analysis, each unitary mMRC score increase between the two assessments corresponded to a concurrent 6MWD reduction of 22.5 m (95%CI −40.1 to −4.8 m, p = 0.013). Although the prevalence of dyspnea reduced significantly over time, roughly one-third of patients remained affected eight months after COVID-19. The 6MWD was a sensitive measure in capturing the functional correlates of presence, severity and evolution of dyspnea. Full article

Oxidative Balance Score (OBS) summarizes diet- and lifestyle-related exposures relevant to redox homeostasis, but the evolutionary behavior of OBS-associated epigenetic markers remains unclear. In this study, we conducted a covariate-adjusted epigenome-wide association study (EWAS) in Korean Genome and Epidemiology Study (KoGES) cohorts, adjusting for OBS and cell-type proportions. We identified 26 robust OBS-associated CpG sites. We then mapped these loci to the Mammalian Methylation Consortium dataset (GSE223748) and examined age-related methylation dynamics across nine mammalian species, using chronological age as a proxy for cumulative oxidative burden. Cross-species analyses showed that human correlation patterns were only partially preserved, with greater concordance observed in primates than in several non-primate species. Furthermore, 1000-iteration bootstrap resampling and null-model sensitivity analyses (Robinson-Foulds distance) demonstrated that the interspecies clustering topology of these 26 CpGs is statistically robust and distinct from random genomic noise. These findings suggest partial evolutionary preservation of age-related methylation behavior at human OBS-associated loci, providing a comparative framework for redox-related epigenetic dynamics. Full article

Wearable physiological sensing is widely used in driving research to infer drowsiness and stress, but whether driving context itself is reflected in wearable signals, independent of any drowsiness label, is rarely examined directly; any such signal reflects the driver’s physiological response to context, not the road environment itself. We analyzed 1536 non-overlapping 60-s windows from 18 driving sessions completed by 11 drivers wearing an Empatica EmbracePlus across three fixed real-world routes (Rural, Highway, Urban; round-trip distances 22.4–47.1 km). Because these windows are correlated pseudo-replicates of the 18-session experimental unit rather than independent observations, we report both the window-level mixed-effects comparison a naive analysis would present as primary (27 of 32 features significant after false-discovery-rate correction) and a corrected session-level analysis with driver-clustered standard errors, covariate adjustment for trip duration, sleep, pre-drive sleepiness, and route order. Using a single omnibus test per feature, only 2 of 32 features (skin conductance response count and accelerometer movement count) remain significant once the statistical unit matches the experimental unit and pairwise multiplicity is properly controlled. A random forest evaluated with leave-one-driver-out cross-validation achieved only 34.9% accuracy (macro-F1 = 0.33; 95% CI [20.0%, 49.9%]) against a 27.0–32.7% baseline, versus 90.6% accuracy (95% CI [89.1%, 92.0%]) under a naive ungrouped cross-validation that leaks driver identity across folds; the 55.6-point gap is clearly distinguishable from zero (95% CI [42.0, 69.2]). A four-way modality ablation (physiology, accelerometry, temperature, all combined) found no dominant sensor channel and the combined-channel model did not outperform accelerometry; these results are consistent with a diffuse cross-modal signature rather than a physiology-specific one. Driving context is statistically distinguishable from wrist-worn wearable signals once analyzed at the correct statistical unit, but the signal is diffuse across sensor channels, and person-independent classification remains weak at this sample size; naive cross-validation and uncorrected pairwise-multiplicity designs common in this literature can substantially overstate both classification and statistical testing results. Full article

Machine-learning intrusion detection for the Internet of Things (IoT) routinely exceeds 99% accuracy on single datasets but fails when transferred to new networks or flow exporters. We formalize five failure modes, define a 23-feature canonical schema, and adapt three datasets (CICIoT2023, TON_IoT, Bot-IoT) to build a full six-pair transfer matrix across three classifiers, each with three random seeds. Random forest attains the highest mean AUC (0.740), yet its balanced accuracy collapses to chance (0.50–0.51) exclusively on CICFlowMeter-sourced pairs while remaining strong (0.72–0.87) on Zeek- and Argus-sourced pairs. This exporter-dependent collapse is reproduced across structurally unrelated classifiers, establishing it as our central finding. A controlled synthetic test confirms that CICFlowMeter’s directional heuristic destroys variance (up to 10⁴ reduction) and causes a small, consistent transfer cost (+0.004 AUC), though full degradation requires compounded effects. Aggregate distributional distance does not predict transfer success (r = −0.17), ruling out a simple divergence explanation. Prior correction provides the largest ablation gain. Source-domain coverage is necessary but not sufficient for transfer, and we observed no universal sample-count threshold. Flow exporter provenance emerges as a major upstream confounder and a directly implicated contributing mechanism, though exporter identity is confounded with dataset identity and is not established as the sole determinant of cross-dataset performance. Full article

Historic urban areas are important repositories of urban history, cultural memory, and local identity. Yet how historical and cultural resources (HCRs) are associated with street vitality, particularly in comparison with streetscape factors, remains insufficiently understood. Taking Chengdu’s historic urban area as a case study, this study integrates streetscape images, point-of-interest (POI) data, and HCR data. It employs machine learning with model interpretation techniques to identify key predictors and nonlinear relationships. The study evaluates the associations of HCRs with street vitality in terms of density, hierarchy, and accessibility, while also assessing streetscape factors within the same analytical framework Results show that HCRs are among the most important explanatory variables associated with street vitality. General HCRs are associated with broadly distributed everyday vitality, whereas high-tier HCRs show stronger localized associations with vitality patterns. The association between high-tier HCRs and street vitality exhibits a clear distance-decay pattern, with the strongest positive associations observed within approximately 650 m. Streetscape factors also exhibit threshold effects: the positive association between vegetation and vitality levels off above a visual proportion of approximately 0.25, whereas building visual proportions between 0.20 and 0.40 are associated with greater vitality. This study broadens the analytical framework for street vitality by incorporating HCRs as key explanatory factors and examining their associations with vitality patterns. Full article

This study examines whether pro-climate lobbying intensity is associated with corporate default risk. Using a panel of 4176 firm-year observations from U.S.-listed firms, we measure financial stability using distance-to-default and pro-climate lobbying intensity as annual pro-climate lobbying expenditure scaled by total assets. Fixed-effects estimates show that pro-climate lobbying intensity is positively and significantly associated with distance-to-default, indicating lower default risk. Economically, a one-standard-deviation increase in lobbying intensity corresponds to an approximately 0.084-unit increase in distance-to-default, equivalent to 1.39% of its sample mean. The evidence is consistent with signaling theory, as costly climate engagement may signal transition preparedness, and with stakeholder theory, as alignment with climate-conscious stakeholders may lower regulatory, reputational, and financing risks. The relationship remains evident after entropy balancing, controlling for lagged distance-to-default in a dynamic specification, and replacing distance-to-default with the Altman Z-score. It is also qualitatively robust to replace the comprehensive lobbying measure with a narrower text-based proxy that identifies pro-climate lobbying through explicit climate-related keywords. Split-sample analyses show a stronger association among firms with at-or-above-median environmental and social performance and among firms with at-or-above-median cash-flow and earnings volatility, suggesting that climate-policy engagement is most informative under greater operating uncertainty; these patterns remain descriptive pending formal coefficient-comparison tests. Overall, the study contributes to the corporate political activity, climate-finance, and credit-risk bodies of literature by showing that pro-climate lobbying carries information relevant to financial resilience and that its relevance varies with firms’ sustainability performance and operating uncertainty. Full article

The spring–mass model (SMM) provides a whole-body representation of elastic behavior during running. Effective contact time (t_ce), effective aerial time (t_ae), rebound asymmetry (REB), vertical stiffness (k_vert), and step frequency (SF) describe complementary temporal and mechanical features of elastic bouncing, yet little is known whether these characteristics change across a competitive distance-running season. Therefore, this study examined pre- to post-season changes in SMM-derived elastic bounce characteristics in university runners tested at the same within-runner velocity. Twelve university runners from the same team completed a six-minute treadmill run with synchronized motion capture before and after a seven-week season. Bilateral kinematics and kinetics were collected and elasticity variables computed. No significant changes were observed in elastic bounce characteristics. SF increased by 1.65 steps/min (p = 0.409, d = 0.25), but not significantly. kvert showed small decreases (left: p = 0.374, d = 0.28; right: p = 0.613, d = 0.16). REB remained unchanged (left: p = 0.904, d = 0.04; right: p = 0.871, d = 0.05). The competitive season had little influence on group-level elastic bounce characteristics tested at a given running velocity, although individual responses varied. Full article

Alveolar bone preservation following tooth extraction requires biomaterials with appropriate biological and physical properties. Here, particle size and morphology of the biomaterial play a critical role in bone regeneration. Tooth particle grafts represent a promising alternative because of their compositional similarity to bone; however, current dentin processing methods often lack standardization and produce heterogeneous particle populations. This study evaluated a novel impact-driven fragmentation system designed to produce dentin particles with controlled size distributions through adjustment of a predefined clearance distance. Fresh porcine teeth were fragmented under three operating conditions (250, 300, and 400 µm clearance distances). Particle characterization was performed using scanning electron microscopy (SEM), laser diffraction granulometric analysis, morphometric measurements of particle length and width, descriptive statistical analysis, and linear regression analysis to evaluate dimensional relationships. Laser diffraction analysis demonstrated that increasing clearance distance produced progressively larger particles, with volume mean diameters of 222, 289.6, and 356 µm for the 250, 300, and 400 µm conditions, respectively. The 300 µm condition generated the most homogeneous particle population, exhibiting the lowest span value (0.34) and the narrowest particle size distribution. SEM observations revealed elongated particles with consistent morphology across all conditions. Morphometric analysis confirmed proportional increases in particle length and width with increasing clearance distance, while aspect ratios remained relatively constant (2.02–2.37). Regression analysis showed a positive linear relationship between particle length and width, supporting a common fragmentation mechanism across all operating conditions. Controlled impact fragmentation enables predictable dentin particle production, allowing for particle size regulation through a defined mechanical parameter while preserving particle morphology. The 300 µm clearance condition provided the most favorable balance between particle size control and distribution homogeneity, suggesting that this approach may contribute to the standardized preparation of tooth-derived graft biomaterials for future bone regeneration applications. Full article

Suboptimal telemetry data quality fundamentally degrades dispatch optimization and thermal load forecasting in smart district heating networks. Existing preprocessing routines rely heavily on isolated, unidimensional thresholds and bidirectional interpolation, routinely inducing high false-alarm rates during legitimate peak operations and causing acausal information leakage. To resolve these limitations, this study proposes an end-to-end data enhancement framework combining an Enhanced Isolation Forest with a strictly causal Long Short-Term Memory (LSTM) sequence imputation architecture. The anomaly detection module integrates Seasonal-Trend decomposition using Loess (STL) to eliminate diurnal cyclical masking, adopts an inverse-variance weighting scheme to prioritize discriminative variables, and implements Tikhonov-regularized Mahalanobis distance metric traversal to capture coupled thermodynamic covariance distortions. For sequential recovery, the causal LSTM network reconstructs unobserved states using solely historical antecedents, with mass flow rate algebraically recovered via thermal energy balance to preserve cross-parameter physical consistency. Validated on continuous hourly field SCADA observations across a complete heating season ($N=2904$), the proposed detection scheme achieves an F1-score of 94.55% with a low false positive rate of 2.45%, outperforming conventional isolation trees. In sequence reconstruction across a 672 h benchmark, the causal LSTM achieves normalized mean squared errors of 0.1196 for contiguous block voids and 0.1104 for single-point missing values, substantially surpassing classical Lagrange interpolation. Downstream deployment into a Bayesian-optimized Gated Recurrent Unit (GRU) load forecasting model demonstrates that this upstream data quality enhancement reduces the root mean squared error from 174.75 to 56.36 kWh/h (a 67.75% relative reduction), lowers the mean absolute percentage error from 12.17% to 4.05%, contracts error variance from 30,538.13 to 3240.7, and achieves a high goodness-of-fit ($R^2=0.9917$). These findings provide an empirical bridge between upstream physics-consistent telemetry refinement and downstream predictive operational reliability in industrial thermal systems. Full article

Plant patches are a common feature of dryland ecosystems, but how within-patch spatial position is associated with plant functional traits–and whether these patterns differ among organs—remains poorly understood. We sampled twigs, leaves, and flowers of the C₄ desert plant Salsola collina from the centers and edges of natural patches in arid Xinjiang, China, using five plots with three patches per plot. For each organ, we measured the concentrations of carbon (C), nitrogen (N), phosphorus (P), sucrose (SUC), fructose (FRU), total soluble sugars (SS), and starch (ST), and calculated non-structural carbohydrate (NSC) concentrations as the sum of SS and ST. Treating the plot as the experimental unit and accounting for paired center–edge observations within plots, we evaluated the effects of patch position, organ type, and their interaction using two-way ANOVA and distance-based multivariate analyses. Organ type accounted for the largest proportion of multivariate trait variation (R² = 0.90), whereas patch position and the Patch × Organ interaction each explained approximately 4% (all p < 0.001). Significant Patch × Organ interactions were detected for 14 of 17 traits, indicating that center–edge differences varied among organs for most traits. N concentrations were higher at patch edges in all three organs, whereas P concentrations were higher in twigs and flowers only; C showed no significant center–edge differences. NSC concentrations were higher at patch edges in all three organs, while SS was higher in edge twigs and leaves and ST was higher in edge flowers. These patterns were accompanied by contrasting organ-specific differences in carbohydrate composition and elemental stoichiometry. Because environmental variables were not measured concurrently, the observed patterns should be interpreted as spatial associations rather than demonstrated environmental mechanisms. These findings indicate that organ identity and within-patch position are jointly associated with variation in the carbon-, nutrient-, and carbohydrate-related traits of S. collina, highlighting the importance of considering organ-specific variation when evaluating fine-scale spatial heterogeneity in dryland plants. Full article

In the years following the 2018 eruption of Kīlauea, vegetation has begun colonizing the new landscape. This study investigates the environmental factors driving early primary succession on the 2018 Lower East Rift Zone lava flow. A Multi-Vegetation Recovery Index (MVRI), derived from five Sentinel-2 vegetation indices using principal component analysis, was used to monitor vegetation growth from 2018 to 2025. Environmental predictors included distance to existing vegetation, wind exposure, elevation, slope, land surface temperature (LST) change, and lava morphology. A multivariate linear regression was used to evaluate the influence of these factors on MVRI change between 2019 and 2025. Because pāhoehoe and ‘a‘ā lava were unevenly distributed across the flow, propensity-score matching was used to compare morphologies under similar environmental conditions. MVRI exhibited a significant positive temporal trend, indicating substantial vegetation recovery during the study period. Distance to existing vegetation was the strongest predictor of MVRI change (β = −0.249, p = 0.001), followed by elevation (β = 0.232, p = 0.002). Slope, LST change, terrain wind exposure, and lava morphology were not statistically significant predictors. The full multivariate regression model explained 30.5% of the observed variation in MVRI change (R² = 0.305). After identifying paired samples with comparable environmental conditions, mean MVRI change did not differ significantly between pāhoehoe and ‘a‘ā lava. These results indicate that proximity to surviving vegetation and broader environmental gradients are more strongly associated with early vegetation recovery than lava morphology or modeled wind exposure. Full article