Three dimension (3D) Multi-input-multi-output (MIMO) scheme, which exploits another dimension of the spatial resource, is one of the enabling technologies for the next generation mobile communication. As the elevation angle in 3D-MIMO channel model might varies against the height of the base station transmit antenna, it has to be taken into account carefully. In this paper, the impact of antenna height on the channel characteristics of 3D MIMO channel is investigated by using the intelligent ray launching algorithm (IRLA). Three typical street scenarios, i.e., the straight street, the fork road and the cross road, are selected as benchmarks. On the basis of simulations, joint and marginal probability density functions (PDFs) of both the elevation angle of departure (EAoD) and the elevation angle of arrival (EAoA) are obtained. The elevation angle spread (AS) and the delay spread (DS) under various antenna heights are also discussed. Simulation results indicate that the PDFs of EAoD and EAoA vary characteristics under different street scenarios. Moreover, the minimum value of the DS can be achieved when the antenna height is half of the building height.

Lightning has received a lot of attention in scientific literature during the recent decade, not only because it is an impressive atmospheric phenomenon but also its associations with severe storms that cause unprecedented damages to agriculture, electric power networks, property, and life. This study assessed the Spatio-temporal characteristics of lightning occurrence with elevation in Uganda using lightning flash and elevation datasets for a period of fifteen years (1998-2013). Datasets used in this study included daily lightning flashes as captured by Lightning Imaging Sensor (LIS) aboard on Tropical Rainfall Measurement Mission (TRMM) satellite and elevation data in form of Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM). Spatio-temporal results indicated that ~80% of areas with an elevation that ranges from 800-1200 m above mean sea level (masl) in Uganda had severe lightning occurrences and ~20% of areas with an elevation greater than 1200 m (masl) had severe lightning occurrences. The country received an enhanced number of lighting events with the highest number in 1999. Subsequently, a reduced trend was observed from 2002 to 2007 followed by an increment in the number of lightning events in (2010, 2011, 2012, and 2013). The intensity of the events decreased gradually though two peaks were observed, (1998-2001) and (2010-2013). Furthermore, results indicate escalations in the frequency and duration of lightning events from 60 times in 1998 to approximately 200 times in 2013 and from 1000 microseconds in 1998 to more than 2000 microseconds in 2013. Generally, the country experienced an enhanced increase in lighting occurrences over the study period which therefore calls for urgent actions to combat the root cause and also provide effective measures to reduce the impacts of lightning strikes.

In the late Pleistocene, a prehistoric hippo species was distributed from Africa to the Asia including Pakistan, India, and Java island. This study aims to model habitat suitability of Asian hippo known as a Hippopotamus sivalensis spp. in east Java. The measured parameters included the fossil locality, vegetation cover, elevation, and distance to the river in a forest river basin sizing 6652 Ha. Those parameters using GIS were weighted, overlaid, and interpolated to determine the most suitable habitats. The model projected that the suitable habitats of H. sivalensis spp. were in the central of the basin near the river. The largest suitable habitats were located in the eastern parts of basin which were dominated by forests

Background/Objectives: In case of myocarditis, electrocardiogram (ECG) may suggest the pattern of ST segment elevation myocardial infarction (STEMI) or non-ST segment elevation myocardial infarction (NSTEMI). NSTEMI pattern is less frequent in case of myocarditis, but it remains unclear if presence of ST segment elevation in case of myocarditis is related to more severe condition and more damage in myocardium. Methods: This is a retrospective study, involving 38 patients, admitted with myocarditis. Patients were divided into two groups – patients with ST segment elevation (STE) pattern in ECG (25) and patients without ST segment elevation (non-STE) pattern (13). The data compared were epidemiology, laboratory and instrumental tests results. Data was analysed using IBM SPSS Statistics v26.0. A p value <0,05 was concerned as statistically significant data. Results: C-reactive protein (CRP) levels were higher in STE group (103.40±82.04mg/l vs 43.54±61.93mg/l, p = 0.017). Left ventricle ejection fraction (LVEF) was significantly higher in non-STE pattern group (49.71±4.14 vs 56.58±3.99, p<0.001). Lower LVEF correlates with higher TnI levels (r= -0.353, p=0.032) and higher CRP levels (r = -0.554, p<0.001). Lower left ventricle (LV) strain correlates with higher levels of Troponin I (TnI( (r = -0.641, p = 0.013).Conclusions: LVEF in STE group was lower comparing to non-STE pattern group. STE pattern was associated with higher CRP levels. Higher TnI levels in case of myocarditis were associated with lower LV strain and lower LVEF, higher CRP levels also correlated with lower LVEF. Based by 6 months echocardiographic follow-up – the prognosis of myocarditis is favorable.

The number of visible navigation satellites in a multi-constellation system increases exponentially compared to a single constellation, significantly increasing the complexity of the system state equations in the data fusion algorithm of a tightly coupled navigation system. This results in an exponential increase in the computation volume for navigation and positioning, seriously affecting real-time performance. To address this, a low-cost, low-computation satellite screening algorithm is proposed. It adaptively changes the number of constellation satellites by setting different screening thresholds and adjusting the number of remaining satellites. The algorithm then sorts and differentiates the azimuth and elevation angles of the screened satellites to form a selection strategy that divides the space region multiple times, enabling fast satellite selection. This approach greatly reduces the calculation amount while ensuring positioning accuracy. Experimental results show that the proposed algorithm maintains positioning differences within 5 meters compared to traditional satellite selection algorithms, and the time consumed for selecting a single epoch is greatly reduced. This indicates that the algorithm is suitable for low-cost multi-constellation satellite positioning systems.

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, which can compromise the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

Elevation has been a cornerstone of biodiversity research, and changes in the environmental factors behind it influence biodiversity and community patterns. Exploring the potential reasons behind liverwort community patterns has been a matter of multiplied interest for ecologists. In the present study, we assessed the liverwort plants of the Sygera Mountain from 3100 m to 4500 m decaying wood, tree, soil, and rock using complex field surveys in 2017 and 2019, and investigated the effects of altitude and various climatic factors on the diversity and community composition of the Sygera Mountain. Also, we used including ANOVA, mixed effects modeling, NMDS, ANOSIM, and CCA, which helped to critically assess the liverwort diversity with respect to environmental variables. The results of the study showed a bimodal variation in the richness of liverwort communities with altitude, with peaks occurring at 3500 m and 4100 m. The variation with altitude was significant, with communities at 4100 m and 4300 m being associated with arid environments, and those at 3100 m and 3300 m favoring areas of higher mean annual temperatures and precipitation. Among the climatic factors, the variation in mean diurnal rang, precipitation, and annual temperature was found to be the determinant of liverwort communities. The results suggest that elevation and daily temperature fluctuations play a crucial role in the distribution and community structure formation of mountain liverwort plants. This study deepens our understanding of liverwort ecology and emphasizes the importance of climatic variables in determining plant community composition.

In order to investigate the vertical distribution pattern of soil fauna community in low-altitude mountain areas. On July 8th, 2022, a low hill was selected as the study area to collect soil arthropods were collected through trap. Leaf litter, vegetation type and distribution quantity were investigated at each sampling site while soil fauna were collected. Also, the soil physical and chemical parameters were measured simultaneously. The results of one-way ANOVA showed that there were significant differences (P

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using UAV-acquired imagery. However, with the increased availability of UAVs with onboard GNSS RTK, georeferencing without GCPs is a promising alternative. However, systematic elevation error remains a problem of this technique. We aimed to analyze the reasons for this systematic error and propose strategies for the elimination of this error. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100m with vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° a 15°). Each of these flights was performed twice to produce a full double grid. Models were calculated from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights from the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS-RTK data and the deviation in the determined internal orientation parameters (focal length). Besides, we have shown that a combination of two flights with different image acquisition axis can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area - water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the upper Mississippi River. The Otsu's automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each pre-defined polygon around each gauging station. In addition to the commonly used green-band based water indices, Landsat 8 and 9 OLI's ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadow), and AWEInsv (AWEI without shadow), where v represents the visible light band used in the water index. At each station, about 60-80 Landsat 8 or 9 images during 2013-2023 were used for assessing the performances of the twenty water indices through comparing the estimated WSEs with the measured WSEs. Results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices.

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of a terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and solution based on prediction of the weight mask by an encoder-decoder architecture, subse-quently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. Each sample corresponds to a 10 by 10 meter area of the stream channel and adjacent land. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. The proposed solution was compared with existing methods for measuring WSE in small streams using a drone. The results indicate that the solution outperforms previous photogrammetry-based methods and is second only to the radar-based method, which is considered the most accurate method available. The solution is characterized by a high degree of explainability and generalization.

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.

Carbon (C) losses due to the conversion of natural forests adversely affect the abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C storage in rubber plantations across elevation gradients is poorly understood. In this study, we investigated biomass and C storage along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. We investigated the C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths. A significant negative correlation was observed between total ecosystem C stocks and elevation in both age groups of rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha–1 was recorded at 900 m in 8-year-old plantations, whereas in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha–1 was found at 700 m. The total ecosystem C stocks decreased to its lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha–1 and 125.75 Mg C ha–1, respectively. We concluded that the total ecosystem C stocks were negatively correlated with elevation and significantly decreased from 8.05% to 51.55% and 11.46% to 42.96% between 700 m and 1100 m in both 8-year-old and 12-year-old plantations, respectively. However, no correlation was observed between the total soil C stocks and elevation in either age group of rubber plantations. Regardless of elevation gradient, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other factors contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stocks along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass and C storage capacity decreased above 900 m and that age and elevation are key factors for biomass and C storage in monoculture rubber plantations.

(1) Background: The aim of the work was to characterize the climatic evolution and change based on the Multi Criteria Classification through the dynamics of bioclimatic indices in viticulture across Italy and its regional administrative boundaries, focusing on latitudes and elevations. (2) Methods: The impact of climate change on viticulture in Italy was analysed and spatialized with reference to historical data from 1991 to 2021 and to the Future Scenarios up to 2080 assumed by SSP2-4.5 and SSP5-8.5, taking into account 13 GCMs. (3) Results: The bioclimatic indices have all shown a significant trend in the historical period, with an increase in temperature and a decrease in precipitation, reflecting their effects on the entire Italian territory with respect to HI, up to 44°N for CI and up to 46°N for DI, regardless of altitude. The Future Scenarios highlighted a shift towards the warmer classes of the two temperature-based indices (HI and CI) for both SSPs, especially for altitudes up to 900 m a.s.l.. The DI-based classification based on DI remained relatively stable in Italy over time, although DI values will become increasingly negative in the near future. (4) Conclusions: The climate in Italy is warming, especially in the south and in the coastal regions. By 2080, more areas will be “Very Hot” with “Warm Nights.” Drought will also increase and have a negative impact on viticulture. The importance of higher latitudes and altitudes in mitigating the effects of climate change justifies the continuing trend towards the relocation of vineyards as a medium-term solution and as an alternative to targeted cultivation methods that must be adopted in the short-term to safeguard the suitability of an area to quality viticulture.

Background: A significant percentage of younger patients with myocardial infarction have premature coronary artery disease (CAD). The aims of this study were to analyze all-cause mortality and major adverse cardiovascular events (MACE- cardiovascular death, non-fatal reinfarction and stroke and target vessel revascularization) during eight-year follow-up in STEMI patients with premature CAD. Method: we analyzed 2,560 STEMI patients without previous CAD and without cardiogenic shock at admission who were treated with primary PCI. CAD was classified as premature in men aged <50 years and women <55 years. Results: Of the 2,560 patients analyzed 630(24.6%) patients had premature CAD. Patients with premature CAD have fewer comorbidities and better initial angiographic findings, as compared to patients without premature CAD. The incidence of non-fatal adverse ischemic events was similar to the incidence in older patients. Premature CAD was an independent predictor for lower mortality (HR 0.50, 95%CI 0.28-0.91) and MACE (HR 0.27, 95%CI 0.15-0.47). In patients with premature CAD, EF<40% was the only independent predictor of mortality (HR 5.59 95%CI 2.18-8.52) and MACE (HR 4.18, 95%CI 1.98-8.13). Conclusion: Premature CAD was an independent predictor for lower mortality and MACE. In patients with premature CAD, EF<40% was an independent predictor of eight-year mortality and MACE.

Qinghai spruce (Picea crassifolia) is a ecologically important species in the forest ecosystem of the Qilian Mountains region in China. Natural regeneration of this species is critical to maintaining forest ecosystem function. We here analyzed several biological indicators among naturally re-generating Qinghai spruce across several elevations in the Pailugou watershed. Specifically, seedling density, basal diameter (BD), and plant height were measured, as were soil physico-chemical parameters, at 2700 m, 3000 m, and 3300 m above sea level. Differences in the biological indicators and correlations between the indicators and soil parameters were then assessed at each elevation. The results showed that soil stoichiometry was more sensitive to changes in el-evation than seedling indicators were. Furthermore, seedling density was positively correlated with soil pH, whereas BD was positively correlated with the carbon to nitrogen ratio (C/N), the carbon to phosphorus ratio, and soil organic carbon (SOC) contents. None of the analyzed soil parameters had a significant impact on elevation-specific differences in seedling density. However, soil pH, SOC, and C/N significantly affected variations in seedling diameter at each elevation. Finally, soil pH, SOC, C/N, and the carbon to phosphorus ratio significantly affected variations in seed-ling height at each elevation. This study provides a strong theoretical basis for further under-standing of the mechanisms associated with Qinghai spruce regeneration, ulti-mately contrib-uting to rational protection and management strategies for this important natural resource.

Browsing of wild ungulates can have profound effects on the structure and composition of forests. In the Swiss National Park, the density of wild ungulates including red deer (Cervus elaphus), ibex (Capra ibex), and chamois (Rupicapra rupicapra) is exceptionally high due to strict protection and the absence of large predators. We examined count data of larch (Larix decidua), cembra pine (Pinus cembra), spruce (Picea abies), upright mountain pine (Pinus mugo subsp. uncinata), and mountain ash (Sorbus aucuparia) of four sampling years between 1991 and 2021 and modelled how different topographic and location factors affected the probability of browsing on saplings of larch, cembra pine, and spruce. Despite the high density of wild ungulates, the numbers of saplings and young trees increased over the past 30 years. The probability of browsing on saplings was highest for larch at a height of 10 – 40 cm and increased with increasing elevation. In our study area, open grasslands are mainly located above the tree line, which might explain the positive correlation between elevation and the probability of browsing. Other factors like exposition and slope, available food resources and disturbance by humans did not have clear effects on the probability of browsing.

Background: Congestive heart failure is the inability to pump blood throughout the body which causes the walls to be unable to pump adequately. Purpose: This study aimed to ascertain the effect of a contrast bath with a 30 º elevation on the reduction of edema in congestive heart failure patients at Ulin General Hospital, Banjarmasin. Methods: The design of this study was quasi-experimental with an unequal control group design. The population is patients with congestive heart failure, and the sample used is successive sampling. 34 respondents were divided into two groups, 17 respondents in the intervention group and 17 in the control group. Results: The Mann-Whitney result value P = 0.027 (P < 0.05) represented a significant reduction in Bath contrast edema with 30º feet elevation in the intervention group and control group patients with congestive heart failure. Conclusion: Based on the results of this study, it is expected to be able to apply contrast showers with an elevation of 30 º in nursing services to reduce the degree of edema in congestive heart failure patients.

The aim of this study was to evaluate implants survival rate, marginal bone loss, surgical and prosthetic complications of implants placed through sinus floor elevation and tilted implants en-gaged in basal bone in order to bypass maxillary sinus. 60 patients were enrolled for this study. According with residual bone height of posterior maxilla the sample was divided in three groups of 20 patients: Group A (lateral sinus floor elevation), Group B (transrectal sinus floor elevation) and Group C (tilted implants employed to bypass sinus floor). Follow-up visits were performed one week after surgery, at 3, 6 months and then once a year for next 4 years. The outcomes were implants survival rate, marginal bone loss and surgical and prosthetic complications. Although the Group A, B and C have demonstrated an implants survival rate of 83.3%, 86,7% and 98,3% respectively, the statistically analysis showed that there was no statistically significant difference between groups. Statistically significant differences between the groups were also not found con-cerning marginal bone loss, as recorded by intra-oral X-ray measurements during follow-up. About complications it wasn’t possible to perform a statistical analysis. To as to reduce potential surgical risks implants placement in basal bone should be preferred.

Zagros is an important region with high quality and quantity karstic water resources in the Middle East. This region provides a dominant part of potable and agricultural water needs for its inhabitants as well as agricultural water needs for nearby regions. Therefore, studying karstic water resources in Zagros by accurate methods such as stable isotopes techniques is very important. In this investigation, hydrological characteristics of groundwater resources including groundwater origin, recharge rate and recharge elevation have been studied using stable isotopes (¹⁸O and ²H). The results show that stable isotopes signatures in groundwater resources show notable variations across Zagros and groundwater resource mainly plot on south and west Zagros meteoric water lines. In addition, recharge elevation and recharge rate in groundwater resources also show significant variations in Zagros. Finally, the stable isotopes signatures in precipitation and groundwater has been used to study the role of each dominant air mass (contribution percentage of precipitation events originate from each air mass) in groundwater resources recharge using Simmr package in R language. Overall, groundwater resources in Zagros is recharged by precipitation events originate from various air masses and they have various recharge rates and recharge elevations.

ST-elevation myocardial infarction (STEMI) remains one of the leading causes of morbidity and mortality worldwide. The identification of novel metabolic and imaging biomarkers could unveil key pathophysiological mechanisms at the molecular level and promote personalized care in patients with acute coronary syndromes. We studied 38 patients with STEMI who underwent primary percutaneous coronary intervention and thrombus aspiration. We sought to correlate serum ceramide levels with micro-CT quantified aspirated thrombus volume and relevant angiographic outcomes, including modified TIMI thrombus grade and pre- or post-procedural TIMI flow. Higher ceramide C16:0 levels were significantly, but weakly correlated with larger aspirated thrombus volume (Spearman r=0.326, p=0.046), larger intracoronary thrombus burden (Nagelkerke R2=0.236, p=0.030) and worse pre- and post-procedural TIMI flow (Nagelkerke R2=0.210; p=0.049 and Nagelkerke R2=0.277; p=0.039, respectively). Ceramides C24:0 and C24:1 were also significantly associated with larger intracoronary thrombus burden (Nagelkerke R2=0.311; p=0.008 and Nagelkerke R2=0.423; p=0.001, respectively). In conclusion, serum ceramide levels (mainly C16:0 and C24:1) were higher among patients with larger intracoronary and aspirated thrombus burden. This suggests that quantification of serum ceramides might improve risk-stratification of patients with STEMI and facilitate a more individualized approach in everyday clinical practice.

The Combined Elevation Test (CET) is a musculoskeletal screening technique (MST) replicates the streamline position in swimming and is commonly used in various sports. Although the CET is widely used, no normative data exist within an adolescent population. Therefore, the purpose of this study was to develop a normative data set for the CET within an adolescent population and to evaluate the influence various demographic and anthropometric variables. Data was collected for 416 participants aged between 8 and 18 years old. Age and arm span showed a significant correlation with CET scores (arm span r_s (105) = .478, p = .000, age r_s (416) = .238 p = .000). Regression analysis further quantified the influence of arm span and age on CET scores accounting for 23.1% and 5.3% of variability respectively. These results can be used as a reference point for clinicians and coaches who are using the CET within their assessment.

Primary percutaneous coronary intervention (pPCI) has revolutionized the prognosis of ST-segment ele-vation myocardial infarction (STEMI) and is the gold standard treatment. As a result of its success, the number of pPCI centres has expanded worldwide. Despite decades of advancements, clinical outcomes in STEMI patients have plateaued. Out-of-hospital car-diac arrest and cardiogenic shock remain a major cause of high in-hospital mortality, whilst the growing burden of heart failure in long-term STEMI survivors presents a growing problem. Many elements aiming to optimize STEMI treatment are still subject to debate or lack sufficient evidence. This review provides an overview of the most contentious current issues in pPCI in STEMI patients, with an emphasis on unresolved questions and persistent challenges.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

The Topographic Ruggedness Index (TRI) is one of the essential measurement in topographic analysis, as well as in explaining biodiversity and geo-diversity. This study demonstrate how to utilize Geographic Information Systems (GIS) to generate TRI for Nyanga District using Digital Elevation Model (DEM). Though the TRI strongly depends on a local scale slope derived from an average adjacent neighbor slope algorithm, and selection of different lag distances in the computation of spatial variability, the measurement demonstrated that areas of higher altitude have higher ruggedness index. The TRI model was generated by computing focal statistics to determine focal sum of DEM and of DEM ². The final TRI was computed using Math Algebra (Raster Calculator) to generate layers of different ruggedness suitable for topographic and biodiversity analysis. The algorithm presented could also be used for smaller areas with high quality data and corrected DEMs. Despite widespread adoption, ruggedness metrics require thorough testing using both artificial landscapes and real world applications.

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at global scale due to computational expense. Instead of predicting arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network gaussian mixture model, also known as a mixture density network (MDN). Training data was created from differencing geocorrected GEDI L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on regional distribution predictive capability. On average, the relative error of equivalent standard deviation of predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlate to qualitative understanding of prediction accuracy and instrument performance.

We provide quantitative results from GIS-based modelling of urban emission functions for a range of representative low- and mid-rise locations, ranging from individual streets to residential communities within cities as well as entire towns and city regions with the aim of whether lantern photometry or built environment has the dominant effect on light pollution. We demonstrate the scalability of our work by providing results for the largest urban area modelled to date, comprising the central 117 km2 area of Dublin City and containing nearly 42,000 public lights. Our results show a general similarity in the shape of the azimuthally-averaged emission function for all areas examined, with differences in total light output distribution depending primarily on the nature of the lighting and, to a smaller extent, on the obscuring environment including seasonal foliage effects. A comparison with global satellite observations shows that they are consistent with the deduced angular emission function for other low-rise areas worldwide. We further validate our approach by comparing results for a range of urban locations by the close agreement observed in a detailed comparison of with calibrated imagery from the International Space Station. To our knowledge, this is the first such detailed quantitative verification of light loss calculations.

This study introduces an advanced method for climate downscaling using a Super-Resolution Generative Adversarial Network (SRGAN), referred to as Elevation-integrated TEmperature and Precipitation SRGAN downscaling (E-TEPS), to enhance the spatial resolution of climate data, specifically 2m-temperature above the surface and total precipitation, from the high-resolution Euro-Mediterranean Center on Climate Change (CMCC) dataset over Italy. Traditional Numerical weather prediction (NWP) as well as global climate models (GCMs) generally lack the resolution required for local applications, and this research addresses that gap by refining GCM outputs to provide detailed local climate insights. E-TEPS incorporates elevation maps as auxiliary inputs, significantly improving the accuracy of downscaled temperature and precipitation data. Utilizing Google Cloud's computational resources, the model processes large climate datasets and runs complex training operations efficiently. The system is exceptionally fast, delivering all results in under 10 seconds. The model demonstrates superior performance over two interpolation methods (bicubic and linear), used as traditional downscaling methods, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), higher Pearson Correlation for temperature, and higher agreement values for the precipitation. Visual comparisons confirm that E-TEPS generated high-resolution outputs exhibit fewer artifacts and better detail preservation, particularly in regions with complex terrain and variable climatic conditions. These findings underscore the potential of our model in delivering precise, high-resolution climate predictions, thereby enhancing the performance of early-warning systems and supporting more effective climate-related decision-making. This paper is the first part of a two-part series; the second part will demonstrate the integration of this downscaling method with the FourCastNet global forecasting model to enhance climate predictions at a regional level, particularly focusing on high-resolution forecasts for Italy.

Over the last decades, an important number of regional and global Digital Elevation Models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs require to be expressed in the same Coordinate Reference System (CRS), sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention), and with heights relative to the same Vertical Reference System (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements, or disparities, may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect or curvature. Therefore, the control of DEM planimetric accuracy is certainly the most important task to perform at first before any comparison. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy, by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have been already computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been performed in a 11x11 grid with a 0.1-pixel step in both directions. The Copernicus DEM GLO-30 has been interpolated by a “classical” bicubic to achieve these 11x11 displacements. This validation method has been applied in four 10 km x 10 km DEMIX tiles showing different height distributions. Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with with typical errors (e_b) ranging from 12 to 20 % of the pixel size (worst case in Croatia).These errors in displacement retrievals are not constant in the 11x11 grid and the overall error E_b depends on the roughness (altimetry variations) encountered in the different tiles. The second idea of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d=1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles making the bicubic parameter vary between -1.5 and 0.0 by step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around -0.95) and higher in mountainous areas (around -0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r=0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

Background: Arm elevation plays an important role in simple and complex daily tasks. Muscles that stabilize the shoulder girdle also function as respiratory accessory muscles. Dual demand on these muscles, particularly in patients with chronic respiratory conditions, leads to dyspnea and fatigue of the upper limb, making simple daily activities difficult. Various tests, including Grocery Shelving Task (GST), are designed to assess functional performance during upper limb activities. The aim of the study is to derive reference values for GST among the population of the United Arab Emirates Methods: This cross-sectional study was conducted with 220 healthy individuals of both genders between the ages of 20 and 69. Duration required to complete GST was recorded. The correlation between duration and age, height, body mass and body mass index (BMI) were also assessed. Results: Reference values for duration (in seconds) in females for age groups 20-29, 30-39, 40-49, 50-59 and 60-69 are 43.5±5.5, 43.1±8.08, 48.1±6.7, 44.9±7.1, 46.7±6.5 respectively. Values for males of age groups 20-29, 30-39, 40-49, 50-59 and 60-69 are 45.7±6.7, 44.8±8.9, 42.5±10.01, 53.4±4.2, and 53.9±5.4 respectively. Conclusion: Reference values for GST duration were obtained. Weak correlations were found between duration and age, height, body mass, BMI of the participants.

ST-segment elevation myocardial infarction (STEMI), a major cause of mortality worldwide, has been shown through epidemiological studies to have a relationship with short-term exposure to air pollutants; however, the association between air pollutants and the outcome of STEMI has not been well studied. The aim of this study was to estimate the impact of air pollutants on the outcomes of STEMI. Data on particulate matter <2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) at each of the 11 air monitoring stations in Kaohsiung City were collected between January 1, 2012, and December 31, 2017. Medical records of non-trauma patients aged > 20 years who had visited the ED with a principal diagnosis of STEMI were extracted. The primary outcome measure was in-hospital mortality. After adjusting for potential confounders and meteorological variables, we found that an increase in the interquartile range (IQR) in NO2 was associated with an elevated risk of in-hospital mortality in patients with STEMI. Moreover, during the warm season, an IQR increase in NO2 corresponded to a higher risk of in-hospital mortality in patients with STEMI on lag 3 (OR=3.266; 95% CI:1.203–8.864, p=0.02). Conversely, an IQR increase in PM10 was associated with an increased risk of in-hospital mortality in patients with STEMI on lag 3 (OR=2.792; 95%CI:1.115–6.993, p=0.028) during the cold season. Our study suggests that exposure to NO2 (during warm season) and PM10 (during cold season) may contribute to a higher risk of poor prognosis in patients with STEMI.

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr⁻¹. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.

Unmanned Aerial Vehicle (UAV) based remote sensing (RS) studies in glaciology are mainly focusing on obtaining accurate high-resolution data from UAV images. Studies for identifying and minimising the challenges faced during the UAV-based RS data acquisition survey on inaccessible and harsh terrains of mountain glaciers is limited. This study aims to examine the practical challenges faced during UAV surveys of glaciers and derive strategies to minimize them. To the authors' knowledge, this is the first study that addresses such problems over the Himalayan region. Here, the UAV surveys were conducted using a fixed-wing commercial-grade off-the-shelf UAV (eBee plus, SenseFly) on three glacier sites (East Rathong, Hamtah and Panchinala-A) located in different zones and climate regimes lying within the Indian part of Himalayas. From UAV collected photos, the study was able to generate ultra-high-resolution ortho-mosaicked images and Digital Elevation Models (DEMs) at 0.1m GSD. UAV-derived DEMs was able to achieve vertical (horizontal) accuracy of 0.45 and 0.21 m (0.15 and 0.1 m) with 3 and 6 ground control points (GCPs) for an area of 0.75 km2 and 1.38 km2. Accuracy assessment of UAV DEMs generated with and without GCPs indicate that GCPs are must to obtain decimetre level accurate DEM especially on glaciers with steep-valleyed terrains. The utility of the obtained ultra-high-resolution ortho-mosaicked images was demonstrated by generating glacier surface feature maps. Based on the challenges observed during UAV surveys, the study identifies and recommends best-suited locations on a glacier and its adjacent regions for conducting UAV surveys efficiently in the glaciated terrain of Himalayas and possibly beyond. Recommendations reported in this article shall minimise the challenges faced and involved risks for data acquisition and thus enable UAVs to cover more glaciated area successfully.

The acquisition of high resolution morpho-bathymetric data on the Calabro Tyrrhenian continental margin (Southern Italy) enabled us to identify several mass-wasting processes, including shallow gullies, shelf-indenting canyons and landslides. In particular, we focus our attention on submarine landslides occurring from the coast down to -1700 m, with mobilized volumes ranging from some hundreds up to tens of millions of cubic meters. These landslides also show a large variability of geomorphic features in the headwall, translational and toe domain. Based on their morphology and distribution, four main classes of coastal/submarine landslides have been recognized: a) rocky coastal/shallow-water failures characterized by large hummocky deposits offshore; b) large-size and isolated scars with associated landslide deposits, mostly occurring on open slope environment and lower part of tectonically-controlled escarpments; c) a linear array of coalescent and nested landslide scars occurring in the upper part of tectonically-controlled escarpments and canyon flanks; d) a cauliflower array of small and coalescent scars occurring in canyon headwall. The latter two classes of landslides are also characterized by a marked retrogressive evolution and their landslide deposits are generally not recognizable on the morpho-bathymetric data. By integrating the morpho-bathymetric dataset with the results of previous studies, we also discuss the main factors controlling the variability in size and morphology of these submarine landslides to provide insights on their failure and post-failure behavior.

In several Indonesian tropical cities, such as Makassar City, frequent flooding during the peak rainy season poses a significant challenge. This study seeks to address this issue by devising effective policies to reduce flood exposure. The main obstacle lies in accurately determining the flood inundation area. To overcome this, the study utilizes multiple satellite data sources on a cloud platform, integrating them with land use data, and DEM data, aligning these with the local government's urban land use plan and existing drainage networks. The research aims to quantify the affected area, identify the predominant inundated land cover, and assess the efficiency of Makassar's drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Mangala sub-districts results in severe flooding, encompassing a total area of 35.28 km². The most affected land use type is cultivation, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km², is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions

Objective: To explore the application effect of Atropine injection combined with nursing intervention on the interventional treatment of PATENTS with ST-segment elevation myocardial infarction. Methods: A total of 60 patients with ST-segment elevation myocardial infarction treated in our hospital from January 2022 to January 2023 were selected as the research objects. According to different nursing methods, they were divided into conventional group (n = 30) and systematic group (n = 30), both groups were treated with percutaneous coronary intervention (PCI). The conventional group received routine nursing before and after operation. On the basis of routine nursing, systematic nursing cooperation was given in the system group. The time of opening infarct vessels, the incidence of total adverse reactions during interventional treatment, mortality, success rate of treatment, improvement of cardiac function and length of hospital stay were compared between the two groups. Results: The time of opening infarct vessel in system group was significantly better than that in routine group (P < 0.05). The total adverse reaction rate in system group was significantly lower than that in routine group (P < 0.05); The mortality of routine group was significantly higher than that of system group (P < 0.05). The success rate of system group was significantly higher than that of routine group (P < 0.05); The number of patients with improved cardiac function and length of hospital stay in system group were significantly better than those in routine group (P < 0.05). Conclusion: Good intraoperative cooperation and nursing is an important guarantee for patients with ST-segment elevation myocardial infarction, which can effectively reduce the occurrence of adverse reactions during interventional treatment, improve the success rate of treatment, and reduce the death of patients during interventional treatment.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

A waterbody detection technique is an essential part of digital elevation model (DEM) generation to delineate land-water boundaries and set flattened elevations. This paper describes the technical methodology for improving the initial tile-based waterbody data that are created during production of the ASTER GDEM, because without improvement such tile-based waterbody data are not suitable for incorporating into the new ASTER GDEM Version 3. Waterbodies are classified into three categories: sea, lake, and river. For sea-waterbodies, the effect of sea ice is removed to better delineate sea shorelines in high latitude areas, because sea ice prevents accurate delineation of sea shorelines. For lake-waterbodies, the major part of the processing is to set the unique elevation value for each lake using a mosaic image that covers the entire lake area. Rivers present a unique challenge, because their elevations gradually step down from upstream to downstream. Initially, visual inspection is required to separate rivers from lakes. A stepwise elevation assignment, with a step of one meter, is carried out by manual or automated methods, depending on the situation. The ASTER GWBD product consists of a global set of 1º latitude-by-1º longitude tiles containing water body attribute and elevation data files in geographic latitude and longitude coordinates and with one arc second posting. Each tile contains 3601-by-3601 data points. All improved waterbody elevation data are incorporated into the ASTER GDEM to reflect the improved results.

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill-merge-spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level-set method based on graph theory. The proposed level-set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level-set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine-resolution LiDAR-derived DEMs show that the raster-based level-set algorithm is much more efficient (~150 times faster) than the vector-based contour tree method. The proposed level-set algorithm has great potential for being applied to large-scale ecohydrological analysis and watershed modeling.

Assessing observation noise is a critical step in the development of a stochastic model for GNSS navigation and positioning. This process ensures that the statistical properties of the observational data are accurately characterized, leading to more reliable and precise positioning results. Traditionally, the one sigma values for observation noise, 0.3 m for code and 3 mm for carrier phase, alongside elevation-dependent weighting schemes, have been standard. However, these may no longer be suitable due to significant advancements in GNSS systems, receivers, and antennas. Previous research predominantly focused on code type and PPP techniques, often limited by the inability to separately assess observation types across different frequency bands due to ionospheric delay. Furthermore, these studies were generally constrained by limited experimental data. This research advocates for the use of ultra-short baselines to eliminate atmospheric delay and other error sources, providing a detailed assessment of observation noise for various frequency bands, GNSS systems, and receiver & antenna types over an extensive period and across multiple baselines. The findings suggest a need to reconsider traditional one sigma values and elevation-dependent weighting schemes for current applications.

Digital Elevation Model (DEM) is a representation of elevation data that represent terrain with or without overlaying objects of the Earth. It is the ideal and most widely used method for determining topography. DEMs are generated from various techniques such as traditional Surveying, Photogrammetry, InSAR, LiDAR, Clinometry and radargrammetry. It has been observed that mostly LiDAR-generated DEMs provide the best accuracy. The unavailability of LiDAR data in most of the region restricts global researchers from high-resolution and accurate DEMs. The recent launch of ICESat-2 with a 13m beam footprint and 0.7m pulse interval, promises elevations at high orbital precision. Its accuracy is of the order of few centimeters in complex topography, because of this ICESat-2 proves to be a good source to generate high-accuracy DEMs. ICESat-2 provides discrete photon data with elevations of points on the Earth’s surface. Traditional interpolation techniques tend to over-smooth the estimated space and still are unable to justify the complicated continuity in the topographical data. Machine learning algorithms are widely being used to extract patterns and spatial extent in geographic data. To estimate a DEM from ICESat-2 LiDAR point data, machine learning regression algorithms are implemented in this study. The present study has been performed for a plain region of Ghaziabad, Uttar Pradesh, India. Studies have shown that Cartosat-1 DEM V3 R1 product provides an accuracy of the order of 2m in predominantly plain regions, hence taken for this region. Current work focuses on comparing various regression-based machine-learning techniques to interpolate DEM from ICESat-2 data The RMSE of the interpolated DEM resulted from the Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, and Multi-Layer Perceptron Regressor was 7.13m, 7.01m, 7.15m, and 3.76m, respectively when evaluated against the TANDEM-X DEM of the same region. The MLP Regressor is found to perform the best among the four algorithms tested.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Backgroundː Previous studies have shown that both serum creatinine and D-dimer levels were associated with atherosclerotic coronary artery disease (CAD). We aimed to determine whether DCR is associated with coronary Gensini score in patients with ST-elevation myocardial infarction (STEMI). Methodsː 337 STEMI patients with complete D-dimer and creatinine and other necessary information were included in the analysis. According to the values of the DCR, patients were divided into the lower DCR group (DCR ≤ 1.42, n = 173) and the higher DCR group (DFR > 1.42, n = 174), and the differences between the two groups were compared. Multivariate linear and multivariate logistic regression analyses were performed to determine independent predictors of Gensini score. Resultsː High DCR group had higher Gensini score compared with low DCR group (P < 0.05). DCR was positively correlated with Gensini score (r=0.493, P < 0.001). Multiple linear regression analysis showed that Previous MI (r=11.312, P=0.035) and DCR (r=5.129, P<0.001) were independent risk factors associated with Gensini score. Multivariate logistic regression analysis showed that, compared to the group 1, DCR is independent risk factor for Group 2, Group 3, Group 4 (P <0.001). DCR is positively correlated with coronary Gensini score in STEMI patients and can be used as an independent predictor of higher Gensini score. Conclusionsː As a new and useful clinical marker, DCR is positively correlated with coronary Gensini score in STEMI patients and can be used as an independent predictor of higher Gensini score.

Degradation of environment quality is currently the prime cause of the recent occurrence of natural disasters; it also contributes in the increase of the area that is prone to natural disasters. This research is aimed to map the potential of areas around Pesanggrahan river in DKI Jakarta by segmenting the Digital Elevation Model derived from LIDAR data. The objective of this segmentation is to find the watershed lines of the DEM image. Data processing in this research is using LIDAR data which take the ground surface data, which is overlaid with Jakarta river map and subsequently, the data is then segmented the image. The expected result of the research is the flood potential area information, especially along the Pesanggrahan river in South Jakarta.

Background: As a relatively high number of ST-segment elevation myocardial infarction (STEMI) patients develop major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI), our aim was to determine the significance, and possible predictive value of QRS complex width and ST-segment elevation. Methods: Our patient sample included 200 PCI-treated STEMI patients, which were divided into two groups, based on the duration of symptoms: (I) less than 6 hours, and (II) 6-12 hours. For every patient, ECG was performed at 6 different time points. Long-term follow-up lasted 6 years from the STEMI onset. Statistical analysis was performed. Results: The mean age was 60.6±11.39 years, and 142 were male. Wider QRS complex, more pronounced ST-segment elevation, and more frequently absent ST-segment resolution were more common in the second group. Presentation of MACE was more frequent in this group. An increase of pre-PCI QRS complex width by 1 msec increases the risk of 6-year MACE by 3%, and an increase in pre-PCI ST-segment elevation by 1mm increases the risk of 6-year MACE by 3.26 times. Conclusion: To the best of our understanding, this study represents a pioneer research that utilized these ECG elements in the context of predicting a 6-year MACE. A wider QRS complex and a more pronounced ST-segment elevation were associated with longer total ischemic time. More research is needed to confirm these findings.

The purpose of the study was to identify factors characterizing a decrease in LV global systolic function in patients with ST-segment elevation myocardial infarction (STEMI) after revascularization using cardiac magnetic resonance imaging (MRI)-based ischemic injury pattern and laboratory parameters sensu left ventricular global systolic function. A total of 109 STEMI patients were examined. The patients underwent contrast-enhanced cardiac MRI with a 1.5 Tesla GE SIGNA Voyager (GE HealthCare, USA) on the 7th-10th days from the onset of the disease. According to cardiac MRI analysis, the patients were divided into the following groups with regard to left ventricular ejection fraction (LVEF) values: group 1 – patients with LVEF ≥50%; group 2 – patients with mildly reduced LVEF 40-49%; group 3 – patients with low LVEF <40%. A predominance of most parameters of the ischemic injury pattern was noted in patients with mildly reduced and low LVEF versus patient group with LVEF ≥50%. Some risk factors for a decrease in LVEF <50% systolic function in STEMI patients after revascularization were revealed: male gender; time from the onset of the anginal attack to revascularization; coronary artery status; several LV parameters; ischemic injury characteristics; natriuretic peptide and troponin I levels.

Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack (open-access) monitoring observations needed to signal whether further actions are needed, and hence to learn by doing. This submission presents an open access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts ("notches") in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems, as well as developing guidelines to better design future restoration efforts with foredune notching.

Interferometry Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique for studying the earth's surface topography and deformations. It is used to generate high-quality Digital Elevation Models (DEMs). DEMs are a crucial and primary input to various topographical quantification and modelling applications. The quality of input DEMs can be further improved using fusion methods, which combine multi-sensor or multi-temporal datasets intelligently to retrieve the best information amongst the input data. This research study is based on developing a Neural Network based fusion approach for improving InSAR based DEMs in plain and hilly terrains. The study areas comprise of relatively plain terrain from Ghaziabad and hilly terrain of Dehradun and their surrounding regions. The training dataset consists of DEM elevations and derived topographic attributes like slope, aspect, topographic position index (TPI), terrain ruggedness index (TRI), and vector roughness measure (VRM) in different land use land cover classes of the study areas. The spaceborne altimetry ICESat-2 ATL08 photon data is used as a reference elevation. A Feed Forward Neural Network with backpropagation algorithm is trained based on the prepared training samples. The trained model produces fused DEMs by learning the relationship between the input and target samples. This is used to predict elevations in the test areas. The accuracy of results from the models are assessed with TanDEM-X 90 m DEM. The fused DEMs show significant improvement in terms of RMSE over the input DEMs with improvement factor of 94.65 % in plain area and 82.62 % in hilly area. The study concludes that the ANN with its universal approximation property is able to significantly improve the fused DEM.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Space-based Interferometric Synthetic Aperture Radar (InSAR) applications have been widely used to monitor the cryosphere over past decades. Because of temporal decorrelation, interferometric coherence often severely degrades on fast moving glaciers. In addition, higher sensitivity ambiguity occurs in large baseline configurations, which are needed for extracting topographic information over low relief areas such as the surface of a glacier. TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) observations, which overcome the temporal decorrelation because of their simultaneous measurements by two satellite constellations, have used a short baseline sufficient for generation of excellent digital elevation models in most locations around the world. However, it remains difficult to estimate detailed topographic characteristics over low slope glacier surfaces because of the relatively less sensitive height ambiguity from the small baselines. In this study, we used the TanDEM-X pursuit monostatic mode with large baseline formation following a scientific phase timeline to develop highly sensitive topographic elevation models of the Petermann Glacier of Northwest Greenland. As expected, coherent interferometric phases (> 0.8) were well maintained over the glaciers despite their fast movement thanks to the nearly simultaneous TanDEM‐X measurements. The height ambiguity, which defined as the altitude difference correspondent to phase change of flattened interferogram, of the dataset was 10.63 m, which is favorable for extracting topography in a low relief region. We validated the TanDEM‐X derived glacial topography by comparing it to the SAR/Interferometric radar altimeter observations acquired by CryoSat‐2 and the IceBridge Airborne Topographic Mapper laser altimeter measurements. Both observations showed very good correlation within a few meters of the offsets (‐12.5 – ‐3.1 m) with respect to the derived glacial topography. Because of highly sensitive ambiguity, we could successfully extract detailed geomorphological features on the glaciers. Routine TanDEM-X observations will be very useful to better understand the dynamics of glacial movements and topographic change.

The catastrophic failure of two dams in Libya in the night of 10th and 11th September 2023, resulted in a significant amount of water and debris destroying a large part of the city of Derna, which is located downstream of the two dams. The flood caused more than 6000 fatalities and the dis-placement of thousands of people from the city. The dams failed due to heavy rainfall caused by the Medicane Daniel, resulting in intense precipitation and the dams overspilling after reaching full capacity. This paper analyses the consequences of the dam breach by modelling the flow discharge and the flooding of the city of Derna. The analysis compares the computed quantities with the findings of the post-event satellite image analysis from UNOSAT and Copernicus, the field reports, and information gathered from the social media through AI detection. The results allows to better understand the dynamics of the dam breach, its initial parameters and an assessment of the accu-racy of the results.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

Little is known about how differences in water availability within the “super humid” tropics can influence the distribution of understory plant species and the composition of understory plant communities. We investigated variation in the physiological drought tolerances of understory plants and plant communities across a large elevation and precipitation gradient. We established 58 understory plots along a gradient of 400 – 3600 m asl elevation and 1000 – 6000 mm yr-1 rainfall in and around Manu National Park in southeastern Peru. Within the plots, we sampled all understory woody plants and measured three metrics of physiological leaf drought tolerance - turgor loss point (TLP), cuticular conductance (Gmin), and solute leakage (SL) - and assessed how the community-level means of these traits related to mean annual precipitation (MAP) and elevation (in the study gradient temperature decreases linearly and vapor pressure deficit increases monotonically with elevation). We did not find any correlations between the three metrics of leaf drought tolerance, suggesting that they represent independent strategies for coping with low water availability. Despite being widely used metrics of leaf drought tolerance, neither TLP nor Gmin showed any significant relationships with elevation or MAP. In contrast, SL, which has only recently been developed for use in ecological field studies, increased significantly at higher precipitations and at lower elevations (i.e., plants in colder and drier habitats have lower SL, indicating greater drought tolerances). Our results illustrate that differences in water availability may play a strong role in structuring plant communities even in in the wet tropics and highlight the potential for SL assays to be an efficient and effective tool for measuring drought tolerances in the field.

Covid-19 pandemic affected outcome in ST-elevation myocardial infarction (STEMI) patients in majority of countries. We aimed to assess retrospectively admissions, treatments, complications and mortality of STEMI patients in the northeast of Slovenia in the first (March-May 2020) and the second wave (October-December 2020) of Covid-19 pandemic and compare them with the data from 2019 (March-October). Comparing 2019 and both waves of Covid-19 pandemic we observed nonsignificant differences in the number of STEMI admissions, in baseline characteristics, in the use of primary percutaneous coronary intervention (PCI), either within the first 3 (40.5%* vs 30.2% vs 25%*, *p = 0.074) and 6 hours, nonsignificant differences in TIMI III flow after primary PCI and in hospital complications, except for significant increase in hospital heart failure (23.3% vs 42%, p = 0.015) and mitral regurgitation in the second wave (10% vs 26.9%, p = 0.008) of the pandemic with nonsignificant increase in hospital mortality (8.9% vs 9.4% vs 13.6%) in both waves of the pandemic. We conclude, that with increased severity of Covid-19 pandemic in the second wave there was a longer delay to primary PCI in STEMI patients, resulting in significantly increased hospital heart failure and nonsignificantly increased hospital mortality

Structure from Motion with Multi-View Stereo photogrammetry (SfM) is increasingly utilised in geoscience investigations as a cost-effective method of acquiring high resolution (sub-meter) topographic data, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via an Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via a handheld digital camera, ‘Ground’) SfM in modelling a hillslope gully system in dry-tropical savanna, and to assess the strengths and limitations of the approach at different scales. A UAV survey covered an entire hillslope gully system (0.715 km²), whereas a Ground survey covered a single gully within the broader system (650 m²). SfM topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate UAV SfM can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., 0.1 m resolution with ~ 0.5 – 1.3 m elevation error), while ground-based SfM is more capable of quantifying gully morphology (e.g., 0.01 m resolution with ~ 0.1 m elevation error). Key strengths of SfM for these applications include: the production of high resolution 3D topographic models and ortho-photo mosaics, low survey instrument costs (< $AUD 3,000); and rapid survey time (4 and 2 hours for UAV and Ground survey respectively). Current limitations of SfM include: difficulties in reconstructing vegetated surfaces; uncertainty as to optimal survey and processing designs; and high computational demands. Overall, this study has demonstrated great potential for SfM to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in tropical savanna landscapes and elsewhere.

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

This study examines the activities conducted at the archaeological site of Aptera in Crete, Greece. The research was part of the DIACHRONIC LANDSCAPES International Design Workshop, organized by the CAM (Center for Mediterranean Architecture), TUC (Technological University of Crete School of Architecture), and UNIFE (University of Ferrara Department of Architecture). This article outlines the methods used for data acquisition and processing on a territorial scale, which generated digital outputs necessary for the analysis and design phases of the workshop, as well as for further examination of the results. The collected data, obtained through low-cost aerial photogrammetric surveying and GNSS terrestrial coordinate detection, were integrated in a Structure from Motion workflow that led to the creation and exportation of various digital outputs, such as point clouds, DTM, DSM, orthophotos, and contour lines. An accuracy analysis was performed to evaluate the effectiveness and efficiency of the digital models compared to the implemented surveying strategies, including the Ground Control Point and Quality Check Point marker positioning strategy. The resulting digital models proved to be valuable assets for analysis and design within the workshop and provided insightful prospects for future research and territorial-scale projects.

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aims of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant Arabis alpina in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs rivalled the accuracy of LiDAR, putting the current paradigm of LiDAR being the more accurate of the two methods into question. We obtained DEMs at spatial resolutions of 6.25cm-8m for PHOTO and 50cm-32m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, we found that SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50cm in such studies.