Search Results (779)

This study investigates the spatial relationship between population distribution and tree cover loss in Nepal from 2000 to 2020, using satellite-based forest cover and population data along with statistical and geospatial analysis. Two statistical methods—linear regression (LR) and Geographically Weighted Regression (GWR)—were used to assess the influence of population on forest cover change. The correlation between total population and forest loss at the national level suggested little to no direct impact of population growth on forest loss. However, sub-national analysis revealed localized forest degradation, highlighting the importance of spatial and regional assessments to uncover land cover changes masked by national trends. While LR showed a weak national-level correlation, GWR revealed substantial spatial variation, with the coefficient of determination values increasing from 0.21 in 2000 to 0.59 in 2020. In some regions, local R² exceeded 0.75 during 2015 and 2020, highlighting emerging hotspot clusters where population pressure is strongly linked to deforestation, especially along major infrastructure corridors. Using very high-resolution spatial data enabled pixel-level analysis, capturing fine-scale deforestation patterns, and confirming hotspot accuracy. Overall, the findings emphasize the value of spatially explicit models like GWR for understanding human–environment interactions guiding targeted land use planning to balance development with environmental sustainability in Nepal. Full article

The $N_k$-index (k-distance degree index) of a connected graph G was first introduced by Naji and Soner as a generalization of the distance degree concept, as $N_k\left(G\right)={\displaystyle \sum _{k=1}^{d\left(G\right)}}\left({\displaystyle \sum _{v\in V\left(G\right)}}{d}_k\left(v\right)\right)k$, where the distance between u and v in G is denoted by $d(u,v)$, the diameter of a graph G is denoted by $d\left(G\right)$, and the degree of a vertex v at distance k is denoted by $d_k\left(v\right)=\{u,v\in V\left(G\right)d(u,v)=k\}$. In this paper, we extend the study of the $N_k$-index of graphs. We introduced some graph transformations and their impact on the $N_k$-index of graph and proved that the star graph has the minimum, and the path graph has the maximum $N_k$-index among the set of all trees on n vertices. We also show that among all trees with fixed maximum-degree $\Delta$, the broom graph $B_{n,\Delta }$ (consisting of a star $S_{\Delta +1}$ and a pendant path of length $n-\Delta -1$ attached to any arbitrary pendant path of star) is a unique tree which maximizes the $N_k$-index. Further, we also defined and proved a graph with maximum $N_k$-index for a given number of n vertices, maximum-degree $\Delta$, and perfect matching among trees. We characterize the starlike trees which minimize the $N_k$-index and propose a unique tree which minimizes the $N_k$-index with diameter d and n vertices among trees. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

In causal inference research, accurate estimation of individualized treatment effects (ITEs) is at the core of effective intervention. This paper proposes a dual-structure ITE-estimation model based on Bayesian Additive Regression Trees (BART), which constructs independent BART sub-models for the treatment and control groups, estimates ITEs using the potential outcome framework and enhances posterior stability and estimation reliability through Markov Chain Monte Carlo (MCMC) sampling. Based on psychological stress questionnaire data from graduate students, the study first integrates BART with the Shapley value method to identify employment pressure as a key driving factor and reveals substantial heterogeneity in ITEs across subgroups. Furthermore, the study constructs an ITE model using a dual-structured BART framework (BART-ITE), where employment pressure is defined as the treatment variable. Experimental results show that the model performs well in terms of credible interval width and ranking ability, demonstrating superior heterogeneity detection and individual-level sorting. External validation using both the Bootstrap method and matching-based pseudo-ITE estimation confirms the robustness of the proposed model. Compared with mainstream meta-learning methods such as S-Learner, X-Learner and Bayesian Causal Forest, the dual-structure BART-ITE model achieves a favorable balance between root mean square error and bias. In summary, it offers clear advantages in capturing ITE heterogeneity and enhancing estimation reliability and individualized decision-making. Full article

Increasing global population and threat from climate change are imposing economic, social, and ecological challenges to global food production. The demand for food is increasing, necessitating enhanced agricultural production with minimal environmental impacts. To meet this demand, sustainable intensification of both crops and livestock is necessary. This is more urgent in sub-Saharan Africa (SSA), a region characterized by low productivity and environmentally degrading agriculture. Integrated Agriculture-livestock-forestry (ALF) systems could be a key form of intensification needed for achieving food security and economic and environmental sustainability. The synergetic interactions between ALF nexus provide a mechanism to foster interconnectedness and resource circulation where practices of one system influence the outcomes in another. These systems enhance long-term farm sustainability while serving the farmers’ environmental and economic goals. It provides opportunities for improving food security, farmer incomes, soil health, climate resilience and the achievement of several UN Sustainable Development Goals. It is therefore crucial to strengthen the evidence supporting the contribution of these systems. On this basis, this paper reviews the potential pathways through which ALF nexus can enhance incomes, food security and climate change mitigation in SSA. The paper discusses the pathways through which the integration of crops, livestock and trees enhance (i) food security, (ii) incomes, (iii) soil health and (iv) mitigation of climate change in SSA. We argue that implementing ALF systems will be accompanied by an advancement of enhanced food security, farmer livelihoods and ecological conservation. It will foster a more balanced and sustainable sub-Saharan African agricultural systems. Full article

Understanding the hierarchical patterns of building heights is essential for sustainable urban development and planning. This study presents a novel approach for detecting and comparing building height hierarchies in four major bay areas: the San Francisco Bay Area, the New York Bay Area in the United States, the Tokyo Bay Area in Japan, and the Guangdong-Hong Kong-Macau Greater Bay Area in China. Kernel density estimation was first used to create continuous spatial distributions of building heights, forming the basis for our analysis. The approach then uses the contour tree algorithm to abstract and visualize these hierarchies. A structural similarity index is proposed to compare the hierarchies by identifying the maximum common sub-contour tree across the different contour trees. The results reveal that all four bay areas exhibit a multi-core hierarchical structure, with the greater bay area exhibiting the most complex pattern. Quantitative comparison reveals that the building height hierarchies of the New York Bay Area and Tokyo Bay Area are most similar (similarity index = 0.74), while those of the San Francisco Bay Area and Greater Bay Area are the least similar (similarity index = 0.17). Our approach provides a practical tool for understanding building height hierarchies and can be readily applied to analyze diverse spatial patterns. Full article

After decades of calls for more sustainable land use systems, there is still a lack of consensus on an appropriate way forward, especially for tropical and subtropical agroecosystems. Land Maxing utilises appropriate, community-based interventions to fortify and maximise the multiple, long-term benefits and interest flows from investments that rebuild all six essential capitals of sustainable development (natural, social, human, physical, financial and political/corporate will) for resource-poor smallholder communities in tropical and subtropical countries. Land Maxing adds domestication of overlooked indigenous food tree species, and the commercialization of their marketable products, to existing land restoration efforts while empowering local communities, enhancing food sovereignty, and boosting the local economy and overall production. These agroecological and socio-economic interventions sustainably restore and intensify subsistence agriculture replacing conventional negative trade-offs with fortifying ‘trade-ons’. Land Maxing is therefore productive, regenerative, remunerative and transformative for farming communities in the tropics and sub-tropics. Through the development of resilience at all levels, Land Maxing uniquely addresses the big global issues of environmental degradation, hunger, malnutrition, poverty and social injustice, while mitigating climate change and restoring wildlife habitats. This buffers subsistence farming from population growth and poor international governance. The Tropical Agricultural Association International is currently planning a programme to up-scale and out-scale Land Maxing in Africa. Full article

The autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we proposed an autonomous positioning method based on multi-view reference images rendered from the scene’s 3D geometric mesh and apply a bag-of-words (BoW) image retrieval pipeline to achieve efficient and scalable positioning, without utilizing deep learning-based retrieval or 3D point cloud registration. To minimize the number of reference images, scene coverage quantification and optimization are employed to generate the optimal viewpoints. The proposed method jointly exploits a visual-bag-of-words tree to accelerate reference image retrieval and improve retrieval accuracy, and the Perspective-n-Point (PnP) algorithm is utilized to obtain the drone’s pose. Experiments are conducted in urban real-word scenarios and the results show that positioning errors are decreased, with accuracy ranging from sub-meter to 5 m and an average latency of 0.7–1.3 s; this indicates that our method significantly improves accuracy and latency, offering robust, real-time performance over extensive areas without relying on GNSS or dense point clouds. Full article

Tusavirus 1 of species Protoparvovirus incertum 1 (family Parvoviridae) was first identified in humans and later in small ruminants (caprine and ovine). This study reports the full-length coding sequences (~4400–4600 nt) of three novel tusavirus-related protoparvoviruses from ovine (“misavirus”, PV540792), for the first time bovine (“sisavirus”, PV540793) and subsequently from caprine (“gisavirus” PV540850/51) fecal samples, using next-generation sequencing (NGS) and PCR techniques. Their NS1, VP1 and VP2 proteins shared 61–63% amino acid identities with each other and with tusaviruses, suggesting these three viruses belong to three novel species in the genus Protoparvovirus. Phylogenetic analyses placed them with tusaviruses on a separate main branch, implying a shared origin among these most likely ruminant protoparvoviruses. A small-scale epidemiological investigation on 318 ruminant enteric samples using novel generic NS1 primers found misavirus in 14/51 (27.5%) ovine and sisavirus in 19/203 (9.4%) bovine samples from multiple Hungarian farms. Tusavirus was present in 5/51 (9.8%) ovine and 15/62 (24.2%) caprine samples, all from one farm. The highest prevalences for all three viruses were found in animals aged 2–12 months, though sporadic cases were also found in other age groups. Partial NS and VP sequence-based phylogenetic trees showed virus-specific lineages for misa-, sisa-, gisa- and tusaviruses, with various strains forming sub-lineages. These findings suggest the presence of multiple genotypes and/or members of additional species, which was supported by a VP sequence-based hierarchical cluster analysis. The study’s viruses were mostly phylogenetically separated by host; however, two bovine sisavirus strains with diverse phylogenetic localizations in the NS (belonging to bovine sisaviruses) and VP1 trees (distantly related to ovine misaviruses) could indicate previous (interspecies?) recombination events. Full article

This paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gaussian noise (AWGN) and Rayleigh fading to mimic realistic environments. Traditional estimators, such as MMSE and LMMSE, often underperform in such heterogeneous and nonlinear conditions due to their analytical rigidity. To overcome these limitations, we introduce a data-driven approach using a decision tree regressor trained on 18,000 signal samples across 36 SNR levels. Simulation results show that support vector machine (SVM) achieved 91.34% accuracy and a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB. Random forest achieved 91.01% accuracy and a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB. The proposed tree model attained 90.83% and 97.63% accuracy with BERs of 0.0917 and 0.0237, respectively, at the corresponding SNR values. The distinguishing advantage of the tree model lies in its inference efficiency. It completes predictions on the test dataset in just 45.53 s, making it over three times faster than random forest (140.09 s) and more than four times faster than SVM (189.35 s). This significant reduction in inference time makes the proposed tree model particularly well suited for real-time and resource-constrained WSN scenarios, where fast and efficient estimation is often more critical than marginal gains in accuracy. The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

Precise spatial localization of broadleaf species is crucial for efficient forest management and ecological studies. This study presents an advanced approach for segmenting and classifying broadleaf tree species, including Japanese oak (Quercus crispula), in mixed forests using multi-spectral imagery captured by unmanned aerial vehicles (UAVs) and deep learning. High-resolution UAV images, including RGB and NIR bands, were collected from two study sites in Hokkaido, Japan: Sub-compartment 97g in the eastern region and Sub-compartment 68E in the central region. A Mask Region-based Convolutional Neural Network (Mask R-CNN) framework was employed to recognize and classify single tree crowns based on annotated training data. The workflow incorporated UAV-derived imagery and crown annotations, supporting reliable model development and evaluation. Results showed that combining multi-spectral bands (RGB and NIR) with canopy height model (CHM) data significantly improved classification performance at both study sites. In Sub-compartment 97g, the RGB + NIR + CHM achieved a precision of 0.76, recall of 0.74, and F1-score of 0.75, compared to 0.73, 0.74, and 0.73 using RGB alone; 0.68, 0.70, and 0.66 with RGB + NIR; and 0.63, 0.67, and 0.63 with RGB + CHM. Similarly, at Sub-compartment 68E, the RGB + NIR + CHM attained a precision of 0.81, recall of 0.78, and F1-score of 0.80, outperforming RGB alone (0.79, 0.79, 0.78), RGB + NIR (0.75, 0.74, 0.72), and RGB + CHM (0.76, 0.75, 0.74). These consistent improvements across diverse forest conditions highlight the effectiveness of integrating spectral (RGB and NIR) and structural (CHM) data. These findings underscore the value of integrating UAV multi-spectral imagery with deep learning techniques for reliable, large-scale identification of tree species and forest monitoring. Full article

The multi-station registration of concrete-filled steel tubular (CFST) arch rib segments poses significant challenges due to structural complexity and environmental constraints during terrestrial laser scanning, requiring multi-angle acquisition for comprehensive coverage. This study introduces a cascaded registration framework comprising: (1) a coarse registration method utilizing local geometric features of segmented tubular joints, where equidistant cross-section partitioning extracts inherent circularity constraints from cylindrical segments, and (2) a refined registration stage employing the Coherent Point Drift (CPD) algorithm with k-d tree acceleration for computational efficiency. Experimental results demonstrate that the coarse registration achieves 31 mm RMSE with R²= 0.889, eliminating 88.9% of initial misalignment. The CPD refinement reduces RMSE to 4 mm (87% precision improvement), reaching sub-centimeter accuracy with exceptional congruence (R² = 0.995, residual error = 0.5%). Notably, k-d tree acceleration decreases computational time by 34.2% (13.30 s vs. 20.21 s) compared to conventional CPD. Validated on 2.2 m CFST specimens, this method provides an efficient solution for multi-station point cloud registration of complex steel structures. Full article

A previously buried paleosol was found on the continental shelf during a study of sea floor scour, nucleated by large artificial reef structures such as vessel hulks, barges, train cars, military vehicles, etc., called “scour nuclei”. It is a relic paleo-land surface of sapling-sized tree stumps, root systems, and fossil animal bone exhumed by scour processes active adjacent to the artificial reef structure. Over the span of five research cruises to the site in 2022–2024, soil samples were taken using hand excavation, PONAR grab samplers, split spoon, hollow tube auger, and a modified Shelby-style push box. High-definition (HD) video was taken using a Remotely Operated Vehicle (ROV) and diver-held cameras. Radiocarbon dating of wood samples returned ages of 42,015–43,417 calibrated years before present (cal yrBP). Pollen studies, together with the recovered macrobotanical remains, support our interpretation of the site as a freshwater forested wetland whose keystone tree species was Taxodium distichum—bald cypress. The paleosol was identified as an Aquult, a sub-order of Ultisols where water tables are at or near the surface year-round. A deep (0.25 m+) argillic horizon comprised the bulk of the preserved soil. Comparable Ultisols found in Georgia wetlands include Typic Paleaquult (Grady and Bayboro series) soils. Full article

Optimal path planning algorithms offer substantial benefits in high-density storage (HDS) systems in modern smart manufacturing. However, traditional algorithms may encounter significant optimization challenges due to intricate architectural configurations and traffic constraints of the HDS system. This paper addresses these issues by introducing a two-step novel path planning method: (1) the mesh-tree grid map topological representation and the (2) Lattice-Hopping (LH) algorithm. The proposed method first converts the layout of an HDS system into a mesh-tree grid hierarchical structure by capturing and simplifying the spatial and geometrical information as well as the traffic constraints of the HDS system. Then, the LH algorithm is proposed to find optimal shipping path by leveraging the global connectivity of main tracks (main track priority) and the ‘jumping’ mechanism of sub-tracks. The main track priority and the ‘jumping’ mechanism work together to save computational complexity and enhance the feasibility and optimality of the proposed method. Numerical and case studies are performed to demonstrate the superiorities of our method to properly modified benchmark algorithms. Algorithm scalability, robustness, and operational feasibility for industrial production in modern smart manufacturing are also displayed and emphasized. Full article