The root-knot nematode, Meloidogyne javanica, is one of the most damaging plant-parasitic nematodes, affecting chickpea and causing substantial yield losses worldwide. The damage potential and population dynamics of this nematode in chickpea in Ethiopia have yet to be investigated. In this study, six chickpea cultivars were tested using 12 ranges of initial population densities (Pi) of M. javanica second-stage juveniles (J2): 0, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 J2 (g dry soil)-1 in a controlled glasshouse pot experiment. The Seinhorst yield loss and population dynamics models were fitted to describe population development and the effect on different measured growth variables. The tolerance limit (TTFW) for total fresh weight ranged from 0.05 to 1.22 J2 (g dry soil)-1, with corresponding yield losses ranging from 31 to 64%. The minimum yield for seed weight (mSW) ranged from 0.29 to 0.61, with estimated yield losses of 71 and 39%. The 'Haberu' and 'Geletu' cultivars were considered good hosts, with maximum population densities (M) of 16.27 and 5.64 J2 (g dry soil)-1 and maximum multiplication rate (a) values of 6.25 and 9.23, respectively. All other cultivars are moderate hosts for M. javanica; therefore, it is crucial to initiate chickpea-breeding strategies to manage the tropical root-knot nematode M. javanica in Ethiopia.

The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (similar to 70 mu m in size), while the weak/absent 2-mu m absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.

Understanding the mechanical behaviour of water ice-bearing lunar soil is essential for future lunar exploration and construction. This study employs discrete element method (DEM) simulations, incorporating realistic particle shapes and a flexible membrane, to investigate the effects of ice content, initial packing density, and gravitational conditions on lunar soil behaviour. Initially, we calibrated DEM model parameters by comparing triaxial tests on lunar soil without ice to physical experiments and the angle of repose simulations, validating the accuracy of our approach. Building on this, we conducted simulations on water ice-bearing lunar soil, examining stress-strain responses, shear strain, bond breakage, deviatoric fabric, and N-ring structures. DEM simulations demonstrate that increasing ice content from 0 % to 10 % elevates peak strength from 85 kPa to 240 kPa in loose samples and from 0.2 MPa to 1.62 MPa in dense samples. This strengthening aligns with microstructural stabilization evidenced by 5-ring configurations and narrowed branch vector distributions. Strain field analysis reveals greater deformation magnitudes in icy regolith, suggesting a trade-off between enhanced load-bearing capacity and reduced ductility. These quantified mechanical responses, including strength gain, structural stabilization, and strain localization, reveal the dual engineering implications of water ice in lunar soil.

The MAJIS (Moons And Jupiter Imaging Spectrometer) instrument, part of the JUICE (JUpiter ICy moons Explorer) mission, is a crucial tool for investigating the composition and dynamics of Jupiter's atmosphere, and the surfaces and exospheres of its icy moons. To optimize observational planning and assess instrument performance, we have developed a radiometric simulator that accurately models MAJIS expected signal from various Jovian system targets. This simulator incorporates instrumental parameters, the spacecraft trajectory, observational constraints, and Jupiter's radiation environment. It provides essential outputs, including Signal-to-Noise Ratio (SNR) predictions and optimized instrument settings for different observational scenarios. By simulating both radiometric performance and de-spiking strategies to mitigate the impact of Jupiter radiation belt, the tool aids in refining observation strategies throughout the MAJIS operations. Several scientific applications demonstrate the simulator capabilities, from mapping the surfaces of Ganymede and Europa to detecting exospheric emissions and atmospheric composition on Jupiter. This simulator is a critical asset for maximizing MAJIS scientific return and ensuring optimal data acquisition during MAJIS exploration of the Jovian system. Study cases are presented for illustrating the capability of the simulator to model scenarios such as high-resolution mapping of Ganymede, exosphere characterization and hotspot detection on Io and Europa. These simulations confirm the potential of MAJIS for detecting key spectral features with high signal to noise ratio so as to provide major contributions to the main goals of the mission: habitability and compositional diversity in the Jovian system.

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Climate change is transforming the ice-free areas of Antarctica, leading to rapid changes in terrestrial ecosystems. These areas represent <0.5% of the continent and coincide with the most anthropogenically pressured sites, where the human footprint is a source of contamination. Simultaneously, these are the locations where permafrost can be found, not being clear what might be the consequences following its degradation regarding trace element remobilisation. This raises the need for a better understanding of the natural geochemical values of Antarctic soils as well as the extent of human impact in the surroundings of scientific research stations. Permafrost thaw in the Western Antarctic Peninsula region and in the McMurdo Dry Valleys is the most likely to contribute to the remobilisation of toxic trace elements, whether as the result of anthropogenic contamination or due to the degradation of massive buried ice and ice-cemented permafrost. Site-specific locations across Antarctica, with abandoned infrastructure, also deserve attention by continuing to be a source of trace elements that later can be released, posing a threat to the environment. This comprehensive summary of trace element concentrations across the continent's soils enables the geographical systematisation of published results for a better comparison of the literature data. This review also includes the used analytical techniques and methods for trace element dissolution, important factors when reporting low concentrations. A new perspective in environmental monitoring is needed to investigate if trace element remobilisation upon permafrost thaw might be a tangible consequence of climate change.

Subsea pipelines in Arctic environments face the risk of damage from ice gouging, where drifting ice keels scour the seabed. To ensure pipeline integrity, burial using methods like ploughs, mechanical trenchers, jetting, or hydraulic dredging is the conventional protection method. Each method has capabilities and limitations, resulting in different trench profiles and backfill characteristics. This study investigates the influence of these trenching methods and their associated trench geometries on pipeline response and seabed failure mechanisms during ice gouging events. Using advanced large deformation finite element (LDFE) analyses with a Coupled Eulerian-Lagrangian (CEL) algorithm, the complex soil behavior, including strain-rate dependency and strainsoftening effects, is modeled. The simulations explicitly incorporate the pipeline, enabling a detailed analysis of its behavior under ice gouging loads. The simulations analyze subgouge soil displacement, pipeline displacement, strains, and ovalization. The findings reveal a direct correlation between increasing trench wall angle and width and the intensification of the backfill removal mechanism. Trench geometry significantly influences the pipeline's horizontal and vertical displacement, while axial displacement and ovalization are less affected. This study emphasizes the crucial role of trenching technique selection and trench shape design in mitigating the risks of ice gouging, highlighting the value of numerical modeling in optimizing pipeline protection strategies in these challenging environments.

In sensitive ecosystems of the Arctic, even slight disruptions may produce serious damage. Therefore, the extent of contamination in such zones should be evaluated. A comparison was made between concentrations of metals in Sanionia uncinata in three areas of the European Arctic: (1) the vicinity of the Polish Polar Station in the SW part of Spitsbergen on Wedel Jarlsberg Land, (2) Longyearbyen (Spitsbergen) influenced by local sources of pollution and (3) Iceland relatively free from local pollution. The tested hypothesis was that S. uncinata from Iceland contains significantly lower concentrations of metals than the same moss from Spitsbergen. The maximum concentrations of metals in the examined moss from Longyearbyen reached values for Cr and Mn higher than those known as harmful for plants and for Ni and Zn values within the harmful ranges with no visible harmful effects. S. uncinata from Iceland contained significantly lower concentrations of Cd, Mn, Pb compared to this species from Spitsbergen. S. uncinata seems to be a useful indicator for metal fallout in the European Arctic. This study presents the effects of local sources of contamination on metal levels in S. uncinata from Longyearbyen, Wedel Jarlsberg Land and Iceland as well as verification of S. uncinata as a suitable bioindicator in this Arctic area. The benefit of the study is a to better understanding contamination problems of Arctic habitats.

Predictive modeling of dielectric heating in porous foods is challenging due to their nature as multiphase materials. To explore the relationship between the topological structure of multiphase foods and the accuracy of dielectric mixture models, the degree of anisotropy of two cooked rice samples with 26 and 32 % porosity was determined, and their dielectric properties were estimated using the Lichtenecker (LK), Landau-LifshitzLooyenga (LLL), and Complex Refractive Index Mixture (CRIM) equations. These properties were used in a predictive finite-element model for reheating an apparent homogeneous rice sample on a flatbed microwave (MW) for 120 s. The results were compared with experimental data and a validated two-element model. Unlike LK and LLL equations, the CRIM equation predicted heat accumulation towards the edges of the container at the two values of porosity ratio evaluated, in accordance with the experimental results and the isotropic nature of the sample. The simulated temperature distributions suggest that the three evaluated equations could predict the MW heating behavior of rice to some extent, but that in order to obtain more accurate results, it could be useful to obtain an empirical topology-related parameter specific for this sample. These results can provide insight on the relationship between the topology of the porous structure in the sample and the adequacy of different dielectric mixture models.

Investigating water ice content at different locations on the Moon is crucial for crewed space missions and serves as a foundation for establishing lunar bases, which necessitates lunar soil sampling to gather information. Aiming to minimize the water ice loss caused by heat generation during drilling, this paper proposes a water ice highconservation sampling system based on frozen CO2 spray cooling. The thermodynamic and hydrodynamic models of the frozen CO2 generation subsystem and heat exchange subsystem are established. The impact of design parameters, flow and thermal conditions, and operation modes on water content has been analyzed. The spray cooling method indirectly affects the lunar soil temperature by reducing the drill bit temperature to increase the water conservation ratio (WCR) during drilling. The method combines frozen CO2 sublimation heat flow and jet cooling flow. Jet cooling is closely associated with the temperature difference between the fluid and the drill bit, as well as the flow velocity. Meanwhile, sublimation heat flow depends on the temperature difference between the drill bit and the saturation temperature of frozen CO2, along with the content of frozen CO2. Jet cooling is predominant at lower mass flow rates, while sublimation cooling prevails at higher rates. In addition, the time the lunar soil is at low-sublimation temperature is an important factor in WCR. Thus, to increase WCR, one can enhance flow velocity by reducing the nozzle diameter, raise sublimation heat flow by increasing mass flow and lowering the initial temperature, and maintain lunar soil at low-sublimation temperatures by increasing cooling time, duty ratio and decreasing the cooling period. Among others, increasing the cooling time has the most significant effect. The increasing slopes of WCR with cooling durations are about 20 %/100 s (at 0.4 g/s, liquid CO2) and 10 %/100 s (at 0.1 g/s, liquid CO2). However, the cooling time should not exceed the drilling time. This study provides an effective water ice conservation system that is useful for other planetary sampling missions.