Technological advances in game-mediated robotics provide an opportunity to engage children with cerebral palsy (CP) and other neuromotor disabilities in more frequent and intensive therapy by making personalized, programmed interventions available 24/7 in children’s homes. Though shown to be clinically effective and feasible to produce, little is known of the subjective factors impacting acceptance of what we term assistive/rehabilitative (A/R) gamebots by their target populations. This research describes the conceptualization phase of an effort to develop a valid and reliable instrument to guide the design of A/R gamebots. We conducted in-depth interviews with 8 children with CP and their families who had trialed an exemplar A/R gamebot, PedBotHome, for 28 days in their homes. The goal was to understand how existing theories and instruments were either appropriate or inappropriate for measuring the subjective experience of A/R gamebots. Key findings were the importance of differentiating the use case of therapy from that of assistance in rehabilitative technology assessment, the need to incorporate the differing perspectives of children with CP and those of their parents into A/R gamebot evaluation, and the potential conflict between the goals of preserving the quality of the experience of game play for the child while also optimizing the intensity and duration of therapy provided during play.

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.

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.

Current satellite remote sensing methods struggle to detect and map forest degradation, a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height (hϕ) is a promising variable for measuring forest disturbances, as it is closely related to mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrain, despite the fact that much of the worlds’ remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between hϕ and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising the level of multilooking in the interferometric processing chain strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available.

Free route airspace (FRA) is a new concept of European air transport. It has been designed to eliminate the negative impact of air traffic on the environment, minimize fuel consumption, simplify and expand flight planning. In our research, FRA is modelled by using graph theory, networks, and convex analysis. We also looked for answers to the question which are the basic mathematical properties of airspace? Basic mathematical properties are expressed using axioms. Therefore, in our work, we state the general, basic axiom of airspace based on our FRA research. The axiom is given using practical, significant entry points and geodetic coordinates.

The complex progression, specialized care, and comorbidities of chronic diseases place a financial and health burden on society. In some instances, the high hospital readmission rate is also a result of chronic diseases. The readmission rates can be decreased if the quality of life for patients is increased and healthcare costs are reduced through effective management and preventive measures. As a result, healthcare professionals, service providers, and policy-makers are looking for innovative approaches to reduce healthcare costs while enhancing the quality of care. There is an emerging trend in deploying technological advancements like artificial intelligence (AI), the Internet of Things (IoT), sensors, wearables, social media, mobile apps, and genomics to decrease hospital readmissions. In some instances, predictive analytics, early warning systems, personal-ized care management, remote monitoring and Telehealth, decision support systems, patient education and engagement, and other areas of artificial intelligence and machine learning have outperformed traditional approaches in lowering hospital readmissions. Although AI effectively reduces readmission, there are potential risks if it is not combined with efficient interventions. Therefore, real-time monitoring and intervention are required for AI systems to increase patient safety and decrease hospital readmissions. To achieve autonomy, prevent harm, achieve fairness, and achieve explicability, the ethical principles relating to AI systems should be taken into ac-count. This study examines the significant impact on health and the economy of hospital read-missions for chronic diseases and the role of AI in reducing readmissions.

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.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

The relocation of the Indonesian capital from Jakarta to Ibu Kota Nusantara (IKN) East Kalimantan IKN will affect several sectors. The change in land use from vegetated to developed land will lead to local climate changes, including the emergence of the urban heat island (UHI) phenomenon, which is reflected in a higher land surface temperature (LST) than the surrounding area. In this study, an analysis of UHI propagation was conducted on a seasonal (DJF, MAM, JJA, and SON), with 5 yearly periods from 2001 to 2020, study area in IKN and its buffer cities (Balikpapan, Samarinda, and Bontang). Terra-MODIS satellite data are processed using Google Earth Engine (GEE) platform and output is done using GrADS. UHI (high LST) is found in urban areas/buffer cities both spatially and temporally in every season, while UHI has not yet occurred in IKN (low LST) and suburban, rural, hilly areas, vegetated areas, and water bodies (lakes, rivers). The cross-sectional latitudinal and longitudinal analysis showed that the LST DJF2015-2020 from highest to lowest temperature are in Balikpapan City (36.84 OC), Samarinda City (36.13 OC), Bontang City (32.3 OC); and IKN zero point (28.82 OC). The UHI propagation was observed in 2001-2005, 2006-2010, 2011-2015, and most during 2016-2020. Seasonally, the UHI was most pronounced in the SON season, while on lowest UHI was observed in the MAM season.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

Introduction Older people with complex, chronic conditions often receive insufficient or inefficient care provision. And few instruments are able to measure their perception on care provision. The "User Reported Measure of Care Coordination" instrument has been satisfactorily used to evaluate chronic care provision and integration. The aim of this study is to validate this instrument into Spanish. Methods The questionnaire was adapted and validate in two phases: translation and cultural adaptation of the questionnaire, and psychometric property measurement. Study population were chronic care conditions patients. Results A total of 332 participants completed test re-test as part of the questionnaire validation process. The final version of the questionnaire had 6 domains: Health and Well-being (D1), Health day to day (D2), Social Services (D3), Planned Care (D4), Urgent Care (D5) and Hospital Care (D6). Cronbach's alpha for the overall questionnaire was 0.86, indicating good internal consistency. When analysing each domain, only Planned Care (D4) and Urgent Care (D5) had Cronbach’s Alphas slightly lower than 0.7, although this could be related to the low number of items in each domain. A good temporal stability was observed for the distinct subscales and items, with intraclass correlation coefficients varying from 0.412 to 0.929 (p < 0.05). Conclusion The adapted version of the “User Reported Measure of Care Coordination” into Spanish proved to be a practical tool for use in our daily practice and an efficient instrument for assessment of care coordination in chronic, complex conditions in older people across services and levels of care.

Amid urgent global climate and biodiversity crises, the strategic restoration of degraded forests stands as a vital countermeasure. This study pioneers a novel approach for the identification and prioritization of potential degraded forest areas suitable for restoration (PDFR), utilizing the advancements in Earth observation data. Utilizing Landsat data within the Google Earth Engine, our PDFR method applies a nuanced, phenology-based threshold classification to accurately map forest covers at a 30-m resolution, distinguishing prime restoration areas such as evergreen, semi-evergreen, deciduous, and flooded forests, and categorizing them into varying levels of degradation using Siem Reap, a province in Cambodia as a case study. The projections indicate a promising potential for carbon sequestration through restoration of the critically (~96,693 ha), highly (48,878 ha), moderately (46,487 ha), and slightly degraded (75,567 ha) forests, estimating a capture of 193.73 TgCO2 in Siem Reap from 2021 to 2030 upon comprehensive restoration initiatives. As Earth observation technologies continue to evolve, the PDFR method emerges as a strategic blueprint for data-driven policy formulation, fostering sustainable forest management and aligning with the global commitments delineated in the Glasgow Forests Declaration for 2030.

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.

Considering the ever-increasing role of rare-earth elements (REE) in the modern hi-tech field, their effective use has a tremendous significance, although the production process is inevitably linked to the large volumes of industrial ammonia effluents and heavy metal wastes. In the process of metallurgical separation of metals, the emission of large volumes of noxious gases and radioactive substances is inevitable. Lean technogenic raw material processing is sensible under the condition of the development of non-waste technology. The lack of competent regulations governing the disposal of waste containing REE has an impact on adjacent territories, accumulating in water bodies and, as a result, in the human body. Such an impact cannot pass without a trace, however, the ambiguity of opinions in the scientific community regarding the toxic effects of REE on living organisms determines the relevance of a more detailed study of this issue. The paper presents experimental and theoretical results of studies of ion flotation in the aqueous solutions containing ytterbium cations and a surfactant as a collector - sodium dodecyl sulphate (NaDS).

Earth Observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: Atmosphere, Water, Agriculture, Land, Disaster Risk Reduction (DRR), Cultural Heritage, Energy, Marine Safety and Security (MSS) and Big Earth Data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and Land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high resolution and near real time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems and services.

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

The construction of earth buildings, both throughout history and in the current day, is well-established worldwide. Despite New Zealand's pre- and post-colonial history of earth construction, earth buildings as residential homes have not been well-received or popularised throughout present-day New Zealand. This research aims to identify the reasons for this lack of awareness and to determine methods that promote earth buildings in New Zealand. This research is based on data collected from semi-structured interviews conducted with subject matter experts and via an online questionnaire completed by members of the Earth Building Association New Zealand (EBANZ). The data collected revealed the experiences and perceptions of all participants regarding the advantages, challenges and promotion of earth buildings specific to New Zealand. Upon analysing these responses, key reoccurring themes were identified and compared. Regarding New Zealand's lack of awareness of earth buildings, interview and questionnaire participants responded that this shortcoming was due to earth construction being a very niche market and lacking commercial marketing. Education was the most frequently reoccurring theme raised by all participants as the top promotional tool for raising awareness of earth buildings. The results of this research can be applied to future work regarding obstacles that limit the growth of New Zealand's earth building industry, as well as research on the role of New Zealand's education system in exposing the next generation of builders, designers, and consumers to earth construction.

Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.

Homochirality is an obvious feature of life on Earth. On the other hand, extraterrestrial samples contain largely racemic compounds. The same is true for any common organic synthesis. Therefore, it has been a perplexing puzzle for decades how these racemates could have formed enantiomerically enriched fractions as a basis for the origin of homochiral life forms. Numerous hypotheses have been put forward as to how preferentially homochiral molecules could have formed and accumulated on Earth. In this article, it is shown that homochirality of the abiotic organic pool at the time of formation of the first self-replicating molecules is not necessary and not even probable. It is proposed to abandon the notion of a molecular ensemble and to focus on the level of individual molecules. Although the formation of the first self-replicating, most likely homochiral molecule is a seemingly improbable event, on a closer look, it is almost inevitable that homochiral molecules have formed simply on a statistical basis. In this case, the non-selective leap to homochirality would be one of the first steps in chemical evolution directly out of a racemic “ocean”. Moreover, most studies focus on the chirality of the primordial monomers with respect to an asymmetric carbon atom. However, any polymer with a minimal size that allows folding to a secondary structure, would spontaneously lead to asymmetric higher structures (conformations). Most of the functions of these polymers would be influenced by this inherently asymmetric folding. To summarize, simple and universal mechanisms may have led to homochiral self-replicating systems in the context of chemical evolution. A homochiral monomer pool is deemed unnecessary and probably never existed on primordial Earth.

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.

Commercially available oxidized (carboxylic groups) and non-oxidized multiwalled carbon nanotubes were studied as adsorbents of cerium(III) in batch operation mode. Several variables affecting the rare earth adsorption were investigated, including: the stirring speed applied to the system, the pH of the solution and the metal concentration and carbon dosages. Although the removal of cerium from the solution is different and dependent upon the adsorbent type: i) adsorption in non-oxidized multiwalled carbon nanotubes, ii) cation exchange in the case of using oxidized multiwalled carbon nanotubes, the adsorption kinetics, the rate law and the isotherm models are the same for both adsorbents: pseudo-second order, film diffusion and Langmuir Type-1, respectively. Cerium is desorbed from loaded adsorbents using acidic solutions.

Interdisciplinary conferences play a vital role in advancing explainable Artificial Intelligence (AI) within increasingly intersecting healthcare and computational biology arenas. Challenges result-ing from algorithmic bias and the necessity for domain-specific Explainable AI (XAI) techniques underscore the increasing significance of transparent and interpretable AI systems used in patient care. Collaborative efforts between clinicians, data scientists, and various stakeholders are nec-essary to innovate solutions tailored to healthcare needs. Specialized conferences may foster these interdisciplinary interactions, providing platforms for knowledge exchange, networking, and collaborative partnerships. The legal, ethical, and societal dimensions of medical AI advocate for an integrative approach that aligns technological advancements with patient-centered care val-ues. The role of specialized conferences is therefore essential for shaping future directions in ex-plainable AI and computational biology that contribute to improved patient outcomes and healthcare innovations.

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.

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.

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.

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. Adopting the Design Science Research paradigm, this study aims to evaluate the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools as an artifact. While a previous study completed the first steps of the Design Science Research, by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines, this work seeks to qualitatively evaluate their application in a practical experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case and responding to a questionnaire and a focus group interview about how they perceived these guidelines. A correlation analysis confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines' definition and could decide if they agreed or disagreed with their presence during the experiment. We conclude that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.

This work investigates centered polygonal lacunary functions restricted from the unit disk onto symmetry angle space which is defined by the symmetry angles of a given centered polygonal lacunary function. This restriction allows for one to consider only the p-sequences of the centered polygonal lacunary functions which are bounded, but not convergent, at the natural boundary. The periodicity of the $p$-sequences naturally gives rise to a convergent subsequence, which can be used as a grounds for decomposition of the restricted centered polygonal lacunary functions. A mapping of the unit disk to the sphere allows for the study of the line integrals of restricted centered polygonal that includes analytic progress towards closed form representations. Obvious closures of the domain obtained from the spherical map lead to four distinct topological spaces of the "broom topology'' type.

The present work demonstrates the optimization of the ligand structure in the series of bis-phosphine oxide and carbamoylphosphine oxide representatives for efficient coordination of Tb3+ and Eu3+ ions with the formation of the complexes exhibiting high Tb3+- and Eu3+-centered luminescence. The analysis of the stoichiometry and structure of the lanthanide complexes obtained by XRD method reveals the great impact of the bridging group nature between two phosphine oxide moieties on the coordination mode of the ligands with Tb3+ and Eu3+ ions. The bridging imido-group facilitates the deprotonation of imido-bisphosphine oxide ligand followed by the formation of tris-complexes. The spectral and PXRD analysis of the separated colloids indicates that the high stability of the tris-complexes provides their safe conversion into polystyrenesulfonate-stabilized colloids under the solvent exchange method. The red Eu3+-centered luminescence of the tris-complex exhibits the same specificity in the solutions and the colloids. The pronounced luminescent response on the antibiotics ceftriaxone allows to sense the latter in aqueous solutions with the LOD value equal to 0.974 M.

An inter- and transdisciplinary concept has been developed, focusing on the scaling of industrial circular construction using innovative compacted mineral mixtures (CMM) derived from various soil types (sand, silt, clay) and recycled mineral waste. The concept aims to accelerate the systemic transformation of the construction industry towards carbon neutrality by promoting the large-scale adoption and automation of CMM-based construction materials, which incorporate natural mineral components and recycled aggregates or industrial by-products. In close collaboration with international and domestic stakeholders in the construction sector, the concept explores the integration of various CMM-based construction methods for producing wall elements in conventional building construction. Leveraging a digital urban mining platform, the concept aims to standardize the production process and enable mass-scale production. The ultimate goal is to fully harness the potential of automated CMM-based wall elements as a fast, competitive, emission-free, and recyclable alternative to traditional masonry and concrete construction techniques. To achieve this objective, the concept draws upon the latest advances in soil mechanics, rheology, and automation and incorporates open-source digital platform technologies to enhance data accessibility, processing, and knowledge acquisition. This will bolster confidence in CMM-based technologies and facilitate their widespread adoption. The extraordinary transfer potential of this approach necessitates both basic and applied research. As such, the proposed transformative, inter- and transdisciplinary concept will be conducted and synthesized using a comprehensive, holistic, and transfer-oriented methodology.

The boosted interest in the use of rare earth elements (REEs) in modern technologies had also increase the necessity of their recovery from various sources, including raw materials and wastes. Though Hydrometallurgy plays a key role in these recovery processes, some drawbacks (apparent or not) of these processes, had led to the development of a more environmental friendship subclass named Solvometallurgy, in which non-aqueous solvents substituted to the aqueous media of the hydrometallurgical processing. The non-aqueous solvents chosen for these usages are, besides ionic liquids (ILs), the so-named Deep Eutectic Solvents (DEEs). The utilization of DEEs included the leaching of REEs from the different sources containing them, and also in the separation-purification steps necessary for the yielding of these elements, normally as oxides or salts, in the most purified form. This work reviewed the most recent literature (2023 year) about the utilization of deep eutectic solvents in the recovery of REEs from various sources, and in the coupling of these two (DESs and REEs) to derived compounds to be used in other fields.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who don’t have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This work compiles design guidelines derived from a systematic literature review to contribute in the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science databases for works published between 2018 and 2021 and discovered 14 papers. We compiled 14 requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The collected guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse, since virtual content creation is one of its bases.

Modern surface analysis techniques are expanding significantly possibilities in description and attribution of historical artifacts. The present study is aimed to compare analytical ability of the X-ray fluorescent analysis (XRF) typically applied for studies ceramic art items with novel surface analysis methods: time-of-flight secondary ions mass spectroscopy (SIMS) and X-ray photoelectron spectroscopy (XPS) that were applied on measurements of elemental compositions of powder raw materials for porcelain fabrication: pegmatite P-1, Vesco-prima clay, kaolin, bone earth, dolomite, and feldspar. It was shown that SIMS successfully measures light elements unnoticeable for XRF such as lithium, beryllium, boron and heavy impurities not found out by XRF such as cobalt, cerium, copper, rubidium, vanadium. XPS has found thorium in the kaolin sample that was not detected by SIMS and XRF. The paper provides also values of chemical shifts of some XPS lines observed in materials under examination. The obtained information on relations of basic and impurity elements revealed by XRF and SIMS together with XPS chemical shifts data could be used to expose contemporary falsifications of historical porcelain and to attribute porcelain items during conservation process.

Humanity’s path to avoiding extinction is a daunting and inevitable challenge which proves difficult to solve, partially due to the lack of data and evidence surrounding the concept. We aim to address this confusion by addressing the most dangerous threats to humanity, in hopes of providing a direction to approach this problem. Using a probabilistic mode, we observed the effects of nuclear war, climate change, asteroid impacts, artificial intelligence and pandemics, which are the most harmful disasters in terms of their extent of destruction on the length of human survival. We consider the starting point of the predicted average number of survival years as the present calendar year. Nuclear war, when sampling from an artificial normal distribution, results in an average human survival time of 60 years into the future starting from the present, before a civilization-ending disaster. While climate change results in an average human survival time of 193 years, the simulation based on impact from asteroids results in an average of 1754 years. Since the risks from asteroid impacts could be considered in the far future, it can be concluded that nuclear war, climate change, and pandemics are presently the most prominent threats to humanity. Additionally, the danger from superiority of artificial intelligence over humans, although abstract in its sense, is a factor of careful study and could also have wide ranging implications, impeding man’s advancements towards becoming a more advanced civilization.

This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. Flooded areas are identified using a change-detection method based on the normalized difference. The HAND algorithm is used to delineate zones for processing. The approach was tested and calibrated at small scale to identify optimal parameters for flood detection. It was then applied to the whole of the island of Madagascar after the cyclone Batsirai in 2022. The proposed method is enabled by the computing power and data availability of Google Earth Engine and Google Colab. The results show satisfactory accuracy in delineating flooded areas. The advantages of this approach are its rapidity, online availability and ability to detect floods over a wide area. The approach relying on Google tools thus offers an effective solution for generating a large-scale synoptic picture to inform hazard management decision-making. However, one of the method’s drawbacks is that it depends to a large extent on frequent radar imagery being available at the time of flood events and on free access to the platform. These drawbacks will need to be taken into account in an operational scenario.

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.

The Direction-of-Arrival (DoA) estimation methods are highly versatile and find extensive applications in satellite communication. The DoA methods are employed across a range of orbits, from Low Earth Orbits (LEO) to Geostationary Earth Orbits (GEO). They serve multiple applications, including altitude determination, geolocation and estimation accuracy, target localization, and relative and collaborative positioning. This paper presents a novel approach for modeling the DoA angle using a closed-form expression, incorporating the boresight angle and satellite and Earth station position data. The method uses the geographic coordinate system in the satellite communication system, precisely the latitude and longitude of the Earth station and altitude parameters of the satellite stations, to calculate the Earth station’s elevation angle and accurately model the DoA angle. Furthermore, this paper performs a comprehensive comparative analysis of various DoA methods to gain deeper insights into the performance of DoA estimation in multi-antenna systems operating under spatially correlated channels. Accordingly, this paper evaluates DoA estimation performance using root-mean-square-error (RMSE) statistics for uplink and downlink conditions through extensive Monte Carlo simulations. The simulation’s effectiveness is validated against the Cramer-Rao Bound (CRB) performance metric for the Additive white Gaussian noise (AWGN) case. (i.e., thermal noise). The simulation results demonstrate improved RMSE performance in satellite systems by incorporating spatial correlation into the system model.

Heterometallic rare-earth clusters have garnered much attention because of their interest-ing solid-state structures and versatile applications in catalysis and material and polymer chemistry over the last four decades. Particular interest is rare earth–transition metal co-ordination compounds that behave as single-molecule magnets (SMMs) because of their potential use in information storage, spintronic devices, and magnetic refrigeration sys-tems. Another implementation involves using lanthanide clusters in luminescent-based sensing for environmental protection and security screening or in photocatalytic energy conversion reactions. Heterometallic rare earth complexes offer the promise of catalytic performance of many organic reactions due to their high oxygenophilicity, Lewis acidity, high coordination numbers, and ability to change the coordination environment quickly. Therefore, they are commonly applied as catalysts in asymmetric synthesis, oxidation re-actions, and CO2 conversions or as initiators in ring-opening polymerization and copoly-merization of cyclic monomers. They are also attractive single-source molecular precur-sors for functional inorganic materials. This review focused on the unique features and properties of the heterometallic rare earth complexes, which are closely correlated with their molecular structure. Our summary is a valuable resource, providing researchers with an insightful exploration of the heterometallic rare-earth complexes that demonstrated multimetallic synergy and cooperativity effects in various applications.

The continuous nature of space and time is a fundamental tenet of many scientific endeavours. That digital representation imposes granularity is well recognized but whether it is possible to address space completely remains unanswered. Part 1 argues that Hales’ proof of Keppler’s conjecture on the packing of hard spheres suggests the answer to be ‘no’, providing examples of why this matters in GIS generally and spatio-temporal GIS in particular. Part 2 seeks to resolve the dichotomy between continuous and granular space, showing how a continuous space may be emergent over a random graph. However, its projection into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a ‘smooth’ spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation.

The local and global order in dense packings of linear, semi-flexible polymers of tangent hard spheres are studied by employing extensive Monte Carlo simulations at increasing volume fractions. Chain stiffness is controlled by a tunable harmonic potential for the bending angle whose intensity dictates the rigidity of the polymer backbone as a function of the bending constant and equilibrium angle. The studied angles range from acute to obtuse ones, reaching the limit of rod-like polymers. We analyze how packing density and chain stiffness affect the ability of chains to self-organize at the local and global levels. The former corresponds to crystallinity as quantified by the Characteristic Crystallographic Element (CCE) norm descriptor, while the latter is computed through the scalar orientational order parameter. In all cases, we identify the critical volume fraction for the phase transition and gauge the established crystal morphologies, developing a complete phase diagram as a function of packing density and equilibrium bending angle. A plethora of structures is obtained, ranging from random hexagonal closed packed morphologies of mixed character and almost perfect face centered cubic (FCC) and hexagonal close-packed (HCP) crystals at the level of monomers, to nematic mesophases, with prolate and oblate mesogens at the level of chains. For rod-like chains, hysteresis is observed between the establishment of long-range nematic order and crystallization, while for right-angle chains both transitions are synchronized. A comparison is also provided against the analogous packings of monomeric and fully flexible chains of hard spheres.

This comprehensive statement explores the concept of educational excellence, emphasizing the limitations of traditional metrics and proposing a holistic model for excellent educational systems. The discussion delves into the ambiguity surrounding excellence, challenges in measurement, and the influence of social, cultural, and economic factors on education. The objectives of the study include shedding light on the nature of excellence, its global significance, and introducing a model for improving school performance. The theoretical framework integrates constructivism, Maslow's Hierarchy of Needs, and Ecological Systems Theory to emphasize student-centered learning, holistic development, and environmental factors. The proposed model envisions an educational ecosystem prioritizing personalized, inclusive, and relevant education, with key components such as student-centered learning, inspirational educators, curriculum innovation, and community involvement. The abstract concludes by highlighting the model's potential implications in transforming education, challenging the status quo, and fostering well-rounded individuals equipped for a rapidly changing world.

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.

Mapping and prediction of inundated areas is increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observed flooding patterns for training, calibration, validation and benchmarking. At regional to continental scale, satellite earth observation is the established method for surface water extent (SWE) mapping and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared to low-resolution SWE maps derived from satellite earth observation (EO). We conclude that UAS have significant potential for SWE mapping at intermediate scales, can bridge the scale gap between ground observations and satellite EO and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation.

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. Therefore, the restoration of degraded landscapes is essential to maintain the functionality of ecosystems, especially those with greater social, economic and environmental vulnerability. Nevertheless, policy-makers are frequently challenged by deciding on where to prioritize restoration actions, which usually includes to deal with multiple and complex needs under an always short budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. To help decision-makers taking informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called "RePlant alpha" which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis, with at least six indicators and for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

In this work, we present preliminary results of radar observations of near-Earth Objects (NEOs) carried out by European radio telescopes in the framework of the European Space Agency (ESA) project “NEO observation concepts for radar systems”, which was aimed to derive the functional requirements of a radar system, evaluate the available European assets to perform NEO radar observations and carry out test radar campaigns. In the first part of the project, we carried out the performance analysis of a possible European planetary radar system. Instrumental features, as much as issues like the impact of weather conditions on signal propagation at different radio frequencies, were considered. This paper is focused on the test campaigns, performed in the years 2021-22 in collaboration with the Jet Propulsion Laboratory (JPL), which led to the observation of several asteroids including 2021 AF8, (4660) Nereus and 2005 LW3, which allowed us to derive astrometric measurements, as well as to measure physical properties, such as rotation periods, and detect an asteroid’s satellite. The obtained results demonstrate that European radio astronomical dishes, although employed only as receivers (in bistatic or multistatic configurations) and for a limited amount of time, are able to provide a significant contribution to the constitution of a European network to increase the opportunities for NEO monitoring and studies, if a transmitting antenna - equipped with a suitable high-power transmitter - were made available.

Antibiotic resistance is a pressing global and planetary health challenge. Links between climate change, antibiotic use and the emergence of antibiotic resistance have been well documented, but less attention has been given to the impact(s) of earth systems on specific bacterial livestock diseases at a more granular level. Understanding the precise impacts of climate change on livestock health – and in turn the use of antibiotics to address that ill-health – is important in providing an evidence base to tackle such impacts and to develop practical, implementable and locally acceptable solutions within and beyond current antibiotic stewardship programmes. In this paper, we set out the case for better integration of earth scientists and their specific disciplinary skill set (specifically, problem-solving with incomplete/fragmentary data; the ability to work across four dimensions and at the interface between the present and deep/geological time) into planetary health research. We then discuss a methodology that makes use of risk mapping, a common methodology in earth science but less frequently used in health science, to map disease risk against changing climatic conditions at a granular level. This will enable predictions of future disease risk and risk impacts based on predicted future climate conditions, and thus provide an evidence base for planetary health activists to influence policy and develop mitigations. Our case study – of climate conditions’ impact on livestock health in Karnataka, India – clearly evidences the benefit of integrating earth scientists into planetary health research.

Children with Autism Spectrum Disorder have deficits in social interaction and expressing and understanding emotions. Many robots for children with ASD have been proposed. However, few studies were found about how to design a social robot for children with ASD. Studies reviewed employed non-experimental studies to evaluate a social robot, but the methodology to design a social robot is rare. This study proposes a design path for a social robot for emotional communi-cation for children with ASD, following User-Centered Design approach. This design path is ap-plied with a study case, evaluated with an expert group on psychologist, human-robot interaction, human-computer interaction, and parents of children with ASD. Results show than following the design path proposed to design a social robot to communication emotions for children ASD is favorable.

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 (

In this study, multi-layer ceramic capacitors (MLCCs) detached from lighting sector's waste electrical-electronic equipment (WEEE) are characterised for the presence of rare earth elements (REEs) and precious metals (PMs). Their digestion was carried out with HNO3 & aqua regia on a heating plate and characterised with the use of inductively coupled plasma optical emission spectroscopy (ICP-OES) & scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). The contents of REEs and PMs are 0.84 wt% and 0.60 wt%, respectively, and create an economic stored value in MLCCs that is essentially defined by PMs of 98.67% and by Pd 78.37%. The content of the main elements is: Nd 0.366 wt%, Y 0.220 wt%, Dy 0.131 wt%, Ag 0.467 wt%, and Pd 0.105 wt%. The results confirm the need of selective removal and separate recycling process of MLCCs from WEEE drivers.

This review explores the potential implementation of lithium tetraborate (Li2B4O7) as a scintillator medium for neutron detection applications. Several characteristics required for the neutron detection process suggest that Li2B4O7 could be a suitable material for scintillation-based neutron detection systems. The inherently large neutron capture cross-section due to 10B and 6Li isotopes, and the ease with which Li2B4O7 can be enriched with these isotopes, combined with the facile inclusion of rare-earth dopants are all expected to improve luminescent properties as well as neutron detection efficiency of Li2B4O7. The electronic structure of doped and undoped Li2B4O7 are explored using photoemission and inverse photoemission spectroscopies, optical measurements, and theoretical computational studies such as density functional theory. The scintillation properties are further enhanced because of the wide bandgap, and transparency towards the photons that are emitted following neutron capture.

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.

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.

Global climate change and associated environmental extremes present a pressing need to understand and predict social-environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we use the Wisdom-Knowledge-Information-Data (WKID) Innovation framework to inform solutions-oriented science in an integrated Research to Commercialization (R2C) model that explores market viability for value-added analytics. We conduct a case study using this R2C model to address the wicked wildfire problem. By integrating WKID and human centered design (HCD), through an industry-university partnership called the Climate Innovation Collaboratory between the University of Colorado Boulder and Deloitte Consulting, we systematically evaluated 39 different user stories across 8 user personas and identified common gaps, how to define information technologies that add value, and how to develop a marketable product. This R2C model could enable transition of knowledge to operational implementation informing policy and decision makers, tasked with addressing some of our most challenging environmental problems.

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

In space application, hybrid pixel detectors of the Timepix family have been considered mainly for measurement of radiation levels and radiation dosimetry in low earth orbits. By the example of the Space Application of Timepix Radiation Monitor (SATRAM), we demonstrate the unique capabilities of Timepix-based miniaturized radiation detectors for particle separation. Using a novel method for proton spectrum reconstruction, we were able to measure the spectrum of protons trapped in the inner Van-Allen radiation belt, for the first time with a single-layer detector. We assess the measurement stability and the resiliency of the detector to the space environment demonstrating that, even though degradation is observed, data quality has not been affected significantly over more than 10 years. Based on the SATRAM heritage and the capabilities of the latest-generation Timepix-series chips, we discuss their applicability for use in a compact magnetic spectrometer for deep-space mission or in the high radiation environment of the Jupiter radiation belts, and their capability for use as single-layer X- and γ-ray polarimeter. The latter was supported by measurement of the polarization of scattered radiation in a laboratory experiment, where a modulation of 80% was found.

The present article summarises Earth Observation (EO)-based rice mapping strategies since 1979, with a focus on data, methodologies, and methods based on 3,700 research publications across global literature and its comparison with the Vietnamese Mekong Delta (VMD). Various quan-titative analyses were conducted through bibliometric analysis using the VOS viewer and Scopus database. Optical images, particularly MODIS and Landsat time series datasets, were found to be the most commonly utilized. Landsat data had the highest share in the global context, while MODIS data research dominated in the VMD, while Sentinel series data and the Google Earth Engine (GEE) platform became more popular in recent years. The research on rice mapping using UAVs has been gradually creeping into global rice mapping research but is a loophole yet to be implemented in the VMD. The most widely used approaches for rice mapping globally were Random Forest, Support Vector Machine, and Principal Component Analysis. Indices like EVI, NDVI, and RVI were commonly used for rice mapping and monitoring. The findings underscore the critical role of EO-based rice mapping studies in the VMD in addressing sustainability and food security chal-lenges.

The orbital elactronic structure of Rare Earth Elements (REEs) contains many unpaired electrons which render them capable of storing large amount of magnetic energy in addition to being critical for hitech applications. Geological deposits are their conventional sources, and the current supply chain relies on production from these deposits. However, given their critical roles in the anticipated global energy transition, there is the need to explore other viable sources to supplement current and future supplies chains. REEs occur in coal as accessory minerals and their concentration in coal ash to levels that rival those of geological deposits has been estab;ished by sophisticated analytical chemical methods. Conmventional hydrometallurgical processes rely on acid leaching, using tioxic mineral acids. Meanwhile, organosulfonic acids have pKa values that rival those of conventional minerals acid and can, therefore, be used in hydrometallurgy but their uses in this regard are not well documented in the literature. In this extensive review, we have covered geological sources of REEs exaustively in addition to showing the potential of organosulfonic acids as environmentally benign lixiviants for REEs extraction from coal ash. We have also shown how process optimization can be achieved using advance technologies while using organisulfonic acids. Moreover, we have shown current and future global market trends regarding the production of select organosulfonic acids, and the anticipated global increase in their production motivates the use of organosulfonic acids as viable lixiviants for REEs extraction from caol ash deposits.

This study explores the complexities of risk communication practices in orthodontics, specifically examining the perspectives of orthodontists in Greece and Slovakia. Informed by the foundational importance of effective communication in health sciences, particularly in facilitating valid consent and shared decision-making, this research addresses gaps identified in the literature concerning the consistent communication of potential treatment risks based on demographic and cultural characteristics. The study identifies 15 potential critical risks with orthodontic treatment. These risks include root necrosis; temporary undesired changes to the occlusion; sleep difficulties; not achieving an ideal result; development of black triangles between teeth; taking additional X-rays; speech difficulties; using a protective splint during sports; duration of treatment; number of visits; transmission of infectious diseases; and swallowing orthodontic appliances. The questionnaire, distributed electronically to orthodontists in Greece (n=570) and Slovakia (n=210) from Sept. 2022 to Dec. 2022, aimed to assess risk communication practices, with consideration to country, gender, and degree-related variations. 168 valid questionnaires (91 from Slovakia and 77 from Greece) were obtained indicating significant disparities in the risks emphasized and preferred forms of consent. The Greek orthodontists focused more on the risks involved such as on relapse, root resorption, temporal occlusal changes, and failure of desired movement, while the Slovak practitioners tend to be interested more about sleeping difficulties, temporal occlusal changes and not achieving an ideal result. They also tend to obtain written or digital consent from patients or their parents/guardians more frequently than the Greek team. Male orthodontists discussed specific risks more frequently, including relapse and extractions, whereas females preferred written or digital consent. PhD-trained orthodontists prioritized certain risks, indicating the need for tailored approaches. This study underscores the dynamic nature of risk assessment in orthodontic practice, emphasizing its ethical and strategic dimensions. The findings advocate for tailored risk communication strategies, that recognize individual, contextual, and cultural factors and the need of an orthodontic informed consent protocol for tailored communication approach for patients to elevate the standard of care in European orthodontics. The reliance on digital tools reflects contemporary trends in enhancing patient understanding, thereby supporting ongoing innovation in orthodontic practices.

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.

Purpose: This study aims to provide a foundational understanding of patient-centric perceptions of telemedicine implementation in rural Minnesota. Methods: This retrospective cohort study utilized a mail questionnaire to assess patient attitudes and behaviors regarding telemedicine during the emergency response to COVID-19. The target population was patients of a Primary Care clinic in eastern Minnesota. Descriptive statistics were used to assess respondents’ level of agreement with survey items scored on a 0-10 scale. Results: A majority of the respondents indicated that they were satisfied with using telemedicine (73.96%); that they gained an additional sense of control over their health condition due to the availability of telemedicine (52.08%); that they would support the use of telemedicine in the future (61.05%); that telemedicine increased access to care (65.63%); telemedicine was preferred over the clinic (61.46%), and that they would purchase new equipment to increase the utility of telemedicine services (54.17%). 51.04% of the respondents indicated a 10/10 level of satisfaction with telemedicine services. 48.96% indicated the strongest level of disagreement related to difficulty using telemedicine or a preference for in-person clinical appointments. Conclusions: Patients were found to have favorable perceptions of telemedicine overall. Though providers continue to recognize the benefit of face-to-face visits, their patients show an increased predilection for virtual care and telemedicine will likely remain an essential tool for providers to reach patients who would otherwise choose not to seek care.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

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.

We present results on the spontaneous crystallization of freely-jointed polymers of hard spheres obtained in an unprecedentedly long Monte Carlo (MC) simulation on a system of 54 chains of 1000 monomers. Starting from a purely amorphous configuration and after a transitory dominance of the hexagonal closed packed (HCP) polymorph, the system crystallizes in a final, stable, face centered cubic (FCC) crystal of very high perfection. Through refined metrics we gauge the degree of ordering and identify the regions of the phase transition and the corresponding morphologies. An analysis of chain conformational characteristics, of the spatial distribution of monomers and of the volume accessible to them shows that the phase transition is caused by an increase in translational entropy that is larger than the loss of conformational entropy of the chains in the crystal compared to the amorphous state. Polymer chains in the crystal adopt ideal random walk statistics as their great length renders local conformational details, imposed by the geometry of the FCC crystal, irrelevant.

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.

Philips Visual Patient Avatar represents an alternative method of patient monitoring that, according to computer-based simulation studies, enhances diagnostic accuracy, confidence and reduces workload. After its clinical integration, we assessed pediatric anesthesia providers' perspectives on this technology. This is a single-center qualitative study, conducted at the University Hospital Zurich using in-depth individual interviews. We aimed to identify the advantages and limitations of Visual Patient Avatar in pediatric anesthesia and to assess children and parents' reactions from caregivers' perspective. Thematic analysis was used to identify the dominant themes. Fourteen members of the institution's pediatric anesthesia team were interviewed. The most prevalent themes were: children's positive reactions towards the Visual Patient Avatar (92.9%) and enhanced speed in problem identification (71.4%). Additionally, 50% of participants reported finding the Visual Patient Avatar useful for diverting children's attention during anesthesia induction, and 50% suggested that its vital sign thresholds should be adaptable for different age groups. The study revealed that the Visual Patient Avatar was recognized as a valuable tool in pediatric anesthesia, enabling prompt identification of underlying issues and receiving positive feedback from both children and parents. The most commonly voiced wish for improvement in the study was the ability to customize the Visual Patient Avatar's thresholds for different age groups.

Educational institutions worldwide have adopted e-learning due to the COVID-19 pandemic. While this shift to online learning (OL) has presented challenges for students and teachers, it has also sparked innovative educational approaches. This study investigates college students' perceptions regarding OL, specifically focusing on gender differences in the experience with OL A quantitative survey gathered information from distinct college students in Gulf Cooperation Council (GCC) nations who experienced OL during the pandemic. The survey included questions related to satisfaction with OL and specific aspects such as course content delivery, interaction with instructors, behavioral changes, and challenges with OL. Male and female student OL experience was compared statistically. The results conclude that male students are more satisfied than female students. Students struggled with internet connection, OL adaptation, focus, and workload during OL. Understanding these gender differences in students' satisfaction with OL is crucial for educators and institutions as they strive to optimize the effectiveness of e-learning strategies. By recognizing and addressing male and female students' unique needs and challenges, educational institutions can enhance the overall educational experience during times of crisis and beyond.

Blood gas analysis plays a central role in modern medicine. Advances in technology have expanded the range of available parameters and increased the complexity of their interpretation. By applying user-centered design principles, it is possible to reduce the cognitive load associated with interpreting blood gas analysis. In this international, multicenter study, we explored anesthesiologists' perspectives on Visual Blood, a novel visualization technique for presenting blood gas analysis results. We conducted interviews with participants following two computer-based simulation studies: the first utilizing Virtual Reality (VR) (50 participants) and the second without VR (70 participants). Employing the template approach, we identified key themes in the interview responses and formulated six statements, which were rated using Likert scales: from 1 (strongly disagree) to 5 (strongly agree) in an online questionnaire. The most frequently mentioned theme was the positive usability features of Visual Blood. The online survey revealed that participants found Visual Blood to be an intuitive method for interpreting blood gas analysis (median 4, IQR 4-4, p<0.001). Participants noted that minimal training was required to effectively learn how to interpret Visual Blood (median 4, IQR 4-4, p<0.001). However, adjustments are necessary to reduce visual overload (median 4, IQR 2-4, p<0.001). Overall, Visual Blood received a favorable response. The strengths and weaknesses derived from these data will help optimize future versions of Visual Blood to improve the presentation of blood gas analysis results.

In this study, the extraction of rare earth elements (REEs) from chloride solutions containing mixtures of these metals with solutions of the mixtures of P507 and Cyanex 272 (1:1) of various concentrations was experimentally studied. It was shown that the distribution coefficients of all REEs decrease with increasing concentration of these metals in the initial solution, which is associated with the loading of the organic phase. The most significant improvement in the extraction is observed for heavy group of rare earth elements. The extractability of REEs enhances with increasing atomic number of the element, as is typical for the extraction of these metals with acidic organophosphorus extractants. The data obtained show that separation factors of adjacent rare earth elements mainly decrease with increasing concentration of metals in the initial aqueous solution. Increasing the concentration of the extractant mixture does not have a significant effect on the values of the adjacent REE separation factors. Analysis of the experimental results shows that two commercial products can be obtained from a concentrate of the medium-heavy group. In the first cascade, yttrium can be separated, in the second, a concentrate of dysprosium and terbium can be obtained.

With young adults (18-30 years) exhibiting the lowest vegetable intake, primarily due to in-adequate cooking skills, this study aims to explore the usability and desirability of educational tools such as written recipes and cooking videos to foster sustainable eating habits. Employing a descriptive, qualitative, and user-centered design, three focus groups were held at a major urban university campus in New Zealand, engaging students, and staff within the target age group. Discussions with 15 participants identified key facilitators for vegetable-based cooking, including access to ingredients, ease of recipe visualization, and cost-effectiveness. Conse-quently, 13 vegetable-centric recipes and instructional videos were created, incorporating nu-tritional value, affordability, and preparation efficiency. These resources, tailored to young adults' preferences, were designed to mitigate the identified barriers to vegetable use, con-tributing to sustainable food practices. The application of a user-centered approach in devel-oping educational content yielded a set of recipes and videos that not only address the gaps in cooking skills among young adults but also promote the broader goals of sustainability in food consumption patterns. This approach offers actionable insights for practitioners and food marketers to enhance vegetable utilization and sustainable eating behaviors in this demo-graphic.

The worldwide population is undergoing a fundamental change in its age structure, which challenges the health and social services system. The need to migrate towards a more person-centered and coordinated model of care that supports the optimization of abilities and capacities for older people has to be matched. In this sense, eHealth technologies can play a fundamental role. In this paper, through a questionnaire-based data collection on 30 primary (older people) and 32 secondary (informal caregivers) end-users, we share the sustainable way to develop a product optimizing the user experience and ensuring adoption. Both older adults and caregivers consider the use of smartphone and smartwatch to be very important, but also the use of digital devices for healthcare can be helpful. Seniors care about self-monitoring of health parameters using wearable devices, regardless of their health status, and would like to be included in the process of making good health decisions, because they need to feel in control of their healthcare process. Digital solutions in health and care can support the wellbeing of older adults in many areas of their daily lives at home and in their communities, but only if such innovation is designed around the natural voice of the intended target.

The work reports a facile synthesis of high thermally-stable nanocrystalline anatase TiO2 nanoparticles (NPs) doped with different atomic concentrations (0.5, 1.0, 3.0, and 6.0%) of Gd3+ and Nd3+ ions by a template-free and one-step solvothermal process, using titanium(IV) butoxide as titanium precursor and dimethyl sulfoxide (DMSO) as solvent. The structure and morphology of the Gd3+, Nd3+, and 0.5%Gd3+-0.5%Nd3+/doped TiO2 NPs have been characterized by using various analytical techniques. The Gd3+/ and Nd3+/TiO2 molar ratios were found to have a pronounced impact on the crystalline structure, size, and morphology of TiO2 NPs. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) studies revealed the proper substitution of Ti4+ by Gd3+ and Nd3+ ions in the TiO2 host lattice. The as-prepared Gdx/TiO2, Ndx/TiO2, and Gd1.0/Ndx/TiO2 bimetallic NPs, x = 0.5, 1.0, 3.0, and 6%, have been investigated as electrocatalysts for hydrogen evolution reaction (HER) in 1.0 M KOH solution using a variety of electrochemical techniques. At any doping percentage, the Gd1.0/Ndx/TiO2 bimetallic NPs showed higher HER catalytic performance than their corresponding counterparts i.e. Gdx/TiO2 and Ndx/TiO2. Upon increasing the Nd content from 0.5 to 6.0%, the HER catalytic performance of the Gd1.0/Ndx/TiO2 bimetallic NPs was generally enhanced. Among the studied materials, the bimetallic Gd1.0/Nd6.0/TiO2 NPs emerged as the most promising catalyst with an onset potential of -22 mV vs. RHE, a Tafel slope of 109 mV dec-1, and an exchange current density of 0.72 mA cm-2. Such HER electrochemical kinetic parameters are close to those recorded by the commercial Pt/C (onset potential: -15 mV, Tafel slope: 106 mV dec-1, and exchange current density: 0.80 mA cm-2), and also comparable with those measured by the most active electrocatalysts reported in the literature. The synergistic interaction of Gd and Nd is thought to be the major cause of the bimetallic catalyst's activity.

The relationship between living things and their respective environments highly dependent on body temperature regulation. The human capacity to effectively thermoregulate evolved at a time when the environmental temperature was likely around 25°C during the Eocene epoch, some ~ 50-60 million years ago. This effectively meant that homeothermy settled on a core temperature balance point of ~ 37°C. When Homo split from chimpanzee around 5 million years ago the Earth was entering a cooling period where the balance point temperature was always well above that of the environment and body heat balance could be maintained. Following this cooling period, the Earth’s rewarming by 7 °C took over approximately 5,000 years, whereas the current estimates indicate 0.7 °C over the past 100 years; ten times the rate of ice-age-recovery warming, or 20 times faster compared with the last 2 million years. As such, if the predicted continued rise in global temperature continues, and surface temperature reaches values where heat load cannot be dumped as the body temperature balance point is at or near the environmental temperature, areas of the Earth would become inhospitable. This effectively means that we will need to deal with both physiological and behavioral limitations since our ability to adapt will be limited by a thermoregulatory strategy that evolved over millions of years for a different kind of environment, not one that is predicted to change rapidly over the next century. This paper outlines the basis on which Homo settled on a thermoregulatory balance point and what limitations this presents for us in the future.

The Sm3+/Gd3+ co-doped glasses in the B2O3–GeO2–PbO and B2O3–GeO2–Bi2O3 systems have been synthesized in the composition range of 40B2O3–40GeO2–(15-17)PbO(Bi2O3)–(3-2)Sm2O3–(2-1)Gd2O3 and 42.5B2O3–42.5GeO2–(11.25-12.75)PbO(Bi2O3)–(2.25-1.5)Sm2O3–(1.5-0.075)Gd2O3. The physical, structural, and spectral properties of the produced glasses have been studied. The fabricated glass samples demonstrated changes in temperature-sensitive luminescence parameters in 298-673 K temperature range. We found out several photoluminescence bands attributed with transitions from different Stark sublevels. By using a combination of two temperature sensitive and one non-sensitive transitions we calculated fluorescence intensities ratio (FIR). This procedure made it possible to increase the sensitivity of luminescence thermometry in several times. Such method could be very useful for the luminescent thermometry materials based on Ln3+ ions with multiplied f-f transitions.

Dietary supplements are widely embraced by cancer patients seeking complementary and integrative approaches to their care. Unfortunately, many patients do not discuss supplement use with physicians, often due to perceptions of physician indifference or negativity toward supplements. This communication gap exposes patients to unreliable information sources and, potentially, unnecessary risk. As the healthcare landscape evolves, patients increasingly value physicians who recognize their pivotal role in shaping healthcare decisions. This patient-centered perspective emphasizes the provision of evidence-based information tailored to individual needs, open discussions on potential risks, and shared decision-making. It underscores the importance of respecting patient autonomy, offering alternative options, documenting preferences, and ensuring ongoing support while coordinating with the healthcare team. To address these needs, healthcare providers should transform their perspectives and become expert guides who embrace patients as informed, empowered participants. This approach prioritizes open dialogue that considers both facts and uncertainties regarding dietary supplement use, allowing for mutually informed decision-making. Here, we review the literature and present a practical approach emphasizing open discussions, transparency, and respect for patient autonomy. Following this approach, healthcare providers can empower patients to navigate the complexities of dietary supplement use in the context of cancer care, ultimately safeguarding patient safety and well-being.

Al-Zn-Mg-Cu alloy has high specific strength, good corrosion resistance, fracture toughness and fatigue resistance. It is one of the most important structural materials in the fields of aviation, aerospace, weapons, transportation and so on, which plays a huge role in the field of aerospace.In order to optimize the strength, toughness and corrosion properties of Al-Zn-Mg-Cu alloy, alloying has always been the focus of research on Al-Zn-Mg-Cu alloy. The effects of main alloying elements, trace alloying elements and rare earth elements on the microstructure and properties of Al-Zn-Mg-Cu aluminum alloy were briefly introduced in the paper, and the future research directions were prospected.

Rammed earth in a built environment has vapor diffusion characteristics and humidity control ability each of which are respectively attributed to the porous structures and the hydrophilic properties. Indeed, these structures and properties, which allows easily absorbing water parti-cles, hence, affecting the durability of a rammed earth wall. This paper presents the water repel-lency method for rammed earth walls which synthesizes siloxane copolymers containing fluo-rine. The water-repellent properties are investigated by measuring the contact angle, water ab-sorption rate, and compressive strength after spray coating with the synthesized siloxane co-polymers on the surface of the rammed earth specimens under study. The water contact angle of the specimen coated with a siloxane copolymer containing 10 mol.% of a silane monomer with a fluorine group is about 140o. The water absorption of the specimen obtained after immersing in water for 24 hours is low at about 3.5 wt.%. In addition, the compressive strength is remained more than 80% of the corresponding strength of the specimen which is not immersed into water. It is confirmed that the use of a 10% by volume of the siloxane copolymer containing the fluo-rine group may assure the water-repellent performance and the economic competitiveness.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

Point defects induced by doping rare earth elements RE (Nd, Er) into the magnesium oxide host were investigated by classical atomistic simulations utilising the General Utility Lattice Program (GULP). Formation and association energies were calculated for the potential defect structures. Both isolated defects and defect complexes were considered. The most energetically favourable structures of defect complexes were found for rare earth doped and Li co-doped systems. The correlation between the association energy and the structure of the defect complex was investigated. The influences of Li were revealed with respect to energy and structure. The simulation results contribute to the understanding of the point defects of doped MgO and how Li influences the doping of rare earth elements in the MgO host.

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.

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.

This article presents research on selected materials with the aim of determining their rare earth element (REE) content and attempting to intensify it. The test material was coal slurry, which is a waste product from the operation of a preparation plant of a closed coal mine. Research on REE concentration intensification tests was carried out using magnetic separation. Laboratory analyses determining the REE content in the samples obtained, were carried out using the inductively coupled plasma ionisation mass spectrometry (ICP-MS) technique. Carrying out this research by the KOMAG Institute of Mining Technology, is a continuation of work related to making mining waste economically useful and determining a new source of REE recovery. Previous research and development work showed REE content (scandium 40.49 ppm) in the coal slurries studied. The result of the work carried out is the determination of the economic utility of REE recovery from the studied waste.

Autonomous electronic area, territory, or space protection is getting more important. One of the areas of application of the system is to monitor the quarantine zones created during epidemics. The idea to create a system of mobile sensory units for defending quarantine zones was born during one of the phases of the Covid epidemic in 2020. The paper contains a brief technical description of a mobile sensor units and their network. This network surrounds an area of interest. The work includes a mathematical and data model of the protected area. In addition, the paper presents a method for distributing mobile sensor units in areas of interest.

For the last two decades, an increasing demand for low-cost adsorbents for drinking water treatment has been challenged in the rural areas of developing countries that have problems with arsenic (As) contamination. In this work, spent bleaching earth (SBE), a residual material that is formed during the process of refining vegetable oil, has been utilized to prepare the new magnetic adsorbent Fe-SBE/C by modifying its surface area through calcination and co-precipitation with iron. The experimental results demonstrate that Fe-SBE/C exhibits a substantial adsorption capacity towards both arsenite (As(III)) and arsenate (As(V)). Optimal pH conditions were found to be pH 10 for As(III) and pH 3 for As(V), resulting in adsorption percentages of 76.7% and 94.1%, respectively. Other experimental parameters such as calcination conditions, adsorbent dosage, iron-loading ratio, and the presence of co-existing ions were thoroughly investigated. The kinetic study revealed that the adsorption processes follow the Elovich and intraparticle diffusion models, suggesting a combined adsorption mechanism of boundary layer control and chemisorption. Furthermore, the isotherm followed the Langmuir model with a maximum adsorption capacity of 202.61 μg g-1 for As(III) and 187.61 μg g-1 for As(V). Through thermodynamic analysis and reinforcement by activation energy data, this research validates that the observed adsorption process is indicative through chemisorption. The regeneration study revealed the potential for multiple cycles of arsenic removal, highlighting the practical applicability of Fe-SBE/C in environmental contexts. In conclusion, the findings of this study underscore the efficacy of Fe-SBE/C in arsenic removal and present it as a promising and environmentally friendly solution for mitigating arsenic pollution.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

For colloids of NaYF4:Yb3+/Tm3+ nanoparticles in DMSO by the method of time-resolved lumi-nescence spectroscopy with nanosecond pulsed excitation at a wavelength of 975 nm the photo-physical processes that determine the course of kinetic curves have been revealed. It has been found that the luminescence rise time decreases with an increase of the concentration of activators and sensitizers due to increase of the efficiency of energy transfer from sensitizers to activators. Cross-relaxation of the excited states of activators provides a decrease of the lumi-nescence decay time with an increase of the concentration of activators and a constant concentration of the sensitizer. There was no correlation between the time of luminescence decay with the change of the concentration of sensitizers and constant concentration of activators due to the competition of the processes of energy back transfer from activators to sensitizers and the "feeding" of activators by excitations coming from remote sensitizer ions.

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.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (&lt;0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

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.

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.

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.

Patients with a psychotic disorder frequently report oral health problems, while mental health nurses (MHNs) seem not to be fully aware of these problems and the risk factors. Therefore, this study aims to develop supportive tools for MHNs regarding oral health to increase sensitization among MHNs and to provide MHNs with knowledge to recognize (potential) oral health problems in patients with a psychotic disorder. We used a human-centered design in which the user, MHNs as well as experts by experience, were placed in the centre of the research process. Problems and needs in MHNs working with patients with a psychotic disorder were addressed. To identify key issues of problem as well as the needs in terms of resources, we started with focus groups (n = 9). We analysed the data thematically based on context of patients and MHNs in practice regarding oral health, preferred oral health tools focused on MHN, and intended outcomes of tools. A Multi Criteria Decision Matrix was developed and analysed (n = 9) to identify the most optimal and viable solution based on established criteria and issues that are prevalent in the work of MHNs. Most promising result was the development of a brochure with an awareness screener. The brochure with awareness screener was developed as a low-fidelity prototype for MHNs regarding oral health in patients with a psychotic disorder, based on the latest scientific evidence. After testing it, the feasibility was tested through semi-structured interviews (n=19). MHNs and experts by experience were satisfied with the tool and provided recommendations for adjustments to the content. Significant augmentations to the brochure include a clinical lesson and a toothbrush with toothpaste for patients. We can conclude that a brochure with an awareness screener is feasible for MHNs. Future steps aiming to further refine and optimize care processes for oral health tools in MHNs, should take refining eligibility criteria for psychiatric populations and the language level of the target group of a tool into account.

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.

Artificial intelligence (AI) has grown in healthcare in recent years. The UK government recognises AI’s potential to enhance NHS services, yet research on AI in primary care (PC) has received limited attention. AI acceptance presents unique challenges in PC, characterised by fragmented structures, heterogeneous data sources, and multiple government departments. The organisational levels within PC are categorised as macro, meso, and micro levels. Existing research has predominantly focused on micro-level stakeholders. Our online survey addressed this research gap by encom-passing stakeholder perspectives at all levels. The results demonstrate the critical role of me-so-level stakeholders in facilitating AI acceptance. Importantly, a lack of understanding of AI terminology and concepts, concerns over potential job displacement, and the importance of em-pathy in patient care are highlighted as key challenges. Stakeholders also express the need for standardised AI terminology, comprehensive training, and regulatory standards to ensure equi-table and effective AI utilisation. This study lays the foundation for future in-depth interviews and further exploration of AI's role in PC. Observations in secondary care indicate that practitioners have substantial concerns about AI, how it works, and its limitations. Explainable AI can help technologists address such concerns, but first, we need to understand primary care's information needs.

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.

The study aimed to investigate how distributed leadership influences adolescents' social and emotional competence, considering the roles of student-centered instructional practices and teacher self-efficacy. Utilizing survey data from 7,246 Suzhou adolescents enrolled in the SESS program, the study employed multilevel structural equation modeling for analysis. The findings indicated a positive correlation between distributed leadership and adolescents' social and emotional competence, as evaluated by teachers. Moreover, distributed leadership was observed to exert an indirect impact on adolescents' self-assessment and teacher-rated social and emotional competence. This influence operated through several mediators: student-centered instructional practices, teacher self-efficacy, and the sequential mediating pathway of student-centered instructional practices → teacher self-efficacy. This study underscores the significance of supportive elements in fostering students' social and emotional competence. It offers valuable insights into the everyday practices of teachers, principals, and administrators. Additionally, it sheds light on how distributed leadership contributes to enhancing adolescents' overall social and emotional development.

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 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 pursuit of sustainable wellbeing is one of the research objectives of positive experience design. Driven by this goal, the purpose of this paper is to provide an impact-centered sustainable positive experience design model. Firstly, the literature review method was used to define the research status and concept of impact-centered sustainable design. Secondly, an impact-centered sustainable positive experience design model was constructed, and relevant formulas for concept generation and concept evaluation were proposed. Thirdly, design verification was conducted through a workshop. Finally, the Technology Acceptance Model (TAM) questionnaire was used to evaluate and discuss the design model. An impact-centered sustainable positive design model was proposed, which includes the important impact dimensions of sensory experience and meaningful experience on users’ quality of life at different levels: healthy living (pleasure index, health behavior), harmonious community (social connectivity, social contribution), and livable environment (living environment, environmental contribution). Based on positive experience related theory, this study takes long-term impacts as the starting point for sustainable positive experience design, which helps designers to generate design concepts from a systematic and long-term perspective.

The Yellow River Basin (YRB) is a crucial ecological zone and an environmentally vulnerable re-gion in China. Understanding the temporal and spatial trends of terraced-field areas (TRAs) and the factors underlying them in the YRB is essential for improving land use, conserving water re-sources, promoting biodiversity, and preserving cultural heritage. In this study, we employed ma-chine learning on the Google Earth Engine (GEE) platform to obtain spatial distribution images of TRAs from 1990 to 2020 using Landsat 5 (1990－2010) and Landsat 8 (2015－2020) remote sens-ing data. The GeoDa software platform was used for spatial autocorrelation analysis, revealing distinct spatial clustering patterns. Mixed linear and random forest models were constructed to identify the driving force factors behind TRA changes. The research findings reveal that TRAs were primarily concentrated in the upper and middle reaches of the YRB, encompassing provinc-es such as Shaanxi, Shanxi, Qinghai, and Gansu, with areas exceeding 40,000 km2, whereas other provinces had TRAs of less than 30,000 km2 in total. The TRAs exhibited a relatively stable trend, with provinces such as Gansu, Qinghai, and Shaanxi showing an overall upward trajectory. Conversely, Shanxi and Inner Mongolia demonstrated an overall declining trend. When com-pared with other provinces, the variations in TRAs in Ningxia, Shandong, Sichuan, and Henan appeared to be more stable. The linear mixed model (LMM) revealed that farmland, shrubs, and grassland had significant positive effects on the TRA, explaining 41.6% of the variance. The ran-dom forest model also indicated positive effects for these factors, with high R² values of 0.983 and 0.86 for the training and testing sets, respectively, thus outperforming the LMM. The findings of this study can contribute to the restoration of the YRB's ecosystem and support sustainable devel-opment. The insights gained will be valuable for policymaking and decision support in soil and water conservation, agricultural planning, and environmental protection in the region.

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.

The present work provides continuity with studies where currently the industrial market is characterized by a highly variable demand in terms of quantities and flexibility of manufacturing or mass customization, which translates into a more demanding production context in terms of the continuous changes required to the production systems, the effect of which results in an increase in the fatigue subjected to the machines that make up the production systems. On the other hand, current production systems tend to use highly communicative and sensorized cyber-physical systems; these characteristics can be used to integrate them into decision-assisted systems in order to improve the availability of the industrial plant. The assisted system developed focuses on collecting and taking advantage of historical knowledge of industrial plant failures and breakdowns. By ideally integrating the Reliability Centered Maintenance (RCM) methodology and Case-Based Reasoning (CBR) algorithms implemented in a Java application, it is possible to design maintenance plans adjusted to a real and changing operational context of any industrial plant. As a result, faster and more accurate decisions are made, as they are based on data. This article focuses on improving certain aspects of the developed assisted system by adding more value to it by incorporating Fuzzy Logic (FL) techniques. The aim is to improve the way of entering information about risk factors and their relative importance, by incorporating natural language, instead of a numerical score, resulting in increased precision in the calculation of the Risk Priority Number (RPN) of the new cases incorporated into the assisted system.