An updated, binary-coded message has been developed for transmission to extraterrestrial intelligences in the Milky Way galaxy. The proposed message includes basic mathematical and physical concepts to establish a universal means of communication followed by information on the biochemical composition of life on Earth, the Solar System’s time-stamped position in the Milky Way relative to known globular clusters, as well as digitized depictions of the Solar System, and Earth’s surface. The message concludes with digitized images of the human form, along with an invitation for any receiving intelligences to respond. Calculation of the optimal timing during a given calendar year is specified for potential future transmission from both the Five-hundred-meter Aperture Spherical radio Telescope in China and the SETI Institute’s Allen Telescope Array in northern California to a selected region of the Milky Way which has been proposed as the most likely for life to have developed. These powerful new beacons, the successors to the Arecibo radio telescope which transmitted the 1974 message upon which this expanded communication is in part based, can carry forward Arecibo’s legacy into the 21st century with this equally well-constructed communication from Earth’s technological civilization.

Current hydrogen production is based on the reforming process leading to the emission of pollutants; therefore, a substitute production method is imminently required. Water electrolysis is an ideal alternative for large-scale hydrogen production, as it does not produce any carbon-based pollutant byproducts. Production of green hydrogen from water electrolysis using intermittent sources (e.g., solar, eolic) would facilitate clean energy storage. However, the electrocatalysts currently required for water electrolysis are noble metals, making this potential option expensive and inaccessible for industrial applications. Therefore, there is a need to develop electrocatalysts based on earth-abundant and low-cost metals. Nickel-based electrocatalysts are a fitting alternative because they are economically accessible. Extensive research has focused on developing nickel-based electrocatalysts for hydrogen and oxygen evolution. Theoretical and experimental work have addressed the elucidation of these electrochemical processes and the role of heteroatoms, structure, and morphology. Even though some works tend to be contradictory, they have lit up the path for efficient nickel-based electrocatalysts. For these reasons, herein, a review of recent progress is presented.

Based on the cloud platform of Google Earth Engine (GEE), this study selected Landsat 5/8 and Sentinel-2 remote sensing images and used Support Vector Machine (SVM) classification method to classify the 35 years of intertidal salt marshes in China, and verified the classification results in combination with field survey. Finally, combining with various driving factors, the reasons and laws affecting the changes of salt marshes species and area were discussed and analyzed. The main results of the study are as follows:The main types of salt marshes plants in China include Phragmites australis, Spartina alterniflora, Suaeda salsa, Scirpus mariquete, Tamarix chinensis, Cyperus malaccensis and Sesuvium portulacastrum. The results salt marshes classification indicated that 166999.32 ha in 1985, 172893.87 ha in 1990, 174952.29 ha in 1995, 125567.51 ha in 2000, 93257.97 ha in 2005, 102539.04 ha in 2010, 96302.92 ha in 2015, and 115722.75 ha in 2019. The main driving factors of salt marsh change from 1985 to 2015 are reclamation, mudflat aquaculture, climate change, coastal zone erosion, invasion of alien species, and natural competition and succession among salt marshes species. The results can be used to quantitatively analyze the salt marshes carbon storage in space and time, and provide data support for the protection of salt marsh wetlands, the restoration of ecological functions and the implementation of "carbon neutral".

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (R_{NDVI_TM(i)}), the NDVI median phenology standardization model (R_{NDVI_AM(i)(j)}), and the NDVI median spatiotemporal standardization model (R_{NDVI_ZM(i)(j)}). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for R_{NDVI_TM(i)}, R_{NDVI_AM(i)}, and _(j)R_{NDVI_ZM(i)(j)}, respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and were applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community.

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The synthesizing of ongoing satellite missions by ESA (Sentinel 2) and NASA (Landsat7/8) provides this unprecedented opportunity at a global scale; nonetheless, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a complete stream processing in the Google Earth Engine cloud platform to generate harmonized surface reflectance images of Landsat7,8 and Sentinel 2 missions. The harmonized images were generated for two agriculture schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during the period 2018-2019. We evaluated the performance of several pre-processing steps needed for the harmonization including image co-registration, brdf correction, topographic correction, and band adjustment. This study found that the miss-registration between Landsat 8 and Sentinel 2 images, varied from 10 meters in Ninh Thuan, Vietnam to 32 meters in Bekaa, Lebanon, and if not treated, posed a great impact on the quality of the harmonized dataset. Analysis of a pair overlapped L8-S2 images over the Bekaa region showed that after the harmonization, all band-to-band spatial correlations were greatly improved from (0.57, 0.64, 0.67, 0.75, 0.76, 0.75, 0.79) to (0.87, 0.91, 0.92, 0.94, 0.97, 0.97, 0.96) in bands (blue, green, red, nir,swir1,swir2, ndvi) respectively. We demonstrated that dense observation of the harmonized dataset can be very helpful for characterizing cropland in highly dynamic areas. We detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns (likely cycles of paddy rice) in Ninh Thuan province only during the year 2018. We fitted the temporal signatures of the NDVI time series using harmonic (Fourier) analysis. Derived phase (angle from the starting point to the cycle's peak) and amplitude (the cycle's height) were combined with max-NDVI to generate an R-G-B image. This image highlighted croplands as colored pixels (high phase and amplitude) and other types of land as grey/dark pixels (low phase/amplitude). Generated harmonized datasets that contain surface reflectance images (bands blue, green, red, nir, swir1, swir2, and ndvi at 30 meters) over the two studied sites are provided for public usage and testing.

The genetic bases of Alzheimer's disease (AD) and frontotemporal dementia (FTD) have been comprehensively studied, which is not the case for atypical cases not classified into these diagnoses. In the present study, we aim to contribute to the molecular understanding of the development of non-AD and non-FTD dementia by surveying rare pathogenic variants. The analysis was performed on 91 patients, the DNA samples of which were pooled and then subjected to whole exome sequencing. Among detected variants two are rare pathogenic and show significant difference in frequency between the analyzed pool and gnomAD Bulgarian control exomes. The rs373478202, G>T, B9D1 variant, most likely present in heterozygous state; is causative for the autosomal recessive ciliopathy- Joubert syndrome, which involves specific CNS malformation. The rs148918985, C>T, ASS1 variant leads to arginosuccinate enzyme deficiency and in compound heterozygosity with other rare pathogenic variant detected in the same gene causes the late-onset form of citrullinemia type I. This disorder causes high ammonia levels, which can lead to cerebral dysfunction. In conclusion, the detected rare pathogenic variants suggest that, in certain cases, the development of non-AD and non-FTD dementia can be mediated by structural alterations in neuronal cilia or by hyperammoniemia.

The experiments described in this research article were designed to test the effect of rare variants into genomic prediction in dairy cattle. Common polymorphisms are able to explain only a small proportion of the underlying genetic variation of complex phenotypes. Variants representing functional mutations with large effects on complex phenotypes are expected to be rare due to natural (humans) or artificial (livestock) selection pressure. Therefore, it is important to check whether the use of rare variants could increase the accuracy of ranking of animals by providing the tool for more precise differentiation among the bulls with high additive genetic merit. The goal of our study was to verify whether including rare variants in a genomic selection model allows for a more accurate description of the additive genetic background of traits under selection in dairy cattle. We used the linear mixed model for comparison SNP estimates for Holstein-Friesian cattle of the two data sets – a set containing only single nucleotide polymorphisms defined by minor allele frequency ≥ 0.01, which is routinely used in the Polish genomic evaluation system (46,216 SNPs), and a set containing SNPs selected based only on the call rate (54,378 SNPs). Based on the SNP estimates we also calculated DGV and GEBV and compared them between both data sets. In all the analyses we used production, fertility, conformation and udder health traits. We also assessed the time required for the two most computationally demanding components of genomic selection: preparing genotype data, and estimation of SNP effects between those two data sets. The results of our study indicated that the analysis including rare variants resulted in changes in the individual ranking of the top 100 male and female candidates, but had no effect on the outcome of the quality of EBV prediction as expressed by the Interbull validation test.

Salvia ceratophylloides (Ard.) is an endemic, rare, threatened plant species recently rediscovered in very few individuals in two different sites of South Italy. The study of within-plant variation more than among-plant one is fundamental to understand the plant adaptation to the local conditions, especially in rare species, and consequently to preserve plant biodiversity. Here, we reported the variation of the morpho-ecophysiological and metabolic traits between the sessile and petiolate leaf of S. ceratophylloides plants in two different sites for understanding the adaptation strategies for surviving in these habitats. The S. ceratophylloides individuals exhibited different net photosynthetic rate, maximum quantum yield, light intensity for the saturation of the photosynthetic machinery, stomatal conductance, transpiration rate, leaf area, fractal dimension and some VOCs between the different leaf types. This within-plant morpho-physiological and metabolic variation was depended on the site. These results provide empirical evidence of sharply within-plant variation of the morpho-physiological traits and VOCs profiles in S. ceratophylloides which could be because of adaptation to the local conditions.

Searching for neutrinoless double beta decay is a top priority in particle and astroparticle physics, being the most sensitive test of lepton number violation and the only suitable process to probe the Majorana nature of neutrinos. In order to increase the experimental sensitivity for this particular search, ton-scale detectors operated at nearly zero-background conditions with a few keV energy resolution are required. In this scenario, cryogenic detectors have proven effective in addressing many of these issues simultaneously. After long technical developments, the CUORE experiment established the possibility to operate large scale detectors based on this technology. Parallel studies pointed out that scintillating cryogenic detectors represent a suitable upgrade for the CUORE design, directed towards higher sensitivities. In this work, we review the recent development of cryogenic detectors, starting from the status of the art and outlying the path toward next-generation experiments.

Pyrochlore group minerals are the main raw phases in granitic rocks of the Katugin complex-ore deposit that stores Nb, Ta, Y, REE, U, Th, Zr, and cryolite. They are of three main generations: primary magmatic (I), early postmagmatic (II), and supergene (III) pyrochlores. The primary magmatic phase (generation I) is fluornatropyrochlore with high concentrations of Na2O (to 10.5 wt.%), F (to 5.4 wt.%) and REE2O3 (to 17.1 wt.%) but low CaO (0.6-4.3 wt.%), UO2 (to 2.6 wt.%), ThO2 (to 1.8 wt.%), and PbO (to 1.4 wt.%). Pyrochlore of this type is very rare in nature and limited to a few occurrences, such as rare-metal deposits of Nechalacho in syenite and nepheline syenite (Canada) and Mariupol in nepheline syenite (Ukraine). It may have crystallized synchronously with or slightly later than melanocratic minerals (aegirine, biotite, and arfvedsonite) at the late magmatic stage when Fe from the melt became bound making impossible the formation of columbite. Second generation pyrochlore formed at the early postmagmatic stage of the Katugin deposit. It differs from that of first generation in lower Na2O concentrations (2.8 wt.%), relatively low F (4 wt.%), and les occupancy of the A and Y sites at similar contents of other components. Generation III pyrochlore is a product of supergene alteration processes. It is compositionally heterogeneous and contains K, Ba, Pb, Fe, and significant Si concentrations but low Na and F. Its compositions mostly fall within the filed of hydro- and kenopyrochlore.

The separation and enrichment can be targeted to enrich the rare and precious metals in fly ash and reduce the cost of leaching and recovering of fly ash. Regarding their different properties, the single-component separation was used to obtain uncompleted burned carbon, glass microbeads, minerals, and other characteristic components from the ash. Also, the mineral composition of each component was analyzed by electron microscopy. The metal minerals were mainly concentrated in the mineral components. Besides, the electron probe micro-analysis shows that the Pt content in the minerals of fly ash was significantly correlated with the metal contents of Ni and Cu. After the obtainment of the characteristics of fly ash metal enrichment, the heavy minerals with Cu, Ni, Pt, Pd, and other target metal elements were enriched by gravity separation and flotation. The enrichment coefficients of Cu, Ni, Pt, and Pd were 1.45, 1.33, 1.90 and 1.60, respectively, and the recovery rates were 77%, 81%, 97% and 88% respectively. Since the yield of heavy minerals obtained by separation was 62.24%, it indicated the physical separation method could significantly reduce the cost of leaching and recovering of fly ash metal resources.

Raw earth bricks made from the soil of the Chalky Champagne region (France) have been used for at least two millennia in construction, a promising heritage in the context of reducing the carbon emissions of buildings. The present experimental study aims at measuring the physical, mechanical, thermal, and hydric properties of adobes collected from a local village barn. Results show a high chalk content estimated at 71%, and a clay content, acting as binder, at 14%. Despite limited load-bearing capacity, these lightweight adobes are suitable for current single-story constructions, while their hydrothermal properties classify them as excellent moisture regulators for occupants. In association with other bio-sourced materials such as starch-beet pulp bricks, Chalky Champagne adobes yield promising insulating properties, and meet the criteria defined by current energy standards.

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30-m spatial resolution in the typical Mediterranean Ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10-m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10 %; commission and omission errors < 8.20 %; Dice coefficients > 91.90 %, and relative biases < 3.44 %. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most extensive and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution for further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

Cuba is struggling with a growing environmental problem: the uncontrolled spread of the allochthonous weed species marabou (Dichrostachys cinerea) throughout the country. Over the last 70 years, marabou has become a formidable invasive species that poses a threat to Cuban biodiversity and agricultural productivity. In this paper, we present a free and affordable method for regularly mapping the spatial distribution of the marabou based on the Google Earth Engine platform and ecological surveys. To test its accuracy, we develop an 18-year analysis (2000–2018) of marabou dynamics using the Valle de los Ingenios, a Cuban UNESCO World Heritage Site, as an experimental model. Our spatial analysis reveals clear patterns of marabou distribution and highlights areas of concentrated growth. Temporal trends illustrate the dynamic nature of the species, identifying periods of expansion and decline. In addition, our system is able to detect specific, large-scale human interventions against the marabou plague in the area. The results highlight the urgent need for remedial strategies to maintain the fragile balance in the region.

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of B factor at the regional scale is fundamental in predicting regional soil erosion and the dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B factor from Sentinel-2 data and proposed a new method, namely trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat-7, Landsat-8). We then calculated and mapped the B-factor in Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall erosivity and rainfall, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10-m resolution land use data, enhancing the B factor calculation accuracy from the region to the spot level. The B factor computation procedure developed in this study is applicable to various river basin and regional scales for regional soil erosion monitoring.

Global Earth Observation (EO) is becoming increasingly important in understanding and addressing critical aspects of life on our planet about environmental issues, natural disasters, sustainable development and others. EO plays a key role in making informed decisions on applying or reforming land use, responding to disasters, shaping climate adaptation policies etc. EO is also becoming a useful tool for helping professionals make the most profitable decisions, e.g., in real estate or the investment sector. Finding similarities in landscapes may provide useful information regarding applying contiguous policies, taking alike decisions or learning from best practices on events and happenings that have already occurred in similar landscapes in the past. However, current applications of similar landscape retrieval are limited by moderate performance and the need for time-consuming and costly annotations. We propose splitting the similar landscape retrieval task into a set of smaller tasks that aim at identifying individual concepts inherent to satellite images. Our approach relies on several models trained with Unsupervised Representation Learning (URL) on Google Earth images to identify these concepts. We show the efficacy of matching individual concepts for tackling the task of retrieving similar landscape(s) to a user-selected satellite image with a proof-of-concept application of the proposed approach on the geographical territory of the Republic of Cyprus. Our results demonstrate the efficacy of breaking up the landscape similarity task into individual concepts closely related to remote sensing instead of trying to capture all concepts and image semantics with a single model like a single RGB semantics model.

In recent years, the concept of Fiducial Reference Measurements (FRM) has been developed to highlight the need for precise and well-characterised measurements tailored explicitly to the post-launch calibration and validation (Cal/Val) of Earth observation satellite missions. The confidence that stems from robust unambiguous uncertainty assessment of space observations is fundamental to assessing the changes in the Earth system and climate model prediction, and delivering the essential evidence-based input for policy makers and society striving to mitigate and adapt to climate change. The underlying concept of an FRM has long been a core element of a Cal/Val program, providing a ‘trustable’ reference against which performance can be anchored or assessed. The ‘FRM’ label was created to embody into such a reference a set of key criteria. These criteria included the establishment of documented evidence of uncertainty with respect to a community-agreed reference (ideally SI-traceable) and specific tailoring to the needs of a satellite mission. It, therefore, facilitates comparison and interoperability between products and missions in a cost-efficient manner. CEOS Working Group Cal/Val (WGCV) is now putting in place a framework to assess the maturity and compliance of a ‘Cal/Val reference measurement’ in terms of a set of community-agreed criteria which define it to be of CEOS-FRM quality. The assessment process is based on a maturity matrix that provides a visual assessment of the state of any FRM against each of a set of given criteria; making visible where it is mature and where evolution and effort needs to be done. This paper provides the overarching definition of what constitutes an FRM and introduces the new CEOS FRM assessment framework.

As a radiant light source within the dynamic range of most spacecraft payloads, the moon pro-vides an excellent reference for on-orbit radiometric calibration. This research hinges on the pre-cise simulation of lunar spectral irradiances and the Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradi-ances, utilizing datasets obtained from Lunar Reconnaissance Orbiter Camera (LROC). The re-search also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit space-craft calibration procedures, thereby boosting the fidelity of data gathered from satellite obser-vations.

Stray light is a critical aspect for high performance optical instruments. When stray light control by design is insufficient to reach the performance requirement, correction by post-processing must be considered. This situation is encountered for example in the case of the Earth observation in-strument 3MI, whose stray light properties are complex due to the presence of many ghosts dis-tributed on the detector array. We implement an iterative correction method and discuss its con-vergence properties. Spatial and field binning can be employed to reduce the computation time but at the cost of a decreased performance. Interpolation of the stray light properties is required to achieve high performance correction. For that, two methods are proposed and tested. The first interpolate the stray light in the field domain while the second applies a scaling operation based on a local symmetry assumption. Ultimately, the scaling method is selected and a stray light reduction by a factor of 58 is obtained at 2σ (129 at 1σ) for an extended scene illumination.

We present a plausible solution to the now forty seven year old paleoclimatology riddle of the so-called Faint Young Sun Paradox via the combined hypothesis of the conservation of the state of radiation balance between the Earth and Sun and that of an expanding Earth, where, in the face of a changing (increasing) Solar luminosity, the Earth would maintain steady temperatures by re-adjusting the height of its atmosphere. That is to say, depending on whether or not the radius of the solid Earth is changing, this re-adjustment of the height of the Earth’s atmosphere would mean two things — i.e.: (1) either the height increases — in which event the Earth accretes matter from its immediate surroundings (i.e., the obvious pool formed by the Solar wind) thereby increasing the mass of the Earth’s atmosphere, or: (2) the height decreases — in which event the Earth naturally expels matter from its atmosphere, thereby decreasing the effective mass of the Earth. We demonstrate that if –- as the current state of the art ITRF observations seem to indicate, namely that — the Earth’s landmass is steadily expanding globally at a paltry rate of ∼+0.45±0.05mm·yr−1, and, that the Earth’s atmosphere is to have a present radial vertical height of about one third of the Earth’s radius (∼2860km) from the Earth’s surface, then, one can (might) with relative ease, explain not only the presence of liquid water on the Earth’s surface some ∼3.20±0.70Gyr ago during the Archaean eon when the Sun was about 75% of its current luminosity, but also the present radial expansion rate of the Earth. When all is said and done, the Earth system is herein cast as an auto-self-regulating incubator where the auto-self-regulating mechanism is as a result of the Earth’s atmosphere responding by automatically re-adjusting its height.

The moon always use the same side to face toward the earth, but there is a dead angle in the mainstream theory of explaining this phenomenon. That is, it cannot explain why the moon doesn't rotate around the axis which is a straight line to connect the mass centers of the earth and moon. Because the numerous meteorite impact craters on the lunar surface indicate that the moon is completely possible to obtain external momentums and rotate around this axis. This paper proposes a plain explanation, that is, the universal gravitation between the earth and moon as well as the earth's magnetic field have formed a trinity restraint mechanism on the moon. According to this explanation, the moon's rotation can be locked, and the mechanism of lunar libration has been revealed out, which can also confirm mutually with the natural phenomenon that the moon has sought a balance in the swing. In addition, with the help of all kinds of detection data from the Apollo moon landings and other circumlunar spacecraft, as well as the studies and analysis of lunar soil samples, the conclusion is that as far as a whole for the moon, it belongs to paramagnetic substances, and its relative permeability is between 1.008 and 1.03. Although the magnetic flux density of the earth on the lunar orbit has been dropped below 0.0008125 nT or lower due to the impact of the solar wind, but it can be used as a reason to lock the moon without rotating around the axis which is a straight line to connect the mass centers of the earth and moon. If another main reason to cause the existence of this fact cannot be found, even if the magnetic flux density of the geomagnetism in lunar orbit is very small, it also should not be artificially ignored. In this regard, we can artificially change the intensity of the earth's magnetic field, and carefully observe the lunar libration and in the distance between the earth and the moon, to verify the arguments in this paper.

A proper transmission of the orientation between surface and underground workings, by means of vertical shafts, is an important challenge in the mining industry, especially when the mine exceeds 200 meters deep. In fact, this study is developed in a mine located to 700 meters deep. Likewise, this paper assesses the accuracy of this operation, in a case study, using the two shafts plumbing and gyroscope methods in order to compare and analyse the planimetric displacement of the base line due to different source of errors in each one. Upsides and downsides of both methods are analysed in the paper. Some disadvantages in each method have been reduced thanks to the technological progress, especially in the two shaft plumbing method. The different sources of error that affect the measures are thoroughly analysed in the study with the aim to compensate them and achieve the required precision for an underground infrastructure. Mine ventilation has been found as one of the most important sources of error in the plumbing method due to intake and return airflow. In this direction, the paper unfolds some measures to reduce the ventilation influence and details a compensation method to reduce ventilation errors.

This paper reports the efforts made and experiences gained in developing the Flood Prevention and Emergency Response System (FPERS) powered by Google Earth Engine, with focus on its applications at the three stages of floods. At the post-flood stage, FPERS integrates various remote sensing imageries, including Formosat-2 optical imagery, to detect and monitor barrier lakes, synthetic aperture radar imagery to derive an inundation map, and high-spatial-resolution photographs taken by unmanned aerial vehicles to evaluate damage to river channels and structures. At the pre-flood stage, a huge amount of geospatial data are integrated in FPERS and are categorized as typhoon forecast and archive, disaster prevention and warning, disaster events and analysis, or basic data and layers. At the during-flood stage, three strategies are implemented to facilitate the access of the real-time data: presenting the key information, making a sound recommendation, and supporting the decision-making. The example of Typhoon Soudelor in August of 2015 is used to demonstrate how FPERS was employed to support the work of flood prevention and emergency response from 2013 to 2016. The capability of switching among different topographic models and the flexibility of managing and searching data through a geospatial database are also explained, and suggestions are made for future works.

Reliable evapotranspiration (ET) estimation is a key factor for water resources planning, attaining sustainable water resources use, irrigation water management, and water regulation. During the past few decades, researchers have developed a variety of remote sensing techniques to estimate ET. The Earth Engine Evapotranspiration Flux (EEFlux) application uses Landsat imagery archives on the Google Earth Engine platform to calculate the daily evapotranspiration at the local field scale (30 m). Automatically calibrated for each Landsat image, the EEFlux application design is based on the widely vetted Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model and produces ET estimation maps for any Landsat 5, 7 or 8 scene in a matter of seconds. In this research we evaluate the consistency and accuracy of EEFlux products that are produced when standard US and global assets are used. Processed METRIC products for 58 scenes distributed around the western and central United States were used as the baseline for comparison. The goal of this paper is to compare the results from EEFlux with the standard METRIC applications to illustrate the utility of the EEFlux products as they currently stand. Given that EEFlux is derived from METRIC, differences are expected to occur due to differing calibration methods (automatic versus manual) and differing input datasets. The products compared include the fraction of reference ET (ETrF), actual ET (ETa), and surface energy balance components net radiation (Rn), ground heat flux (G), and sensible heat flux (H), as well as Ts, albedo and NDVI. The product comparisons show that the intermediate products of Ts, Albedo, and NDVI, and also Rn have similar values and behavior for both EEFlux and METRIC. Larger differences were found for H and G. Despite the more significant differences in H and G, results show that EEFlux is able to calculate ETrF and ETa values comparable to the values from trained expert METRIC users for agricultural areas. For non-agricultural areas such as semi-arid rangeland and forests, the automated EEFlux calibration algorithm needs to be improved in order to be able to reproduce ETrF and ETa that is similar to the manually calibrated METRIC products.

Coal gasification with carbon dioxide is a process for generating clean gaseous fuels and relieving greenhouse effect. Zhundong coal has high alkali and alkali earth metals (AAEMs) content, medium volatile and low ash in nature. Isothermal CO₂ gasification of char derived from Zhundong coal (R-char) and char from acid washing R-char (AR-char) are performed in thermo-gravimetric analyzer (TGA). The effect of AAEMs is investigated on the gasification behavior in the range of temperatures 1073 K to 1273 K. The carbon conversion increases rapidly with increasing reaction temperature and CO₂ concentration. R-char has high gasification rate and carbon conversion compared with AR-char. The accuracy of the free-model approach for calculating activation energy at different conversions is validated by compared with different kinetic models (volume reaction model, distributed activation energy model). Moreover, R-char gasification with CO₂ shows a compensation effect as the Arrhenius parameters (EA and k0) increase or decrease simultaneously.

Background: Modulator therapies improve weight and body mass index (BMI) in cystic fibrosis (CF) patients. We aimed to compare the nutritional risk index (NRI) in adult CF patients receiving modulator (MT) or only non-modulator (conventional) therapies (non-MT). Methods: A single-center prospective cohort study was conducted between June-December 2023. The NRI based on weight gain and albumin was calculated at beginning and end of a 12 weeks period in both groups. This design was pragmatic, since based on individual patient access to MT for 12 weeks. Results: 107 patients were included [mean (SD) age: 23.85 (4.98) years, 54.7% male, 46.7% MT]. In the MT group, mean (SD) weight (kg) and albumin (g/dL) increased significantly [changes: +3.09 (2.74) and +0.17 (0.37); p

An laboratory procedure has been developed to obtain lanthanum oxide from spent fluid catalytic cracking catalyst, commonly used in the cracking the heavy crude oil process. Two different spent fluid catalytic cracking catalysts, which are mainly formed by silica and alumina, and a certain amount of rare earths were leached under several conditions to recover the rare earth from the solids waste. Subsequently, liquid phases were subjected to a liquid-liquid extraction process, and lanthanum was quantitatively stripped using oxalic acid to obtain the corresponding lanthanum oxalates. After the corresponding thermal treatment, these solids were transformed into lanthanum oxide. Both, lanthanum oxalates and oxides solids have been characterized by wide techniques in order to investigate the purity of the phases.

From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), rare disease, and genomics. Over fifteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. A key component of these efforts has been the development of resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI). This paper presents findings from a survey conducted by the NBSTRN team with input from one of theNBSTRN's expert workgroups, the Bioethics and Legal Workgroup. With responses from 88 members of the NBS community including researchers and state NBS programs, the survey found that individuals rely on internal and collegial ELSI resources and highlighted a substantial demand for structured, accessible training and resources. The survey’s insights emphasize the importance of privacy and informed consent in NBS research, and suggested a need for improved collaborative tools and educational programs focused on ELSI for the NBS community. The survey results may inform future efforts in ELSI and NBS research including the development of expanded ELSI tools that leverage digital platforms and consultative services.

Background: Circulating rare cells participate in breast cancer evolution as systemic components of the disease and thus, are a source of theranostic information. Exploration of cancer-associated rare cells is in its infancy. We determined the validity of a rare-cell detection platform and demonstrated the usefulness of a certain rare cell subset as a novel approach to characterize the breast cancer system. Methods: Linearity of the Rarmax® platform was established using a spike-in approach. The platform includes red blood cell lysis, leukocyte depletion and high-resolution fluorescence image recording. Rare cell analysis was conducted on 28 samples (before and after surgery) from 14 patients with breast cancer, 20 healthy volunteers and 9 non-cancer control volunteers. In-depth identification of rare cells, including circulating tumor cells, endothelial-like cells, erythroblasts and inflammation-associated cells, was performed using a phenotype and morphology-based classification system. Results: The platform performed linearly over a range of 5-950 spiked cells, with an average recovery of 84.6%. Circulating epithelial and endothelial-like cell subsets have been demonstrated to be associated with or independent of cancer with tumor presence. Furthermore, certain cell patterns may be associated with treatment-related adverse effects. The sensitivity in detecting tumor-presence and cancer-associated abnormality before surgery was 50% and 85.7%, respectively and the specificity was 100% and 96.6%, respectively. Conclusion: The present study supports the idea of a cancer-associated rare cell abnormality to represent tumor entities as well as systemic cancer. The latter is independent of the apparent clinical cancer.

The time required to reach a correct diagnosis is one of the most important problems for rare disease (RD) patients. Diagnostic delay can be intolerably long, to the point that it is usually described as a “diagnostic odyssey” and, sometimes, a diagnosis might never occur. The International Rare Disease Research Consortium proposed, as ultimate goal for 2017-2027, to enable all people with a suspected RD to be diagnosed within one year if the disorder is known, and to enter a globally coordinated diagnostic and research pipeline for the unsolved cases. In-depth analysis of the genotype through next generation sequencing, together with a standardized in-depth phenotype description and sophisticated high-throughput approaches, have been applied as diagnostic tools to increase the chance of a timely and accurate diagnosis. This approach is very fruitful as, according to the Orphanet database, from 2010 to March 2017 more than 600 new RDs have been described and about 3600 genes linked to RDs have been identified. However, combination of -omics and phenotype data and international sharing of this information raise ethical concerns. Values to be assessed include not only patient autonomy but also family implications, beneficence, non-maleficence, justice, solidarity and reciprocity, which must be respected and promoted and, at the same time, balanced among each other. In this work we suggest that, to maximise patients involvement in the search for a diagnosis and identification of new causative genes, undiagnosed patients should have the possibility to: 1) actively participate in the description of their phenotype; 2) choose the level of visibility of their profile in matchmaking databases; 3) express their preferences regarding return of new findings, in particular which level of Variant of Unknown Significance (VUS) significance should be considered relevant to them. The quality of the relation between individual patients and physicians, and between the patient community and the scientific community is critically important for making the best use of available data and combining efforts in the search for matches with similar cases worldwide that will help to solve unsolved cases. The contribution of patients to collecting and coding data comprehensively is critical for efficient use of data downstream of data collection.

The scope of this work is to empirically check and prove the practical applicability of the Primordially Hydridic Earth (hereinafter PHE) concept for early exploration of the resources of naturally occurring hydrogen. With the PHE concept postulates interpreted within the local geological, tectonic, petrological and geophysical context, the reconnaissance plan, as well as the field exploration and data acquisition programs, were put together and implemented in the field. The results obtained from the surface (

The earth pressure acting on soilbag reinforced retaining structures subjected to surcharge loads under non-limited states, is crucial for designing these structures. In this study, the earth pressures are investigated through model tests of the soilbag reinforced retaining wall. Findings from these tests reveal a non-linear distribution of lateral earth pressure on the wall when subjected to surcharge loads in non-limited states, with an observed escalation in pressure corresponding to increased surcharge loads. Compression tests on soilbags were conducted to analyze their lateral stress-strain behavior. Insights from these tests facilitated the development of a predictive method for estimating lateral pressure on soilbag reinforced retaining walls under similar conditions. This method, rooted in the principle of force equilibrium in differential elements, has its accuracy and reliability substantiated by the alignment of its predictions with the model test outcomes.

The identified Urban Heat Island (UHI) phenomenon, coupled with diminished vegetation and anthropogenic heat release, poses a significant environmental challenge for major urban centers. Particularly worrisome is the UHI effect in hot and temperate climates such as the Mediterranean region during the summer season. This exacerbates discomfort and heightens the risks of diseases linked to high temperatures. Additionally, it contributes to environmental consequences, including elevated electricity consumption due to increased demand for cooling [1]. The utilization of Remotely Sensed imagery for estimating Land Surface Temperature has become more prevalent in various applications associated with evaluating urban micro-climates and the Urban Heat Island (UHI) phenomenon. This study presents an algorithm for the automatic mapping of Land Surface Temperature (LST) from Landsat-8 data using a vegetation index-based technique in the Google Earth Engine (GEE) platform. The primary aim is to discern patterns and trends in LST over a 5-year period, from 2017 to 2021. The application was tested in the urban area of Thessaloniki, Northern Greece, which is particularly representative of the Mediterranean urban environment. Computation of Normalized Difference Indices (NDVI, NDBI, NDWI) for the study area was also performed within the GEE platform and correlation analysis was implemented to evaluate the impact of the urban landscape on the distribution of LST.

For the territory of the Republic of Tatarstan (Russia), a spatial model of forest communities was built as a basis for assessing the sequestration potential of ecosystems. Combination of multi-temporal and multi-spectral satellite imagery from the Landsat 8 and Landsat 9 platform as input data and Google Earth Engine cloud platform were used. The set of 292 vegetation indices and metrics computed from the pre-processed imagery, were combined into dataset. The Weka X-Means clustering algorithm was trained and applied to study area. The unsupervised classification was carried out by vegetation classes in the Braun-Blanquet system. The results of unsupervised classification were verified using data from more than 17,000 relevés with geographic references from the Flora database. For automatic classification, the EuroVeg Checklist expert system in the JUICE 7.1 package was used. The proposed methodology for obtaining initial data and unsupervised classification, supported by an automated expert system, made it possible to obtain a picture of the vegetation distribution in the study area with sufficient accuracy, and in the future, it will be used to assess the sequestration potential of the ecosystems of the region under study.

As part of the Earth Climate Observatory space mission concept for the direct observation from space of the Earth Energy Imbalance, we propose an advanced camera suite for the high resolution observation of the Total Outgoing Radiation of the Earth. For the observation of the Reflected Solar Radiation, we propose the use of two multispectral cameras covering the range from 400 to 950 nm, with a nadir resolution of 1.7 km, combined with a high resolution RGB camera, with a nadir resolution of 0.57 km. For the observation of the Outgoing Longwave Radiation, we propose the use of 6 microbolometer cameras, with each a spectral bandwidth of 1 μm in the range from 8 to 14 μm, and with a nadir resolution of 2.2 km.

This research focuses on monitoring the spatiotemporal changes of urban surface water in Dhaka City from 1990 to 2021, utilizing satellite imagery and the Google Earth Engine (GEE) platform. Surface water is essential for urban, environmental, and agricultural ecosystems, and its dynamics have significant implications for water resource planning and environmental management. The main objectives of this study are to assess the extent of urban surface water coverage over the last three decades and identify trends of water loss or gain in the study area. The study employs Landsat 5 TM and Landsat 8 OLI/TIRS imagery, integrating GEE with machine learning coding and WRI techniques to extract and analyze surface water data efficiently. Traditional remote sensing methods for dynamic monitoring are time-consuming and cumbersome, but GEE offers a user-friendly and accurate approach, providing easy access to satellite data and cloud-based processing. The results reveal a concerning trend in urban surface water coverage, indicating a significant reduction from 36.23 km² in 1990 to 5.83 km² in 2021, representing a loss of approximately 20 square kilometers or 45 percent of surface water over the last three decades. The decline is attributed to factors such as unplanned urban expansion, rapid real estate development, and increased industrial and economic activities in the study area. The developed algorithms utilizing GEE offer valuable insights into the maximum and minimum extent of surface water, enabling effective surface water planning and management. These findings contribute to sustainable water resource management and environmental preservation in Dhaka City.

Cooking is a very decentralized and private way of energy consumption in human activities. The existing investigation and statistical analysis can’t effectively calculate the proportion of population relying on clean cooking fuel and utilization technology in the region. Therefore, based on the big data of the earth, this paper adopts the combination of spatial analysis and statistical analysis to determine the survey sample area, and according to the economic conditions, topographic characteristics, national policies for new energy construction and living habits of the provinces and cities under investigation, the questionnaire survey is conducted for the research area to calculate the proportion of the regional population relying on clean cooking fuels and utilization technologies. Taking the south of the Yangtze River in China as an example, the paper effectively calculates that 88.25% of the population depends on clean cooking fuel and technology in this region, of which 89.81% are in urban areas and 79.87% are in rural areas. Analysis of the survey data shows that the proportion of the population using clean cooking fuels and technologies is related to factors such as economic development, income and resource endowment. There is a large urban–rural gap in terms of energy consumption tendency and structure in the south of the Yangtze River in China and cooking energy consumption in rural households also varies from region to region. The technical ideas and conclusions of the paper have high reference application value, which can help promote the upgrading of clean energy utilization and provide data basis for relevant decision and policy making.

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

Rail impedance directly affects the transmission performance of track circuit . Considering the condition of earth stratification, for the difficult to calculate the rail impedance due to the semi-infinite integration interval and the oscillation of the integrand by using the Carson formula, The truncation method is proposed to divide the impedance formula is divided into definite integral and tail integral. The integral is approximated by the spline function, and the tail integral is calculated by using the exponential integral and Euler formula. Based on it, the rail impedance calculation formula of track circuit is obtained. The electromagnetic field model of track circuit with earth stratification is simulated by finite element method, and the correctness of the method is verified. Based on the formula, the influence of current frequency, soil depth and conductivity on rail impedance is studied. The relative error between the calculated results of rail impedance and the simulation results of finite element is within 5%. It can be seen that the formula has high accuracy and correctly reflects the law of rail impedance variation with current frequency, soil depth and resistivity. It provides a reliable reference for the theoretical calculation of rail impedance of track circuit.

We have an unprecedented ability to map the Earth’s surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to use these tools to improve scientific knowledge and decision making. Here we present a trained U-Net model that can map and delineate ground mounted solar arrays using publicly available Sentinel-2 imagery, and that requires minimal data pre-processing and no feature engineering. By using label overloading and image augmentation during training, the model is robust to temporal and spatial variation in imagery. The trained model achieved a precision and recall of 91.5% each and an intersection over union of 84.3% on independent validation data from two distinct geographies. This generalizability in space and time makes the model useful for repeatedly mapping solar arrays. We use this model to delineate all ground mounted solar arrays in North Carolina and the Chesapeake Bay watershed to illustrate how these methods can be used to quickly and easily produce accurate maps of solar infrastructure.

The evolutionary origin of the genome remains elusive. Here, I hypothesize that its first iteration, the protogenome, was a multi-ribozyme RNA. It evolved, likely within liposomes (the protocells) forming in dry-wet cycling environments, through the random fusion of ribozymes by a ligase and was amplified by a polymerase. The protogenome thereby linked, in one molecule, the information required to seed the protometabolism (a combination of RNA-based autocatalytic sets) in newly forming protocells. If this combination of autocatalytic sets was evolutionarily advantageous, the protogenome would have amplified in a population of multiplying protocells. It likely was a quasispecies with redundant information, e.g., multiple copies of one ribozyme. As such, new functionalities could evolve, including a genetic code. Once one or more components of the protometabolism were templated by the protogenome (e.g., when a ribozyme was replaced by a protein enzyme), and/or addiction modules evolved, the protometabolism became dependent on the protogenome. Along with increasing fidelity of the RNA polymerase, the protogenome could grow, e.g., by incorporating additional ribozyme domains. Finally, the protogenome could have evolved into a DNA genome with increased stability and storage capacity. I will provide suggestions for experiments to test some aspects of this hypothesis.

In this paper, we introduce a computational procedure that enables autonomous LEO laser trackers endowed with INSs to increase the current accuracy when shooting at middle distant medium-size LEO debris targets. The code is designed for the trackers to throw the targets into the Atmosphere by means of ablations. In case that the targets are eclipsed to the trackers by the Earth, the motions of the trackers and targets are modeled by equations that contain post-Newtonian terms accounting for the curvature of space. Otherwise, when the approaching targets become visible for the trackers, we additionally use more accurate equations, which allow to account for the local bending of the laser beams aimed at the targets. We observe that under certain circumstances the correct shooting configurations that allow to safely and efficiently shoot down the targets, differ from the current estimations by distances that may be larger than the size of many targets. In short, this procedure enables to estimate the optimal shooting instants for any middle distant medium-size LEO debris target.

Existing rammed earth construction methods have disadvantages such as increased initial costs for manufacturing the large formwork and increased labor costs owing to the labor-intensive construction techniques involved. To address the limitations of existing rammed earth construction methods, an autonomous rammed earth construction method is introduced herein. As this autonomous rammed earth construction method uses a modular formwork, alternative materials must be used in the construction to satisfy the requirements for the early-age binder performance. Accordingly, this study evaluates the use of an epoxy emulsion composed of epoxy and a hardener to enhance the performance of the binder. Preliminary experiments were conducted to determine the optimal formulation of the epoxy emulsion, following which the compressive strength, water loosening, shrinkage, rate of mass change, and microstructure of several red clay binder specimens with and without epoxy emulsion were analyzed at early ages. The results confirmed that the epoxy emulsion can be applied to satisfy the performance requirements for autonomous rammed earth construction by improving the durability and strength of the binder at early ages.

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. Satellite imagery can monitor algae biomass over large areas. The detection of chlorophyll a (chl-a) concentrations in small lakes is hindered by the low spatial resolution of conventional ocean colour satellites. The short time-series of the newest generation of space-borne sensors (e.g. Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes' chl-a, it is still unclear how the spatial and temporal variability of chl-a concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30-m resolution imagery to assess lakes' chl-a concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of chl-a, taking advantage of the long-time span of the LT 5 and 7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50 % of the chl-a interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake's trophic state, with models performing in average twice as better in lakes with higher chl-a concentration (> 20 µg/l) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes' annual mean chl-a concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of chl-a provide a tool for assessing the impacts of environmental change.

Pulmonary arteriovenous malformations (PAVMs) are vascular anomalies resulting in abnormal connection between pulmonary arteries and veins. In 80% of cases, PAVMs are present from birth, but clinical manifestations are rarely seen in childhood. These congenital malformations are typically associated with Hereditary Hemorrhagic Telangiectasia (HHT), a rare disease that affects 1 in 5,000/8,000 individuals. HHT disease is frequently caused by mutations in genes involved in the TGF-β pathway. However, approximately 15% of patients do not have a genetic diagnosis and, among the genetically diagnosed, more than 33% do not meet the Curaçao Criteria. This makes clinical diagnosis even more challenging in the pediatric age group. Here, we introduce an 8 years old patient bearing a severe phenotype of multiple diffuse PAVMs caused by an unknown mutation which ended in lung transplantation. Phenotypically, the case of study follows a molecular pattern HHT-like. Therefore, molecular biology and cellular functional analysis has been performed in primary endothelial cells (ECs) isolated from the explanted lung. The findings revealed a loss of functionality in lung endothelial tissue and a stimulation of endothelial to mesenchymal transition. Understanding the molecular basis of this transition could potentially offer new therapeutic strategies to delay lung transplantation in severe cases.

The R23Cu7Mg4 (R = Ca, Eu) intermetallics, studied by single crystal X-ray diffraction, were found to be isostructural with the Yb23Cu7Mg4 prototype (hP68, k4h2fca, space group P63/mmc), forming a small group inside the bigger 23:7:4 family, otherwise adopting the hP68-Pr23Ir7Mg4 crystal structure. The observed structural peculiarity is connected with the divalent character of the R component and with a noticeable volume contraction, resulting in a clear clustering of title compounds inside the whole 23:7:4 family. The occurrence of fragments typical of similar compounds, particularly Cu-centered trigonal prisms and Mg-centered core-shell polyicosahedral clusters with R at vertices, induced the search of significant structural relationships. In this work, a description of the hexagonal crystal structure of the studied compounds is proposed as a linear intergrowth along the c-direction of the two types of slabs R10CuMg3 (parent type: hP28-kh2ca, SG 194) and R13Cu6Mg (parent type: hR60-b6a2, SG 160). The ratio of these slabs in the studied structure is 2:2 per unit cell, corresponding to the simple equation 2XR10CuMg3 + 2XR13Cu6Mg = 2XR23Cu7Mg4. This description assimilates the studied compounds to the (Ca,Eu,Yb)4CuMg ones, where the same slabs (of p3m1 layer symmetry) are stacked in a different way/ratio, and constitutes a further step towards a structural generalization of R-rich ternary intermetallics.

Many of the potential immune therapeutic targets are similarly affected in adult-onset neurodegenerative diseases such as Alzheimer’s (AD) disease, Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD), but also in a seemingly distinct Niemann-Pick type C disease with primarily juvenile-onset. This strongly argues for an overlap in pathogenic mechanisms. The commonly researched immune targets include various immune cell subsets such as microglia, peripheral macrophages, or regulatory T cells (Tregs), the complement system, and other soluble factors. In this review, we will compare these neurodegenerative diseases from a clinical point of view and point out the common pathways and mechanisms of protein aggregation, neurodegeneration and/or neuroinflammation that could potentially lead to shared treatment strategies. We also describe the common therapeutic approaches in treating the immune dysfunctions in these disorders, moving from immunization to microbiome regulation and stem cell treatment.

Quantification of bone loss for follow-up comparisons has not been routinely performed in patients with parietal thinning so far. This study compares different methodological approaches including both 2- and 3-dimensional methods in computerized tomographies of a single patient. The 2-dimensional method provides accurate measurements of bone thickness, while the 3-dimensional method allows for a more comprehensive view of the bone's structure, including parameters like shape, size, and thickness. The techniques provided valuable data for the treating physician and suggest a possible effect of corticosteroids in this individual patient. The study concludes that 3-dimensional reconstruction of computerized tomographies with subsequent 2-dimensional analyses appears justified for routine radiological practice to increase clinical awareness of the size of the parietal bone defects and allow for quantification of bone loss during long-term follow-ups. Further validation of the proposed analytical approaches is warranted in larger studies to investigate patterns of bone thinning across the skull and changes in bone volumes over time, with specialized software to be approved for such analyses.

During the COVID-19 pandemic, individuals with symptoms other than cough or fever have refrained from seeking medical advice. However, a delay in treatment might lead to serious consequences. At the same time, digital health initiatives have emerged to overcome this bottleneck of healthcare. Herein, we report the results of a multi-center initiative using a combination of patient history and artificial intelligence (AI) to identify individuals with rare neuromuscular diseases. First, a questionnaire with 46 items was developed by interviewing patients with muscular dystrophies, amyotrophic lateral sclerosis, Morbus Pompe, neuropathies, and myasthenia gravis. Second, patients with proven neurological diseases answered the questionnaire. Third, a combination of classifiers (artificial neural network, support vector, and random forest) was trained and, finally, the system was challenged with new questionnaires. Users with an abnormal questionnaire pattern received a unique code for data privacy and contact details for a neurologist for further advice. The neurologists confirmed or refuted the AI-based diagnosis. The questionnaire was accessed 3122 times, leading to 853 unique codes. Only for a few patients the computer-based diagnoses and the confirmed final diagnoses were reported to us. However, for these few patients, the genetic testing and high CK levels finally ended their long-lasting diagnostic odyssey.

Solar geoengineering (SG) solutions have many advantages compared to the difficulty of carbon removal (CR): SG produces fast results, is shown here to have much higher efficiency than CR, is not related to fossil fuel legislation, and is something we all can participate in brightening the Earth with cool roofs, and roads. SG requirements detailed previously to mitigate global warming (GW) have been concerning primarily because of overwhelming goals and climate circulation issues. In this paper, the advantages of annual solar geoengineering (ASG) to mitigate yearly global warming increases are explored and detailed as it provides higher feasibility in geoengineering applications. ASG area modification requirements found here are generally 50 to possibly higher than 150 times less compared to the challenge of full SG GW mitigation reducing circulation concerns. Results indicate that there are mixed technologies that can help meet annual mitigation Earth brightening goals. As well, results show much higher feasibility for L1 space shading compared to prior literature estimates for full GW mitigation. However, stratosphere injections appear challenging in the annual approach. Because ASG earth brightening area requirements are much smaller than those required for full mitigation, we have concerns that worldwide negative SG would interfere with making positive advances for several reasons. Negative SG currently dominates yearly practices with the application of dark asphalt roads and roofs. This issue is discussed.

Mechanical robustness and high energy efficiency of composite materials are immensely important in modern stretchable, self-powered electronic devices. However, the availability of these materials and their toxicities are challenging factors. This paper presents the mechanical and energy-harvesting performances of low-cost natural rubber composites made of stearic acid-modified diatomaceous earth (mDE) and carbon nanotubes (CNTs). The obtained mechanical properties were significantly better than those of unfilled rubber. Compared to pristine diatomaceous earth, mDE has higher reinforcing efficiencies in terms of mechanical properties because of the effective chemical surface modification by stearic acid and enhanced filler-rubber interactions. The addition of a small amount of CNT as a component in the hybrid filler systems not only improves the mechanical properties but also improves the electrical properties of the rubber composites and has electromechanical sensitivity. For example, the fracture toughness of unfilled rubber (9.74 MJ/m3) can be enhanced by approximately 484% in a composite (56.86 MJ/m3) with 40 phr (per hundred grams of rubber) hybrid filler, whereas the composite showed electrical conductivity. At a similar mechanical load, the energy-harvesting efficiency of the composite containing 57 phr mDE and 3 phr CNT hybrid filler was nearly double that of the only 3 phr CNT-containing composite. The higher energy-harvesting efficiency of the mDE-filled conductive composites may be due to their increased dielectric behaviour. Because of their bio-based materials, rubber composites made by mDE can be considered eco-friendly composites for mechanical and energy harvesting applications and suitable electronic health monitoring devices.

Different values of the Hubble constant for extragalactic objects are not considered here. We give a number of examples of the extreme accordance of expansion rates of different fields of knowledge with the cosmological expansion rate. The coincidence of the expansion rates means that a common cause is almost inevitable. All these examples are gravitationally bound in themselves and in this case are subject to cosmological expansion. According to standard theory, this should not happen. We therefore question the common boundary of gravity and expansion for both theoretical and observational reasons and conclude that all gravitationally dominated objects participate in cosmological expansion or scale drift, contrary to general doctrine. The space expands with its contents while numerically maintaining distance, radius, rotation time and density. What is generally interpreted as an expansion is obviously a scale drift with a drift rate that corresponds to the size of the Hubble constant. The Earth is subject to expansion and scale drift. This results in numerically constant measured values. This drift apparently also applies to distant galaxies and other objects. The cosmological red shift is not interpreted here as a Doppler effect and numerical increase in distances, but in accordance with standard theory as an expansion or drift of the space-time scale. The expansion of the radii of galaxies makes the assumption of dark matter superfluous. The continents and our everyday environment are not subject to expansion or scale drift.

Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.

Rural electrification rate (RER) in Africa is still low to date. Several countries in Sub-Saharan Africa have tried to address this problem using conventional single-phase two-wire or three-phase three-wire systems, however at large costs due to the nature of dispersed rural load centers, low load demand, and low population density. Another solution of off-grid generation creates associated health problems. Therefore, this paper undertakes a review of a single wire earth return (SWER) network as a RER improvement solution. The paper undertakes intensive literature review to elucidate challenges and solutions to the implementation of SWER technology. Advantages of SWER technology discussed make it the choice for RER improvement in Sub-Saharan African countries. After that, a case study is selected in rural Tanzania, and a preliminary SWER network design is undertaken.

Coral reefs are critical ecosystems globally for marine fauna, biodiversity and through the services they provide to humanity. However, they are significantly threatened by anthropogenic stressors, such as climate change. By combining 9 environmental variables and ecological and health-based thresholds obtained from the available literature, we develop, using fuzzy logic (discontinuous functions), a Coral Reef Stress Exposure Index (CRSEI) for remotely monitoring coral reef exposure to environmental stressors. Our approach capitalises on the abundance of readily available satellite Earth Observation (EO) data available in the Google Earth Engine (GEE) cloud-based geospatial processing platform. CRSEI values from 3157 distinct reefs were generated and mapped across 12 important coral reef ecosystem regions. Quantitative analyses indicated that the index detected significant temporal differences in stress and was, therefore, able to capture historic change at a global scale. We also applied the CRSEI to three case-study reef ecosystems, previously well-monitored for stress and disturbance using other methods. PCA analysis indicated that depth, current, sea surface temperature (SST) and SST anomaly accounted for the greatest contribution to the variance in stress in these three regions. The CRSEI corroborated temporal and spatial differences in stress exposure from known disturbances within these reference regions, in addition to identifying the potential drivers of inter- and intra-region differences in stress, namely depth, degree heating weeks and SST anomaly. We discuss how the index can be further improved in future with site-specific thresholds for each stress variable, and the incorporation of additional variables not currently available in GEE. This index provides an open access tool, built around a free and powerful processing platform, that has broad potential to assist in the regular monitoring of our increasingly imperilled coral reef ecosystems, and, in particular, those that are remote or inaccessible.

The goal of this study was to analyze the forest degradation in the Reserve for San Rafael National Park, Paraguay, during the period 2005-2019. This Reserve is one of the most important forest remnants of the Upper Paraná Atlantic Forest Ecoregion. A multitemporal analysis of degradation was carried out due to the occurrence of three disturbances: forest fires, a twister and illicit crops, using the Continuous Degradation Detection (CODED) algorithm, for which 3 factors were considered: variations due to pixel in the NDFI index values before, during and after every disturbance registered. In this context, the phenomenon with the greatest impact in terms of magnitude of degradation were the forest fires of 2005, being that year at the same time, the one that reported the highest degradation values. Secondly, there are the illicit crops established until the first semester of 2019, and lastly, the twister that occurred in 2017. Our findings demonstrate that CODED algorithm can detect multi-temporal degradation events in a Subtropical Broadleaf Forest, and the post-disturbance regeneration process after every disturbance tends to occur immediately. The response in terms of degradation-regeneration is highly variable, depending of the nature and severity of each disturbance and the vegetation recovery dynamics.

Culverts are often required under earth embankment to allow for the crossing of a watercourse, like streams, to prevent the road embankment from obstructing the natural waterway. The opening of the culvert is determined based on the waterway required to accommodate the design flood, whereas the thickness of the culvert section is designed based on the loads applied to the culvert. This paper studies some design parameters of box culverts, such as the thickness of the haunch, the coefficient of earth pressure, the thickness of box culvert, and depth of fill on the top slab, to show the effect of haunch on the stresses of the box culvert. The study investigated the variation in stresses and the cost comparison made for different width of the box culvert. The percentage reduction in the cost of culvert based on the presence of haunch is presented.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

One of the IRDiRC goals for 2017-2027 is to achieve definitive diagnosis for rare undiagnosed diseases within one year, as diagnosis delay remains one of the pending issues in the rare diseases field. The Spanish Undiagnosed Rare Diseases Program (SpainUDP) was created in response to this challenging scenario to cover patients’ needs and after seeing the success of the UDP in USA. SpainUDP offers a multidisciplinary approach to those patients who have long sought a diagnosis without any success. During a first phase of the protocol, undiagnosed cases are sent to SpainUDP by individual patients, patient organizations or hospitals. After a carefully analysis of phenotype, data from sequencing experiments (WES) is processed with a standard pipeline and a detailed standardized phenotypic information (mapped to HPO) is connected to genetic data. In addition, the participation of SpainUDP in international initiatives such as the European projects RD-Connect and Solve RD, the Undiagnosed Diseases Network International (UDNI), and the MatchMaker Exchange platform, allows the establishment of a global data sharing strategy across multiple projects submitting data to these international initiatives. From the official beginning of the program (at the end of 2015) until early 2018, 147 cases were accepted in SpainUDP. During this time, 37 cases (25 %) dropped out the program due to several reasons. The remaining 110 cases are distributed as follows: phenotypic and genotypic (WES) characterization was finished in 30 cases, of which 20 (67 %) were diagnosed; 21 cases are pending on variants validation by Sanger; in 25 cases, WES is ongoing and 34 cases are in a deep phenotypic characterization. As a conclusion, SpainUDP aims to achieve a diagnosis following two recommendations of the IRDiRC: the patients’ diagnosis in a period of time as short as possible and the promotion of data sharing (especially genomic) at the international level.

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts, with each exhibiting varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between melioidosis morbidity rate and the environmental indicators was evaluated using geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.

This study aims to reveal the revolutionary Gamma Distribution Manifold (GDM) info-geometric analysis combined with potential Gamma distribution (GD) applications to advance Satellite Earth Observations (SEOs). An exposition of the GDM Fisher Information Matrix (FIM) was undertaken, together with the analytic investigation of the existence of FIM’s inverse. The derivation of GDM’s Geodesic Equations (GEs) is undertaken. The solution of the obtained Geodesic Equations is optimally complicated, and it provides more extensions to numerous open problems. Notably, the current paper provides a first-time unification between three multi-interdisciplinary fields, namely, information geometry theory, probability theory, and Satellite earth observations. Looking at the other side of the coin, the provided revolutionary approach in this current work reports the significant impact of info-geometric analysis on the probabilistic dynamics as well as the influential role of information geometry on satellite and space industry. This poses numerous open issues that initiates a new form of contemporary unification between these inter-disciplinaries to discover another visionary thinking out of traditional statistical frames. In other words, refraining ourselves from using classical statistical frames of thinking and delving into a new info-geometric vision of statistical distributions as well as examining real life applications of information geometry to advance satellite earth observations. The curtain is drawn on this paper by providing several challenging open problems in combination with a concluding remark and the next phase of research.

A survey of the trends in metal-ligand coordination and hydrogen bonding has been carried out on complexes of singly deprotonated cyanuric acid CY− and alkaline earth metals, synthesised from aqueous solution and characterised by X-ray crystallography. Involvement of cyanurate in the metal coordination increases from Mg through Ca and Sr to Ba relative to coordination by water, in parallel with the increasing ionic radius and coordination number. The incidence of bridging of metal centres by both water and cyanurate also increases in this series. The Mg complex contains hexaaqua-coordinated cations, uncoordinated CY− anions, and additional water molecules. Ca and Sr complexes feature polymeric chains of coordinated metal ions, the Ca complex with uncoordinated anions and the Sr complex with all potential ligands coordinated. The Ba complex has a two-dimensional coordination network. Extensive hydrogen bonding plays a key role in generating a three-dimensional network in all four structures. The competition between cyanurate and water for coordination to the alkaline earth metal cations, and that between coordination and hydrogen bonding propensities of the cyanurate anion, appear to be finely balanced, with the structural outcomes depending on the relative sizes and degrees of hardness of the four cations of the group.

With the rapid development of Internet of Things (IoT)-based near-earth remote sensing technology, the problem of network intrusion for near-earth remote sensing systems has become more complex and large-scale. Therefore, it is essential to seek an intelligent, automated, and robust network intrusion detection method. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the characteristics of existing near-earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes. The proposed network applies spatiotemporal graph neural networks to the network intrusion detection of near-earth remote sensing systems and validates the effectiveness of the proposed method on the latest flow-based dataset.

Desertification is a global environmental and socio-economical issue threatening humanity's survival and development. The Shiyang River Basin ecosystem is vulnerable and prone to desertification. In addition, establishing the quantitative analysis of desertification driving factors and understanding their relative contribution, separately or combined, is still an unresolved problem. The present study applied geographic information system (GIS) techniques and a geographic detector model to quantify desertification spatial extent and driving mechanisms. This research utilized Fractional Vegetation Cover (FVC) to elucidate desertification spatial heterogeneity. The 30 years Coefficient of Variation (CV) of the Normalized Difference Vegetation Index (NDVI) was a dependent variable and indicator of ecosystem terrestrial conditions; Elevation, near-surface air temperature, precipitation, wind velocity, land cover change, soil salinity, road buffers, waterway buffers, and soil types were independent variables. The results showed that 89.41% of the total area is under desertification risk, where 20.99% is extremely desertified, 34.45% is severely desertified, 12.05% is moderately, and 21.92% is slightly desertified. The results from the Geodetector model showed that Power Determinant (PD) values ranged between 0.004 and 0.270. Elevation and soil types had the highest contributing factors with PD values of 0.270 and 0.227, whereas precipitation, soil salinity, the buffer of the waterway, and wind velocity played a moderate role with PD values of 0.146, 0.117, 0.107, and 0.071. Near-surface air temperature, road buffer, and land cover dynamics exhibited lower impact with PD values of 0.028, 0.013, and 0.004. In most cases, investigating the interaction between driving factors resulted in a mutual or non-linear enhancement. There was an apparent linear and mutual enhancement between elevation and soil salinity, precipitation, and soil types with values of 0.3513, 0.3232, and 0.3204, respectively. In addition, there was a mutual enhancement between soil salinity and soil types with a value of 0.2962. On the other hand, a non-linear enhancement was observed between Elevation and near-surface air temperature (0.3116), Elevation and Land cover dynamics (0.2759), soil types and near-surface air temperature (0.2687), land cover dynamics and soil types (0.234), precipitation and near-surface air temperature (0.2248), precipitation and wind velocity (0.2248), and between land cover dynamics and precipitation (0.223). This research revealed irrefutable evidence that environmental factors might be the primary drivers of ecosystem disturbance, provided the basis for the environmental footprint of desertification mechanism, and might be a cornerstone for future policy on ecological restoration sustainability in the Shiyang River Basin.

Scale invariance is expected in empty Universe models, while the presence of matter tends to suppress it. As shown recently, scale invariance is certainly absent in cosmological models with densities equal to or above the critical value ρ_c = 3(H₀)²/(8πG). For models with densities below ρc, the possibility of limited effects remains open. If present, scale invariance would be a global cosmological property. Some traces could be observable locally. For the Earth-Moon two-body system, the predicted additional lunar recession would be increased by 0.92 cm/yr, while the tidal interaction would also be slightly increased. The Earth-Moon distance is the most systematically measured distance in the Solar System, thanks to the Lunar Laser Ranging (LLR) experiment active since 1970. The observed lunar recession from LLR amounts to 3.83 (±0.009) cm/yr; implying a tidal change of the length-of-the-day (LOD) by 2.395 ms/cy. However, the observed change of the LOD since the Babylonian Antiquity is only 1.78 ms/cy, a result supported by paleontological data, and implying a lunar recession of 2.85 cm/yr. The significant difference of (3.83-2.85) cm/yr = 0.98 cm/yr, already pointed out by several authors over the last two decades, corresponds well to the predictions of the scale-invariant theory, which is also supported by several other astrophysical tests.

Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT can achieve approximate performance compared to the upper bound with low complexity.

The authors of this paper use an original method of diatomaceous earth fractionation, which allows for obtaining a filler with a specific particle size distribution. The method makes it possible to separate small, disintegrated and broken diatom frustules from those which maintained their original form in diatomaceous earth. The study covers a range of tests conducted to prove that such a separated diatomic fraction shows features different from the base diatomite used as an epoxy resin filler. We have examined mechanical properties of a series of diatomite/resin composites considering the weight fraction of diatoms and the parameters of the composite production process. The studied composites of Epidian 601 epoxy resin cross-linked with amine-based curing agent Z-1 contained 0 to 70% vol. of diatoms or diatomaceous earth. Samples were produced by casting into silicone moulds in vacuum degassing conditions and, alternatively, without degassing. The results have shown that the size and morphology of the filler based on diatomaceous earth affects mechanical and rheological properties of systems based on epoxy resin.

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper we automate a method (thresholding of the short-wave infrared band) for classifying inundation, using Landsat imagery and Google Earth Engine. We demonstrate the method in the Okavango Delta, northern Botswana, a complex case study due to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery. Inundation classifications in the Okavango Delta have predominately been implemented on broad spatial resolution images. We present the longest time series to date (1990-2019) of inundation maps at high spatial resolution (30m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with accuracy ranging from 91.5 - 98.1%. Use of Landsat imagery resulted in consistently lower estimates of inundation extent than previous studies, likely due to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use.

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEI_ns, and AWEI_s) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEI_ns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEI_ns and AWEI_s are better than NDWI, MNDWI and MNDWI2. AWEI_ns works better than AWEI_s under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEI_ns.

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Science (ESS) are no exception. However, within ESS specialized visualization requirements and data models --- particularly for those data arising from numerical models --- often make general-purpose visualization packages difficult, if not impossible, to effectively use. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR's utility in the study of a numerically simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

In this study, the use of recycled diatomaceous earth as the extraction phase in a the solid phase microextraction (SPME) technique for the determination of polycyclic aromatic hydrocarbons (PAHs) in river water samples, with separation/detection performed by gas chromatography-mass spectrometry (GC-MS), is proposed. The optimized extraction conditions are extraction time 70 min at 80 ºC with no addition of salt. The limits of quantification were close to 0.5 μg L-1 with RSD values lower than 25% (n = 3). The linear working range was 0.5 μg L-1 to 25 μg L-1 for all analytes. The method was applied to samples collected from the Itajaí River (Santa Catarina, Brazil) and the RSD values for repeatability and reproducibility were lower than 15% and 17%, respectively. The efficiency of the recycled diatomaceous earth fiber was compared with that of commercial fibers and good results were obtained, confirming that this is a promising option for use as the extraction phase in SPME.

The variability of meteorological parameters such as temperature and precipitation, and climatic conditions such as intense droughts, are known to impact vegetation health over southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole/Dipole Mode Index (DMI) is important as these factors drive the variability of temperature and precipitation. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform in order to analyze the response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP), during the study period. The MODIS-enhanced vegetation index and burned area index were also analyzed for this period. The area-averaged Modern Retrospective Analysis for Research Application (MERRA) model maximum temperature and precipitation were also extracted using the JavaScript code editor in the GEE platform. This procedure demonstrated a strong reversal of both the NDVI and Enhanced Vegetation Index (EVI), leading to signs of a sudden increase of burned areas (strong BAI) during the strongest El Niño period. Both the Theilsen method and the Mann–Kendall test showed no significant greening or browning trends over the whole time series, although the annual Mann–Kendall test, in 2003 and 2014–2015, indicated significant browning trends due to the most recent strongest El Niño. Moreover, a multi-linear regression model seems to indicate a significant influence of both ENSO activity and precipitation. Our results indicate that the recent 2014–2016 drought altered the vegetation condition in the HiP. We conclude that it is vital to exploit freely available GEE resources to develop drought monitoring vegetation systems, and to integrate climate information for analyzing its influence on protected areas, especially in data-poor counties.

A plant’s health and productivity is influenced by its associated microbes. Although the common microbiome is often thought to be the most influential, significant numbers of rare or uncommon microbes (eg. specialized endosymbionts) may also play an important role in the health and productivity of certain plants in certain environments. To help identify rare/specialized bacteria and fungi in the most important angiosperm plants, we contrasted microbiomes of the shoots, roots and rhizospheres of Arabidopsis, Brachypodium, maize, wheat, sugarcane, rice, tomato, coffee, common bean, cassava, soybean, switchgrass, sunflower, Brachiaria, barley, sorghum, and pea. Plants were grown inside sealed jars on sterile sand or field soil. About 95% and 86% of fungal and bacterial diversity inside plants was uncommon, however judging by read abundance, up to half of the mycobiome consists of uncommon fungal cells, while less than 11% of bacterial endophytes are rare. Uncommon seed transmitted microbiomes consisted mostly of Proteobacteria, Firmicutes, Bacteriodetes, Ascomycetes and Basidiomycetes that most heavily colonized shoots, to a lesser extent roots and least of all rhizospheres. Soil served as a more diverse source of rare microbes than seeds, replacing or excluding the majority of the uncommon seed transmitted microbiome. With the rarest microbes, their colonization pattern could either be the result of stringent biotic filtering by most plants, or uneven/stochastic inoculum distribution in seeds or soil. Several strong plant-microbe associations were observed such as seed transmission to shoots, roots and/or rhizospheres of Sarocladium zeae (maize), Penicillium (pea and Phaseolus), and Curvularia (sugarcane), while robust bacterial colonization from cassava field soil occurred with the cyanobacteria Leptolyngbya into Arabidopsis and Panicum roots, and Streptomyces into cassava roots. Some abundant microbes such as Sakaguchia in rice shoots or Vermispora in Arabidopsis roots appeared in no other samples, suggesting they were infrequent, stochastically deposited propagules from either soil or seed (impossible to know based on the available data). Future experiments with culturing and cross inoculation of these microbes between plants may help us better understand host preferences and their role in plant productivity, perhaps leading to their use in crop microbiome engineering and enhancement of agricultural production.

Desmoplastic small round cell tumor (DSRCT) is an extremely rare, aggressive sarcoma affecting adolescents and young adults with male predominance. Generally, it originates from serosal surface of abdominal cavity. The hallmark characteristic of DSRCT is the EWSR1-WT1 gene fusion. This translocation up-regulates the expression of PDGFRα, VEGF and other proteins related to tumor and vascular cell proliferation. Current management of DSRCT includes a combination of chemotherapy, radiation and aggressive cytoreductive surgery plus intra-peritoneal hyperthermic chemotherapy (HIPEC). Despite advances in multimodal therapy, outcomes remain poor since the majority of patients present disease recurrence and die within 3 years. The dismal survival makes DSRCT an orphan disease with urgent need of new drugs. The treatment of advanced and recurrent disease with tyrosine kinase inhibitors, such as pazopanib, sunitinib, and mTOR inhibitors have been evaluated in small studies. Recent works using comprehensive molecular profiling of DSRCT identified potential therapeutic targets. In this review, we aim to describe the current studies conducted to better understand DSRCT biology and to explore the new therapeutic strategies under investigation in preclinical models and in early phase clinical trials.

Seed bank survival underpins plant population persistence but studies on seed bank trait-environment interactions are few. Changes in environmental conditions relevant to seed banks occur in desert ecosystems owing to solar energy development. We developed a conceptual model of seed bank survival to complement methodologies using in-situ seed bank packets. Using this framework, we quantified the seed bank survival of two closely related annual desert plant species, one rare (Eriophyllum mohavense) and one common (Eriophyllum wallacei) and the seed bank-environment interactions of these two species in the Mojave Desert within a system that emulates microhabitat variation associated with solar energy development. We tracked 4,860 seeds buried across 540 seed packets and found, averaged across both species, that seed bank survival was 21% and 6% for the first and second growing seasons, respectively. After two growing seasons, the rare annual had a significantly greater seed bank survival (10%) than the common annual (2%). Seed bank survival, across both species, was significantly greater in Shade (10%) microhabitats compared to Runoff (5%) microhabitats and Control microhabitats (3%). Our study confers insight into this early life-stage across rare and common congeners and their environmental interactions using a novel conceptual framework for seed bank survival.

To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics are important to be characterized but its atomistic details are hard to obtain both experimentally and computationally. We review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: One is the Onsager-Machlup action method, and the other is the weighted ensemble method. Some applications of above methods to biomolecular systems are also discussed and illustrated.

CubeSats, known for their compact size and cost-effectiveness, have gained significant popularity. However, their limited size imposes restrictions on optical aperture and, consequently, Ground Resolution Distance in Earth Observation missions. To overcome this limitation, the concept of deployable optical payloads with segmented primary mirrors which can unfold like petals has emerged, enabling larger synthetic apertures and enhanced spatial resolution. This study explores the potential benefits of leveraging Additive Manufacturing (AM) and Topology Optimization (TO) in the realm of ultra-precision machining, specifically single point diamond machining. The goal is to reduce fixture weight while improving stiffness to minimize deformations caused by rotational and cutting forces which compromise optical performance. Through Finite Element Analysis, this research compares conventionally machined fixtures with those produced using AM and TO techniques. The results reveal that concept designs created through TO can achieve a remarkable 68% reduction in weight. This reduction makes the assembly, including the machining fixture and 12U deployable segments, manageable by a single operator without the need for specialized lifting equipment. Moreover, these innovative designs lead to substantial reductions of up to 86% and 51% in deformation induced by rotational and cutting forces, respectively.

During disaster response, clouds or darkness can prevent the use of optical images for detecting consequences of natural disasters, including landslides. In these situations, radar images can be used to detect changes more rapidly. However, Synthetic Aperture Radar (SAR) backscatter intensity images are underutilized for landslide detection. Unfortunately, there remains a lack of understanding about how to interpret landslide signatures in SAR imagery. In this study, we investigate how the morphometric features and material properties of landslides, and preexisting land cover, control their expression in SAR backscatter intensity change images. Trends in the spatial and temporal signatures of over 1000 landslides in 30 diverse case studies are investigated, using multi-temporal composites and dense time-series of Sentinel-1 C-band SAR backscatter intensity data. The results show that the orientation of landslide surfaces relative to the sensor, pre-existing land cover, and the roughness of the landslide surface, determine whether landslides will produce an increase or decrease in backscatter intensity values. In certain cases, we can identify morphometric features of landslides (e.g. scarps, transit zone, deposits, ponding) and material properties. Generally, we see that landslides appear most clearly with a strong increase in intensity when they occur in herbaceous vegetation or non-vegetated ground surfaces, due to an increase in surface roughness. While in forested or densely vegetated areas, landslides produce a more complex signature with both decreases due to radar shadow and vegetation removal, and an adjacent edge of increased intensity due to double bounce and direct return from vertical tree trunks and convex edges. In most cases, rough deposits produce an increase in intensity, while smooth deposits (e.g. from mudslides) exhibit specular reflection, and thus show decreased values. Landslides are less visible in cases with pre-event very rough ground, or mixed vegetation conditions. The conceptual model developed can aid interpretation of landslides in SAR imagery, and provide domain knowledge needed to train models for automatic landslide detection.

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.

The altitude of the habitat is one of the important regulators of species survival. Kaçkar Mountains National Park is located in the Eastern Black Sea region of Turkey. This is the first study on the benthic diatom flora of the high mountain lakes in Kaçkar Mountains National Park, which is situated between 2782 and 3075 m a.s.l. A total of 84 diatom species were identified from benthic communities of 15 habitats in summer (19 July, 28 August) and autumn (10 September) months of 2020. The genus Pinnularia (13 species) formed the basis of the taxonomic list followed by Eunotia (5 species), Navicula (5 species), and Frustulia (4 species) genera, respectively. The waters in all the studied lakes were fresh, low-saline, with low-alkaline or circumneutral pH and organically uncontaminated as evidenced by prevailed bioindicator groups. Statistical methods and comparative floristic results confirm the role of the lake altitude for the diatom species distribution. Species richness was higher in lakes with lower altitude, but a statistical approach also revealed a tendency for it to increase with altitude if species richness increased. Further studies will be needed to continue exploring this pattern. In the first round of analysis, diatom species groups were categorized according to IUCN categories, revealing one potentially threatened and one rare species. To protect studied high mountain lakes, their ecological conditions must be constantly monitored in the Kaçkar Mountains National Park.

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. The solar wind O6+ ions which are the primary charge state for oxygen ions in solar wind are considered. The line emissivity of the charge-exchange born O5+ ions are calculated by Spectral Analysis System for Astrophysical and Lab oratory (SASAL). It is found that the emission lines from the O5+ ranges from 105.607 to 118.291 eV with the strong line at 107.047 eV. We then simulated the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm-2 s-1 sr-1 on the dayside, which is 4.5 times that before the solar wind structure reaches the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at north pole.

The ongoing expansion in volume, velocity and variety of geospatial data has stimulated the development of artificial intelligence applications and associated online cartographic visualizations. In this context, the cartographic communication through peer-reviewed media evolved to incorporate Findability, Accessibility, Interoperability and Re-usability (FAIR) principles. In this paper, we review the functionalities proposed by current international peer-reviewed media for accessing, exploring and re-using geospatial data, and situate them on a diagram conceived for peer-reviewed cartography in the artificial intelligence era. We then present an overview of the cartographic infrastructure and workflow currently available in the Terra Digitalis journal for the online publication of massive spatiotemporal data. This free and opensource based solution allows a smooth integration, from data submission to final peer reviewed publication, of dynamic, interactive 2D and 3D maps. As a proof of concept, we present two maps derived from massive spatiotemporal data, related, respectively, to a climate numerical model (raster data) and a bike sharing system’s origin-destination dataset (vector data). We argue that the combination of technologies used in the journal provides an efficient and low-cost solution, unmatched by other publication media, to boost the accessibility and FAIR principles for geospatial big data.

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

During HVDC earth return operation systems, a high magnitude current will be injected into soil through earth electrode, the potential on the surface would change widely and produce unfavorable effects on the AC systems around. This paper presents an effective finite element method (FEM) coupling electric field with thermal field to evaluate the electrical field induced by the injected DC current. Firstly, owe to the characteristic of FEM, this method can consider arbitrary soil and earth electrode structure. Secondly, by setting the electrical and thermal parameters of soil as a function of temperature at the same time, the dynamic coupling process of electric field and thermal field is simulated accurately. Thirdly, to deal with the singular point in FEM subdivision and the huge computation in traditional three-dimensional FEM, the FEM coupling 2-D earth electrode with 3-D soil based on "shell" theory is introduced. Finally, based on the suggested method, the effect of abnormal resistance region (ARR) near DC earth electrode on electric field distribution is analyzed.

Ambitious biodiversity goals to protect 30% or more of the Earth’s surface by 2030 (30x30) require strategic near-term targets. To define areas that must be protected to prevent the most likely and imminent extinctions, we propose Conservation Imperatives—16,825 unprotected sites harboring rare and threatened species and spanning ~164 Mha of the terrestrial realm. We estimate that protecting the Conservation Imperatives would cost approximately US$169 billion [90% probability: $146 — $228 billion]. Globally, 38% of the 16,825 sites are either adjacent to or within 2.5 km of an existing protected area, potentially reducing land acquisition and management costs. These sites should be prioritized for conservation action over the next five years as part of a broader strategy to expand the global protected area network. The expansion of global protected areas between 2018–2023 incorporated only 7% of sites harboring range-limited and threatened species, highlighting a renewed urgency to conserve these habitats. Permanently protecting only 0.74% of land found in the tropics, where Conservation Imperatives are concentrated, could prevent the majority of predicted near-term extinctions once adequately resourced. We estimate this cost to be US$29 billion to US$46 billion per year over the next five years. Multiple approaches will be required to meet long-term protection goals: providing rights and title to Indigenous Peoples and Local Communities (IPLCs) conserving traditional lands, government designation of new protected areas on federal and state lands, and land purchase or long-term leasing of privately held lands.

Abstract: Power generation using dielectric elastomer (DE) artificial muscle is attracting attention because of its light weight, low cost, and high efficiency. Since this method is a system that produces electricity without emitting carbon dioxide nor using rare earths, it would contribute to the goal of environmental sustainability. In this paper, the background of DEs, the associated high-efficient wave energy generation (WEG) systems that we developed using DEs, as well as the latest development of its material are summarized. By covering the challenges we face and the achievements that we’ve reached, we can discuss the opportunities to build the foundation of a recycled energy society through the usage of these WEGs. On the other hand, to make these possibilities commercially successful, the advantages of DEs need to be integrated with traditional technologies. To achieve this, we also consider the method of using DEs alone and a system used in combination with an oscillating water column. Finally, the current status and future of DEGs are discussed.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

Recent advancements have revolutionized interplanetary spacecraft missions by combining low-thrust propulsion systems with planetary Gravity-Assist (GA) maneuvers. This study investigates/improved a novel dual-step efficient-robust homotopy algorithm, grounded in indirect optimal control principles, to analyze fuel-optimal bang-bang control problems. The methodology integrates efficient techniques, including the homotopic method, compatible normalization, and improved switching function detection, aimed at enhancing optimal convergence in solving low-thrust gravity-assist spacecraft missions. To illustrate practical application, an Earth-to-Jupiter interplanetary scenario utilizing Mars-GA is examined. Results demonstrate the algorithm's superior efficacy in achieving fuel efficiency, robust convergence, and remarkable accuracy compared to existing solutions.

Abstract: Earth’s Phanerozoic history is marked by about 100 major events (“golden spikes”) indicating the beginnings of new stages in the Geologic Time Scale (GTS). Stage boundaries signify major deterministic events that are relatively well-known and can be correlated worldwide, mostly on the basis of appearances or disappearances of fossil species. The latest stage (Anthropocene) is human-made and currently involves rapid (approximately linear) increase in average atmospheric temperature. The main cause of Earth’s past and current temperature increases is addition of CO2 to the atmosphere. There is continuous exchange of greenhouse gases between atmosphere and oceans, which (per unit of volume) contain 50 to 60 times as much CO2 as the atmosphere. Abundance of new very precise worldwide observations is allowing multifractal modeling of weather and climate during the latest Quaternary stages during which some variables like temperature of the atmosphere and frequency of forest fires can be described by using the Pareto-lognormal frequency distribution model with parameters that are subject to continuous (deterministic) changes, similar to those in the earlier Phanerozoic.

In this paper presents analytical findings regarding the influence of gravity disturbance on INS. A data from the high-precision gravity model, EGM2008, we introduce a novel real-time gravity disturbance compensation approach for INS. This method estimates gravity disturbances along paths for platforms on both land and water, utilizing the Multi-Layer Perceptron (MLP) technique. To estimate these gravity disturbances, we developed four distinct MLP models and compared their training results using Half-Mean Square Error (half-MSE) and Root Mean Square Error (RMSE). This comparative analysis allowed us to identify the MLP model that exhibited the best performance. In order to validate the proposed gravity compensation method, field test was conducted using a test vehicle. The results of this field test unequivocally demonstrated the effectiveness of our approach in enhancing positioning accuracy. Over the course of a 2-hour field experiment, it was evident that employing the proposed gravity compensation method improved positioning accuracy by a notable 24%.

Abstract: This paper summarises an early and successful piece of citizen science, performed within The University of Queensland Seismograph Stations (UQSS) observatory, in cooperation with colleagues at CSIRO. It was designed to en-courage young STEM students from Brisbane high schools to engage in “real” re-search, back in 1995. Having completed the project report, their (analog) results sat in a cupboard until the report was dusted off and the project was re-analysed in 2022 by an honors student, considering timely climate change applications for the study. This is a time when science is changing considerably from analog to digital medium and operational methods. The original project was called Earthquake generated T phases on BRS Seismograph (Brisbane, Q’ld)- a predictor for Tasman Sea Tsunamis? [1] Fortunately, seismology is a very collaborative field. The research question has since changed. There is a lot of data analysis involved in the science of recording earthquake signals, with auxiliary definitive catalogues, observers logbooks, housing of the recordings themselves (analog and digital) and the software mediums that change over time. In other words, a lot of headaches can be encountered in longitudinal data collection study such as this. The citizen science students used a pre-prepared decadal collection (1980-90) derived from the BRS observatory data catalogue. BRS is part of the global World-Wide Seismograph Station Network (WWSSN). Currently in Australia, university earth science observatories have diminished, and in their place, public seismic networks (PSN) have evolved, either in back-yard sheds or school science labs. The level of expertise required fits the role of advancing citizen science for a real science advantage. This is a topical citizen disaster preparedness action area for today’s climate emergency, which is threatening the globe and all lifeforms. Citizen engagement or mobilisation is an essential preparedness strategy. Keywords: citizen science 1; seismology 2; earth observation 3; climate change 4; education for sustainable development 5; disaster preparedness 6; disaster alerts 7

Globally, saline lakes occupying 23% by area 44% by volume among all the lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, pollution, and invasive species. India’s largest saline lake, Sambhar is currently shrinking at the rate of 4.23% due to illegal saltpan en-croachment. This research article aims to identify the trend of migratory birds and monthly wetland status. Birds survey was conducted for 2019, 2020 and 2021 and combined with literature data of 1994, 2003, and 2013 for visiting trend, feeding habit, migratory and resident ratio, and ecological diversity index analysis. Normalized Difference Water Index was scripted in Google Earth Engine. Results state that it has been suitable for 97 species. Highest NDWI values for the was whole study period was 0.71 in 2021 and lowest 0.008 in 2019 which is highly fluctuating. The decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If the causal factors are not checked, it might completely desiccate by 2059 as per its future prediction. Certain steps are suggested that might help conservation. Least, the cost of restoration might exceed the revenue generation.

Based on measured astronomical position data of heavenly objects in the Solar System and other planetary systems, all bodies in space seem to move in some kind of elliptical motion with respect to each other. According to Kepler’s 1^st Law, “orbit of a planet with respect to the Sun is an ellipse, with the Sun at one of the two foci.” Orbit of the Moon with respect to Earth is also distinctly elliptical, but this ellipse has a varying eccentricity as the Moon comes closer to and goes farther away from the Earth in a harmonic style along a full cycle of this ellipse. In this paper, our research results are summarized, where it is first mathematically shown that the “distance between points around any two different circles in three dimensional space” is equivalent to the “distance of points around a vector ellipse to another fixed or moving point, as in two dimensional space”. What is done is equivalent to showing that bodies moving on two different circular orbits in space vector wise behave as if moving on an elliptical path with respect to each other, and virtually seeing each other as positioned at an instantaneously stationary point in space on their relative ecliptic plane, whether they are moving with the same angular velocity, or different but fixed angular velocities, or even with different and changing angular velocities with respect to their own centers of revolution. This mathematical revelation has the potential to lead to far reaching discoveries in physics, enabling more insight into forces of nature, with a formulation of a new fundamental model regarding the motions of bodies in the Universe, including the Sun, Planets, and Satellites in the Solar System and elsewhere, as well as at particle and subatomic level. Based on the demonstrated mathematical analysis, as they exhibit almost fixed elliptic orbits relative to one another over time, the assertion is made that the Sun, the Earth, and the Moon must each be revolving in their individual circular orbits of revolution in space. With this expectation, individual orbital parameters of the Sun, the Earth, and the Moon are calculated based on observed Earth to Sun and Earth to Moon distance data, also using analytical methods developed as part of this research to an approximation. This calculation and analysis process have revealed additional results aligned with observation, and this also supports our assertion that the Sun, the Earth, and the Moon must actually be revolving in individual circular orbits.

Growing popularity of buildings with integrated sub-systems, requires a review of methods to optimize the preheat of ventilation air. An integrated system permits using geothermal heat storage parallel to the direct outdoor air intake with additional treatment in the mechanical room as a part of building automatic control system. Earth Air Heat Exchanger (EAHX) has many advantages but also has many unanswered questions. Some of the drawbacks are: a possible entry of radon gas, high humidity in the shoulder seasons as well as the need for two different air intake sources with a choice that depends on the actual weather conditions. While in winter, the EAHX may be used continuously to ensure thermal comfort, in other seasons, its operation must be automatically controlled. To generate the missing information about the EAHX technology we have examined two nearly identical EAHX systems, one placed in ground next to the building and the other under the basement slab. In another project, we have reinforced the ground storage action by heat exchanger placed on the return pipes of the hydronic heating system. Effectively, the information provided in this paper, shows advantages of merging both these approaches while the EAHX could be placed under the house or near the basement foundation that is using an exterior basement insulation.

Growing popularity of smart and integrated buildings requires a review of methods to optimize the preheat of ventilation air. An integrated system permits using heat ex-changers located in the mechanical room or in the future even using an exterior wall as a heat exchanger. One may ask the question how does the earth-air heat exchanger (EAHX) technology fitts into this function. EAHX has many advantages but also has many unanswered questions. Some of the drawbacks are: a possible entry of radon gas, high humidity in the shoulder seasons as well as the need for two different air intake sources with a choice that depends on the actual weather conditions. While in winter, the EAHX may be used continuously to ensure thermal comfort, in other seasons, its operation must be automatically controlled. To generate the missing information about the EAHX technology we reviewed literature and examined two nearly identical EAHX systems, placed either in ground next to the building or under the basement slab. Effectively, the information provided in this paper, shows advantages of merging both these approaches while the EAHX shoud be placed under the house or near the basement foundation.

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from Sentinel-1 SAR, Global Surface Water, HydroSHEDS, Global Human Layer Settlement, and MODIS Land Cover, our primary ob-jective is to delineate flood extents and compare them with centennial flood-prone areas, offering a comprehensive assessment of exposure during the period from July to October 2022 across Sen-egal's 14 regions. The findings underscore a total inundation area of 2 951 square kilometers, impacting 297 142 people, 175 square kilometers of urban and 16 square kilometers of crops. Notably, August wit-nessed the largest flooded areas, reaching 780 square kilometers, constituting 0.40% of the coun-try's surface. Subsequent regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experi-enced varying extents of flooding, with August data showing a 1.34% overall overlap compared to centennial flood-prone areas derived from hydrological and hydraulic modeling. This low per-centage reveals the distinct purposes and natures of the two approaches (remote sensing and modeling), as well as their complementarity. Turning to flood exposure, August emerges as the most critical month, affecting 76 595 people (0.43% of the total population). Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 100 707, 57 648, 31 579, and 26 581 people, respectively. These findings emphasize the imperative for comprehensive disaster preparedness and mitigation efforts. The study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies.

In the current work, the stable queueuing system's Generalised Z-Entropy (GZE) of Rényian formalism is examined in its fractal dimension. Notably, fractal dimension’s resulting behaviour that corresponds to the GZE parameters is examined through numerical tests. This research makes a substantial generalization in the literature by fusing fractal geometry and information theory to shed light on how entropy and complexity interact. More fundamentally, the significant role of fractal dimension to advance Big Data Analytics(BDAs) is highlighted. Closing remarks combined with open problems and the next phase of research are provided.

The rescue, digitisation, quality control, preservation and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth's climate system. In combination with land and air measurements, the historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for en-hancing predictive climate models and reanalyses. This paper provides an overview of marine data rescue activities covering the past centuries, presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitisation, quality control and the de-velopment of temporally high resolution meteorological and climatological observations from the Sea, will greatly help to further complete our understanding and knowledge of the Earth system including extremes, as well as improving the quality of reanalysis.