Search Results (280)

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

Windthrow determines major changes in tree stand evolution due to the felling or breaking of either isolated trees or entire stands. It has a major ecological, social and economic impact. Wood loss resulting from tree felling and cross-cutting operations is a less-studied aspect related to windthrow. Wood loss is represented by high stumps, broken or split stems, wood lost in the felling of trees that remain standing, wood lost in felling cuts that attempt to remove the stem from the root plate of partially or totally uprooted trees and wood lost as a result of stem cross-cutting. The study focused on estimating losses and their indices in a spruce tree stand located in the Curvature Carpathians. Windthrow took place in this tree stand in February 2020. The results showed that the total wood loss index is 7.747%. The main losses are represented by wood losses in high stumps (5.319%). The amount of wood loss depends on the proportion of uprooted or standing trees, ground inclination and the uprooting direction of trees as opposed to ground inclination, as well as on tree dimension. Tree volume significantly influences wood loss in high stumps (p < 0.001). The closer the uprooting direction is to the highest slope, the higher the tree stump becomes. Wood loss caused by stem breaking and splitting represents 2.280%, tree felling cuttings and stem removal from the root plate in uprooted trees account for 0.138% while loss resulting from stem cross-cutting represents 0.10%. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

Potato early dying disease complex (PED) leads to premature senescence and rapid decline in potato plants. Unlike potato wilt caused solely by Verticillium species, PED symptoms are more severe due to the synergistic effects of multiple pathogens, including root-lesion nematodes, fungi such as Colletotrichum and Fusarium, and soft-rot bacteria. To investigate the microbiome responsible for PED, soil and stem samples from healthy-looking and symptomatic plants were analyzed using amplicon-targeted next-generation sequencing (Illumina MiSeq and PacBio technologies). Samples were collected from four locations in New Brunswick, Canada from fields previously rotated with barley or oat. Comparative analysis of the bacterial, fungal, and eukaryotic diversity in soil samples showed minimal differences, with only bacterial alpha diversity influenced by the plant health status. Verticillium dahliae was abundant in all soil samples, and its abundance was significantly higher in the stems of diseased plants. Additional fungal species implicated in PED, including Plectosphaerella cucumerina, Colletotrichum coccodes, Botrytis sp., and Alternaria alternata, were also identified in the stems. This study highlights the complex, plant-associated microbial interactions underlying PED and provides a foundation for microbiome-informed disease management strategies. Full article

In this study, a dynamic load forecasting model was developed for offshore wind turbines, based on the COAWST (Coupled Ocean-Atmosphere-Wave-Sediment Transport) model, the GRU (Gated Recurrent Unit) algorithm, and a data assimilation module. The model was able to forecast aerodynamic, wave, and current loads acting on the turbines. Four groups of forecasting tests were conducted to evaluate the model’s performance under different strategies and to assess the impact of atmospheric assimilation on improving dynamic load forecasts. The wind turbines in Cangnan Offshore Wind Farm, located in the west of the East China Sea, were chosen as the study object. The results indicated that the model achieved high forecasting accuracy, with the RMSEs (root mean square errors) of 275.59 kN, 335.85 kN, and 313.51 N, for the aerodynamic, wave, and current loads. The errors were reduced by about 13%, 10.09%, and 6.7% when compared with the original COAWST model, and were also lower than the atmospheric and oceanic reanalysis data. Atmospheric data assimilation was demonstrated to reduce the forecasting RMSE of aerodynamic load by about 12%, and its error improvement was able to be combined with GRU-based error correction. Additionally, atmospheric assimilation mitigated the reduction in temporal variability caused by forecasting error correction, preventing a decrease in the standard deviation of aerodynamic load forecasts. However, atmospheric assimilation had minimal impacts on wave and current load forecasts, with the RMSEs increased by about 2.5% and 0.1%, and had almost the same performance in correlation coefficients and standard deviations. Full article

Accelerating the recovery of compacted soils caused by logging machinery using bioengineering techniques is a key goal of Sustainable Forest Management. This research was conducted on an abandoned skid trail with a uniform 15% slope and a history of heavy traffic, located in the Nav forest compartment of northern Iran. The main objectives were to assess (a) soil physical properties 35 years after skidding by a tracked bulldozer, (b) the impact of natural alder regeneration on soil recovery, and (c) the contribution of alder fine-root development to the restoration of compacted soils in beech stands. Soil physical properties and fine root biomass were analyzed across three depth classes (0–10 cm, 10–20 cm, 20–30 cm) and five locations (left wheel track (LT), between wheel tracks (BT), right wheel track (RT)) all with alder trees, and additionally control points inside the trail without alder trees (CPWA), as well as outside control points with alder trees (CPA). Sampling points near alder trees (RT, LT, BT) were compared to CPWA and CPA. CPA had the lowest soil bulk density, followed by LT, BT, RT, and CPWA. Bulk density was highest (1.35 ± 0.07 g cm⁻³) at the 0–10 cm depth and lowest (1.08 ± 0.4 g cm⁻³) at 20–30 cm. The fine root biomass at 0–10 cm depth (0.23 ± 0.21 g dm⁻³) was significantly higher than at deeper levels. Skid trail sampling points showed higher fine root biomass than CPWA but lower than CPA, by several orders of magnitude. Alder tree growth significantly reduced soil bulk density, aiding soil recovery in the study area. However, achieving optimal conditions will require additional time. Full article

A study was conducted in Sierra Leone to identify cassava plants that are asymptomatic and symptomatic to cassava mosaic disease (CMD) and collect planting materials for field trial establishment; determine the prevalence of CMD caused by African cassava mosaic virus (ACMV) and East African cassava mosaic virus (EACMV) using the Nuru App and virus indexing techniques; and assess selected agronomic traits in cassava. A total of 80 cassava farms spanning four provinces (Southern, Eastern, Northern, and North-West) were surveyed in April 2022. Findings showed that the cassava variants of the experiment and locations significantly (p < 0.001) affected CMD incidence, severity, growth, and fresh storage root yield traits. The CMD incidence (87.0%) and whitefly abundance (144.8) were highest, and the CMD severity was moderate (4.0) for the plants derived from cuttings obtained from symptomatic Cocoa mother plants, while plants derived from cuttings of improved mother plants exhibited no visible symptoms of the disease and the lowest population (45.1) of whiteflies. The Nuru app is inefficient for phenotypically detecting CMD at 3 months after planting (MAP), while at 6, 9 and 12 MAP, the app efficiently detected the disease using a molecular analysis technique. Resistant, non-diseased plants derived from cuttings obtained from SLICASS 4 mother plants produced the highest fresh storage root yield (54.9 t ha⁻¹). The highest storage root yield loss was recorded in the plants obtained from cuttings of symptomatic variety Cocoa mother plants harvested at Matotoka grassland ecology, Bombali District (90.2%), while those harvested from cuttings of asymptomatic variety Cocoa mother plants grown at the four test environments had a similar storage root yield loss ranging from 40.3 to 46.2%. Findings suggest the importance of genetic variability, environmental adaptation, utilization of diseased-free materials, and phytosanitation as disease management strategies for increased production. These findings provide important insights into the distribution, impact, and spread of CMD and whitefly abundance in the studied areas in Sierra Leone that could be exploited for cassava production, productivity, conservation, and population improvement. Full article

Urban vulnerability creates structural imbalances, leading to unsafe conditions and urban decline. One of the key root causes of urban vulnerability is significant changes in urban layout morphology, which significantly influences the determination of accessibility potential, causing some areas to grow while others decline. This study aims to examine the morphological structural factors that influenced physical vulnerability, with a focus on commercial buildings, which were affected by the transformation of urban structure resulting from the layout and connectivity of the transportation network at the global, local, and community levels, depending on their location; these factors contribute to spatial vulnerability in varying degrees. This study applied an indicator-based quantitative research methodology, constructing a Physical Vulnerability Index (PVI) by using Principal Component Analysis (PCA) to create new factors or components and compare physical vulnerability levels across different areas. The research findings found that the most influential morphological structural factor on physical vulnerability was micro-level morphology, primarily due to the relationship between the configuration of space and the level of usage popularity. The second most influential factor is macro-level morphology, resulting from the relationship between the accessibility potential of urban-level and neighborhood-level transportation networks. Full article

This paper introduces a method of monitoring infrared channel calibration stability through direct comparison of calibrated radiances by two Advanced Baseline Imager (ABI) on two geostationary (GEO) platforms. This GEO-GEO comparison is based on radiances in the overlapping area observed by the two ABIs, pixel by pixel, at approximately the same time, location, spectrum, and viewing zenith angle. It was initially developed for GOES-17 and subsequent GOES missions to validate the ABI’s calibration around its local midnight—a subject of particular interest for instruments on three-axis stabilized geostationary satellites. With the cryocooler anomaly of the GOES-17 ABI, however, the GEO-GEO comparison became an indispensable tool to characterize GOES-17 ABI infrared (IR) channel calibration with high frequency, low uncertainty, and in near real time, providing critical feedback to root cause investigation and mitigation options. Later, the GEO-GEO comparison was applied to the GOES-18 ABI as originally intended and was proved successful. It confirms that, with few exceptions, radiometric calibration for all ABIs is stable to within 0.1 K when the radiance fluctuation is converted to the brightness temperature at 300 K. Full article

The continuous provision of nitrogen (N) to the crop is critical for optimal cotton production; however, the constant and excessive application of synthetic fertilizers causes adverse impacts on soil, plants, animals, and human health. The current study focused on the short-term effects (one-year study) of adding different rates of clinoptilolite zeolite, as part of an integrated nutrient management plan, and different rates of inorganic N fertilizer to improve soil and crop performance of cotton in three locations (ATH, MES, and KAR) in Greece. Each experiment was set up according to a split-plot design with three replications, three main plots (zeolite application at rates of 0, 5, and 7.5 t ha⁻¹), and four sub-plots (N fertilization regimes at rates of 0, 100, 150, and 200 kg N ha⁻¹). The results of this study indicated that increasing rates of the examined factors increased cotton yields (seed cotton yield, lint yield, and lint percentage), with the greatest lint yield recorded under the highest rates of zeolite (7.5 t ha⁻¹: 1808, 1723, and 1847 kg ha⁻¹ in ATH, MES, and KAR, respectively) and N fertilization (200 kg N ha⁻¹: 1804, 1768, and 1911 kg ha⁻¹ in ATH, MES, and KAR, respectively). From the evaluated parameters, most soil parameters (soil organic matter, soil total nitrogen, and total porosity), root and shoot development (root length density, plant height, leaf area index, and dry weight), fiber maturity traits (micronaire, maturity, fiber strength, and elongation), fiber length traits (upper half mean length, uniformity index, and short fiber index), as well as color (reflectance and spinning consistency index) and trash traits (trash area and trash grade), were positively impacted by the increasing rates of the evaluated factors. In conclusion, the results of the present research suggest that increasing zeolite and N fertilization rates to 7.5 t ha⁻¹ and 200 kg N ha⁻¹, respectively, improved soil properties (except mean weight diameter), stimulated crop development, and enhanced cotton and lint yield, as well as improved the fiber maturity, length, and color parameters of cotton grown in clay-loam soils in the Mediterranean region. Full article

To study the mantle structure of the North China Craton (NCC) and its tectonic implications, in particular, the evolution of the rift systems in the Trans-North China Orogen (TNCO), we used teleseismic data recorded by 250 portable seismic stations to invert for the P-wave velocity (Vp) structures of the mantle beneath the NCC. Our results show a large-scale low-Vp anomaly in the shallow mantle and high-Vp anomalies in the deeper upper mantle beneath the eastern NCC, with fine-scale high-Vp anomalies at the lithosphere–asthenosphere boundary, indicating multi-stage lithospheric delamination during the Cenozoic. In the Yan Mountains (YanM), an east–west striking high-Vp anomaly between 60 to 200 km depths and low heat flow suggest the preservation of a thick mantle root. In the TNCO, high-Vp bodies in the upper mantle and the upper part of the mantle transition zone (MTZ) are imaged. The shallower high-Vp anomaly located beneath the Shanxi–Shaanxi Rift (SSR), along with an overlying local-scale low-Vp anomaly, indicates local hot material upwelling due to lithospheric root removal. The India–Eurasia collision’s far-field effects are proposed to cause lithospheric thickening, subsequent root delamination, and the formation and evolution of the SSR. Full article

Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum within noisy contexts while also minimizing the number of trials. For radiation survey and source location, the aid of such a machine learning algorithm could significantly cut down on time and health risks required for maintenance and emergency response scenarios. Maintaining the explainability while increasing the efficiency of the search has been found possible by including the high uncertainty data that is picked up while the agent is in transit. Now that the paths of transit matter to data acquisition they could be optimized as well. This paper introduces reinforcement learning (RL) to the BO search framework. The behavior of this RL additive is observed in simulation over three different datasets of real radiation data. It is shown that the RL additive can cause significant increases to the score of the maximum point discovered, but the computational time cost is increased by nearly 100% while the reconstructed radiation field root mean square error (RMSE) of the BO+RL algorithm matches BO performance within 1%. Full article

Tracing the root cause of defective products in liquid storage tank (LST) production poses a formidable challenge due to the complex dependencies between production and inspection processes. With associated coupling existing among multiple production processes, and the correspondence between the faults in production processes and inspection links being non-unique, these faults are usually difficult to be directly located via a single inspection process. In this paper, the problem of tracing the root cause of defective LST products, which is caused by process parameter deviations or human operation errors during production, is studied. A root cause tracing method that is based on the dynamic multi-fault diagnosis (DMFD) framework is proposed. First, a factorial hidden Markov model (FHMM) is established to depict the state transition process of the LST product, where its status changes over time and across production processes. This is achieved by considering the product state at each production process as a hidden state and the outcomes of each inspection process as an observation state. Then, the Viterbi algorithm is employed to solve the hidden state transition matrix and diagnostic matrix within the framework of the FHMM. Finally, experimental verification is carried out on a real LST assembly production line, and the influence of imperfect testing on the model accuracy is also considered. The experiment is carried out on an LST assembly line that encompasses three discrete links, including the welding of the upper and lower bodies, the installation of check valves, and the installation of sensors. Experimental results demonstrate that the proposed method achieves significantly more superior performance when compared to existing algorithms. Full article

A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years. Full article