Local Variables Research Articles

Rain Fade Analysis on High Frequency Signal: A Review

A reliable transmission of high-frequency signal of more than 10 GHz is crucial for various communication systems, especially in the era of advanced technologies and wireless communications. Rain-induced signal fades pose a significant challenge to signal integrity, making accurate rain rate measurement methods essential for system optimization. This review critically examines the existing techniques for measuring rain rate and the applicability in analysing high-frequency signal fades. Several case studies and practical examples are presented to illustrate the significance of accurate rain rate measurements in predicting and mitigating signal degradation. Researchers are working to enhance existing models to better fit local environmental variables based on all the case studies. Having an accurate estimation of the level of rain attenuation in the signal is one of the components to ensuring the quality of service of a communication signal. Signal availability can be ensured with suitable rain attenuation modelling, assuming that all other communication elements are functioning as planned.

Geographic variability of carbon and nitrogen isotope ratios of nonvolant terrestrial small mammals (Rodentia) across 3 Brazilian biomes

Abstract In this study, we investigated landscape variability of the carbon and nitrogen isotopic composition of nonvolant small mammals in the 3 main Brazilian biomes (Amazon, Atlantic Forest, and Cerrado) while also considering the differential spatial distribution of C4 plants in these biomes. We compiled a subset of data on stable carbon and nitrogen isotope ratios from nonvolant small mammals of the order Rodentia and compared the aggregated isotopic variability at the biome level using classical δ13C–δ15N biplot. The concept of isotopic niche width was used to test whether different foraging attributes drive their isotopic composition, while a Bayesian isotopic mixing model was used to estimate the proportion of 3 main food categories available to these small mammals. We also developed isoscapes in the Brazilian biomes, interpolating animals δ13C and δ15N based on the geographic coordinates of each sample and “sourcescapes” considering geographic variation of the dietary sources. The findings of this study advance our understanding of the foraging ecology of small mammals in biodiversity-rich regions of the Neotropics. Classical methodologies such as stomach content confirmed dietary choices revealed by the stable isotopic composition and also highlighted the importance of C3 and C4 plants in the diet of this group of animals, especially in biomes such as the Atlantic Forest and Cerrado, where there is a predominance of highly altered landscapes. These results confirm that replacing the original vegetation with C4 crops has altered the feeding patterns of small mammals, which could lead to critical ecological changes in the trophic structure of these areas. Vertical stratification of the dietary niche and the interaction between biome and foraging lifestyle were also observed. However, in each biome, there was significant intraspecific and interspecific variation caused by preferences for ingestion of plant and animal material, leading to different degrees of omnivory. Finally, the high local variability between individuals and species requires a larger sampling design that may also contribute to improved spatial resolution of the isoscapes.

State-of-the-art cranial ultrasound in clinical scenarios for infants born at term and near-term.

Neonates admitted to the intensive care unit are at risk of brain injury. Importantly, infants with signs of neurological impairment need prompt diagnosis to guide intervention. Cranial ultrasound (CUS) is the first-line imaging tool for infants born preterm. New developments in this technology, which now incorporates high-resolution equipment, have notably improved the performance of CUS in infants born at term and near-term. On the other hand, the potential of CUS as a diagnostic tool in older infants is less established. The lack of studies focusing on this topic, local protocol variability among clinical sites, and divergent opinions on CUS patterns of disease entities are the main constraints. This review provides an overview of state-of-the-art CUS as a decision-making tool under different clinical scenarios, such as neonatal encephalopathy, seizures, and suspected central nervous system infection. The CUS features that characterize several patterns supporting a diagnosis are detailed, focusing on haemorrhage and infection.

Stochastic and alternating pacing paradigms to assess the stability of cardiac conduction

Reentry, the most common cause of severe arrhythmias, is initiated by slow conduction and conduction block. Hence, evaluating conduction velocity and conduction block is of primary importance. However, the assessment of cardiac conduction safety in experimental and clinical settings remains elusive. To identify markers of conduction instability that can be determined experimentally, we developed an approach based on new pacing paradigms. Conduction across a cardiac tissue expansion was assessed in computer simulations and in experiments using cultures of neonatal murine cardiomyocytes on microelectrode arrays. Simulated and in vitro tissues were paced at a progressively increasing rate, with stochastic or alternating variations of cycle length, until conduction block occurred. Increasing pacing rate led to conduction block near the expansion. When stochastic or alternating variations were introduced into the pacing protocol, the standard deviation and the amplitude of alternating variations of local conduction times emerged as markers of unstable conduction prone to block. In both simulations and experiments, conduction delays were prolonged at the expansion but increased only slightly during the pacing protocol. In contrast, these markers of instability increased several-fold, early before block occurrence. The first and second moments of these two metrics provided an estimation of the site of block and the accuracy of this estimation. Therefore, when beat-to-beat variations of pacing cycle length are introduced into a pacing protocol, the local variability of conduction permits to predict sites of block. Our pacing paradigms may have translational applications in clinical cardiac electrophysiology, particularly in identifying ablation targets during mapping procedures.

Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. “reservoir age” offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age and delta-R estimates therefore have limited applicability. While forward modelling of past R-age variability has been proposed as a means of resolving this problem, this requires accurate a priori knowledge of past global radiocarbon budget closure (i.e. production, and cycling), which we currently lack. In this context, the construction of empirical regional marine calibration curves could provide a way forward. However, the spatial reach of such calibrations and their robustness subject to (uncertain) temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R ages), regardless of the causes of R-age variability. We apply multiple algorithms (k-means, k-medoids, and hierarchical clustering) to outputs from two different numerical models spanning a range of climate states, forcings, and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations, such as increased high-latitude R ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the eastern equatorial Pacific, which cohere with known modern oceanographic processes. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This remains true when cluster assignments obtained from one model simulation are applied to simulated R-age time series from another. Further, model-based clusters are found to be broadly consistent with existing reservoir age reconstructions that span the last ∼30 kyr. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which empirical marine radiocarbon calibration curves may eventually be generated.

Exploring the Drivers Influencing Multidimensional Alpha and Beta Diversity of Macroinvertebrates in Mountain Streams

Understanding the driving mechanisms of diversity across multiple dimensions is a fundamental task in biodiversity conservation. Here, we examined the alpha and beta diversity of macroinvertebrates in the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province, China. We also assessed how much local environmental, land use, climatic, and spatial variables affected these diversities. We found that (1) there were generally weak or no correlations of alpha and beta diversity between taxonomic, functional, and phylogenetic dimensions; (2) compared to both functional and phylogenetic beta diversity, which was mainly determined by nestedness, taxonomic beta diversity was mostly molded by turnover and was much higher; and (3) local environmental variables predominantly influenced taxonomic and functional dimensions of alpha and beta diversity, while spatial factors primarily drove phylogenetic dimension. These results suggest that regulating local habitats is crucial for lotic biodiversity conservation efforts, though spatial processes cannot be overlooked. Furthermore, our findings verify the supplemental role of functional and phylogenetic data in enriching insights provided by taxonomic data alone. This underscores the importance of a multidimensional approach for a more nuanced understanding of community assembly mechanisms, which is crucial for efficient ecosystem management and biodiversity conservation.

Ocean-atmosphere-ice processes in the Ross Sea: A review

The Ross Sea has been the site of extensive investigations since the earliest days of polar exploration. The International Geophysical Year of 1957-58 enhanced research activities with the establishment of scientific stations and the collection of oceanographic observations in the area. While many features of its oceanography, ecology, physics, glaciology, geology, and biogeochemistry are known, recent advances provide new insights into its structure and function, as well as into its relationship to global climate. We present a comprehensive review of the advances of understanding the main processes occurring in the area, such as the formation of dense shelf water and the production of Antarctic Bottom Water (AABW), as well as the main drivers (at both large and local scales) of local dynamics and water mass variability. We also summarize the main modeling applications, which are still limited and need to be improved using high-resolution models and, locally, limited-area models to explain processes driven mainly by thermodynamics and water-mass transformations. The Ross Sea forms the most saline AABW due to the activity of two polynyas in the western sector. A salinity gradient occurs on the shelf, with fresh Low Salinity Shelf Waters concentrated in the eastern Ross Sea, which is influenced by the inflow of fresh water from the Amundsen and Bellingshausen Seas. This freshwater inflow was thought to be the cause of a multi-decadal freshening of the High Salinity Shelf Water, precursor to the AABW, although a rebound in salinity in the Ross Sea has been observed since 2014. The increase in salinity has also affected the production of AABW, with the respective rebound occurring almost simultaneously.

Literature Review on Stochastic Modeling of Wet and Dry Spells

With increasing concerns about climate change and its profound implications for water resources, stochastic modeling of wet and dry spells has emerged as a critical domain within hydrology and climatology, providing insight into the temporal patterns of precipitation and drought occurrences. This review of the literature synthesizes recent advances in the modeling of wet and dry spells using stochastic methods, focusing on publications from 2000 to 2024. The review examines various modeling approaches, including Markov chain models, mixed probability models, non-homogeneous models, and time series approaches. Key findings indicate that Markov chain models are effective in simulating the sequential occurrence of wet and dry spells, with higher-order variants addressing issues of overdispersion. Mixed probability models excel at representing heterogeneity and extremes in precipitation data, especially in regions with distinct wet and dry seasons. Non-homogeneous models are valuable for understanding the temporal dynamics and irregularities of dry spells, revealing significant shifts in their frequency and duration over time. These can be complemented by time-series methods to highlight long-term trends and seasonal variations. The review underscores the importance of regional specificity in modeling approaches to accurately capture local climatic and geographical variability. Recommendations for future research include the integration of hybrid models that combine the strengths of various approaches to improve predictive precision, focusing on the impacts of climate change, and conducting cross-regional comparisons to generalize findings and enhance the robustness of the models.

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

Comprehending and quantifying local variability in plant phenology, alongside its impacts on tree growth, is challenging due to spatially and temporally heterogeneous environmental factors that interact to affect phenological events and periods. Although previous studies have focused on the climatic factors driving phenological events at the stand level, the influences of competition, neighborhood characteristics, species richness, and water availability on plant phenology remain unclear. In the present paper, we used hourly terrestrial LiDAR surveys performed between April 2020 and April 2021 to investigate the influence of local factors (neighborhood and topography) on the phenology of silver birch trees (Betula pendula Roth.). We also examined how phenological events affect growth in tree height and crown area. All response and explanatory variables were estimated using LiDAR time-series data and a field survey campaign. Our findings demonstrate a high within-species variability in plant phenology that is controlled by biotic and abiotic characteristics of the ecosystem. We found that between tree variation in leaf burst, completion of the leaf growth and length of canopy growth period were affected by tree size, neighborhood species richness, level of suppression and competition, which potentially indicate plant responses to light availability. The beginning of senescence, completion of leaf drop, and length of autumn phenology were mostly affected by topographic water index, which reflects water availability and can be linked to nutrient availability and exposure to wind. Furthermore, we demonstrated that an earlier leaf burst, and delayed beginning of senescence were associated with larger absolute growth in canopy area but found no clear relationship between height growth and phenology. Our study highlights the capability of dense, high-quality LiDAR time-series to detect significant phenological variation among individuals of the same species within the same locality, demonstrating the potential of LiDAR time-series as a tool for understanding phenology and its impacts.

The brain's "dark energy" puzzle upgraded: [18F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity.

The brain's resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [18F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [18F]FDG kinetic parameters according to Sokoloff's model, i.e., (irreversible uptake rate), (delivery), (phosphorylation), in a large healthy control group. The parameters' spatial distribution was described at a high spatial resolution. We showed that while is the least redundant, there are relevant differences between and (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [18F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2), estimated from 15O PET data. We found that combining rs-fMRI and CMRO2 led to satisfactory prediction of individual variance (45%). Although more difficult to describe, and were both most sensitive to local rs-fMRI variables, while was sensitive to CMRO2. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.

Efektivitas Pembelajaran Kitab Taisirul Kholaq Dalam Meningkatkan Akhlak Siswa

Juvenile delinquency is an act of violating norms and rules committed during adolescence or the transition from childhood to adulthood. Teenagers are an asset that every country has. This research aims to find out; 1) Local Content Learning of Kitab Taisirul Kholaq at An-Nur Al Anwar Junior High School Bululawang Malang, 2) Student morals at An-Nur Al Anwar Junior High School Bululawang Malang, 3) The Effect of Local Content Learning of Kitab Taisirul Kholaq on Student Morals at An-Nur Al Anwar Junior High School Bululawang Malang. This research was conducted with a quantitative approach using the type of correlation research, used to determine the correlation or relationship of local content learning Kitab Taisirul Kholaq on student morals. The sample used in this study was 15% of the population with a purposive sampling technique of 54 students. Data collection techniques using observation, questionnaires, and documentation. The results showed 1) it is known that the minimum value of the local content variable is 36, the maximum value is 62, the mean is 44.13, Std Deviation is 4.434, Variance is 19.662 from 54 respondents. 2) The minimum value of the student morals variable is 46, the maximum value is 83, the mean is 62.07, Std Deviation is 6.375, Variance is 40.636 from 54 respondents. 3) There is an influence of local content learning of the book of Taisirul Kholaq on student morals at An-Nur Al Anwar Junior High School with a moderate level of strength.

Repetitive Transcranial Magnetic Stimulation (rTMS) Treatment Reduces Variability in Brain Function in Schizophrenia: Data From a Double-Blind, Randomized, Sham-Controlled Trial.

There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham. 83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group. We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected). Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing.

Signature of the western boundary currents in local climate variability.

The western boundary currents are characterized by narrow, intense ocean jets and are among the most energetic phenomena in the world ocean. The importance of the western boundary currents to the mean climate is well established: they transport vast quantities of heat from the subtropics to the midlatitudes1, and they govern the structure of the climatological mean surface winds2-6, precipitation4-6 and extratropical storm tracks7-13. Their importance to climate variability is much less clear, as the tropospheric response to extratropical sea surface temperature (SST) variability is generally modest relative to the internal variability in the midlatitude atmosphere12-14. Here we exploit novel local analyses based on high-spatial-resolution data to demonstrate that SST variability in the western boundary currents has a more robust signature in climate variability than has been indicated in previous work. Our results indicate that warm SST anomalies in the major boundary currents of both hemispheres are associated with a distinct signature of locally enhanced precipitation and rising motion anomalies that extend throughout the depth of the troposphere. The tropospheric signature closely mirrors that of ocean dynamical processes in the boundary currents. Thus, the findings indicate a distinct and robust pathway through which extratropical ocean dynamical processes influence local climate variability. The observational relationships are also reproducible in Earth system model simulations but only when the simulations are run at high spatial resolution.

Universal versus taxon-specific drivers of helminth prevalence and intensity of infection.

Two key epidemiological parameters, prevalence and mean intensity of infection, together capture the abundance of macroparasite populations, the strength of density-dependent effects they experience, their potential impact on host population dynamics and the selective pressures they exert on their hosts. Yet, the drivers of the extensive variation observed in prevalence and mean intensity of infection, even among related parasite taxa infecting related hosts, remain mostly unknown. We performed phylogenetically grounded Bayesian modelling across hundreds of amphibian populations to test the effects of various predictors of prevalence and intensity of infection by six families of helminth parasites. We focused on the potential effects of key host traits and environmental factors pertinent to focal host populations, i.e. the local diversity of the amphibian community and local climatic variables. Our analyses revealed several important determinants of prevalence or intensity of infection in various parasite families, but none applying to all families. Our study uncovered no universal driver of parasite infection levels, even among parasite taxa from the same phylum, or with similar life cycles and transmission modes. Although local variables not considered here may have effects extending across taxa, our findings suggest the need for a taxon-specific approach in any attempt to predict disease dynamics and impacts in the face of environmental and climatic changes.

Blue Carbon Assessment in the Salt Marshes of the Venice Lagoon: Dimensions, Variability and Influence of Storm‐Surge Regulation

AbstractSalt marshes are intertidal coastal ecosystems shaped by complex feedbacks between hydrodynamic, morphological, and biological processes. These crucial yet endangered environments provide a diverse range of ecosystem services but are globally subjected to high anthropogenic pressures, while being severely exposed to climate change impacts. The importance of salt marshes as “blue carbon” sinks, deriving from their primary production coupled with rapid surface accretion, has been increasingly recognized within the framework of climate mitigation strategies. However, large uncertainties remain in salt marsh carbon stock and sequestration estimation. In order to provide further knowledge in salt marsh carbon assessment and investigate marsh carbon pool response to management actions, we analyzed organic matter content in salt marsh soils of the Venice Lagoon (Italy) from 60 sediment cores to the depth of 1 m and estimated organic carbon stock and accumulation rates in different areas. Organic carbon stocks and accumulation rates were highly variable in different marshes, being affected by organic and inorganic inputs and preservation conditions. Our estimates suggest that the studied marshes store 17,108 ± 5,757 tons of carbon per square kilometer in top 1‐m of soil and can accumulate 85 ± 25 tons of carbon per square kilometer per year. However, flood regulation may reduce the annual marsh CO2 sequestration potential by more than 30%. Our results contribute valuable information for regional carbon assessments, reinforcing the need for integrated coastal management policies to preserve the ecosystem services of coastal environments, and underscore the importance of considering local variability and methodological variations.

Essential Oils of Some Potential Medicinal Plants and their Wound Healing Activities.

The wound has been recognised as a deep cut or tearing of the epidermis, which is also referred to as trauma and harm to the body tissues. Healing of wounds requires a coordinated series of cellular processes, including cell attraction, proliferation, differentiation, and angiogenesis. These processes involve interactions between various cells, such as macrophages, endothelial cells, keratinocytes, fibroblasts, growth hormones, and proteases. The outcome of wounds can be fatal if not treated properly, resulting in chronic wounds, chronic pain, and even death. Wound healing is replacing missing tissue with tissue repairs and regeneration. Some local variables are the presence of tissue maceration, foreign objects, biofilm, hypoxia, ischemia, and wound infection. Sustained growth factor delivery, siRNA delivery, micro-RNA targeting, and stem cell therapy are all emerging possible therapeutic approaches for wound healing. Traditional approaches, such as Ayurveda, Siddha, and Unani medicines, are also being used for treatment. The therapeutic application of nanoformulations in wound infections has shown various beneficial effects. Several herbal medicines, especially essential oils have shown potential wound healing activities, such as lavender, tea tree, sesame, olive, etc. Various nanoparticles and their nanoformulations have been explored in wound healing therapy. The present review article highlights several aspects of essential oils for wound healing activity through a novel drug delivery system. Further, some patents on wound healing through herbal medicine have been listed.

Hydrological dynamics, wetland morphology and vegetation structure determine riparian arthropod communities in constructed wetlands

Wetland hydrological dynamics often dictate the composition of biological communities found in or near wetlands, either directly or through changes in vegetation composition. However, much remains unknown, particularly regarding how riparian arthropods respond to such dynamics. In this study, we used high-resolution hydrological data, along with presence of grazing livestock and shoreline vegetation height from 41 constructed wetlands in south-western Sweden to explore flood zone areas, flood frequencies, vegetation and grazing as drivers of the resident arthropod communities. The collected material consisted of 26,817 arthropods, where the dominant groups were Diptera (13,258 specimens), spiders (6,207) and Coleoptera (2,858), which were collected using SLAM (Sea Land and Air Malaise) trapping, along with pitfall trapping and vacuum sampling of riparian arthropods. We found group-specific responses to inundation frequencies, where wetlands with higher frequencies had lower abundances of some beetles and tipulids, and where wetlands with longer low-water table periods contained less trichopterans and heteropterans. In contrast, the size of flood zone areas only affected some wolf spider groups, that were more abundant in wetlands with intermediately sized flood zones. Shoreline vegetation height affected multiple groups, spiders, beetles and dipterans, but in different directions, whereas presence of grazing livestock had limited impact on abundances and community compositions. Given the variable responses to wetland hydrological and structural drivers, it seems that wetland arthropod communities would benefit from a high local wetland habitat variability, or wetlandscapes where individual wetlands have differing hydrological dynamics, morphology and vegetation.

Local Weather and Global Climate Data-Driven Long-Term Runoff Forecasting Based on Local–Global–Temporal Attention Mechanisms and Graph Attention Networks

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff.

Quantitative assessment of concrete microstructure in the diagnosis of surface damage to industrial floor

Diagnostic assessment of industrial floor using the methodology of quantitative evaluation of concrete microstructure is presented. Symptoms of damage included surface dusting and delamination of the top layer. A petrographic analysis of the concrete composition in core specimens was carried out. Excessive air content, accumulation of air voids and the occurrence of oriented cracks, local variability of the phase composition of cement hydration products, including the occurrence of carbonated areas were observed and associated with the premature surface damage

Intrinsic short Marine Heatwaves from the perspective of sea surface temperature and height

Marine heatwaves (MHWs) have recently been recognized as extreme climate events considering their devastating impacts on marine ecosystems. Our study explored the spatial and temporal variability of short (duration <10 days) and long MHWs in nine sub-regions around the Australian coastal region using the original (5-day) and an updated longer duration (10-day) criteria for MHW identification based on gap-free Sea Surface Temperature (SST) analyses from 1981 to 2020. By quantitatively investigating the contribution of ocean warming to short MHWs, we could consider most of the short events as background signals of a dynamic ocean surface over the Australian region. The application of the updated definition highlights areas that are more sensitive to local internal forcings, especially over the main flow of the East Australian Current. Furthermore, the Great Barrier Reef exhibit a larger increasing trend of MHW areas after excluding the short events. By numerically and graphically evaluating the relationship between the sea level anomaly (SLA) and SST metrics over two coastal regions of Australia, it is found that longer MHWs exhibiting two variation trends of large SLA metrics are ENSO dominant in the northwest coastal region (NW), and less ENSO-dominant but geographically-impacted in the southeast coastal region (SETS). However, it is possible that most short events in these two regions are a result of local and intrinsic variability or ocean warming of the water columns rather than the remote modulation of climate modes. Moreover, SLA over the 90th percentile, which successfully observed a subsurface MHW event over the NW region in 2008, has the potential to help identify subsurface MHWs, although limited by application area. Further investigation into the applicability of these, or other similar, updates to the MHW definitions may be warranted, to draw a broadly applicable conclusion to benefit detection and prediction of strong sub-surface MHWs impacting commercial and environmental activities.