Local Changes Research Articles

Miocene ice sheet dynamics and sediment deposition in the central Ross Sea, Antarctica

Drill cores from the Antarctic continental shelf are essential for directly constraining changes in past Antarctic Ice Sheet extent. Here, we provide a sedimentary facies analysis of drill cores from International Ocean Discovery Program (IODP) Site U1521 in the Ross Sea, which reveals a unique, detailed snapshot of Antarctic Ice Sheet evolution between ca. 18 Ma and 13 Ma. We identify distinct depositional packages, each of which contains facies successions that are reflective of past baseline shifts in the presence or absence of marine-terminating ice sheets on the outermost Ross Sea continental shelf. The oldest depositional package (>18 Ma) contains massive diamictites stacked through aggradation and deposited in a deep, actively subsiding basin that restricted marine ice sheet expansion on the outer continental shelf. A slowdown in tectonic subsidence after 17.8 Ma led to the deposition of progradational massive diamictites with thin mudstone beds/laminae, as several large marine-based ice sheet advances expanded onto the mid- to outer continental shelf between 17.8 Ma and 17.4 Ma. Between 17.2 Ma and 15.95 Ma, packages of interbedded diamictite and diatom-rich mudstone were deposited during a phase of highly variable Antarctic Ice Sheet extent and volume. This included periods of Antarctic Ice Sheet advance near the outer shelf during the early Miocene Climate Optimum (MCO)—despite this being a well-known period of peak global warmth between ca. 17.0 Ma and 14.6 Ma. Conversely, there were periods of peak warmth within the MCO during which diatom-rich mudstones with little to no ice-rafted debris were deposited, which indicates that the Antarctic Ice Sheet was greatly reduced in extent and had retreated to a smaller terrestrial-terminating ice sheet, most notably between 16.3 Ma and 15.95 Ma. Post-14.2 Ma, diamictites and diatomites contain unambiguous evidence of subglacial shearing in the core and provide the first direct, well-dated evidence of highly erosive marine ice sheets on the outermost continental shelf during the onset of the Middle Miocene Climate Transition (MMCT; 14.2−13.6 Ma). Although global climate forcings and feedbacks influenced Antarctic Ice Sheet advances and retreats during the MCO and MMCT, we propose that this response was nonlinear and heavily influenced by regional feedbacks related to the shoaling of the continental shelf due to reduced subsidence, sediment infilling, and local sea-level changes that directly influenced oceanic influences on melting at the Antarctic Ice Sheet margin. Although intervals of diatom-rich muds and diatomite indicating open-marine interglacial conditions still occurred during (and following) the MMCT, repeated advances of marine-based ice sheets since that time have resulted in widespread erosion and overdeepening in the inner Ross Sea, which has greatly enhanced sensitivity to marine ice sheet instability since 14.2 Ma.

Structural and physiological implications of witches' broom infection in Xylopia aromatica (Annonaceae) trees

AbstractWitches' brooms are abnormal branches that can affect various tree species in the Brazilian savanna, such as Xylopia aromatica. This disease leads to biomass loss and alters growth patterns. This study investigated the anatomical and physiological impacts of witches' broom on X. aromatica, hypothesizing that the disease induces changes in both infected and noninfected branches of the same tree. Measurements included leaf area, chlorophyll content and photosystem II (PSII) functionality in control and infected plants, as well as branch types (control, uninfected and infected branches for infected plants). Water potential was assessed at predawn and midday, and stem samples were analysed for anatomical and vessel element analysis. Witches' broom disease manifests as short brown stems and small yellowish‐green leaves. While infected and uninfected plants showed no significant differences in leaf area and leaf dry biomass, infected branches had reduced leaf number, area and weight. Chlorophyll content and PSII efficiency were significantly reduced in infected branches, indicating photosynthetic disruption. Water potential was lower at predawn in infected plants but normalized by midday, suggesting an adaptive response. Anatomical changes in infected branches included alterations in vessel elements and parenchymatic rays. Overall, X. aromatica displays localized changes due to witches' broom, which aids in water stress adaptation. Despite these findings, long‐term studies are needed to evaluate the impacts on biomass reduction and reproductive investment in Brazilian savanna areas, crucial for understanding the ecological impact and developing survival strategies for affected plants.

Centrosome-organized plasma membrane infoldings linked to growth of a cortical actin domain.

Regulated cell shape change requires the induction of cortical cytoskeletal domains. Often, local changes to plasma membrane (PM) topography are involved. Centrosomes organize cortical domains and can affect PM topography by locally pulling the PM inward. Are these centrosome effects coupled? At the syncytial Drosophila embryo cortex, centrosome-induced actin caps grow into dome-like compartments for mitoses. We found the nascent cap to be a collection of PM folds and tubules formed over the astral centrosomal MT array. The localized infoldings require centrosome and dynein activities, and myosin-based surface tension prevents them elsewhere. Centrosome-engaged PM infoldings become specifically enriched with an Arp2/3 induction pathway. Arp2/3 actin network growth between the infoldings counterbalances centrosomal pulling forces and disperses the folds for actin cap expansion. Abnormal domain topography with either centrosome or Arp2/3 disruption correlates with decreased exocytic vesicle association. Together, our data implicate centrosome-organized PM infoldings in coordinating Arp2/3 network growth and exocytosis for cortical domain assembly.

Substantial reduction in population exposure to sea level changes along the Chinese mainland coast through emission mitigation

Abstract Rising sea level increases the exposure to flooding and related damage in coastal areas with high population density and substantial economic activity. As global temperatures continue to rise due to climate change, sea levels have been consistently increasing and are projected to continue this upward trend. This study assesses the future exposure at provincial and city levels populations coastal mainland China coast to local sea level changes under five greenhouse gas (GHG) emission scenarios from IPCC-AR6, as well as two low-confidence scenarios accounting for the potential impact of uncertain ice sheet processes with low- and high-GHG emissions. We incorporate spatial heterogeneity into regional sea level projections and population projections from 2020 to 2100, extreme sea levels (ESLs) of 10-, 50-, and 100 year return periods (RP), and local coastal protection standards. Our findings indicate that the inundated areas expand continuously within the century with heightened exposure under higher emission scenarios. Although the coastal population is projected to decline, the fraction of the coastal population exposed to flooding increases across all scenarios, with accelerated growth under higher GHG emissions and higher ESLs. Zhejiang and Jiangsu emerge as the provinces most exposed to sea-level rise, whereas Taizhou, Nantong, Wuxi, Panjin, and Huzhou are identified as the top five cities with the highest population exposure to local sea level rise (SLR). Transitioning towards a sustainable scenario (i.e. SSP1-2.6) rather than a fossil fuel-intensive one (i.e. SSP5-8.5) can reduce the local SLR and substantially mitigate these exposures. Compared to the median projections under SSP5-8.5, aligning GHG emissions with SSP1-2.6 could reduce population exposure substantially in all coastal provinces, especially in Jiangsu, where population exposure to 100 year RP coastal floods would be reduced by ∼1.6 M in 2050 and by ∼5.4 M in 2100.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Physics-informed neural networks for localized assessment in Euler-Bernoulli beams

This study employs the Physics-Informed Neural Networks (PINNs) to address an inverse problem, specifically evaluating local changes in material properties within an Euler-Bernoulli beam through numerical simulations. In our numerical model, a viscoelastic material is strategically introduced to a localized zone of the beam, inducing non-constant and complex alterations in the storage modulus. The training process incorporates the partial differential equation into the neural network's loss function, coupled with essential stability conditions for the machine learning model, enhancing the efficiency of the training process. Notably, this methodology excels in characterizing structural behavior influenced by these intricate local changes in material properties, bypassing the need to identify the entire structure and consequently to deal with uncertainties related to boundary conditions. The effectiveness of the approach is validated through numerical simulations, underscoring its potential for applications in efficiently assessing and understanding local material alterations within complex structural systems.

Optimization of graded and partial metamaterial treatments for enhanced broadband sound insulation in partition panels

Resonant metamaterials can achieve unprecedented vibroacoustic attenuation in lightweight partition panels through small resonators attached on a subwavelength scale. Conventional metamaterial treatments are effective only in a narrow band and typically cover the entire panel surface, posing important challenges for industrialization due to manufacturing and cost constraints. To address these issues, in this study we propose graded and partial metamaterial treatments, which are selectively distributed where they are the most effective, avoiding unnecessary coverage, and include a spatially varying resonance frequency for broadband effectiveness. The related design challenges are tackled by developing a numerical methodology that simultaneously optimizes the distribution and the grading of the treatment. The optimization objective is to maximize the broadband diffuse sound transmission loss (STL) of the metamaterial panel, while constraining the maximum fraction of treated area. Leveraging effective medium modelling, the approach efficiently incorporates local changes in the effective mass density of the metamaterial panel to represent partial treatments. Different design cases are considered for different target bands and panel dimensions. Results demonstrate that graded partial metamaterial treatments can achieve broadband improvements with a limited treated area, effectively suppressing the STL dips due to the modes of the host panel and to coincidence effects.

Characterization of aberrant splicing in pediatric central nervous system tumors reveals CLK1 as a candidate oncogenic dependency.

Pediatric brain cancer is the leading cause of disease-related mortality in children, and many aggressive tumors still lack effective treatment strategies. We characterized aberrant alternative splicing across pediatric brain tumors, identifying pediatric high-grade gliomas (HGGs) among the most heterogeneous. Annotating these events with UniProt, we identified 11,940 splice events in 5,368 genes leading to potential protein function changes. We discovered CDC-like kinase 1 (CLK1) is aberrantly spliced to include exon 4, resulting in a gain of two phosphorylation sites and subsequent activation. Inhibition of CLK1 with Cirtuvivint significantly decreased both cell viability and proliferation in the pediatric HGG KNS-42 cell line. Morpholino-mediated depletion of CLK1 exon 4 splicing reduced RNA expression, protein abundance, and cell viability with concurrent differential expression of 78 cancer genes and differential splicing at functional sites in 193 cancer genes. Our findings highlight a dependency of pediatric HGGs on CLK1 and represent a promising therapeutic strategy.

Passive defect localization on a vibrating plate using Matched-Field Processing

Passive defect localization on a vibrating plate using Matched-Field Processing (MFP). The aim of this study is to investigate the use of MFP for passive localization of a local change in parameter of a vibrating shell in the modal regime. Matched-Field Processing is a beamforming method that uses the correlation between the recorded data and a controllable digital model of wave propagation (replica) to locate a source. Vibration measurements were conducted on an aluminum plate using a laser vibrometer. The plate was tested both with and without damage (added mass) while being excited by a shaker. Replicas were created by performing modal analysis on measurements taken from the undamaged structure. Matched-Field Processing is applied directly to the measured data to locate the active source (shaker) and to the residues (field difference between undamaged and damaged states) to locate a local variation of the parameter (defect). The method's feasibility is also being explored for passive measurements in modal analysis and residue computation.

Climate and Land Use Change Pressures on Food Production in Social-Ecological Systems: Perceptions from Farmers in Village Tank Cascade Systems of Sri Lanka

Climate and land use change pressures are critical to food production in Social-Ecological Systems (SESs). This study assessed farmers’ perceptions of the pressures of climate and land use changes alongside their impacts on food production in Mhahakanumulla Village Tank Cascade System (MVTCS), a SES maintained by traditional agricultural land use systems in the dry zone of Sri Lanka. This study used both rating and ranking scale questions to quantify farmers’ perceptions. The tobit regression model was employed to evaluate how farmer perception was influenced by socio-economic factors. The results showed that most of the farmers had experienced that the climate of the MVTCS area had changed over time, and they perceived variability of rainfall patterns as the most prominent and influential climate change. The increased cost of production, wildlife damage, and land degradation were ranked by the farmers as the most impactful factors of food production due to climate change. The farmers rated deforestation and land clearing as the most influential and impactful changes in land use, while wildlife damage and land degradation ranked as the highest impacts on food production due to land use changes. Among the socio-economic determinants, training and income/profit positively influenced farmer perceptions of the severity of both climate and land use change. The level of farmer’s adaptation to climate change had a negative association with their perception of the severity of climate change. Household size negatively influenced the perceptions of the severity of climate change while positively influencing perceptions of land use change impacts. Among the spatial determinants, farm size and downstream locations of MVTCS positively influenced perceptions of the severity of both climate and land use change. Thus, the effectiveness of adaptation strategies towards climate and land use change pressures depends on how well they are understood by the farmers. The study findings provide helpful insights for formulating localized land use policies and climate change adaptation strategies in these globally important landscapes with a combination of both top-down and bottom-up approaches.

Changepoint detection on daily home activity pattern: a sliced Poisson process method.

The problem of health and care of people is being revolutionized. An important component of that revolution is disease prevention and health improvement from home. A natural approach to the health problem is monitoring changes in people's behavior or activities. These changes can be indicators of potential health problems. However, due to a person's daily pattern, changes will be observed throughout each day, with, eg, an increase of events around meal times and fewer events during the night. We do not wish to detect such within-day changes but rather changes in the daily behavior pattern from one day to the next. To this end, we assume the set of event times within a given day as a single observation. We model this observation as the realization of an inhomogeneous Poisson process where the rate function can vary with the time of day. Then, we propose to detect changes in the sequence of inhomogeneous Poisson processes. This approach is appropriate for many phenomena, particularly for home activity data. Our methodology is evaluated on simulated data. Overall, our approach uses local change information to detect changes across days. At the same time, it allows us to visualize and interpret the results, changes, and trends over time, allowing the detection of potential health decline.

Non-invasive regional parameter identification of degenerated human meniscus

Accurate identification of local changes in the biomechanical properties of the normal and degenerative meniscus is critical to better understand knee joint osteoarthritis onset and progression. Ex-vivo material characterization is typically performed on specimens obtained from different locations, compromising the tissue's structural integrity and thus altering its mechanical behavior. Therefore, the aim of this in-silico study was to establish a non-invasive method to determine the region-specific material properties of the degenerated human meniscus. In a previous experimental magnetic resonance imaging (MRI) study, the spatial displacement of the meniscus and its root attachments in mildly degenerated (n = 12) and severely degenerated (n = 12) cadaveric knee joints was determined under controlled subject-specific axial joint loading. To simulate the experimental response of the lateral and medial menisci, individual finite element models were created utilizing a transverse isotropic hyper-poroelastic constitutive material formulation. The superficial displacements were applied to the individual models to calculate the femoral reaction force in an inverse finite element analysis. During particle swarm optimization, the four most sensitive material parameters were varied to minimize the error between the femoral reaction force and the force applied in the MRI loading experiment. Individual global and regional parameter sets were identified. In addition to in-depth model verification, prediction errors were determined to quantify the reliability of the identified parameter sets. Both compressibility of the solid meniscus matrix (+141 %, p ≤ 0.04) and hydraulic permeability (+53 %, p ≤ 0.04) were significantly increased in the menisci of severely degenerated knees compared to mildly degenerated knees, irrespective of the meniscus region. By contrast, tensile and shear properties were unaffected by progressive knee joint degeneration. Overall, the optimization procedure resulted in reliable and robust parameter sets, as evidenced by mean prediction errors of <1 %. In conclusion, the proposed approach demonstrated high potential for application in clinical practice, where it might provide a non-invasive diagnostic tool for the early detection of osteoarthritic changes within the knee joint.

Mobility patterns in Austrian and Italian municipalities in the decade before and during the COVID‐19 era

AbstractIn European countries, where the demographic transition has reached advanced stages and the natural increase has fallen below zero, migration constitutes a significant component of local population change. We investigate to what extent the dynamics of international migration and internal mobility changed during the first waves of the COVID‐19 pandemic, compared to the previous decade. We focus on Austrian and Italian municipalities to assess the contribution of migration components to local population growth, using official data provided by National Statistical Institutes on inflows and outflows of migrant and native populations, from 2010 to 2020. The adoption of harmonized degrees of urbanization allows us to profile spatial and demographic patterns of mobility, in the Austrian and Italian territories. We apply Bayesian‐Geostatistical models, and Artificial Neural‐Networks to investigate the potential determinants of mobility variability. The results reveal Austrian and Italian population‐specific migration trends. Overall, the trends observed in the decade before the pandemic were either confirmed or further accentuated during the COVID‐19 era. Although rural‐urban mobility generally persisted in both countries, counter‐urbanization trends were detected among Austrian populations during the initial period of the COVID‐19 pandemic. Conversely, urban and intermediate municipalities in Italy maintained their attractiveness and capacity to retain Italian populations. These findings offer new empirical insights into urbanization dynamics in a comparative perspective, which are particularly relevant for the definition of European regional policy aimed at matching local needs with national social cohesion goals.

Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning.

Previous neuroimaging studies have identified significant alterations in brain functional activity in retinal detachment (RD) patients, these investigations predominantly concentrated on local functional activity changes. The potential directional alterations in functional connectivity within the primary visual cortex (V1) in RD patients remain to be elucidated. In this study, we employed seed-based functional connectivity analysis along with Granger causality analysis to examine the directional alterations in dynamic functional connectivity (dFC) within the V1 region of patients diagnosed with RD. Finally, a support vector machine algorithm was utilized to classify patients with RD and healthy controls (HCs). RD patients exhibited heightened dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) between the Visual Network (VN) and default mode network (DMN), as well as within the VN, compared to HCs. Conversely, dFC between VN and auditory network (AN) decreased, and dEC between VN and sensorimotor network (SMN) significantly reduced. In state 4, RD patients had higher frequency. Notably, variations in dFC originating from the left V1 region proved diagnostically effective, achieving an AUC of 0.786. This study reveals significant alterations in the connectivity between the VN and the default mode network in patients with RD. These changes may disrupt visual information processing and higher cognitive integration in RD patients. Additionally, alterations in the left V1 region and whole-brain dFC show promising potential in aiding the diagnosis of RD. These findings offer valuable insights into the neural mechanisms underlying visual and cognitive impairments associated with RD.

Cryo-EM structures of cardiac muscle α-actin mutants M305L and A331P give insights into the structural mechanisms of hypertrophic cardiomyopathy

Cardiac muscle α-actin is a key protein of the thin filament in the muscle sarcomere that, together with myosin thick filaments, produce force and contraction important for normal heart function. Missense mutations in cardiac muscle α-actin can cause hypertrophic cardiomyopathy, a complex disorder of the heart characterized by hypercontractility at the molecular scale that leads to diverse clinical phenotypes. While the clinical aspects of hypertrophic cardiomyopathy have been extensively studied, the molecular mechanisms of missense mutations in cardiac muscle α-actin that cause the disease remain largely elusive. Here we used cryo-electron microscopy to reveal the structures of hypertrophic cardiomyopathy-associated actin mutations M305L and A331P in the filamentous state. We show that the mutations have subtle impacts on the overall architecture of the actin filament with mutation-specific changes in the nucleotide binding cleft active site, interprotomer interfaces, and local changes around the mutation site. This suggests that structural changes induced by M305L and A331P have implications for the positioning of the thin filament protein tropomyosin and the interaction with myosin motors. Overall, this study supports a structural model whereby altered interactions between thick and thin filament proteins contribute to disease mechanisms in hypertrophic cardiomyopathy.

Fostering collective action for adolescent well-being: citizen science in a Colombian semi-rural area.

We present findings from the Our Voice in Barú project, examining adolescent engagement in citizen science to address environmental and social factors affecting well-being in a semi-rural Afro-descendant community in Latin America. The project aimed to identify barriers and enablers of well-being, informing adolescent-led actions for sustainable environmental changes relevant to adolescent health. Utilizing the Our Voice citizen science method, which involved technology-enabled walks, participatory cartography workshops and community meetings, we engaged adolescents and other community members. Complex systems framework was employed to analyze results and assess the promotion of collective action and local change. Local enablers of well-being included availability of quality sports and recreational venues as safe spaces, educational settings and cultural aspects, fostering safety, community pride, and personal growth. Conversely, barriers included environmental contamination, deteriorated sports venues and drug addiction, indicating the community's limited access to services and exposure to harmful substances. Adolescent citizen scientists, in partnership with the research team and relevant community decision-makers, successfully achieved changes aimed at identified barriers, including infrastructural enhancements like the repair of pumps to remove stagnant water, improvements in the quality and safety of recreational venues, and the implementation of community-led initiatives to address substance abuse and promote healthier behaviors. The Our Voice in Barú project empowered adolescents to advocate for change and promoted collective action to tackle barriers identified as significant to their well-being.

Thermal radiation imaging technology and sensor assisted calculation of sports training technology simulation

With the development of sports training science and technology, the real-time monitoring of athletes’ physical condition and training effect is becoming more and more important. As a non-contact measurement method, thermal radiation imaging technology can capture the changes of athletes’ body surface temperature efficiently and in real time, and provide relevant information about physiological state and performance indicators. This paper discusses the application of thermal radiation imaging technology in sports training to help measure athletes’ physical state and training effect, so as to provide scientific basis for improving training efficiency and reducing sports injuries. In this paper, the high resolution thermal imaging equipment is used to systematically analyze the thermal radiation imaging of athletes, and the physiological index monitoring and data processing technology are combined to dynamically observe the temperature distribution and change characteristics of athletes under different training states. Through comparative analysis, the correlation between the data provided by thermal radiation imaging technology in the training process and the traditional physiological indicators was evaluated. The results show that thermal radiation imaging technology can effectively reflect the heat distribution and local body temperature changes of athletes during training. Compared to traditional physiological feedback data, the technology has significant advantages in quickly identifying excessive fatigue and potential injuries in athletes, especially during high-intensity training, and can provide early warning of associated risks. The application of sensor-assisted measurement system will further enhance the accuracy and effectiveness of sports scientific research and provide important support for athletes’ scientific training and health management.

Analysis of mode I crack propagation in glassy polymers under cyclic loading using a molecular dynamics informed continuum model for crazing

Craze and crack propagation in glassy polymers under cyclic mode I loading are investigated by employing a recently developed continuum-micromechanical model for crazing. This model accounts for the local morphology change from microvoids to fibrils during craze initiation, viscoplastic drawing of bulk material into fibrils, and viscoelastic creep recovery of the fibrillated craze matter during unloading. To ensure consistency between the bulk and craze model parameters, the material parameters of the craze model are normalised and calibrated based on a hybrid approach integrating experimental findings from the literature and molecular dynamics results. This yields a generic, yet representative glassy polymer response.In the framework of 2D plane strain finite element simulations, we study brittle as well as ductile glassy polymers and assess the results by drawing comparisons to the experimental and numerical literature. For brittle materials, characterized by a purely elastic bulk behaviour, the model reproduces craze characteristics such as the craze opening contour, the craze length-to-width ratio, a double stress peak at the craze and crack tip, and a non-proportional stress redistribution during loading-unloading cycles. In ductile glassy polymers, the interaction of shear yielding in the bulk and crazing along the ligament is analysed. In particular, shear bands emanate from the crack tip in each loading cycle and arch forward towards the craze. This plastic zone shares resemblance to the so-called epsilon-shaped deformation zone. The current simulations capture normal fatigue crack propagation, where craze and crack growth occur near the peak load in every cycle and the craze length remains relatively constant across the loading cycles. Moreover, findings from this study suggest that plasticity-induced unloading of the craze adjacent to the crack tip impedes crack growth.

Modeling wear of different surface layers of agricultural tools in soil abrasive mass

Uniform quantitative measures have not been developed to identify the areas with the greatest intensification of tool wear. Therefore, this paper developed original approach to assess the wear of cutting elements by identifying the intensity of local volumetric wear. A tribological test was conducted under natural operating conditions in two soil types: sandy loam and loam. The specimens were mounted on a 9-tined cultivator with spring tines aggregated with an agricultural tractor. A method was developed to discretise the local volume changes of a component using surface scanning models developed from 3D scanning. The notion of the rake angle has been defined, allowing the assessment of changes in the shape of the element during wear. The practical usefulness of the developed method was verified on three types of surface layers, i.e. 38GSA steel, ELHARD70 deposited on it, and a G10 layer of carbide. For each of the tested variants, local volumetric consumption was defined based on the identification of the wear grid. These should be the basis for developing the technology of surface strengthening of elements. The work confirmed the complexity of the process of wear of working elements in natural soil conditions, presented in many research works. A different course of wear of the 38GSA steel and the Elhard 70 weld layer was shown, depending on the type of treated soil. In the case of samples made of G10 cemented carbide, the largest volumetric wear for heavy soil occurred outside the place of reinforcement application. The highest volumetric wear in the front zone was recorded for 38GSA steel in heavy soil. Wear results from the modelling are similar to those obtained from the experiment under natural conditions in light soil using the nominal model (CAD) and the actual friction surface.

Projected changes in mean climate and extremes from downscaled high-resolution CMIP6 simulations in Australia

High-resolution climate change projections are required to understand local and regional climate change impacts. We used CCAM (Conformal Cubic Atmospheric Model) to dynamically downscale CMIP6 GCMs (Global Climate Models) over Australia under three emissions scenarios, producing a set of 60 simulations at a 10 km resolution. Previous work has evaluated the performance of the downscaled models in the historical period. Here, we evaluate the impact of end-of-century climate change in the downscaled CMIP6-CCAM models for mean and extreme climate under three Shared Socioeconomic Pathways (SSP126, 245 and 370). We find the changes in mean climate are similar in the host CMIP6 and downscaled models. For extreme temperature, we find that extreme maximum temperatures (TXx) increase by 3.4°C, while extreme minimum temperatures (TNn) warm by 3.0°C. Extreme precipitation generally increases in summer and decreases in winter; however, there is a large amount of inter-model variation in the location and magnitude of change. Consecutive dry days also decrease in most areas in Austral summer and increase in Austral winter. Heatwaves become more frequent and hotter by the end of the century. These results suggest a hotter, wetter Austral summer, with longer, more frequent and more intense heatwaves, and a hotter and drier Austral winter in most areas. This dataset provides useful new high-resolution information on how climate change is likely to impact Australia, which will be a valuable resource to underpin local adaptation responses to future impacts.