Global Model Research Articles

Research on Rapid Detection of Underwater Targets Based on Global Differential Model Compression

Research on Rapid Detection of Underwater Targets Based on Global Differential Model Compression

Collaborative Training of Data-Driven Remaining Useful Life Prediction Models Using Federated Learning

Remaining useful life prediction models are a central aspect of developing modern and capable prognostics and health management systems. Recently, such models are increasingly data-driven and based on various machine learning techniques, in particular deep neural networks. Such models are notoriously “data hungry”, i.e., to get adequate performance of such models, a substantial amount of diverse training data is needed. However, in several domains in which one would like to deploy data-driven remaining useful life models, there is a lack of data or data are distributed among several actors. Often these actors, for various reasons, cannot share data among themselves. In this paper a method for collaborative training of remaining useful life models based on federated learning is presented. In this setting, actors do not need to share locally held secret data, only model updates. Model updates are aggregated by a central server, and subsequently sent back to each of the clients, until convergence. There are numerous strategies for aggregating clients’ model updates and in this paper two strategies will be explored: 1) federated averaging and 2) federated learning with personalization layers. Federated averaging is the common baseline federated learning strategy where the clients’ models are averaged by the central server to update the global model. Federated averaging has been shown to have a limited ability to deal with non-identically and independently distributed data. To mitigate this problem, federated learning with personalization layers, a strategy similar to federated averaging but where each client is allowed to append custom layers to their local model, is explored. The two federated learning strategies will be evaluated on two datasets: 1) run-to-failure trajectories from power cycling of silicon-carbide metal-oxide semiconductor field-effect transistors, and 2) C-MAPSS, a well-known simulated dataset of turbofan jet engines. Two neural network model architectures commonly used in remaining useful life prediction, long short-term memory with multi-layer perceptron feature extractors, and convolutional gated recurrent unit, will be used for the evaluation. It is shown that similar or better performance is achieved when using federated learning compared to when the model is only trained on local data.

Assessing the accuracy of global digital elevation models for hydrological analysis: the cases of Syria and Russia

Digital elevation models provide valuable information about the surface topography. The resolution and accuracy of the data used to create the models are critical for the results of hydrological analysis. Available global digital elevation models obtain high potential in terms of data sources; however, their performance requires meticulous evaluation for different terrain characteristics and applications. Aim. To determine the accuracy and resolution of data provided by current and old global models, namely SRTM 1, SRTM GL1, V3 ASTER, GMTED2010, PALSAR ALOS, and GTOPO 30, using the example of two terrains. The study considers the areas in Syria (Syrian coast) and Russia (Chernoyarsky and Akhtubinsky districts in the Astrakhan region) represented by different elevation models. A matching degree between model and real elevations measured by GPS was determined to carry out the hydrological analysis of the land surface. For this purpose, three statistical measures, including range, standard deviation and correlation were defined by means of BaseCamp, ARCGIS PRO & SAGA_GIS software. Data resolution indicates the degree of detail represented in the DEM dataset, which is established by the spatial sampling interval or the size of the grid cell used to represent the land surface. Data accuracy refers to the matching degree between the elevation values derived from the DEM and the actual data obtained on the surface. Results. In terms of correlation coefficient and standard deviation, the GMTED 2010 and ASTER V3 model for the Syrian region and the ALOS PALSAR and SRTM GL 1 model for the Russian region prove to be the most effective for hydrological analysis in the absence of accurate local models.

Tradeoffs between economy wide future net zero and net negative economy systems: The case of Chile

Given the possible economic consequences, poorer countries have more challenges in delivering their Nationally Determined Contributions. As a developing country, Chile has pledged to attain carbon neutrality by 2050. While Chile has implemented several mitigation measures, it still relies heavily on carbon sequestration, intending to sequester around 65 MtCO2e by 2050. However, heavy reliance on sequestration poses several risks as the literature shows that natural sinks, particularly forest and land, are exposed to severe impacts from global warming and climate change. Fortunately, Chile has significant renewable energy potential, which, if fully utilized, may move the country towards a net negative emissions context. To assess if such a net-negative system is feasible in the context of Chile, a new regional version of the Global Change Analysis Model for Chile is developed. The model is used to investigate the effects and required levels of investment in renewable energy and decarbonization of end-use sectors to achieve economy-wide net negative emissions scenarios. The design of net negative pathways follows a statistical approach based on the expected sequestration capacity in 2050 and its corresponding confidence interval. The results are compared to scenarios that are aligned with the objective of carbon neutrality by 2050. The findings show that obtaining net-zero emissions by 2050 is possible, however achieving net negative systems will be dependent on existing sequestration capacity and the application of economic incentives to boost green energy deployment in Chile as well as to push such green energy, in the form of electricity or e-fuels, into hard to decarbonize final demand sectors, such as transport, mining, and industry demand sectors. The results also indicate that after significantly reducing CO2 emissions from the energy sector (primarily the power sector), the agricultural sector and other urban and industrial sectors still contribute to non-significant levels of CH4 and N2O emissions.

Flammable futures—storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

Abstract Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must consider the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (>5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global Biosphere Management Model (GLOBIOM) to predict resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic fire model used to reproduce and project wildfires based on various criteria and input variables; GLOBIOM is a global economic land use model, which assesses land use competition and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

Modelling geoid height errors for local areas based on data of global models

Abstract The development of global geoid models became feasible following the launch of specialised satellite missions. Today, the root mean square deviation of the heights in global models of high degree & order varies from centimetres to decimetres across different countries. In countries where the accuracy of such models is lower, there is potential to enhance their precision by applying specific corrections. This study presents a novel methodology for locally modelling the height errors of high degree & order global geoid models using levelling sub-benchmarks for GNSS stations. The methodology is based on a combination of optimal interpolation methods, filtering, and the concept of data weighting by gravity anomaly differences. The methodology is aimed at creating a hybrid model that aligns with the local characteristics of the geoid (or quasi-geoid) derived from the traditional levelling network. The advantage of this methodology lies in its ability to reduce the residual height errors of the EGM2008 and EIGEN6C4 models to less than 1 cm when using only four control points. Such results exceed the initial values of the systematic height errors of these models by 90–96 %. For the GECO model, the residual errors are around 2 cm, while for the XGM2019e_2159 model, they reach 3 cm. These results indicate that this methodology can be applied to all global models of high degree & order, although its effectiveness may vary depending on the specifics of a particular model.

Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT

Abstract. In the past, satellite climatologies of gravity waves (GWs) have initiated progress in their representation in global models. However, these could not provide the phase speed and direction distributions needed for a better understanding of the interaction between GWs and the large-scale winds directly. The ESA Earth Explorer 11 candidate CAIRT could provide such observations. CAIRT would use a limb-imaging Michelson interferometer resolving a wide spectral range, allowing temperature and trace gas mixing ratio measurements. With the proposed instrument design, a vertical resolution of 1 km, along-track sampling of 50 km, and across-track sampling of 25 km in a 400 km wide swath will be achieved. In particular, this allows for the observation of three-dimensional (3D), GW-resolving temperature fields throughout the middle atmosphere. In this work, we present the methodology for the GW analysis of CAIRT observations using a limited-volume 3D sinusoidal fit (S3D) wave analysis technique. We assess the capability of CAIRT to provide high-quality GW fields by the generation of synthetic satellite observations from high-resolution model data and comparison of the synthetic observations to the original model fields. For the assessment, wavelength spectra, phase speed spectra, horizontal distributions, and zonal means of GW momentum flux (GWMF) are considered. The atmospheric events we use to exemplify the capabilities of CAIRT are the 2006 sudden stratospheric warming (SSW) event, the quasi-biennial oscillation (QBO) in the tropics, and the mesospheric preconditioning phase of the 2019 SSW event. Our findings indicate that CAIRT would provide highly reliable observations not only of global-scale GW distributions and drag patterns but also of specific wave events and their associated wave parameters. Even under worse-than-expected noise levels of the instrument, the resulting GW measurements are highly consistent with the original model data. Furthermore, we demonstrate that the estimated GW parameters can be used for ray tracing, which physically extends the horizontal coverage of the observations beyond the orbit tracks.

Comorbidity profiles among sputum-positive tuberculosis patients in Cameroon

IntroductionComorbid non-communicable diseases (NCDs) like diabetes, cardiovascular diseases (CVD), kidney diseases, and hypertension, could have implications for tuberculosis (TB) treatment management and increase the disease burden amongst active TB patients.MethodsThis cross-sectional study aimed at profiling comorbidities amongst sputum-positive TB patients in the South West and Littoral regions of Cameroon and was relevant for improving disease management and public health interventions. Diabetes was defined by elevated blood glucose, body mass index (underweight: < 18.5 kg/m2, normal: 18.5– < 25.0 kg/m2, overweight: 25– < 30 kg/m2 and obese: ≥30.0 kg/m2) and hypertension by elevated blood pressure levels (i.e., systolic ≥130 mmHg or diastolic ≥80 mmHg). Socio-demographic and clinical data were collected using case report forms. Descriptive analysis was performed, bivariate logistic regression analysis was computed with at least one comorbidity as the dependent variable (global model) and a multivariable logistic regression analysis was done to provide adjusted odds ratios (final model). The covariate with the highest p-value was removed until p < 0.25 cut-off, using R software version 4.3.1. p-value < 0.05 at 95% confidence interval was considered statistically significant.ResultsFive hundred and forty-nine sputum-positive microscopically confirmed active TB patients were enrolled into this study. Two-thirds (65.8%) of the total patients were male. Overall, 56 sputum-positive TB patients had at least one non-communicable disease, thus a prevalence of 10.2% (95% CI = 7.9–13.0). The most frequently recorded NCD was diabetes 4.4% (95% CI = 3.1–6.7) followed by kidney disease 2% (95% CI = 1.1–3.6), hypertension 0.9% (95% CI = 0.4–2.2), and CVD 0.91% (95% CI = 0.4–2.2). Three TB patients (0.6%) had all four comorbidities examined. Age group (p < 0.001), and level of education (p = 0.049) were factors significantly associated with having at least one comorbidity.DiscussionOur findings showed that diabetes was significantly the most prevalent comorbid NCD amongst sputum-positive TB patients (p < 0.001). HIV status, occupation, body mass index (BMI), and alcohol intake were not significantly associated with having at least one comorbidity. Implementing public health intervention programmes such as systematic screening of TB patients for NCDs especially diabetes is highly recommended for better control of these diseases.

HyFormer: a hybrid transformer-CNN architecture for retinal OCT image segmentation

Optical coherence tomography (OCT) has become the leading imaging technique in diagnosing and treatment planning for retinal diseases. Retinal OCT image segmentation involves extracting lesions and/or tissue structures to aid in the decisions of ophthalmologists, and multi-class segmentation is commonly needed. As the target regions often spread widely inside the retina, and the intensities and locations of different categories can be close, good segmentation networks must possess both global modeling capabilities and the ability to capture fine details. To address the challenge in capturing both global and local features simultaneously, we propose HyFormer, an efficient, lightweight, and robust hybrid network architecture. The proposed architecture features parallel Transformer and convolutional encoders for independent feature capture. A multi-scale gated attention block and a group positional embedding block are introduced within the Transformer encoder to enhance feature extraction. Feature integration is achieved in the decoder composed of the proposed three-path fusion modules. A class activation map-based cross-entropy loss function is also proposed to improve segmentation results. Evaluations are performed on a private dataset with myopic traction maculopathy lesions and the public AROI dataset for retinal layer and lesion segmentation with age-related degeneration. The results demonstrate HyFormer's superior segmentation performance and robustness compared to existing methods, showing promise for accurate and efficient OCT image segmentation. .

Federated Diverse Self-ensembling Learning Approach for Data Heterogenity in Drive Vision

Federated learning (FL) has developed as an efficient framework that can be used to train models across isolated data sources while also protecting the privacy of the data. In FL a common method is to construct local and global models together, with the global model (server) informing the local models and the local models (clients) updating the global model. Most present works assume clients have labeled datasets and the server has no data for supervised learning (SL) problems. In reality, clients lack the competence and drive to identify their data, while the server may host a tiny amount. How to reasonably use serverlabeled and client-unlabeled data is crucial in semi-supervised learning (SSL) and Cclientdata heterogeneity is widespread in FL. However, inadequate high-quality labels and non-IID client data, especially in autonomous driving, decrease model performance across domains and interact negatively. To solve this Semi-Supervised Federated Learning (SSFL) problem, we come up with a new FL algorithm called FedDSL in this work. We use self-ensemble learning and complementary negative learning in our method to make clients’ unsupervised learning on unlabeled data more accurate and efficient. It also coordinates the model training on both the server side and the clients’ side. In an important distinction to earlier research that kept some subset of labels at each client, our method is the first to implement SSFL for clients with 0% labeled non-IID data. Our contributions include the effectiveness of self-ensemble learning by using confidence score vector for calculating only for the current model performing data filtering and initiated negative learning by showing the data filtering performance in the beginning rounds. Our approach has been rigorously validated on two significant autonomous driving datasets, BDD100K and Cityscapes, proving to be highly effective. We have achieved state-of-the-art results and the metric that is utilized to evaluate the effectiveness of each detection task is mean average precision (mAP@0.5). Astonishingly FedDSL performs nearly as well as fully-supervised centralized training approaches, despite the fact that it only uses 25% of the labels in the Global model.

Robust Federated Learning for Mitigating Advanced Persistent Threats in Cyber-Physical Systems

Malware triage is essential for the security of cyber-physical systems, particularly against Advanced Persistent Threats (APTs). Proper data for this task, however, are hard to come by, as organizations are often reluctant to share their network data due to security concerns. To tackle this issue, this paper presents a secure and distributed framework for the collaborative training of a global model for APT triage without compromising privacy. Using this framework, organizations can share knowledge of APTs without disclosing private data. Moreover, the proposed design employs robust aggregation protocols to safeguard the global model against potential adversaries. The proposed framework is evaluated using real-world data with 15 different APT mechanisms. To make the simulations more challenging, we assume that edge nodes have partial knowledge of APTs. The obtained results demonstrate that participants in the proposed framework can privately share their knowledge, resulting in a robust global model that accurately detects APTs with significant improvement across different model architectures. Under optimal conditions, the designed framework detects almost all APT scenarios with an accuracy of over 90 percent.

A new active learning surrogate model for time- and space-dependent system reliability analysis

This study introduces a novel method for time- and space-dependent system reliability analysis, integrating an active learning surrogate model with an innovative parallel updating strategy. A global Kriging model is developed to represent the signs of random samples using efficient global optimization. From a Bayesian perspective, the prediction probabilities of random sample signs within the time-space domain are calculated, and the sample with the lowest prediction probability is chosen to update the global Kriging model. The system extremum for each sample in the time-space domain is determined, and the corresponding random variables, time-space coordinates, and failure modes are selected. To further decrease iteration times, a parallel updating strategy that considers both the predicted probability and the correlation among candidate samples is proposed. Additionally, a new stopping criterion is introduced to balance accuracy and efficiency, terminating the updating process appropriately. The method's accuracy and efficiency are validated through three examples.

Transferability and Explainability of Deep Learning Emulators for Regional Climate Model Projections: Perspectives for Future Applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global climate models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here, we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as perfect prognosis (PP) and model output statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of explainable artificial intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differs between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications. Significance Statement Regional climate model (RCM) emulators are a cost-effective emerging approach for generating comprehensive ensembles of regional climate projections. Promising results have been recently obtained using deep learning models. However, their potential to capture the regional climate dynamics and to emulate other periods, emission scenarios, or driving global climate models (GCMs) remains an open issue that affects their practical use. This study explores the potential of current emulation approaches incorporating new explainable artificial intelligence (XAI) evaluation techniques to assess the reliability and transferability of the emulators. Our findings show that the different global and regional model biases involved in the different approaches play a key role in transferability. Based on the results obtained, we provide some prospects for potential applications of these models in challenging problems.

Improving Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model

AbstractDeficiencies in upper ocean vertical mixing parameterizations contribute to tropical upper ocean biases in global coupled general circulation models, affecting their simulated ocean heat uptake and ENSO variability. To better understand these deficiencies, we develop a suite of ocean model experiments including both idealized single column models and realistic global simulations. The vertical mixing parameterizations are first evaluated using large eddy simulations as a baseline to assess uncertainties and evaluate their implied turbulent mixing. Global models are then developed following NOAA/GFDL's 0.25° nominal horizontal grid spacing OM4 (uncoupled) configuration of the MOM6 ocean model, with various modifications that target biases in the original model. We test several enhancements to the existing mixing schemes and evaluate them against observational constraints from Tropical Atmosphere Ocean moorings and Argo floats. In particular, we find that we can improve the diurnal variability of mixing in OM4 via modifications to its surface boundary layer mixing scheme, and can improve the net mixing in the upper thermocline by reducing the background vertical viscosity, allowing for more realistic, less diffuse currents. The improved OM4 model better represents the mixing, leading to improved diurnal deep‐cycle variability, a more realistic time‐mean tropical thermocline structure, and a better Pacific Equatorial Undercurrent.

Simulation model for electrical and agricultural productivity of an olive hedgerow Agrivoltaic system.

Given the need to promote a more sustainable energy model that contributes to the fight against Climate Change and the consequent advancement of photovoltaics, Agrivoltaics is presented as a solution to the conflict over land use, since it makes it possible to reconcile agricultural production and photovoltaic power simultaneously on the same piece of land. However, it is a new production model and there are still many aspects to research. This work presents a mathematical model to simulate the spatial distribution of the solar radiation in the canopy of the crop of an agrivoltaic plant in which N-S solar trackers are alternately combined with olive groves in hedgerows. In this way, simulation has the advantage of predicting the expected behaviour of one or more configurations of agrivoltaic installations and, on the basis of this simulated behaviour, making decisions during the design phase to optimise their performance. To do this, various models have been integrated to simulate both the behaviour of irradiance on its way from the Sun to the crop, using geometric-vector analysis, and the irradiance absorption processes on its way through the canopy of the crop. Thus, the global model presented allows estimating the electrical and oil production of an agrivoltaic plant such as the one described. From this global model, electrical and oil productions have been systematically simulated for a set of agrivoltaic plants whose design parameters range around the intermediate values of an agrivoltaic facility with olive groves in hedgerows spaced 10 m apart and alternated with 3 m wide and 3 m hight N-S solar trackers. For this intermediate facility, the annual productions of oil and electricity are and , respectively. Finally, the oil and energy productions of all the simulated agrivoltaic facilities and their design parameters have been interrelated obtaining mathematical equations to determine in a simple way the influence on the productions of the geometrical design variables of the agrivoltaic installation (height of the solar trackers, width of the collectors and distance between hedgerows). Therefore, the model presented makes it possible to advance in the evaluation of the possible integration of olive groves in agrivoltaic systems and their profitability, constituting an important benefit for the implementation of agrivoltaics in the Mediterranean regions where olive groves play a significant role in agricultural production systems.

FAM: Adaptive federated meta-learning for MRI data

Federated learning enables multiple clients to collaborate to train a model without sharing data. Clients with insufficient data or data diversity participate in federated learning to learn a model with superior performance. MRI data suffers from inadequate data and different data distribution due to differences in MRI scanners and client characteristics. Also, privacy concerns preclude data sharing. In this work, we propose a novel adaptive federated meta-learning (FAM) mechanism for collaboratively learning a single global model, which is personalized locally on individual clients. The learnt sparse global model captures the common features in the MRI data across clients. This model is grown on each client to learn a personalized model by capturing additional client-specific parameters from local data. Experimental results on multiple data sets show that the personalization process at each client quickly converges using a limited number of epochs. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism. Additionally, the FAM-based sparse global model has fewer parameters that require less communication overhead during federated learning. This makes the model viable for networks with limited resources.

Inertia of Ionospheric Conductance During Electron Precipitation Events

AbstractUsing the SuperThermal Electron Transport (STET) model coupled with the Super‐thermal Proton Electron Atomic Hydrogen—tRansport in the Ionosphere and Thermosphere (SPEAH‐RIT) codes, we demonstrate that temporal variability of ionospheric conductance is defined by several time scales: the temporal variations of the magnetospheric source, the start time of electron precipitation, and the termination of the corresponding source. In these cases, the time scales are defined by dissipation of energetic electrons and effective recombination processes. The results presented in this paper were applied in the regions of pulsating aurora and polar arcs, demonstrating the fact that ionospheric conductance requires some time to form and decay. These time delays constitute an effective “inertia” in the conductance calculation which is not accounted for in many global models which assume an instantaneous connection between precipitation and conductance. Ionospheric conductance inertia influences the temporal variation in ionospheric and magnetospheric electric fields, and as a result, impacts magnetosphere‐ionosphere (MI) dynamics on a global scale.

Constraining the Properties of the Multicomponent Local Interstellar Medium: MHD-kinetic Modeling Validated by Voyager and New Horizons Data

We introduce the first solar-cycle simulations from our 3D, global MHD-plasma/kinetic-neutrals model, where both hydrogen and helium atoms are treated kinetically, while electrons and helium ions are described as individual fluids. Using Voyager/PWS observations of electron density up to 160 au from the Sun for validation of several different global models, we conclude that the current estimates for the proton density in the local interstellar medium (LISM) need a revision. Our findings indicate that the commonly accepted value of 0.054 cm−3 may need to be increased to values exceeding 0.07 cm−3. We also show how different assumptions regarding the proton velocity distribution function in the outer heliosheath may affect the global solution. A new feature revealed by our simulations is that the helium ion flow may be significantly compressed and heated in the heliotail at heliocentric distances exceeding ∼400 au. Additionally, we identify a Kelvin–Helmholtz instability at the boundary of the slow and fast solar wind in the inner heliosheath, which acts as a driver of turbulence in the heliotail. These results are crucial for inferring the properties of the LISM and of the global heliosphere structure.

Comparison of cation exchange capacity extraction methods for soil data harmonization and soil classification in Central and East Europe

Cation exchange capacity and base saturation are of crucial importance for soil characterisation and classification; however, the interchangeability of soil data for international assessment of soil productivity, soil classification, mapping, and modelling is limited due to the lacking comparisons between results obtained using different analytical methods in particular regions of the world. The aim of the present study was to analyse the relationships between cation exchange capacity and base saturation measured using the methods adopted in Central and East Europe, and ‘standard’ techniques required for soil classification suggested by World Reference Base (WRB). This study was carried out using 183 soil samples collected from Luvisols, Retisols, Planosols, Chernozems, Phaeozems, Cambisols, and Arenosols in Poland, representing a wide range of texture, pH, and organic carbon content. A close comparability was found in noncalcareous soils between the ‘total sorption capacity’ (T) measured in Central and East Europe as the sum of base cations and ‘total’ (‘hydrolytic’) acidity, with the ‘standard’ cation exchange capacity (CEC) measured using 1 M ammonium acetate buffered at pH 7. The close correlation between T and CEC values (in noncalcareous soils) facilitates reliable data recalculation and its application in global modelling, soil classification and mapping. For soils containing carbonates, CEC must be measured using reference methods, because no relationship exists between T and CEC. The ‘potential’ base saturation (V) derived on the basis of T may be reliably recalculated to ‘standard’ base saturation (BS), allowing a retrieval of archival data from Central and East European regional databases and published reports. Similarities between the values of cation exchange capacity and base saturation, whether measured or calculated using the local and standard methods, allow a positive verification of previously proposed correlations between the local soil taxa and the reference soil groups of the WRB classification and soil orders defined by USDA Soil Taxonomy. The pH values corresponding to 50 % of ‘standard’, ‘potential’, and ‘effective’ base saturation were estimated at 5.5, 5.2, and 4.8, respectively. Irrespective of the differences between current estimates and previously reported pH threshold values corresponding to 50 % base saturation, the obtained results confirm that field measurements of soil pH may be considered a substitute for laboratory-measured base saturation for some purposes, such as soil classification.

Gap tolerance and molten pool destabilization mechanism in oscillating laser-arc hybrid welding of aluminum alloys

Oscillating laser-arc hybrid welding (O-LAHW) offers significant advantages in enhancing efficiency and mechanical properties. However, in actual production, gap fluctuations can cause instability, limiting its application in large-scale structure manufacturing. In this study, we explored the impact of gap fluctuation on the destabilization of the molten pool in aluminum alloy O-LAHW and identified the maximum gap tolerance for various conditions. High-speed photography revealed that the oscillating molten pool undergoes a transitional state of periodic collapse before complete instability, a behavior distinct from that of a non-oscillating molten pool. We also analyzed the variations in weld geometry prior to destabilization and developed a global force model of the molten pool to identify key geometrical parameters and related driving forces contributing to destabilization. The results show that maintaining a surface tension ratio of over 55% at the root of the molten pool is crucial for its stability. Additionally, the effects of oscillatory behavior and gap variations on the laser-substrate interaction were explored, revealing the physical mechanisms behind the changes in key geometrical parameters of the melt pool cross-section. The centrifugal effect generated by high-frequency oscillation is identified as a crucial mechanism for extending the duration of periodic collapse compared to non-oscillating molten pools. By discussing the interactions between energy absorption, molten pool shape, and molten pool forces, the study reveals the evolution process of weld destabilization and explains the differences in gap tolerance between oscillating and non-oscillating laser-arc hybrid welding, providing a reference for improving weld stability.