Search Results (6,199)

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Electrostatic MEMS micromirrors provide a compact and low-power beam-steering solution for miniaturized laser communication terminals. However, when they are used for quasi-static beam pointing rather than resonant scanning, the nonlinear voltage–angle relationship, bidirectional actuation asymmetry, and terminal-level installation errors can significantly degrade pointing accuracy. In this paper, a nonlinear modeling and differential-voltage control method is investigated for a two-axis electrostatic MEMS micromirror used in a miniaturized laser communication terminal. The device under test is a bonded aluminum MEMS micromirror with a 5.0 mm aperture. Static and dynamic characterization results show that the micromirror achieves maximum mechanical deflection angles of 5.215° and 5.161° along the X and Y axes, respectively, with resonant frequencies of 317 Hz and 319 Hz. To improve the accuracy of quasi-static pointing, the differential-voltage actuation principle is analyzed, and a nonlinear voltage–angle model is established based on measured deflection data. Compared with a first-order linear model, the cubic nonlinear model reduces the root-mean-square fitting error from 0.142° to 0.0127° for the X axis and from 0.132° to 0.0109° for the Y axis. Furthermore, a terminal-level calibration architecture based on a quadrant detector is introduced to map the MEMS angular deflection to the received spot position. The proposed modeling and calibration approach provides an actuator-level basis for accurate beam pointing and closed-loop acquisition in miniaturized laser communication terminals. Full article

Operational landslide susceptibility mapping (LSM) remains challenging in regions with pronounced geo-environmental heterogeneity, where single global models often overlook spatially variable landslide-environment relationships. Northern Guangdong, China, is a typical humid mountainous region where steep terrain, diverse lithology, and highly variable rainfall produce non-stationary landslide controls. To address this challenge, we develop a cluster-informed LSM framework that integrates unsupervised consensus K-means sub-zoning with localized Random Forest (RF) models and SHapley Additive exPlanations (SHAP). We use a harmonized inventory of 1510 landslides (2011–2022), together with twelve 30 m conditioning factors, for model training and validation. Compared with logistic regression, Support Vector Machines (SVM), and Light Gradient Boosting Machine (LightGBM), RF consistently achieves higher accuracy across clusters, and the cluster-wise RF ensemble attains pooled ACC = 0.8212, F1 = 0.8176, and AUC = 0.8956. SHAP highlights both regionally consistent predictors (e.g., NDVI, distance to road) and distinct cluster-specific controls linked to geomorphic and hydrologic settings. The proposed framework enhances predictive accuracy, produces finer susceptibility gradients, and yields better-calibrated probability estimates than a single global model. These results demonstrate that explicitly accounting for geo-environmental heterogeneity can generate interpretable, spatially adaptive susceptibility outputs. By identifying high-risk zones for priority monitoring, land-use regulation, infrastructure protection, and mitigation planning, the proposed framework provides a practical decision-support tool for sustainable mountain development and disaster risk reduction in heterogeneous mountainous regions. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

This study consists of two main components. The first part establishes a seakeeping assessment method, while the second part focuses on hull-form optimization with seakeeping performance as the objective. For the seakeeping analysis, the Lewis conformal mapping method is used to calculate the sectional hydrodynamic coefficients. Strip theory is then applied to obtain the global hydrodynamic coefficients of the hull. The coupled heave and pitch motion responses are calculated and compared with nonlinear time-domain simulation results and experimental data, showing good agreement. A multivariate linear regression model is established to approximate the relationship between the principal hull-form parameters and the heave and pitch RAOs. The comparison between the regression model and strip theory results shows that the prediction error remains within 5%, indicating that the regression model can provide an efficient surrogate objective function for hull-form optimization. The particle swarm optimization (PSO) algorithm is then employed to optimize the hull form, with the ship length, breadth, draft, and block coefficient considered as design variables. To further evaluate the optimized hull, additional calculations are conducted under different Froude numbers and encounter angles. Under head sea conditions with varying Froude numbers, the optimized hull reduces the peak heave RAO by 11.6–31.1% and the peak pitch RAO by 8.6–17.9%. Under different encounter angles at F_r = 0.3, the reductions in peak heave and pitch RAOs are 31.1–33.9% and 16.5–18.8%, respectively. These results demonstrate that the proposed regression assisted PSO optimization framework can effectively reduce the heave and pitch responses of the Wigley hull under the investigated regular wave conditions. Full article

The interplay between special functions and geometric function theory continues to inspire significant advances in the study of analytic and multivalent functions. In this work, we introduce and investigate several new subfamilies of multivalent functions associated with the generalized Mittag-Leffler-type Poisson distribution in the open unit disk. We establish necessary and sufficient conditions characterizing membership in these classes and derive meaningful inclusion relationships among them. Furthermore, we define a novel integral operator linked to the generalized Mittag-Leffler-type Poisson distribution and examine its mapping properties and structural connections with the proposed function classes. The results presented herein not only unify and extend a variety of earlier contributions but also demonstrate the effectiveness of distribution-theoretic methods in the analysis of multivalent functions. Full article

Tertullian provocatively asked (circa 200 AD), “What indeed has Athens to do with Jerusalem? What concord is there between the Academy and the Church?” The relationship between formal philosophy and religion has been a contentious battleground ever since. It has historically come into sharpest focus in biology generally, and evolutionary theory specifically. Charles Darwin clearly won the day in the short-term. His evolutionary functionalism looked like an inglorious abandonment of time-honored metaphysical realism for positivist empirical reductionism with a concomitant horizontalization of perspective that has secularized and flattened the intellectual landscape. But more recently the rise of evo-devo (evolutionary developmental biology) and epigenetic factors have forced a reevaluation of his archrival, Richard Owen. This paper argues that the key to understanding Owen is rooted in his devout Anglicanism through his broad church theology which put Plato into the service of evolutionary theory only now beginning to receive our belated attention. The road to Owen’s evolutionary theory weaved its way from Athens through Jerusalem, finding itself in the contentious intersection of 19th-century Victorian science and religion. Inaccurately mapped by William Paley, Owen’s evolutionary structuralism offered an alternative through science, philosophy, and religion that is only now beginning to be appreciated. Full article

Although it is generally accepted that material characteristics influence the sensing and locomotion of autonomous mobile robots (AMRs), this knowledge is mostly anecdotal and remains fragmented. This study aims to shed light on the relationship between building finishing materials and AMR performance. To address the lack of literature on the subject, this exploratory mixed-methods review combines an AMR market survey, collection of failure cases, and review of robot navigation mechanisms. As a result, with additional expert assessment, this study derived a relational diagram containing five primary relationships for sensing (i.e., color on obstacle detection, texture and transparency on obstacle detection and mapping accuracy) and five for locomotion (i.e., slipperiness and unevenness on speed and path consistency, wheel-mark resistance on surface preservation). Potential research themes (e.g., sensitivity by robot specifications, BIM-based information utilization, and robot-specific signage systems) were also derived by thematic analysis. By establishing a foundational research framework that clarifies how architectural material choices dictate robotic reliability, this review contributes to designing experimental scenarios for future empirical validations in robot-inclusive spaces. Full article

Line confocal sensors provide non-contact, high-resolution, and high-efficiency measurement and can be integrated into optical measurement systems such as Photon for three-dimensional topography measurement of complex surfaces. However, installation-induced tilt-pose errors of the sensor can couple height information with lateral position, thereby reducing the accuracy of profile reconstruction. To address this issue, this paper proposes a calibration-block-based tilt-pose error identification and compensation method for line confocal sensors. Using the known geometric features of the calibration block, the proposed method establishes a mapping relationship between sensor tilt-pose errors and measured profile distortion. Sensitivity analysis is performed to identify the dominant error components, and the tilt-pose errors are estimated in a single identification process, enabling quantitative compensation of the measured point cloud. Experimental results show that, after calibration and compensation, the maximum Z-direction height difference in the overlapping profile region of the calibration block is reduced from 12.782 μm to 0.307 μm. The proposed method requires no complex external alignment devices and provides an effective approach for high-precision integrated applications of line confocal sensors. Full article

Urban green spaces (UGSs) are considered crucial for enhancing older adults’ subjective well-being. However, limited studies have explored the synergistic effects of UGS quality and quantity on satisfaction across green spaces, residential environments, and life domains, making it challenging to uncover the multifaceted sustainable benefits of UGSs on older adults’ subjective well-being. This study drew on multi-source data and place attachment theory to depict neighborhood-accessible UGS quantity (provision, accessibility, and visibility) and quality (cognition, behavior, and affect). Through the geographical visualization of bivariate SHapley Additive exPlanations (SHAP) interaction values extracted from the trained eXtreme Gradient Boosting (XGBoost) model, and the comparison of bivariate SHAP maps with univariate SHAP maps, the study revealed the nonlinear geographic associations between UGS quantity and quality and three types of satisfaction. The results showed that when UGS quantity and quality coexisted, variations in the impact of quantity on older adults’ satisfaction were associated with quality differences. The gain effect of quality on quantity was more significant in areas with limited green space within a 500 m buffer zone. UGSs made a direct contribution to green space satisfaction, while their indirect association with life satisfaction surpassed that of residential satisfaction due to their provision of emotional qualities. This study calls for neighborhood green planning aimed at improving older adults’ subjective well-being, which should shift focus from quantity to quality and balance the relationship between quantity and quality based on regional characteristics. Full article

Background/Objectives: Grief following the death of a partner is a complex psychosocial process associated with an increased risk of prolonged grief, depression and suicidal ideation. Among lesbian, gay, bisexual, transgender, intersex, and queer (LGBTIQ+) individuals, these risks are exacerbated by stigma, relational invisibility and family rejection, often resulting in unrecognized or disenfranchised grief. This scoping review aimed to map the available evidence on the experiences of bereavement following the death of a partner among LGBTIQ+ individuals between 2016 and 2026, identifying study types, recurring themes and knowledge gaps relevant to nursing practice. Methods: A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) extension and the methodology of the Joanna Briggs Institute. Searches were planned in PubMed/MEDLINE, Scopus, CINAHL, PsycINFO and Web of Science (2016–March 2026) using combined terms for grief, partner and LGBTIQ+ populations. Primary qualitative, quantitative and mixed-methods studies, as well as selected grey literature that explicitly addressed grief following the death of a partner in LGBTIQ+ individuals were considered. Results: The search identified 1032 records; after removing duplicates (n = 356), 676 titles/abstracts were screened, and 94 full texts were assessed. Eighteen studies were included, mainly qualitative, and conducted in high-income countries. Key themes included invisibility and lack of recognition of the relationship, managing the disclosure of sexual orientation and gender identity, social isolation and the role of chosen families, and intersectional vulnerabilities in subgroups such as older adults, bisexual people and trans people. Conclusions: The available evidence reflects specific bereavement experiences among LGBTIQ+ individuals that are not adequately captured in traditional models of bereavement care. Significant gaps remain, particularly in Spanish-speaking contexts and in the design and evaluation of nurse-led interventions. This scoping review provides a conceptual basis for future research and for the development of culturally safe clinical practices in supporting LGBTIQ+ individuals through bereavement. Full article

Mapping wildfire patterns in Wildland–Urban Interface (WUI) areas is a fundamental tool for fire management and prevention, particularly in regions where urban expansion occurs in close proximity to natural vegetation. This mapping approach makes it possible to identify critical zones and to support more effective interventions adapted to the specific conditions of each territory. This work analyzed wildfires in the state of Tocantins, Brazil, using detailed geospatial data and advanced analysis techniques and statistics to characterize the dynamics of burned areas. Data used for the project were retrieved from MapBiomas and the Geoprocessing Laboratory of the Public Ministry of Tocantins (LABGEO), applying logistic regression models to explore the relationship between the distance of WUIs and the frequency of wildfires. The methodology covered the spatial distribution of fires and the different dynamics observed by type and size of burned area, allowing for a more detailed analysis. The results indicated significant variations in the proportion of burned areas inside and outside the WUIs, suggesting that proximity to these interfaces plays a critical role in the occurrence pattern of fires. Notably, Palmas, the state capital, stood out as one of the municipalities with the highest concentration of impacts in WUI areas, highlighting the relevance of these zones in environmental risk management. The study emphasizes the importance of adopting regional approaches that consider local specificities in the management and prevention of wildfires. The integration of geospatial data with robust statistical methodologies can guide more effective management strategies, assisting in the planning of public policies adapted to the socio-environmental dynamics of Tocantins. Full article

Psilocybin, a psychedelic drug with reported anxiolytic and antidepressant potential, is rapidly metabolized to its active metabolite psilocin. However, a lack of adequate toxicity studies and tissue distribution studies currently restricts its development and application. This study combined behavioral assays in zebrafish with desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to systematically evaluate the acute neurotoxicity of psilocybin and characterize the in vivo spatial distribution of its active metabolite, psilocin. The novel tank test was used to evaluate zebrafish following a 4 h exposure to psilocybin at three different doses (20, 40, and 80 μM; n = 6 per group). Statistical analysis of the data was performed using ANOVA. Behavioral analyses revealed that exposure to psilocybin induced pronounced neurobehavioral alterations, including hyperactivity and disrupted swimming patterns, as evidenced by significant increases in the number of zone transitions and shuttle frequency. We established a DESI-MSI-based method for quantitative mapping and visualization of psilocin in zebrafish tissues. Methodological validation indicated that a linear relationship between ion intensity, spotted amount (R² = 0.9947), and reproducibility (RSD < 15%) is suitable for quantitative analysis of psilocin in zebrafish tissues. Spatial distribution maps showed that following continuous exposure for 4 h, psilocin was widely distributed across multiple tissues, such as the eye, brain, heart, liver, and kidney, with marked accumulation in the brain and the periportal regions of the liver. Relative psilocin signal intensity revealed a dose-dependent increase in tissue drug levels. The dose-dependent increase in both behavioral hyperactivity and brain psilocin levels points to a consistent relationship, in line with a central site of action. Collectively, these findings demonstrate that DESI-MSI provides a visual and efficient strategy for studying drug distribution in biological tissues from exposed animals. The neurobehavioral toxicity phenotypes and distinct tissue distribution patterns of psilocin uncovered in this study offer critical insights into the biological effects and potential risks of this psychoactive substance. Full article

To establish a quantitative mapping relationship between macro- and micro-parameters in the PFC^2D parallel bonding model, and in view of the inherent complexity of the mutual validation process between laboratory experiments and parameter calibration, this paper takes uniaxial compression tests as the mechanical reference. By combining orthogonal experimental design, Pearson correlation analysis and multivariate analysis of variance, this study systematically investigates the effects of 10 micro-parameters on 6 macro-mechanical indicators (modulus of elasticity E, Poisson’s ratio ν, uniaxial compressive strength ${\sigma }_c$, friction-to-cohesion ratio FCR, crack initiation strength ${\sigma }_{ci}$ and crack damage stress ${\sigma }_{cd}$). To reduce the coupling dimension between cohesion and internal friction angle in the calibration of PFC macro–micro parameters, this paper defines the Friction-to-Cohesion Ratio (FCR) as the ratio of the equivalent macroscopic angle of internal friction to the equivalent macroscopic cohesion, and systematically conducts sensitivity analyses of uniaxial compression simulations. The results indicate that the elastic modulus E is primarily governed by $E^{\ast }$, ${\overline{E}}^{\ast }$, ${\overline{k}}^{\ast }$ and $R_f$; the Poisson’s ratio ν is mainly influenced by $E^{\ast }$, $k^{\ast }$, ${\overline{E}}^{\ast }$, ${\overline{k}}^{\ast }$ and $R_f$; the uniaxial compressive strength ${\sigma }_c$, the crack initiation strength ${\sigma }_{ci}$ and the crack damage stress ${\sigma }_{cd}$ are primarily regulated by ${\overline{\sigma }}_c$ and $R_f$; whilst the Friction-to-Cohesion Ratio (FCR) is mainly affected by ${\overline{\sigma }}_c$, $\overline{\phi }$, $R_f$, $\overline{c}$ and $\beta$; Elasticity parameters and strength parameters are governed by different micro-mechanisms, reflecting the fundamental decoupling of stiffness and strength in the PFC model. This study established a progressive ‘screening–validation–quantification’ sensitivity analysis framework, revealing the directional regulation patterns of various micro-parameters on macroscopic responses, thereby providing a theoretical basis for the targeted optimisation and efficient calibration of micro-parameters in PFC discrete element simulations. Full article

This contribution presents a simulation-based approach for optimizing injection molding processes using digital twins. It combines surrogate modeling via response surface methodology (RSM) with the evolutionary algorithm NSGA-II to efficiently capture complex relationships between process parameters and objectives. A key element is the adaptive enhancement of the training dataset within the decision-relevant region of interest (ADEROI) by a modified greedy max–min algorithm. This strategy closes data gaps, improves model accuracy in the potentially optimal region, and directs additional simulations to informative areas. Leave-one-out (LOO) and hold-out (HO) cross-validations show strong root mean square error (RMSE) and $R^2$ values for deformation, shrinkage, cycle time, and mass. NSGA-II converges after 403 generations and results in 191 Pareto-optimal solutions, which are consolidated into preference-consistent operating points. These points make trade-offs between analyzed objectives’ deformation, shrinkage, and cycle time explicit for process pre-design. Preferred solutions are identified through weighted sums of normalized objectives and inversely mapped process parameters. Their agreement with the physics-based digital twin at the hundredths level supports the plausibility of the selected operating points within the investigated simulation-based workflow. A retrospective benchmark against a scaled single-stage LHS baseline shows that ADEROI achieves ROI-equivalent point density with fewer simulation runs for the investigated case, reducing the estimated runtime by $39.1\%$ and resulting in a $1.64\times$ speed-up. The quantitative validation is limited to one thin-walled PP keyholder component; further geometries, mold layouts, and polymer materials are required to empirically assess generalizability. Full article