Search Results (2,115)

Understanding habitat use and connectivity in estuarine fishes is essential for effective conservation and management. In this study, otolith microchemistry was applied to investigate habitat use and connectivity of the estuarine catfish Genidens genidens across three estuaries in southeastern Brazil. A total of 58 individuals were analyzed using laser ablation inductively coupled plasma mass spectrometry, focusing on strontium-to-calcium (Sr:Ca) and barium-to-calcium (Ba:Ca) ratios. Variations in elemental ratios along otolith transects were used to infer individual ontogenetic patterns along the estuarine–marine gradient. Most individuals exhibited combined use of estuarine and marine environments, while trajectories restricted to freshwater were rare. The apparent complexity of chemical profiles tended to increase with age; however, this pattern disappeared after correction for size-related bias, suggesting that age itself did not significantly influence habitat-use transitions. These patterns are consistent with ecological plasticity and partial migration within populations of G. genidens, although they may also reflect exposure to variable environmental conditions. Sr:Ca ratios were useful indicators of salinity-related transitions, whereas Ba:Ca ratios provided complementary information associated with continental influence. Overall, this study highlights the applicability of otolith microchemistry for investigating habitat-use patterns in estuarine fishes and reinforces the ecological importance of estuaries for feeding, growth, and recruitment in G. genidens, while acknowledging inherent limitations related to environmental variability and proxy interpretation. Full article

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement. Full article

Accurate rainfall information is essential for rainfall–runoff modeling in monsoon-influenced basins, where pronounced spatial variability and limited gauge coverage introduce significant uncertainty. Satellite precipitation products provide spatially continuous estimates but are affected by systematic biases, and improvements in statistical rainfall accuracy do not necessarily translate into hydrologically consistent model forcing. This study interpreted satellite rainfall bias correction through a rainfall–runoff framework in the Phetchaburi River Basin, Thailand, using the DWCM-AgWU hydrological model. Simulations were driven by gauge observations and multiple satellite-based rainfall products (GSMaP, CMORPH, CHIRPS, and PERSIANN-CCS), with bias correction applied using Linear Scaling and Quantile Mapping under rainfall-specific calibration. Results showed that bias correction significantly modified rainfall characteristics in distinct ways. Linear Scaling primarily preserved temporal and spatial structure while adjusting rainfall magnitude, whereas Quantile Mapping improved the distributional representation of rainfall intensities. These differences propagated through hydrological processes, leading to systematic variations in runoff responses across multiple metrics, including water balance consistency, peak magnitude, and timing errors. This suggests that each method performs differently depending on the aspect of system response. Rather than identifying a universally optimal method, the findings highlight trade-offs in how rainfall correction strategies influence hydrological system response. Runoff behavior is interpreted as a process-level indicator of rainfall representation, emphasizing that hydrological consistency depends not only on rainfall accuracy but also on its interaction with model structure. These results suggest a process-oriented perspective for interpreting the role of satellite rainfall products in regulated and monsoon-affected basins. Full article

Recent global events have slowed progress toward achieving the Sustainable Development Goals (SDGs), making robust sustainability reporting (SR) systems critical for monitoring and corrective actions. While research on the link between corporate sustainability performance (CSP) and corporate financial performance (CFP) is extensive, the specific role of sustainability reporting as a communication channel remains insufficiently explored. Therefore, the objective of this paper is to address this gap in the literature by assessing the relevance of sustainability reporting for modeling the relationship between CSP and CFP. In this study, a univariate meta-analysis based on a PRISMA screening framework was performed to assess the unidirectional relationship between SR and CFP, specifically investigating whether SR acts as a moderating or mediating factor in the CSP-CFP nexus. The analysis is limited to 19 high-quality articles published in top-tier accounting journals between 2014 and 2024 to minimize publication bias and ensure reliability. The meta-analysis reveals no statistically significant moderating effect of SR on CFP. Instead, the results confirm a significant mediating effect, particularly when considering the presence of sustainability reports rather than just their specific content. These findings suggest that SR serves as a vital catalyst for corporate communication, providing more positive effects in voluntary compared to mandatory disclosure settings. This paper has both theoretical and practical implications, which are mainly relevant to standard-setters for assessing the efforts of SR disclosure regulation, and is of fundamental importance to managers as it indicates that SR does not relate solely to the practice of conformity, but rather to essential channels of communication and value creation. Full article

Micro-public space (MPS) regeneration is typically evaluated at the point of delivery, yet long-term performance depends on whether everyday stewardship can be sustained thereafter. This study reframes neighbourhood social capital as a governance–environment signal reflecting coordination capacity and examines whether residents’ willingness to participate in post-regeneration co-management is primarily appraisal-driven (perceived value, attitude, and perceived behavioural control) or coordination-driven via a residual direct channel consistent with routine governance. A cross-sectional survey of adults residing within walkable catchments of five regenerated MPS sites in Nan’an District, Chongqing, China ($N=477$), was conducted. An integrated Stimulus–Organism–Response × TPB model was estimated using WLSMV with ordered categorical indicators; indirect effects were assessed via bias-corrected bootstrap confidence intervals. Coordination capacity was strongly associated with perceived value, participation attitude, and perceived behavioural control. In the joint model, only perceived value retained a statistically reliable positive association with stewardship willingness, whereas the incremental contributions of attitude and perceived behavioural control were negligible once the stimulus was included. A residual direct association from coordination capacity to willingness persisted beyond the appraisal block, supporting a direct-dominant interpretation; bootstrap analyses yielded no robust evidence for mediation (BCa 95% CIs crossed zero). These findings suggest that sustaining regenerated micro-spaces requires low-friction governance designs that minimise coordination costs, reinforce soft accountability, and render institutional responsiveness visible to residents. Full article

Spaceborne LiDAR systems, such as ICESat-2, provide critical data for global land cover and topography; however, their performance in rugged, vegetated landscapes requires rigorous local validation. This study evaluates the vertical accuracy of ICESat-2 ATL08 terrain height metrics in the complex Turkish Western Black Sea region, utilizing a reference dataset of high-precision terrestrial GNSS measurements. Following strict IQR-based outlier detection and photon density filtering, 1637 spatially matched segments were analyzed. The h_te_best_fit terrain height metric showed the best agreement with the terrestrial GNSS reference data, yielding an RMSE of 3.37 m and a mean bias of −0.42 m, indicating a slight underestimation of the terrain surface. The univariate analysis revealed a strong positive correlation between terrain slope and vertical error, indicating that slope is the prominent degradation factor contributing to pulse broadening. Additionally, dense forest cover was found to limit ground photon retrieval, leading to increased error margins, whereas nighttime acquisitions offered slightly improved precision. These findings suggest that while ATL08 is a valuable topographic source, slope-dependent corrections are essential for applications in mountainous environments. Full article

This study examines the historical and anticipated effects of climate change on essential hydroclimatic variables (temperature, precipitation, evapotranspiration, and soil moisture) in the Southeastern Anatolia Project (GAP) region of Türkiye, a semi-arid and agriculturally significant basin experiencing heightened water stress. The analysis employs a collection of CMIP6 Global Climate Models (GCM) and integrates three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5), utilizing statistical bias correction methods such as Delta Change, Quantile Mapping (QM), and Empirical Quantile Mapping (EQM) to improve the regional accuracy of the projections. The ACCESS-CM2 model, validated with data from Türkiye’s Meteorological General Directorate (MGM), was chosen for comprehensive spatial mapping, utilizing Inverse Distance Weighting (IDW) interpolation across seven temporal intervals encompassing past, present, and future periods. The findings indicate a steady increase in temperature and evapotranspiration, especially under high-emission scenarios, with temperature rises above +4 °C and considerable water losses anticipated by century’s end. Soil moisture exhibits a declining tendency, particularly in the southern and eastern regions, signifying increasing drought susceptibility. Precipitation patterns demonstrate significant spatial variability and rising uncertainty, with relative error (RE%) values increasing under SSP5-8.5. Historical data from 1963 to 2022 corroborate these conclusions, indicating a progressive shift towards a warmer and drier regional climate. These observations highlight the importance of climate adaptation strategies and water management in the GAP region. The research provides decision-makers a high-resolution, bias-corrected hydroclimatic dataset. Full article

Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit detection + harvesting” framework. First, by integrating MobileNetV4 and Triplet Attention mechanisms, an improved YOLOv8n network is designed, with the improved YOLOv8n Precision reaching 98.148% and FPS reaching 30 FPS on Jetson Nano, achieving a good balance between detection accuracy and computational efficiency suitable for edge deployment. Second, a strawberry three-dimensional coordinate reconstruction method based on weighted 3D centroid reconstruction is proposed, utilizing depth bias adjustment coefficients to improve spatial accuracy. Third, to address localization errors caused by vibration and platform motion, a dynamic compensation and temporal fusion strategy based on an Inertial Measurement Unit (IMU) is proposed. The rotation matrix estimated from IMU data is first used to correct camera pose variations. Then, an adaptive sliding window is employed to smooth the coordinate sequence. Finally, an Extended Kalman Filter (EKF) is applied to further refine the fused results by incorporating temporal dynamics, ensuring that the reconstructed three-dimensional coordinates in the robotic arm reference frame achieve higher stability and continuity. Experimental results in orchard scenarios show that compared with traditional methods, the system has higher localization accuracy, stronger robustness to dynamic disturbances, and higher harvesting efficiency. This work provides a practical and deployable solution for advancing intelligent fruit-harvesting robots. Full article

Two-dimensional Ti₃C₂O₂ MXene has emerged as a promising electrode material for non-enzymatic glucose sensing due to its metallic conductivity and biocompatibility. However, the atomic-scale sensing mechanism remains unclear. This DFT study uses the PBE functional with the D3(BJ) dispersion correction to elucidate glucose–MXene interactions under idealized vacuum conditions. Pristine Ti₃C₂O₂ shows metallic behavior with a density of states of about 8.2 states per electron volt at the Fermi level, dominated by Ti 3d states. β-d-glucose adsorbs onto the surface through hydrogen bonding, with an adsorption energy of −0.82 eV at a separation distance of 2.8 angstroms. Bader analysis indicates a transfer of about 0.15 electrons from MXene to glucose, resulting in a Fermi level shift of about −0.15 eV and an 18% reduction in the density of states at the Fermi level. These changes correspond to an estimated sensitivity of approximately 0.6 μA mM⁻¹ cm⁻² and a detection limit of about 17 µM, consistent with reported experimental performance of MXene-based sensors. Comparative adsorption calculations for common sweat interferents yield −0.45 eV for lactate and −0.25 eV for urea, indicating weaker interfacial affinity than glucose; these values reflect thermodynamic binding strength and possible surface occupation rather than definitive electrochemical selectivity, which additionally depends on redox potential, electron-transfer kinetics, and operating bias. We acknowledge three main limitations: first, the model considers only pure oxygen termination rather than mixed oxygen, hydroxyl, and fluorine terminations; second, the calculations are performed under vacuum rather than in aqueous conditions; third, the study is based on static zero kelvin structures rather than finite temperature dynamics. Despite these idealizations, the results provide baseline mechanistic insights to support rational design of MXene-based glucose sensors. Full article

Background/Objectives: Therapeutic drug monitoring (TDM) of immunosuppressants is essential in treating pediatric kidney diseases; however, repeated venipuncture is burdensome in children. We evaluated whether minimally invasive fingerstick capillary sampling combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS) provides results analytically comparable to those of conventional venous sampling. Methods: Capillary whole blood (2.8 µL) was collected via fingersticks from pediatric patients receiving mycophenolate mofetil, with or without tacrolimus (TAC) or cyclosporine A (CsA). Drug concentrations were quantified using a previously validated simultaneous LC-MS/MS method and compared with conventional venous sampling using linear regression and Bland–Altman analyses. Results: Seventy-four paired samples from 21 patients were analyzed. Strong correlations were observed between capillary and venous samples for mycophenolic acid (MPA), TAC, and CsA (R² > 0.90). Hematocrit correction improved agreement for MPA. Bland–Altman analyses demonstrated acceptable bias across analytes. Conclusions: Fingerstick-based microvolume sampling combined with LC-MS/MS provides analytically reliable immunosuppressant quantification in pediatric patients. Although larger clinical validation is required, this minimally invasive approach may reduce procedural burden and may support future outpatient or home-based TDM strategies. Full article

Business development unfolds within complex adaptive environments marked by nonlinear interaction, structural asymmetry, and recurrent instability. Sustained performance under such conditions requires regulatory structures that preserve coherence while enabling structural transformation. This study advances symmetry evolution as a systems principle that explains the emergence of balance through interaction among decision bias, structural symmetry, and regulatory intensity. An evolutionary regulation framework represents this interaction as a closed-loop dynamic that drives coevolution of regulation and symmetry through recursive feedback. Stability emerges as a property of proportional coupling rather than correction of deviations. Multi-modal simulations representing turbulent decision landscapes demonstrate formation of bounded oscillatory equilibrium under perturbation while preserving exploratory capacity, with a mean recovery interval of 1.01 iterations, compared with 9.56 under fixed regulatory intensity and 47.29 under exogenous adjustment, indicating a substantial reduction in recovery time. Coordinated evolution of regulatory gain and structural symmetry sustains adaptive stability without suppressing innovation dynamics. The study establishes a systemic foundation for resilience and endogenous governance in complex business systems and reframes decision optimization as structural adaptation within evolving regulatory architectures. Full article

Neural language models have achieved remarkable progress in semantic representation learning. However, cross-domain representation learning still suffers from prominent semantic noise propagation issues. Existing methods still face challenges in cross-domain semantic modeling, including limited robustness across different semantic granularities, difficulty in separating transferable semantics from task-irrelevant semantic interference, and insufficient adaptability to specialized scenarios. These issues may reduce feature discriminability in fine-grained semantic tasks and complex application settings. To address these problems, we propose the Diffusion-Coded Attention Network (DCANet), a novel cross-domain representation learning architecture with three synergistic core modules: a multi-granular parallel diffusion masking mechanism for cross-scale context fusion via stochastic path activation, an implicit semantic encoder that distills domain-invariant patterns into adaptive bias codes via shared latent manifolds, and a self-correcting attention topology realizing dynamic semantic purification via closed-loop interactions between local features and global bias states. Extensive evaluations are conducted on nine well-recognized benchmark datasets to verify DCANet’s effectiveness and reliability. Experimental results show that DCANet attains state-of-the-art results on the majority of the benchmark datasets, with significant accuracy improvements on text classification and sentiment analysis tasks. Full article

Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM_2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM_2.5 variability, and precipitation is often expected to reduce particle loads through wet removal. However, humid and wet conditions may coincide with elevated PM_2.5 under specific atmospheric and compositional conditions. Here, we investigate long-term relationships between precipitation regimes and PM_2.5 concentrations in the Metropolitan Region of Belém (Eastern Amazonia) over the period 1980–2024. We combined PM_2.5 from the MERRA-2 reanalysis (including a bias-corrected product) with in situ precipitation records, and classified precipitation conditions using the Standardized Precipitation Index (SPI). We find statistically significant positive long-term tendencies in both precipitation and PM_2.5. Stratified analyses show that PM_2.5 concentrations are significantly higher under wet conditions, with a weak but significant positive relationship between SPI and PM_2.5 (r = 0.23 for the full period; r = 0.24 for the wet class, p-value < 0.01). These findings indicate that increased precipitation in a strong humid tropical urban environment does not necessarily lead to improved air quality. Instead, wet conditions may favor processes such as hygroscopic growth and secondary aerosol formation, contributing to higher PM_2.5 concentrations on a monthly scale. Overall, this study highlights the importance of considering precipitation regimes and associated atmospheric processes when assessing air quality in tropical urban environments. Full article

Background/Objectives: To compare the efficacy of two aligner systems (Invisalign and Eon Aligner) across multiple linear and angular movements. Methods: A total sample of 80 patient cases (n = 40 in each group) was recruited retrospectively. Per case, 3 digital models were retrieved in the form of stereolithography (STL) files. Predicted and achieved tooth movements were measured using the 3Shape Clear Aligner Studio. Initial models were aligned on the predicted and achieved models to create superimposition. Differences in measurement between pre-treatment, predicted, and post-treatment scans were measured. Agreement between the two, Invisalign and Eon, was measured using the interclass correlation coefficient (ICC). Results: Both Invisalign (ICC = 0.82; 95% CI 0.66, 0.9) and Eon Aligner (ICC = 0.75; 95% CI 0.53, 0.87) have shown good agreement when calculating the average differences between the achieved and predicted interpremolar width values. Similar results were found for both intercanine width values (Invisalign: ICC = 0.96; 95% CI = 0.93, 0.98 vs. Eon Aligner: ICC = 0.98; 95% CI = 0.97, 0.99). In Eon cases, good to excellent agreement between the achieved and predicted models was observed for lateral (ICC = 0.89; 95% CI = 0.79, 0.94) and central (ICC = 0.93; 95% CI = 0.87, 0.96) mesiodistal rotations. Conversely, Invisalign displayed moderate strength of agreement for the lateral (ICC = 0.68; 95% CI = 0.40, 0.83) and central (ICC = 0.70; 95% CI = 0.44, 0.84) mesiodistal readings. While both aligners demonstrated some level of predictive capacity towards horizontal movements, they were unreliable in predicting vertical movements. Differences in magnitude of change between initial and achieved values between Eon and Invisalign were noted only for certain teeth in the case of horizontal and vertical movements. Conclusions: Both clear aligner therapy systems were able to achieve satisfactory outcomes in terms of inter-premolar and intercanine width changes. Eon Aligner, on the other hand, outperformed Invisalign in terms of rotational accuracy and horizontal movement precision. Notably, both systems demonstrated poor predictability for vertical movements and suffer from significant systemic bias requiring over-correction. Full article

Ultra-wideband (UWB) technology has been widely adopted for indoor positioning due to its high temporal resolution. However, the accuracy of UWB-based indoor positioning is fundamentally limited by ranging measurement errors, particularly under non-line-of-sight (NLOS) conditions, where systematic bias and uncertainty are introduced into the measured distances. In this paper, a measurement error mitigation method is proposed to improve UWB ranging reliability in complex indoor environments. The method first identifies NLOS measurements using low-dimensional physical features and a lightweight machine learning classifier. Subsequently, an error compensation strategy is applied to correct biased ranging observations, which are then incorporated into a nonlinear least squares positioning model. Experimental results obtained in typical indoor environments demonstrate that the proposed method significantly reduces ranging errors and improves positioning accuracy compared with conventional approaches. The results indicate that the proposed framework effectively enhances measurement robustness without increasing system complexity. Full article