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Open-source legacy data available for training soil organic carbon (SOC) models are limited and not uniformly distributed in space or time. While some process-based models predict SOC changes, most of the large-scale data-driven SOC modeling efforts overlook temporal shifts. Accounting for the expected temporal drift allows us to increase the accuracy of dataset available for machine learning models. Here we present an approach for creating proximity-based distance matrices using the legacy data available in contiguous US (CONUS) and generating spatially resolved temporal shift projections that adjust observations to the target date. The approach was evaluated by comparing SOC observations projected to two reference years, SOC1980 and SOC2020 and without temporal adjustment (SOCno−adj). Stocks of SOC projections showed significant differences between SOCno−adj and SOC2020. Baseline estimate of SOC stocks in CONUS croplands (top 1 m) were higher based on SOCno−adj (14.49 Pg C) compared to SOC2020 (13.29 Pg C), for pasture lands 15.49 Pg (SOCno−adj) and 14.22 Pg C (SOC2020), for forest lands at 39.52 Pg C (SOCno−adj) and 40.83 Pg C (SOC2020). The study results confirmed the validity of our methodology, and its capability to enhance SOC stock projections effectively with temporal adjustments. Potential users of this study's outcomes include many stakeholders involved in carbon incentive programs, including farmers, scientists, policy makers, and industry partners. [ABSTRACT FROM AUTHOR]

In this paper, a new measurement principle for decoupling mechanical and thermal signals in an OFDR measurement with integrated optical fibres is investigated. Previous methods for decoupling require additional measuring equipment or knowledge about the substrate properties. This new method is based solely on simultaneous measurements of two fibres with different temperature sensitivities resulting from different core doping processes. By exposing both fibres to the same thermal and mechanical load, the signal could be differentiated through the signal variations caused by the thermo-optical effect. The two fibres used in the tests have a sufficient response difference in the cryogenic temperature range. Therefore, the method is suitable for various applications, such as high-temperature superconductors as well as cryogenic and space applications. [ABSTRACT FROM AUTHOR]

With the growing demand for environmental sustainability, the cosmetics industry is increasingly turning to natural ingredients with high bioactivity and efficacy. Rice, a staple food in many cultures, particularly in Asia, is renowned for its nutritional and medicinal benefits, as well as its skincare properties. Numerous studies have emphasized the multifunctional benefits of rice in skincare, ranging from its ability to enhance hydration to its effectiveness in addressing signs of aging, positioning it as a promising ingredient in cosmetic formulations. Recently, fermentation has emerged as an innovative technique that significantly enhances the bioactive potential of rice. This process amplifies the production of beneficial compounds, such as organic acids, amino acids, polyphenols, polysaccharides, vitamins, and minerals. Previous studies have shown that rice fermentation products (RFPs) exhibit a range of skincare benefits, including moisturization, antioxidation, anti-inflammation, whitening, and anti-aging effects. This review provides an overview of the fermentation process and advantages of RFPs, followed by a detailed analysis of their key bioactive components and the diverse skincare benefits they offer. Moreover, we discuss the challenges related to the standardization, component analysis, and efficacy evaluation of these products, and we conclude with potential future research directions to fully explore the skincare potential of RFPs. [ABSTRACT FROM AUTHOR]

Land use change and agricultural management have a considerable impact on land use patterns and greenhouse gas (GHG) emissions in dynamic urbanised landscapes. This study evaluated sustainable land allocation strategies in line with the European Green Deal. A constrained cellular automata land use model was employed to assess the impacts of Business-as-Usual (BAU), Land Sharing (LSH), and Land Sparing (LSP) scenarios, using open-access data from Flanders (Belgium). Under BAU, urban expansion reduced unregistered agricultural land by 495 km2, leading to higher GHG emissions despite an 11% increase in green space. LSH increased green space by 36% and enhanced landscape diversity, while LSP improved habitat coherence by 24%. Livestock-related methane (3.09 Mt CO 2 e) dominated GHG emissions, comprising more than 75% of the total, with cattle responsible for 73% of methane emissions. Nitrous oxide emissions reduced from 1.60 Mt CO 2 e to 1.44 (BAU), 1.43 (LSP), and 1.42 (LSH) Mt CO 2 e. Forest sequestration offset up to 34% of total emissions, removing −1.35 Mt CO 2 e. Green Deal measures mitigated emissions in all scenarios, with LSH achieving the highest gains. The results highlight the need for spatial strategies that integrate sustainable agricultural practices and balance productivity, nature conservation, and climate action under the European Green Deal. [ABSTRACT FROM AUTHOR]

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications. [ABSTRACT FROM AUTHOR]

Clarifying the spatio-temporal evolution characteristics of eco-environment quality (EEQ) under land use/cover change (LUCC) and its coordinated relationship is of great importance for formulating reliable environmental protection strategies and measures to promote regional sustainable development. Most studies have emphasized the importance of LUCC for regional ecological quality. However, deeply unraveling the complex interrelationships between them remains a significant challenge, particularly in ecologically fragile regions like the Li River Basin. Therefore, based on the historical land use data and the remote sensing ecological index (RSEI) of the Li River Basin from 1990 to 2020, we analyzed the spatio-temporal evolution characteristics of EEQ and LUCC, and explored the influences and non-linear effects between them by using the bivariate spatial autocorrelation and XGBoost model. The key findings are as follows: (1) Land use/cover (LUC) in the Li River Basin was predominantly characterized by forestland and cropland, which together accounted for approximately 97% of the region. The interconversion between forestland and cropland represented the primary form of regional LUCC, while built-up land demonstrated a growth trend by encroaching on cropland. (2) The EEQ exhibited a volatile upward trend within the research period, with an average RSEI value of 0.5891, indicating a generally favorable ecological condition. (3) A significant negative spatial correlation was observed between land use intensity (LUI) and the RSEI, characterized by H–L, L–H, and non-significant clusters. (4) There was a distinct non-linear relationship that existed between LUCC and the RSEI, underscoring that appropriately regulating regional land use scale can help maintain ecological balance. These findings provide a scientific basis for optimizing land spatial management models and formulating policies to improve ecological environment quality, while also offering a new framework and reference for further ecological research on EEQ influencing factors and driving mechanisms. [ABSTRACT FROM AUTHOR]

Despite growing interest in ancient wheat varieties, the functional and nutritional properties of einkorn (Triticum monococcum) in cereal-based foods remain not fully elucidated. This study examined the chemical composition and functional properties of wholegrain einkorn pasta through cooking and simulated gastrointestinal digestion, comparing it with conventional Triticum durum wheat pasta. While sharing similar macronutrient profiles, einkorn pasta demonstrated higher retention of key compounds including phenolics, tocopherols, and phytosterols throughout cooking and in vitro digestion. Notable findings include enhanced prebiotic activity specifically targeting bifidobacteria populations and preserved antioxidant capacity despite thermal processing. These results demonstrated einkorn's potential as a functional food ingredient, suggesting its capacity to deliver enhanced nutritional benefits through its unique matrix properties. Our findings provide mechanistic insights into ancient grain functionality in modern food applications, with implications for developing nutritionally enhanced pasta products. [ABSTRACT FROM AUTHOR]

Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress. [ABSTRACT FROM AUTHOR]

Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome prediction and diagnosis. However, while some biomarkers are crucial, many lack informative value. To enhance prediction accuracy and understand disease mechanisms, it is essential to integrate relevant features and identify key biomarkers, separating meaningful data from noise and modeling complex associations. To address these challenges, we introduce the High-dimensional Feature Importance Test (HdFIT) framework for machine learning models. HdFIT includes a feature screening step for dimension reduction and leverages machine learning to model complex associations between biomarkers and disease outcomes. It robustly evaluates each feature's impact. Extensive Monte Carlo experiments and a real microbiome study demonstrate HdFIT's efficacy, especially when integrated with advanced models like deep neural networks. Our framework shows significant improvements in identifying crucial features and enhancing prediction accuracy, even in high-dimensional settings. [ABSTRACT FROM AUTHOR]

The conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users' and producers' accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso's landscape, the results indicate the potential of the approach to provide accurate mapping. [ABSTRACT FROM AUTHOR]

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments. [ABSTRACT FROM AUTHOR]

In the engineering of complex technical systems, Systems Engineering (SE) is a key approach that is becoming increasingly relevant in more and more industries due to the ever-increasing complexity of systems. Over the decades of practical application and research, various specializations and forms of the Systems Engineering approach have developed, but there has so far been a lack of an overarching context and positioning in meaningful stages for the introduction of Systems Engineering in companies. For this reason, this research will systematize common Systems Engineering approaches and bring them together in a stage model for Systems Engineering. Based on a systematic literature review, use cases are identified for each approach and stage, which support companies in selecting an approach suitable for their own organization. [ABSTRACT FROM AUTHOR]

Low temperatures up to the cryogenic range represent an increasingly relevant area of application both in space travel, in the transport and storage of liquid gases and in the field of research (e.g. superconductivity). Adhesive bonding is a predestined joining method for joints in these applications. However, the material behaviour of adhesives and in particular the mechanical behaviour of bonded joints at cryogenic temperatures has been little researched. This paper compares the tensile and shear strengths of adhesives and bonded specimens at room temperature and cryogenic temperature and also investigates the strains occurring within the bonded joint. An increase in strength of up to 35% at a temperature of 77 K is found for tensile shear specimens. For tensile specimens, the increase is up to 347% due to embrittlement of the polymeric material. When examining the strains, the largest resulting contractions are found to be up to 0.6% at the interface between the adhesive and the joined part due to different expansion coefficients of the materials. [ABSTRACT FROM AUTHOR]