Search Results (3,780)

Laser-induced breakdown spectroscopy (LIBS) has been used to explore the chemistry of three regions of Mars on respective missions by NASA and CNSA, with CNES contributions. All three LIBS instruments use ~100 mm diameter telescopes projecting pulsed infrared laser beams of 10–14 mJ to enable LIBS at 2–10 m distances, eliminating the need to position the rover and instrument directly onto targets. Over 1.3 million LIBS spectra have been used to provide routine compositions for eight major elements and several minor and trace elements on >3000 targets on Mars. Onboard calibration targets common to all three instruments allow careful intercomparison of results. Operating over thirteen years, ChemCam on Curiosity has explored lacustrine sediments and diagenetic features in Gale crater, which was a long-lasting (>1 My) lake during Mars’ Hesperian period. SuperCam on Perseverance is exploring the ultramafic igneous floor, fluvial–deltaic features, and the rim of Jezero crater. MarSCoDe on the Zhurong rover investigated for one year the local blocks, soils, and transverse aeolian ridges of Utopia Planitia. The pioneering work of these three stand-off LIBS instruments paves the way for future space exploration with LIBS, where advantages of light-element (H, C, N, O) quantification can be used on icy regions. Full article

Background/Objectives: Burn injuries are a major public health concern. This study estimated global, regional, and national burn burdens by total body surface area from 1990 to 2021 and projected trends to 2050. Methods: Utilizing data from the Global Burden of Disease Study 2021, we examined the prevalence, mortality, and years lived with disability (YLDs) according to age, sex, and region. Future trends were predicted using Bayesian meta-regression models and Das Gupta decomposition analysis. Results: In 2021, global prevalence was 12.99 million for severe burns and 235.34 million for mild burns, with age-standardized rates of 158.75 and 2815.26 per 100,000. Severe burns were highest in Southern Latin America (7836.51 per 100,000) and mild burns in the Caribbean (626.94 per 100,000). The largest declines from 1990 to 2021 were in high-income North America for severe burns (−38.22%) and East Asia for mild burns (−73.03%). Females had higher severe burn prevalence at younger and older ages, while males had higher mild burn prevalence from early adulthood. Leading risk factors were fire, heat, and hot substances (38.22% of severe burn YLDs; 53.87% for mild burns). By 2050, severe burns are projected to rise by 233.4% and mild burns by 142.5%, with Eastern Europe showing the largest growth. Conclusions: Although age-standardized burn rates are declining, absolute cases are projected to rise due to population growth and aging, particularly in low- and middle-income countries, underscoring the need for stronger prevention and improved burn care infrastructure. Full article

Preventive conservation (PC) of built heritage has proved to be one of the most efficient and sustainable approaches to ensure its long-term preservation. Nevertheless, the management of all the areas involved in a PC project is complex, often resulting in poor interaction between them. This research proposes a GIS-based methodology for integrating data from different PC areas into a centralised digital model, establishing a Common Data Environment (CDE) to optimise PC strategies for heritage systems in complex contexts. Applying this method to the pavilions of the 1929 Ibero-American Exhibition in Seville (Spain), the study addresses five key PC areas: active follow-up, damage detection and assessment, risk analysis, maintenance, and dissemination and valorisation. The approach involved designing a robust relational database structure—using PostgreSQL—tailored for heritage management, defining several data standardisation criteria, and testing semi-automated procedures for generating multi-scale 2D and 3D GIS (LOD2 and LOD4) entities using remote sensing data sources. The proposed spatial database has been designed to function seamlessly with major GIS platforms (QGIS and ArcGIS Pro), demonstrating successful integration and interoperability for data management, analysis, and decision-making. Geographic web services derived from the database content were created and uploaded to a WebGIS platform. While limitations exist, this research demonstrates that simplified GIS models are sufficient for managing PC data across various working scales, offering a resource-efficient alternative compared to more demanding existing methods. Full article

Chronic kidney disease (CKD) prevalence is rising worldwide and is projected to become the fifth leading cause of death globally by 2040. The high proportion of undiagnosed early-staged CKD and delayed diagnosis is of significant concern. The access to diagnosis and treatment is also limited in low-resource settings. The majority of individuals with kidney disease succumb to cardiovascular disease complications. Furthermore, heart failure and CKD are closely interconnected, with each condition significantly increasing the risk of developing the other. They share common risk factors, such as high blood pressure and diabetes, and their coexistence worsens prognosis and raises mortality rates. The bidirectional relationship between the heart and kidneys becomes even more complex and challenging in the context of cardiorenal syndrome. Emerging medications, such as sodium–glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists, have shown remarkable efficacy in slowing the progression of kidney disease, surpassing the benefits of traditional treatments. This article summarizes the evidence on the early detection of CKD and real-world opportunities to slow the progression of CKD by optimizing cardiorenal guideline-directed medical therapy. Full article

Machine learning-guided predictive models are attractive in rock modeling for different scholars to obtain continuous profiles of rock compressive strength in rock engineering. The major objectives of the study are to assess the implications of machine learning (ML)-based connectionist models to obtain the unconfined compressive strength (UCS) of rock, to perform parametric sensitivity analysis on petrophysical parameters, and to develop an improved correlation for UCS prediction. The least-squares support vector machine (LSSVM) is applied to develop data-driven models for the prediction of UCS. Additionally, the random forest (RF) algorithm is applied to verify the effectiveness of predictive models. A database containing well-logging data is processed and utilized to construct connectionist models to obtain UCS. For the efficacy of predictive models, statistical performance indicators such as the coefficient of determination (CC), average percentage relative error, and maximum average percentage error are utilized in the study. It is revealed that the RF- and LSSVM-based models for predicting UCS perform excellently with high precision. Considering the parametric sensitivity analysis in the predictive models for UCS, the formation compressional wave velocity and formation gamma-ray are the most strongly contributing predictor variables rather than other input variables such as the modulus of elasticity, acoustic shear wave velocity, and rock bulk density. The improved correlation for predicting UCS shows high precision, achieving a CC of 96% and root mean squared error of 0.54 MPa. This systematic research workflow is significant and can be utilized for connectionist robust model development and variable selections in the petroleum and mining fields, such as predicting reservoir properties, the drilling rate of penetration, sanding potentiality of hydrocarbon reservoir rocks, and for the practical implications of boring and geotechnical engineering projects. Full article

Spectroscopic observations of Earth-like exoplanets and ultra-faint galaxies–top scientific priorities for the coming decades–involve measuring broadband signals at rates of only a few photons per square meter per hour. This imposes exceptional requirements on the detector performance, necessitating dark currents below 1 e⁻/pixel/kilo second, read noise under 1 e⁻/pixel/frame, and the ability to handle large-format arrays–capabilities that are not yet met by most existing infrared detectors. In addition, spaceborne LiDAR systems require photodetectors with exceptional sensitivity, compact size, low power consumption, and multi-channel capability to facilitate long-range range finding, topographic mapping, and active spectroscopy without increasing the instrument burden. MCT Avalanche photodiodes arrays offer high internal gain, pixelation, and photon-counting performance across SW to MW wavelengths needed for multi-beam and multi-wavelength measurements, marking them as a critical enabling technology for next-generation planetary and Earth science LiDAR missions. This work reports the latest progress in developing Hg_1−xCd_xTe linear-mode e-APDs at premier industrial research institutions, including relevant experimental data, simulations and major project planning. Related studies are summarized to demonstrate the practical and iterative approach for device fabrication, which have a transformative impact on the evolution of this discipline. Full article

Effective community health initiatives are grounded in strategic collaboration among community-based organizations and local residents to ensure the interventions are responsive to the community’s unique needs. Drawing on insights from community partners and project participants, this study explored key factors that facilitated, hindered, and strengthened community engagement, collaboration, and sustainability in health promotion efforts for underserved populations on the U.S.-Mexico border. This study utilized semi-structured interviews to gather the perspectives and insights of community partners and participants regarding effective health promotion strategies developed through our collaborative project. Data were collected from 15 community partners who have collaborated with our project and 15 participants who had attended our project events (n = 30). Our study identified four major themes: (1) key attributes and perceived value of effective health promotion activities, (2) tailored approaches for engaging underserved populations, (3) strategies for successful partnerships, and (4) ways to enhance health promotion and barriers to overcome. Practice implications are discussed. Full article

This study presents the partial real-world validation of a previously developed framework that integrates Lean Construction principles, Building Information Modeling (BIM), and Emerging Technologies to optimize construction management. While the original framework was validated through expert consensus using the Delphi Method, this research applies it in the context of Saudi Arabia to test its feasibility during the design phase. A case-based approach was adopted involving a confidential mega-scale project. Key Performance Indicators (KPIs) were used to assess impact, including cost and time efficiency, productivity, waste reduction, quality, safety, stakeholder satisfaction, and process automation. Our results revealed a 25% improvement in cost efficiency, a 40% acceleration in design delivery, a 25% increase in productivity, 70% process optimization and automation, 100% elimination of non-value-adding activities, and a 20% enhancement in design quality. Stakeholders reported high levels of satisfaction, citing transparency, real-time collaboration, and enhanced decision-making as major benefits. These findings confirm the framework’s potential for transforming project delivery through integrated digital and Lean strategies. Full article

Urbanization, characterized by population growth and socioeconomic development, is a major driving factor of land use land cover (LULC) change. A spatio-temporal understanding of land cover change is crucial, as it provides essential insights into the pattern of urban development. This study conducted a longitudinal analysis of LULC change in order to evaluate the tradeoffs of urban growth and sustainability challenges in the Himalayan region. Landsat time-series satellite imagery from 1988 to 2024 were analyzed for two major cities in Nepal—Kathmandu metropolitan city (KMC) and Pokhara metropolitan city (PMC). The LULC classification was conducted using a machine learning support vector machine (SVM) approach. For this study period, our analysis showed that KMC and PMC witnessed urban growth of over 400% and 250%, respectively. In the next step, LULC change and urban expansion patterns were predicted based on the urban development indicator using the Cellular Automata Markov chain (CA-Markov) model for the years 2040 and 2056. Based on the CA-Markov chain analysis, the projected expansion areas of the urban area for the two future years are 282.39 km² and 337.37 km² for Kathmandu, and 93.17 km² and 114.15 km² for PMC, respectively. The model was verified using several Kappa variables (K-location, K-standard, and K-no). Based on the LULC trends, the majority of urban expansion in both the study areas has occurred at the expense of prime farmlands, which raises grave concern over the sustainability of the food supply to feed an ever-increasing urban population. This haphazard urban sprawl poses a significant challenge for future planning and highlights the urgent need for effective strategies to ensure sustainable urban growth, especially in restoring local food supply to alleviate over-reliance on long-distance transport of agro-produce in high-altitude mountain regions. The alternative planning of sustainable urban growth could involve adequate consideration for urban farming and community gardening as an integral part of the urban fabric, both at the household and city infrastructure levels. Full article

Clarifying the trade-offs and synergies between land use and ecosystem services in major water source river basins is enhancing regional land resource distribution and safeguarding water-related ecological environments. The Danjiangkou Reservoir Basin—the water source area of the South-to-North Water Diversion Project—land use change characteristics from 2012 to 2022 were focused on in this study. Five categories of ecosystem services, represented by six land use-related indicators, were selected for analysis. The InVEST model was utilized to conduct a quantitative assessment of their spatial and temporal variations. This study investigates the spatial variations of ecosystem services, analyzes their trade-offs and synergies, and explores the impacts of land use changes on the supply and interactions of these services. The findings reveal that cultivated land was served as the dominant source of land use conversion. Specifically, the largest areas of cultivated land conversion were to forest land (240.91 km²), followed by water bodies (144.65 km²) and construction land (38.43 km²). The selected ecosystem services exhibited distinct temporal and spatial variation: water yield, total carbon storage, and habitat quality showed upward trends, whereas total nitrogen output, total phosphorus output, and soil erosion demonstrated declining trends. Overall, the synergy and trade-off relationships among the six ecosystem service indicators weakened; however, the degree of improvement in trade-offs exceeded the decline in synergies. The integration of land use change, ecosystem service functions, and trade-off/synergy relationships into a unified analytical framework facilitates a robust theoretical foundation for basin-scale ecological management. This approach offers a scientific foundation for spatial optimization, ecological redline delineation, and resource allocation within the basin. Full article

Background/Objectives: The mandibular lingula (ML) is a small bony projection on the medial surface of the ramus and serves as the first reference point identified during sagittal split ramus osteotomy (SSRO) or inferior alveolar nerve block (IANB). Anatomical variations in the mandibular ramus have been shown to exist across different populations. Understanding these population-specific differences enhances both clinical safety and diagnostic precision. However, there is a paucity of anthropological data amongst the Mongoloid population, especially in Southeast Asia. Hence, this study aimed to investigate the (i) distance of the lingula to different mandibular anatomical landmarks and its localization, (ii) lingula shape, and (iii) differences between gender and the sides of the mandible amongst the local ethnic groups. Methods: This retrospective cross-sectional study consisted of 206 cone-beam computed tomography (CBCT) images of 77 males and 129 females (mean age 33), with a total of 412 hemimandibles. Measurements were performed on three-dimensionally reconstructed CBCT images. Results: The most common shape was the truncated type. The distance of the lingula to the anterior (LiA), posterior (LiP), superior (LiS), and inferior (LiI) borders of mandible were 17.84 (2.25) mm, 14.46 (3.44) mm, 17.73 (3.00) mm, and 27.05 (4.40) mm, respectively. No significant difference exists between the sides of the mandible. Sexual dimorphism existed for all lingula measurements except LiA. Indians have smaller rami with more anteriorly and inferiorly placed ML than Malay and Chinese. The majority of ML was located 8.55 mm above the occlusal plane. Conclusions: This study provides information on the ML and its surrounding ramus structure in the local population. Such variations must be accounted for in SSRO and IANB. Full article

Timber construction is increasingly promoted in Chile as a route to low-carbon, energy-efficient housing, yet public acceptance remains decisive for its diffusion. This study reports the first large-scale perception survey of timber buildings in Greater Concepción (N = 200) and contrasts key results with an earlier identical survey in Valdivia. Concepción residents strongly recognise timber’s thermal comfort attributes and associate wood housing with lower winter heating demand, a perception markedly stronger than in Valdivia. Conversely, 73% of Concepción respondents believe timber homes burn easily, but a majority also accept that modern engineering can mitigate this risk, indicating scope for targeted technical communication. Environmental perceptions are more ambivalent: although respondents value wood’s renewable origin, 42% doubt that timber construction reduces climate change, and many equate it with deforestation, echoing controversies around Chile’s plantation model. Cluster analysis reveals a techno-optimist subgroup coupling enthusiasm for energy savings with confidence in fire-safety innovations, suggesting a strategic constituency for demonstration projects. By situating end-user attitudes within national decarbonisation goals, this paper argues that region-specific outreach—emphasising verified energy-efficiency gains, certified sustainable forestry and visible fire-safety performance—can convert passive approval into active demand and accelerate Chile’s transition to a net-zero housing stock. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

Intensified human activities in recent years, such as wastewater discharge and agricultural non-point source pollution have led to a decline in lake water quality, especially in the middle and lower reaches of the Yangtze River Basin, which threaten the stability of lake water ecosystems. Therefore, it is necessary to conduct a scientific assessment of the water eco-environmental quality of shallow lakes and implement targeted management measures. Considering the characteristics of shallow lakes, major ecological and environmental issues, and current standards and guidelines, an indicator system method was employed to establish a water eco-environmental quality evaluation system tailored for typical shallow lakes in the middle and lower reaches of the Yangtze River Basin. This evaluation system comprises three criteria layers (aquatic organism, habitat quality, and water quality) and 10 indicator layers. Using survey data from 2022 to 2024 for evaluation, the results showed that the water eco-environmental quality of Lake Gehu was rated as poor, with the lowest score for macrophyte coverage and the highest score for riparian vegetation coverage. This indicates that the shoreline restoration project in Lake Gehu was effective, while the lake water quality still needs improvement. Remedial measures include increasing aquatic vegetation coverage, reducing nitrogen and phosphorus pollution loads, and controlling the occurrence of algal blooms. This evaluation system combines field surveys with remote sensing monitoring data, fully considering historical and current conditions, and can guide local authorities in evaluating lake water environmental quality. The constructed evaluation system is applicable for the assessment of shallow lakes in the middle and lower reaches of Yangtze River Basin. It provides a scientific basis for the continuous improvement of eco-environmental quality and the construction of Beautiful Lakes Initiative, contributing to the management and protection of lake ecosystems. Full article

In 2024, significant increases in surface temperature were recorded in Batam City and Bintan Regency, marking the highest levels observed in regional climate monitoring. The rapid conversion of vegetated land into residential and industrial areas has been identified as a major contributor to the acceleration of local climate warming. Climatological analysis also revealed extreme temperature fluctuations, underscoring the urgent need to understand spatial patterns of temperature distribution in response to climate change and weather variability. This research uses a Cellular Automata–Artificial Neural Network (CA−ANN) approach to model spatial and temporal changes in land surface temperature across the Riau Islands. To overcome the limitations of single-model predictions in a geographically diverse and unevenly developed region, Landsat satellite imagery from 2014, 2019, and 2024 was analyzed. Surface temperature data were extracted using the Brightness Temperature Transformation method. The CA−ANN model, implemented via the MOLUSCE platform in QGIS, incorporated additional environmental variables, such as rainfall distribution, vegetation density, and drought indices, to simulate future climate scenarios. Model validation yielded a Kappa accuracy coefficient of 0.72 for the 2029 projection, demonstrating reliable performance in capturing complex climate–environment interactions. The projection results indicate a continued upward trend in surface temperatures, emphasizing the urgent need for effective mitigation strategies. The findings highlight the essential role of remote sensing and spatial modeling in climate monitoring and policy formulation, especially for small island regions susceptible to microclimatic changes. Despite the strengths of the CA−ANN modeling framework, several inherent limitations constrain its application, particularly in the complex and heterogeneous context of tropical island environments. Notably, the accuracy of model predictions can be limited by the spatial resolution of satellite imagery and the quality of auxiliary environmental data, which may not fully capture fine-scale microclimatic variations. Full article