Search Results (1,110)

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

To address the poor detection performance in low-light underground coal mine belt conveyors caused by information entropy degradation and high background noise, as well as the difficulty in multi-scale target extraction due to uneven entropy distribution, this paper proposes an efficient foreign object detection model named SIRI-YOLO based on an improved YOLOv11n architecture. First, a Self-Calibrating Illumination Network (SCINet) is introduced to restore image information entropy and enhance low-light adaptability. Second, the C2PSA module is enhanced to C2PSA-IRMB by incorporating an Inverted Residual Mobile Block (IRMB), improving multi-scale feature utilization and reducing ineffective entropy increase. Third, an improved Reparameterized Generalized Feature Pyramid Network (RepGFPN) is adopted to strengthen the fusion of high-level semantics and low-level spatial features, reducing information entropy loss during feature pyramid transfer. Finally, the Inner-MPDIoU loss function is introduced to replace CIoU, achieving more accurate entropy minimization from a KL divergence perspective. Experimental results on a dataset containing large coal chunks and anchor rods show that SIRI-YOLO achieves 92.8% mAP@50, 59.4% mAP@50:95, 89.5% precision, and 87.2% recall, with only 2.92M parameters and 70.01 FPS, outperforming mainstream YOLO models. Furthermore, on the public ExDark low-light dataset, SIRI-YOLO improves mAP@50 by 4.2% over YOLOv11n, demonstrating strong generalization across different low-light and complex scenarios. The proposed method effectively handles uneven illumination, scale variation, and complex backgrounds, offering a practical solution for coal mine safety through system entropy reduction. Full article

Self-drilling hollow bar micropiles (HBMPs), which integrate drilling, grouting, and reinforcement into a single process, have broad application prospects in mountainous transmission lines and offshore wind power projects. However, existing research has focused mainly on friction piles in soil layers, and there is a lack of systematic understanding of the load-transfer mechanism and bearing capacity calculation method for rock-socketed HBMPs. Based on field static load tests of rock-socketed HBMPs, this study systematically investigates the vertical bearing behavior and capacity calculation method of single rock-socketed HBMPs through a combination of test data analysis, finite element numerical simulation, and theoretical analysis. The field test results show that the load-settlement curves of rock-socketed HBMPs are of a slowly varying type, exhibiting mixed friction-end-bearing characteristics. After data screening, the average Q-s curve of Pile No. 1 and Pile No. 5 was taken as the benchmark, and the representative ultimate bearing capacity of a single pile determined by the 40 mm settlement criterion is 5860 kN. The test data of Pile No. 3 and Pile No. 4 were retained as independent validation data. A three-dimensional finite element model considering the cohesive contact behavior at the pile–rock/soil interface was established using ABAQUS. After calibration with the test results, the error between the simulated and measured bearing capacity is −3.4%, demonstrating good model reliability. Parametric analysis indicates that the bearing capacity increases linearly with the grouting volume increase rate $V_{\mathrm{inc}}$, with the expansion effect being the main enhancement mechanism; the improvement amplitude under hard rock conditions is significantly smaller than that in cohesive soils. The effect of uniaxial compressive strength $q_u$ of hard rock on bearing capacity is negligible because the capacity is controlled by the pile–rock interface shear strength. The bearing capacity increases approximately linearly with the rock-socketed depth $L_r$, and a minimum rock-socketed depth of 1.0 m is recommended. Analysis of the load-transfer mechanism shows that rock-socketed HBMPs rely mainly on shaft resistance (accounting for 90.6%), and the axial force decays significantly along the pile length. Elastic compression of the pile accounts for 78% of the pile head settlement, and the limited displacement at the pile tip leads to insufficient mobilization of end bearing. A modified bearing capacity formula considering the grouting expansion effect is established with shaft resistance as the core. A hierarchical validation strategy is adopted to test its predictive ability: for the finite element cases not participating in parameter calibration, the prediction error is within ±2%; for the field test piles, the prediction error is +7.9%; and for Pile No. 3 and Pile No. 4, the errors are +1.7% and −2.1%, respectively. These values are significantly better than those of existing methods (errors ranging from −72.1% to +54.5%). The research results can provide a theoretical basis for the design of single HBMP bearing capacity under rock-socketed conditions. Full article

This article examines how young Muslim women in contemporary France live, negotiate, and recalibrate the hijab within a differentiated secular order that distributes the conditions of public visibility unequally across institutional sites. Rather than treating the headscarf as a legal controversy or as a symbolic test of the compatibility of Islam with republican secularism, the analysis asks how visible Muslim femininity is rendered institutionally legible, conditionally tolerable, or professionally problematic across the ordinary spaces of school, work, leisure, and public life, and how women respond when the continuity between faith, body, and public presence is repeatedly subjected to regulation. Drawing on a reflexive thematic analysis of seven in-depth interviews with young Muslim-background women in Paris, the article shows that hijab emerges in the core narratives as an ethical form of composure, governed self-presence, and dignity; that schools, workplaces, and recreational sites act as visibility filters that classify which forms of Muslim femininity can appear as acceptable, neutral, and professionally credible; and that these pressures are negotiated aesthetically through ongoing acts of bodily calibration and respectable self-presentation. To capture this practical labor, the article develops the concept of embodied boundary-work and situates it explicitly in dialogue with Foucauldian accounts of disciplinary normalization and feminist scholarship on the ambivalence of agency under norm-governed conditions. The argument is that the French hijab question is most productively understood through the gendered management of Muslim visibility enacted through institutional norms of fit, neutrality, and appearance, whereby the female body becomes the site where secular governance, moral selfhood, professional sorting, and public belonging concretely intersect. Full article

Assessing drought risk under evolving climate conditions is critical for adaptation planning, yet it remains challenged by projection uncertainty and methodological complexity. This study presents a global probabilistic framework for estimating self-calibrating Palmer Drought Severity Index (scPDSI) drought return periods by integrating observational climate data with statistical modeling. We combined the scPDSI with a stochastic weather generator and generalized extreme value (GEV) analysis to evaluate drought duration extremes at a 2.5° × 2.5° global resolution. The weather generator creates 1000 synthetic time series per grid cell to enable probabilistic assessment, reproducing observed variability, persistence, and long-term trends. To project future risk, we derived return periods by scaling synthetic series using regional temperature change factors from a multi-model CMIP6 ensemble. Results indicate broad agreement with climate model ensembles but highlight regions where nonlinear dynamics drive divergences. We explicitly address critical methodological limitations raised in the recent literature, including the use of scPDSI as a sole indicator, the assumption of stationary variance in the stochastic generator, and the statistical challenges of modeling discrete drought durations with GEV distributions. This framework offers a spatially explicit, observationally grounded tool for decision-makers, while underscoring the necessity of multi-index validation in future global assessments. Full article

Current urban logistics models often struggle to reconcile diurnal traffic dynamics with rigid spatial–temporal regulations. This decoupling causes “cascading infeasibility,” where traffic delays trigger structural regulatory violations and UAV energy depletion. This study formulates a time-dependent vehicle–UAV joint routing problem that strictly couples time-varying speeds with vehicle-restricted zones and no-fly zones. The mixed-integer program minimizes a composite cost by integrating speed curves, geometric detour models, and coupled energy functions. To solve large-scale instances, we propose a hybrid metaheuristic solver (IHGA-VNS-SL) combining genetic algorithms, variable neighborhood search, simulated annealing, and self-learning. Tested on calibrated Wuhan instances, IHGA-VNS-SL quantitatively outperforms baseline heuristics (GA and ALNS). It achieves a tight 2.31% optimality gap against exact solvers (CPLEX) and up to a 20% cost reduction over ALNS, alongside near-zero tardiness. Results demonstrate that this strict coupling effectively mitigates synchronization failures, confirming the framework’s robustness for megacity distribution. Full article

This article presents a new literature-informed benchmark problem for continuous global optimization, inspired by the semantics of weather-aware irrigation scheduling. The problem is formulated as a continuous minimization task in which irrigation decisions are determined under synthetically generated but agronomically motivated weather-dependent water demand, rainfall effects, and nonlinear operational penalties. The proposed benchmark is intentionally synthetic and self-contained requiring no external datasets or field-calibrated parameters while its components are grounded in established concepts from irrigation science such as evapotranspiration-based demand estimation, yield response–water relationships, and weather-dependent scheduling principles. It is designed to be sufficiently challenging for numerical optimization while retaining a clear agronomic interpretation. The article provides the full mathematical formulation of the problem, explains its main components and parameters, and reports an experimental study focused on its optimization behavior using ten established continuous optimizers across five problem dimensions. From this perspective, the proposed problem is positioned within a clear framework for the study of real-world-inspired continuous optimization benchmarks, offering an additional case in which mathematical formulation, practical interpretation, and experimental investigation coexist in a coherent and systematic manner. Full article

Outdoor thermal comfort in hot, arid regions critically influences campus open-space use and the sustainability of university campuses, including reduced cooling energy demand and enhanced livability, yet validated integrated assessments remain scarce. This study aims to explore the relationship among microclimate conditions, thermal comfort, and the sustainable use of campus open spaces in a hot, arid region, with the goal of identifying design strategies that enhance both user comfort and environmental sustainability. The study incorporated: (1) a site audit; (2) exploratory RayMan simulations (n = 180, unvalidated) calculating Physiological Equivalent Temperature (PET) across five zones; and (3) a June survey (n = 156, 52% response rate). Physical analysis revealed height-to-width ratios of 0.13–0.30, representing an 80–91% deficit below the 1.5 minimum commonly recommended benchmark for effective shading in the literature. Unvalidated simulations estimated a mean annual PET of 31.2 °C (SD = 4.8 °C), with 17.6% of annual PET values within the comfort range and 65.2% within the hot range. For June, unvalidated simulations estimated 4% of PET values within the comfort range, while 35.5% of respondents reported thermal comfort (mean ASHRAE 1.66, warm range)—a descriptive discrepancy of 31.5 percentage points. Self-reported social factors (friends: 79.8%) ranked higher than shading space selection responses; behavioral observations are required to confirm actual use patterns. Priority interventions from physical analysis and user reports include optimized shade, cool materials (albedo ≥ 0.60), and intentional greening—subject to validation with calibrated measurements. By linking microclimate modification to increased open-space usability and reduced cooling energy demand, this research contributes to sustainable campus planning frameworks. Pending field validation and seasonal surveys, the quantitative thermal comfort estimates should be considered exploratory rather than conclusive. Full article

As intelligent systems become decision-support tools in the architecture, engineering, and construction (AEC) industry, establishing human–AI trust is critical. However, in engineering consulting, the psychological mechanisms underlying trust formation remain unclear. Grounded in Self-Determination Theory and the Stereotype Content Model, this study utilized multi-wave survey data from Chinese engineering consulting employees to investigate these mechanisms. We examined how perceived autonomy influences human–AI trust through the competitive dual-mediation of warmth perception and competence perception, alongside the asymmetric moderating role of critical thinking. Results reveal that perceived autonomy directly enhances trust. However, social cognition acts as a competitive mechanism: autonomy positively impacts trust via warmth perception but generates a negative indirect effect via competence perception. Furthermore, critical thinking exerts an asymmetric boundary effect; it does not interfere with the intuitive warmth pathway but significantly intensifies the negative indirect effect through the competence pathway. Ultimately, these findings highlight that perceived autonomy exerts a double-edged sword effect in the context of human–AI collaboration. To mitigate professional defensive rejection and calibrate trust, AEC firms should prioritize human-in-the-loop deployment strategies, objective interface designs, and the cultivation of AI collaborative literacy. Full article

The solar corona has an extremely low density, and its brightness is only about one millionth of that of the photosphere. High-dynamic-range imaging of its faint structure is therefore essential for studying coronal heating, coronal mass ejections, and space weather. Quantitative coronagraph imaging requires flat-field measurement and calibration, which underpin intensity calibration, small-scale feature detection, and long-term cyclic analysis. This paper analyzes the coronagraph imaging chain (baffle–optical system–detector) and the origins of flat-field errors, including optical aberrations, stray light, and pixel-response non-uniformity, and summarizes the resulting calibration requirements of next-generation coronagraphs. On this basis, ground-based and space-based flat-fielding methods are systematically reviewed: the ground-based methods include integrating-sphere uniform light sources, opal glass/diffuser plates, clear-sky and thin-cloud backgrounds, and solar disk scanning, while the space-based methods include internal light sources and diffuser plates, attitude-roll and off-corona offset observations, and multi-phase statistical self-consistent flat-fielding. Their accuracy, resource cost, and applicability are compared. The review shows that no single method is simultaneously high-precision, easy to update, and engineer-friendly; a hierarchical, multi-method calibration framework is therefore recommended. Finally, a new method is proposed in which lithographically generated structured light fields, combined with Fourier optics and machine learning inversion, are used to estimate the pixel-response function. Preliminary experiments show that this method achieves a lower residual error than the integrating-sphere and opal glass methods, providing a high-precision reference for future wide-band, high-resolution coronagraph calibration. Full article

This paper investigates the accuracy of conventional inductive current transformers (iCTs) and Rogowski coils (RCs) in measuring distorted currents, evaluating compliance with the WB0 (up to the 13th harmonic) and WB1 (up to the 60th harmonic) accuracy classes according to the IEC 61869-1 standard. A custom reference iCT, calibrated via the ampere-turns method to achieve a superior baseline accuracy (0.02%), served as the primary benchmark. A zero-flux electronic transducer was utilized strictly to verify this reference. Despite inherent core nonlinearity, tested conventional iCTs with reduced to minimum secondary burdens successfully met the class 0.5-WB1 requirements. In the case of tested Rogowski coils, the study reveals that their wideband performance depends on physical design of the particular type. High-sensitivity coils suffer from increased parasitic capacitance and self-inductance, causing significant additional phase shift at higher frequencies, whereas low-sensitivity, small-diameter coils offer superior linearity. Overall, the tested RCs generally ensured compliance with the 0.5-WB1 class across the evaluated frequency range, with certain units successfully achieving the more restrictive 0.2-WB1 class. Ultimately, conventional iCTs remain a highly reliable solution for metering purposes in low-voltage networks, while properly selected Rogowski coils provide a valuable alternative for power quality analysis and harmonic distortion measurements. Full article

This paper addresses the data-driven identification of open-chain robot morphology from finite windows of heterogeneous signals, including commanded joint references, measured joint states, and end-effector pose observations. Unlike conventional calibration procedures that assume a known kinematic topology, the proposed formulation estimates both discrete structural quantities and continuous kinematic coordinates: the number of active joints, the revolute/prismatic token sequence, Product-of-Exponentials (POE) screw axes, and the home pose of the end effector. A temporal transformer encoder is used as the main estimator and compared with a gated recurrent unit (GRU) baseline on the same dataset, with the same output heads and a multitask physics-aware objective. The continuous target is expressed in POE coordinates rather than as a Denavit–Hartenberg table because POE directly represents spatial joint axes and avoids several frame-assignment ambiguities. Simulated results on a noisy benchmark of 48 serial-robot families show that both sequence models recover the discrete structure on the tested in-library trajectories, while their continuous reconstruction errors reveal different trade-offs in screw-axis, home-pose, and trajectory reconstruction accuracy. The study also discusses inactive-slot masking, out-of-library behavior, synthetic-to-real limitations, persistent excitation, and the role of the learned model as an initialization for subsequent calibration refinement. Full article

Hydrogen sulfide (H₂S) is a toxic and biologically relevant gas, necessitating sensitive and interference-resistant detection methods for environmental monitoring. Here, we develop a donor–acceptor molecular platform incorporating a polarized conjugated double bond bridge and demonstrate its application, using YG2 as the representative probe, as a dual-peak ratiometric UV–Vis sensor for H₂S. UV–Vis spectroscopy, supported by ¹H NMR analysis, indicates HS^--induced interaction with the conjugated linkage, leading to disruption of π-conjugation, suppression the intramolecular charge-transfer (ICT) band at 409 nm, and enhancing the locally excited (LE) band at 279 nm. The ratiometric parameter log(Abs₂₇₉/Abs₄₀₉) affords a linear response over the concentration range of 1.0 × 10⁻⁶–1.0 × 10⁻⁴ M with a detection limit of 8.3 × 10⁻⁷ M, providing approximately an order-of-magnitude improvement in analytical sensitivity compared with single-wavelength methods, and the reaction reaches completion within ~10 s. YG2 exhibits excellent selectivity toward H₂S over common anions and enables accurate quantification in real water samples, with recoveries of 95.43–105.86% and relative standard deviations (RSDs) of 0.56–9.58%. These results suggest that YG2 is a rapid, self-calibrating, and spectroscopically interpretable ratiometric probe suitable for reliable H₂S detection in complex aqueous environments. Full article

The self-calibration of micro-component position and orientation is a key step in micro-assembly. To address the limitations of conventional self-calibration methods—where the calibration substrate is fixed and lacks adaptability—this study proposes a droplet-array-based method for self-calibrating micro-component position and orientation. By using a droplet array to form a reconfigurable calibration substrate, the method supports iterative updates of micro-devices and enables synchronous restructuring of the substrate. First, a mechanical model of the self-calibration process is established to analyze the coupling forces exerted by the liquid-bridge array between the calibration substrate and the micro-component, thereby clarifying the mechanism of droplet-array-driven self-calibration. Next, the effects of micro-component material and surface properties on calibration error are examined. Extensive experiments are then conducted to validate the proposed analytical approach. The results show that a droplet array matching the shape and size of the micro-component can be constructed in real time as a calibration substrate. Through the coupling forces generated by the liquid bridges, self-calibration of micro-components with arbitrary shapes and dimensions can be achieved. Calibration accuracy is dependent upon the material and surface roughness of the micro-component. Variations in the micro-component material lead to different forces being applied by the liquid bridge, with the self-calibration error arising from the interplay of these factors. For micro-components of identical material, a smoother surface corresponds to higher calibration accuracy. Full article

Speech-based non-invasive screening offers a cost-effective and scalable approach for the early detection of Alzheimer’s disease (AD). However, the clinical utility of deep learning models remains severely constrained by the scarcity of labeled speech data in low-resource languages, necessitating cross-lingual transfer learning. Conventional domain adaptation paradigms typically assume semantically consistent feature domains and focus heavily on aligning marginal distributions; however, they suffer catastrophic performance degradation when applied to cross-lingual pathologic speech. By analyzing disease-associated representation vectors within a self-supervised HuBERT space, we uncover a systematic mechanism driving this failure, a phenomenon we term cross-lingual polarity flip, where the direction of disease-relative-to-control feature offsets fundamentally reverses between languages. While prior multilingual studies have largely discarded such dimensional inconsistencies as ungeneralizable noise, a 500-round Monte Carlo stability analysis demonstrates that these flips occur in a highly stable, structural manner across 18.3% of top discriminative dimensions. Leveraging this insight, we introduce Monte Carlo Polarity Flip Calibration (MC-PFC), a few-shot framework designed to explicitly rectify flip orientations. Requiring only five labeled support samples per class from the target domain, MC-PFC robustly estimates direction flips via a separability-weighted ensemble voting mechanism. Evaluated on a strictly held-out Chinese blind test set, MC-PFC achieves an area under the receiver operating characteristic curve (AUC) of 0.871, recovering 99.5% of the performance achieved by a full in-domain trained upper bound (AUC = 0.875). Ablation experiments confirm that direction calibration yields a substantial +0.361 AUC gain, vastly outperforming standard distribution alignment (+0.081). This work establishes a data-efficient paradigm for cross-lingual medical analysis, shifting the clinical AI focus from discarding cross-lingual discrepancies to actively modeling and calibrating them. Full article