Search Results (1,001)

Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R²), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered. Full article

We model the short-period cataclysmic variable EZ Lyn with MESA binary evolution and infer its present-day parameters through a staged statistical search. First, we compute a coarse grid of tracks in $(M_{1,0},P_0)$ at fixed $M_{2,0}$ and rank snapshots by a profile likelihood. We then resample the neighbourhood of the minimum to build a refined $\Delta {\chi }^2$ surface. Finally, we sample this surface with an affine-invariant MCMC to obtain posteriors, using a likelihood that treats the one-sided constraint on the donor temperature and the ambiguity of component roles in the binary output. The best-fit snapshot reproduces the observables and identifies EZ Lyn as a period bouncer with a substellar donor. We infer $M_{\mathrm{WD}}=0.850\pm 0.019{\mathrm{M}}_{\odot }$, $M_2=0.0483\pm 0.0137{\mathrm{M}}_{\odot }$, $R_{\mathrm{WD}}=0.0092\pm 0.0001{\mathrm{R}}_{\odot }$, $R_2=0.099\pm 0.005{\mathrm{R}}_{\odot }$, $T_{\mathrm{WD}}=11,500\pm 20\mathrm{K}$, and $T_2=1600\pm 50\mathrm{K}$. The instantaneous mass-transfer rate at the best-fit snapshot is $\dot{M}=3.66\times {10}^{-11}{\mathrm{M}}_{\odot }{\mathrm{yr}}^{-1}$, consistent with the secular range implied by the white-dwarf temperature. Independent checks from the Roche mean-density relation, surface gravities, and the semi-empirical donor sequence support the solution. In population context, EZ Lyn lies in the period-minimum spike and on the low-mass tail of the donor mass–period plane. The classification is robust to modest displacements along the shallow $\Delta {\chi }^2$ valley. We release inlists, tracks, and analysis scripts for reproducibility. Full article

Accurate estimation of global horizontal irradiance (GHI) is essential for optimizing photovoltaic (PV) systems, particularly in regions with distinct climatic characteristics. Geostationary satellites, such as GK2A and COMS, provide consistent and spatially extensive data, offering a practical alternative to ground-based measurements. However, the performance of semi-empirical GHI models has been sparsely evaluated across diverse geographic zones. This study aimed to conduct a comparative analysis of four semi-empirical models—Beyer, Rigollier, Hammer, and Perez—applied to two contrasting locations: Seoul, South Korea (temperate) and Jakarta, Indonesia (tropical). Using satellite-derived cloud indices and ground-based pyranometer data, model performance was evaluated via RMSE, MBE, and their relative metrics. Results indicate that the Hammer model achieves the best performance in Seoul (RMSE: 103.92 W/m²; MBE: 0.09 W/m²), while the Perez model outperforms others in Jakarta with the lowest relative RMSE of 58.69%. The analysis outlines the limitations of transferring models calibrated in temperate climates to tropical settings without regional adaptation. This study provides critical insights for improving satellite-based GHI estimation and supports the development of region-specific forecasting tools essential for expanding solar infrastructure in Southeast Asia. Full article

Sustainable logistics increasingly requires energy-efficient solutions for warehousing systems. However, current energy consumption models often neglect the role of pallet transfer points that act as interfaces between various internal transport subsystems, despite their measurable impact on overall energy demand. This study addresses the energy implications of such transfer points in passive and plus-energy warehouse environments. Using the results of an operational analysis and empirical observations, we propose a dual classification of transfer nodes based on their technological characteristics (manual, semi-automated, automated, integrated) and energy profile (low, medium, high consumption). A novel Energy Performance Index (EPI) is introduced to quantify the energy efficiency of transfer nodes by combining both classification dimensions with weighted coefficients. Practical data indicate that overlooking these interfaces can lead to underestimating total energy use by up to 30%. Furthermore, the results emphasise the importance of technical integration and synchronisation between subsystems to reduce idle consumption and transfer losses. The approach presented in this paper provides a system-level modelling framework for energy assessment and supports the design of more sustainable and energy-conscious warehouse operations. The findings are relevant for logistics planners and system designers aiming to meet passive or plus-energy standards in intralogistics. Full article

The sediment yield resulting from storm erosion has become a focal point of research and a significant area of interest in the upper reaches of the Yangtze River amid changing environmental conditions. The issue of numerous types of erosivity factors (R) in storm erosion sediment yield models, with unclear applicability. This study examines two classical types of erosivity factors: the rainfall erosivity factor (EI₃₀, Zhang Wenbo empirical formula, etc.) and runoff erosivity power. Four combinatorial forms of erosion dynamic factors, encompassing rainfall and runoff elements, were developed. Based on the rainfall, runoff and sediment data of four stations along the Fu River basin–Pingwu station, Jiangyou station, Shehong station and Xiaoheba station from 2008 to 2018, the correlation between different R factors and sediment transport in different watershed areas was studied, and the semi-monthly sediment transport model of heavy rainfall in the Fu River basin was constructed and verified. The results revealed a weak correlation between the rainfall erosivity factor and the sediment transport modulus, making it unsuitable for developing a sediment transport model. In smaller basin areas, the correlation between the combined erosivity factor and sediment transport modulus was strongest; conversely, in larger basins, the relationship between runoff erosivity power and the sediment transport model was most pronounced. The power function relationship between the erosivity factor and sediment transport modulus yielded a more accurate simulation of sediment transport during the verification period, particularly during rainstorms, surpassing that of SWAT. These findings provide a scientific basis for predicting sediment transport during storms and floods in small mountainous basins. Full article

Accurate bathymetric information is crucial for coastal management, navigation, and ecosystem monitoring, yet conventional hydrographic surveys are costly and logistically demanding. This study introduces a two-stage semiempirical model for satellite-derived bathymetry (SDB) based on log-ratio reflectance indices from atmospherically corrected Landsat 8 imagery. The approach combines the optical sensitivity of the green/blue band ratio and the attenuation properties of the red/blue ratio within a parametric regression framework, enhancing both stability and interpretability. The methodology was evaluated in two contrasting coastal environments: the turbid Magdalena-Almejas Lagoon System (Mexico) and the clear-water coral reef setting of Buck Island (U.S. Virgin Islands). Results demonstrated that the proposed model outperformed traditional semiempirical approaches (Lyzenga, Stumpf, Hashim), achieving $R^2=0.8155$ (RMSE = 1.16 m) in Magdalena-Almejas and $R^2=0.9157$ (RMSE = 1.38 m) in Buck Island. Performance was statistically superior to benchmark methods according to cross-validated confidence intervals and was comparable to an artificial neural network, while avoiding overfitting in data-scarce environments. These findings highlight the model’s suitability as a transparent, cost-efficient, and scalable alternative for SDB, particularly valuable in regions where in situ data are limited. Full article

The transition from linear to circular systems remains slow and fragmented, despite the increasing recognition of circular economy (CE) as a strategic pathway to sustainability. This review identifies and categorizes the main policy levers supporting the adoption of Circular Business Models (CBM) in an analytical framework comprising eight determinants: policy agenda, governance, regulation, standardization, economic incentives, information, cooperation, and digitalization. Based on a semi-systematic review of 95 scientific and grey literature sources, the study reveals persistent gaps in policy coherence, governance coordination, and support for high-circularity strategies. The proposed framework offers a practical tool for policymakers to assess existing policy landscapes, identify gaps, and design integrated policy mixes tailored to specific contexts. It also provides a foundation for future empirical research and benchmarking across jurisdictions. By highlighting the interplay between top-down and bottom-up initiatives, the study underscores the need for inclusive, stable, and digitally enabled policy environments to accelerate the circular transition. Full article

Schinus molle L. is a tree commonly found in agricultural fields, deserts, and semi-arid areas of central Mexico. Its distinctive aroma makes it a source of essential oil, extracted mainly from the bark and fruits. The leaves contain phenolic compounds, and their extracts have demonstrated antimicrobial activity. Obtaining these extracts requires a prior drying process. This study aimed to evaluate the effect of convective drying on phenolic compounds in pirul leaves and determine the thermodynamic properties of the process, including the effective diffusivity of water vapor (D) and activation energy (Ea). Drying kinetics were conducted at different air-drying temperatures (30, 40, and 50 °C) at a constant rate of 1 ms⁻¹, and the results were fitted to the second Fick’s law and semi-empirical models. After drying, a decrease in total flavonoid content was observed as the drying temperature increased, with losses of 37%, 49%, and 62% at 30, 40, and 50 °C, respectively. The final values ranged from 37.96 to 21.02 mg QE/100 g of dry leaf. The D varied between 1.32 × 10⁻¹² and 6.71 × 10⁻¹² m² s⁻¹, with an Ea of 66.06 kJ mol⁻¹. The fitting criteria (R², RMSE, AIC/BIC) indicated that the Logarithmic model best described the kinetics at 30–40 °C, while Page was adequate at 50 °C. These findings suggest an inverse relationship between drying temperature and flavonoid content, while higher temperatures accelerate water vapor diffusivity, reducing the processing time, as observed in plant matrices. Full article

Youth violence in Zimbabwe’s high-density suburbs has evolved into a severe public-health emergency, entrenching trauma, fuelling substance abuse, and amplifying structural inequities. Christian churches remain the most pervasive civic institutions in these settings, commanding high moral authority, psychosocial reach, and convening power. However, the mechanisms by which churches mitigate violence, and the constraints they face, continue to be under-researched. Grounded in socio-economic model lens and faith-based social capital theory, this study interrogates the intersections between youth violence and church responses in Zimbabwe’s urban centres. The study adopts a qualitative approach using semi-structured interviews with church leaders. Twenty (20) church leaders from mainline, Pentecostal, and Apostolic traditions were recruited through purposive and snowball sampling to capture denominational diversity and varying levels of programme engagement. Interviews probed leaders’ perceptions of youth-violence drivers, theological framings of non-violence, practical interventions (e.g., trauma-healing liturgies, anti-drug ministries, peer-mentorship schemes), and institutional constraints such as resource scarcity and political pressures. Data was analysed using reflexive thematic analysis. The findings indicate three interconnected mechanisms through which churches mitigate the cycle of violence. Nevertheless, gendered participation gaps, theological ambivalence toward activism, and limited alignment with municipal safety strategies continue to pose challenges to these efforts. By positioning churches within Zimbabwe’s broader violence-prevention ecology, the study offers an empirically grounded blueprint for integrating faith actors into city-level public-health strategies and contributes towards evidence-based, structural solutions to urban youth violence. Full article

This paper addresses the fundamental problem of seed selection in semi-supervised clustering, where the quality of initial seeds has a significant impact on clustering performance and stability. Existing methods often rely on randomly or heuristically selected seeds, which can propagate errors and increase dependence on expert labeling. To overcome these limitations, we propose FA-Seed, a flexible and adaptive model that integrates active querying with self-guided adaptation within the framework of fuzzy hyperboxes. FA-Seed partitions the data into hyperboxes, evaluates seed reliability through measures of membership and association density, and propagates labels with an emphasis on label purity. The model demonstrates strong adaptability to complex and ambiguous data distributions in which cluster boundaries are vague or overlapping. The main contributions of FA-Seed include: (1) automatic estimation and selection of candidate seeds that provide auxiliary supervision, (2) dynamic cluster expansion without retraining, (3) automatic detection and identification of structurally complex regions based on cluster characteristics, and (4) the ability to capture intrinsic cluster structures even when clusters vary in density and shape. Empirical evaluations on benchmark datasets, specifically the UCI and Computer Science collections, show that our approach consistently outperforms several state-of-the-art semi-supervised clustering methods. Full article

Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, traditional propagation models, whether they are deterministic, empirical, or semi-empirical, face limitations when applied to dynamic environments due to their reliance on detailed atmospheric and terrain inputs. Therefore, to address these challenges, we propose a data-driven approach based on graph neural networks (GNNs) to model AIS reception as a function of environmental and geographic variables. Specifically, inspired by attention mechanisms that power transformers in large language models, our framework employs the SAmple and aggreGatE (GraphSAGE) framework convolutions to aggregate neighborhood features, then combines layer outputs through Jumping Knowledge (JK) with Bidirectional Long Short-Term Memory (BiLSTM)-derived attention coefficients and integrates an attentional pooling module at the graph-level readout. Moreover, trained on real-world AIS data enriched with terrain and meteorological features, the model captures both local and long-range reception patterns. As a result, it outperforms classical baselines—including ITU-R P.2001 and XGBoost in F1-score and accuracy. Ultimately, this work illustrates the value of deep learning and AIS sensor networks for the detection of positioning anomalies in ship tracking and highlights the potential of data-driven approaches in modeling sensor reception. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article

Limited-Entry Liner (LEL) technology has emerged as a transformative solution for enhancing hydrocarbon recovery in unconventional reservoirs while addressing challenges in carbon sequestration. This review examines the role of LEL in optimizing acid stimulation, hydraulic fracturing and production optimization, focusing on its ability to improve fluid distribution uniformity in horizontal wells through precision-engineered orifices. By integrating theoretical models, experimental studies, and field applications, we highlight LEL’s potential to mitigate the heel–toe effect and reservoir heterogeneity, thereby maximizing stimulation efficiency. Based on a comprehensive review of existing literature, this study identifies critical limitations in current LEL models—such as oversimplified annular flow dynamics, semi-empirical treatment of wormhole propagation, and a lack of quantitative design guidance—and aims to bridge these gaps through integrated multiphysics modeling and machine learning-driven optimization. Furthermore, we explore its adaptability for controlled CO₂ injection in geological storage, offering a sustainable approach to energy transition. This work provides a comprehensive yet accessible overview of LEL’s significance in both energy production and environmental sustainability. Full article