Search Results (2,838)

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring. Full article

Direct methods provide a model-free approach to solving the crystallographic phase problem and deliver unbiased atomic structures. However, conventional iterative projection algorithms such as Hybrid Input–Output (HIO) face two critical challenges: discontinuous density modification at the protein-solvent boundary and inaccurate molecular envelope reconstruction that fails to account for trapped solvent, particularly in crystals with solvent content approaching the lower limits of direct phasing applicability. We introduced four continuous iterative projection algorithms, including our improved continuous version, which implements smooth density modification at protein-solvent interfaces. To address envelope inaccuracy, we developed a two-step refined reconstruction scheme using sequential large-radius and small-radius Gaussian filters to identify trapped solvent molecules within surface cavities and internal channels. This scheme enhances the performance of both continuous and classical algorithms, including HIO, the difference map, and our improved versions. Benchmarking on 28 protein structures (solvent contents 55–78%, resolutions 1.46–3.2 Å, reported R-factor less than 0.22) showed that the refined envelope scheme increased average success rates of continuous algorithms by 45.7% and classical algorithms by 60.5%. The performance of continuous algorithms and improved classical algorithms proved comparable to the well-established HIO algorithm, forming a top-tier group that exceeded other classical algorithms. Integrating a genetic algorithm co-evolution strategy further enhanced average success rates by approximately 2.5-fold and accelerated convergence through population-wide information sharing. Although the success rate correlates with solvent content, our strategy improved success probability at any given solvent level, extending the practical boundaries of direct methods. The high success rate enabled averaging of multiple independent solutions, which reduced mean phase error by approximately 6.83° and yielded atomic models with backbone root-mean-square deviation (RMSD) typically below 0.5 Å relative to structures reported in the Protein Data Bank (PDB). This work introduces novel algorithms, a refined envelope reconstruction methodology, and an effective optimization strategy with genetic algorithm evolution. The complete framework enhances the capability and reliability of direct methods for phasing protein crystals with limited solvent content and provides a toolkit for addressing challenging cases in structural biology. Full article

Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies. Full article

Enhanced Geothermal Systems (EGS) extend geothermal energy beyond conventional hydrothermal resources but face challenges in creating sustainable heat exchangers in low-permeability formations. This review synthesizes achievements from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a field laboratory advancing EGS readiness in 175–230 °C granitic basement. From 2017 to 2025, drilling, multi-stage hydraulic stimulation, and monitoring established feasibility and operating parameters for engineered reservoirs. Hydraulic connectivity was created between highly deviated wells with ~300 ft vertical separation via hydraulic and natural fracture networks, validated by sustained circulation tests achieving 10 bpm injection at 2–3 km depth. Advanced monitoring (DAS, DTS, and microseismic arrays) delivered fracture propagation diagnostics with ~1 m spatial resolution and temporal sampling up to 10 kHz. A data infrastructure of 300+ datasets (>133 TB) supports reproducible ML. Geomechanical analyses showed minimum horizontal stress gradients of 0.74–0.78 psi/ft and N–S to NNE–SSW fractures aligned with maximum horizontal stress. Near-wellbore tortuosity, driving treating pressures to 10,000 psi, underscores completion design optimization, improved proppant transport in high-temperature conditions, and coupled thermos-hydro-mechanical models for long-term prediction, supported by AI platforms including an offline Small Language Model trained on Utah FORGE datasets. Full article

The integration of Advanced Metering Infrastructure (AMI) provides high-resolution electrical data, essential for enhancing industrial efficiency and monitoring equipment health. However, the utility of this data is frequently compromised by anomalies, underscoring the necessity for robust, automated detection methodologies. This study benchmarks three distinct categories of machine learning models: a statistical baseline (SARIMA), an unsupervised classifier (Isolation Forest), and a deep learning reconstruction model (LSTM-Autoencoder). The evaluation was conducted using a multivariate dataset acquired from bakery manufactory equipment, employing a synthetic anomaly injection framework with a 5% contamination rate. The results indicate significant challenges in accurately detecting anomalies within this dataset. The SARIMA model achieved the highest average F1-Score (0.256), slightly outperforming the Isolation Forest (0.233), while the LSTM-Autoencoder performed the poorest (0.110). Critically, all models exhibited extremely low precision (ranging from 0.074 to 0.204), indicating an unacceptably high rate of false positives. The findings suggest that standard configurations of these models struggle to differentiate between true anomalies and the inherent variability of industrial operations, highlighting the need for advanced optimization and feature engineering for practical deployment. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

Globally, sea turtles face significant threats from human activities, yet detailed information on their habitat use and specific anthropogenic impacts remains limited, particularly in key marine protected areas like Kenya’s Diani-Chale National Marine Reserve (DCNMR). This study utilized drone-based (UAV—unmanned aerial vehicle) monitoring and geospatial analysis to assess sea turtle distribution and habitat use, integrating data from the Allen Coral Atlas. Most sea turtle sightings occurred in reef zones (61.86%), while the reef slope was the most utilized geomorphic feature (26.7% of sightings). The study identified a significant sea turtle hotspot in the northern DCNMR, a region characterized by lower anthropogenic pressure and unique geomorphic features. Between February and July 2024, we conducted monthly UAV surveys (6–10 survey days per month) in the DDCNMR using a DJI Mavic 3 drone, completing multiple standardized 25-min flights per day that each covered ~1 km² via non-overlapping transects at 30–40 m altitude under optimal sea state and visibility conditions, resulting in 233 sea turtle sightings. UAV survey data were summarized descriptively, with sea turtle sightings mapped against geomorphological features as well as benthic habitats from an open source, high-resolution, satellite-based map and monitoring system for shallow-water coral reefs (ACA—Allen Coral Atlas). Allen Coral Atlas data and drone observations indicate that a widened reef slope and estuarine nutrient inputs provide a critical habitat gradient, offering turtles tidal-independent access to shallow foraging flats. Based on these findings, we recommend designating the northern reef slope as a priority no-take zone and conducting seagrass health assessments to guide potential restoration. This research demonstrates the utility of integrating drone surveys with open access geospatial tools to provide the actionable spatial data necessary for targeted sea turtle conservation and informed marine spatial planning. Full article

A newly commercialized single-row multi-fan autonomous unmanned ground vehicle (UGV) sprayer, for use in trellised tree fruit crops, was tested to better understand air and spray patterns prior to wide-scale adoption in the modern apple orchard systems typical to Washington State. This sprayer was equipped with five brown and yellow Albuz ATR80 nozzles per fan (QM-420, Croplands Quantum). The fans were installed in a Q8 configuration, with eight fans (four on each side) staggered near the front and back as a stack to increase vertical span. Air velocity and spray delivery patterns of the commercialized sprayer unit were assessed in laboratory using a customized smart spray analytical system. Previous field trails of this sprayer unit revealed a hardware issue with electric proportional valve controls in fan-nozzle assembly, resulting in uneven spray deposition across V-trellised canopy. Post issue resolution, the sprayer characterization data showed an average Symmetry of 91%, and 84% for air velocity and spray volume delivery on either side. An average Uniformity of 57% and 48%, respectively was recorded for pertinent sprayer attributes across the spray height. Overall, after optimization, the UGV sprayer is suitable for efficient agrochemical application in modern orchard systems. Further evaluation of labor savings, biological efficacy gains from autonomous operation, and a full economic analysis would better inform grower adoption. Commercial viability of this UGV sprayer could also be improved by added features such as variable-rate application enabled by real-time crop sensing or task-map integration. Full article

As a core city in central China and a key node of the Changsha–Zhuzhou–Xiangtan (CZT) Metropolitan Area, Changsha has experienced profound territorial space restructuring amid rapid urbanization and high-quality development. This study focuses on the spatiotemporal evolution characteristics, driving mechanisms, and future optimization paths of production–living–ecological space (PLES) in Changsha, using three key time nodes: 2010, 2020, and 2025. Based on updated land use data (30 m spatial resolution), socioeconomic statistics, and the latest territorial spatial planning policies, we integrated multiple research methods including the land use transfer matrix, dynamic degree model, Logistic regression, and FLUS (Future Land Use Simulation) model. The results reveal the evolutionary law of PLES space from “rapid expansion” (2010–2020) to “quality improvement” (2020–2025) in Changsha and simulate the 2035 PLES layout under three scenarios (natural development, cultivated land protection, and ecological protection) incorporating rigid policy constraints such as urban development boundaries and ecological conservation red lines. This research provides updated scientific support for the coordinated and sustainable development of territorial space in new first-tier cities and metropolitan area cores. Full article

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35${}^{\circ }$ on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article

Scanning Electrochemical Microscopy represents one of the most advanced high-resolution techniques that enables detailed monitoring of electrochemical processes, with a particular focus on corrosion phenomena. Scanning Electrochemical Microscopy has become an indispensable tool in studying the behavior and degradation of protective coatings exposed to aggressive environmental conditions. This technique allows researchers to precisely track local electrochemical reactions on material surfaces, providing valuable information about the stability and effectiveness of coatings. Scanning Electrochemical Microscopy enables the detection of localized current variations in the pA–nA range, allowing the identification of microdefects with nanometric width. In this paper, the basic principles of Scanning Electrochemical Microscopy operation are first presented, including a description of the device. The method of scanning the electrode is discussed through the modes and their interpretation of the obtained data for systems with protective anticorrosive coatings. Furthermore, Scanning Electrochemical Microscopy techniques enable a detailed study of the mechanisms and kinetics of new, modified coatings, which is especially significant in the case of nanoparticle-enriched coatings. Such modifications often enhance the protective properties of materials, and Scanning Electrochemical Microscopy allows monitoring of their performance under real conditions, providing insight into local electrochemical changes that are otherwise difficult to detect with standard methods. Special attention is given to the challenges researchers may encounter during experiments, such as calibration prior to measurement, interpretation of input parameters, and signal analysis. This paper aims to provide a comprehensive overview of the capabilities and limitations of Scanning Electrochemical Microscopy (SECM), emphasizing its importance as a tool for the development and optimization of new, high-performance coatings for industrial applications and scientific research. Full article

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency. Full article

This study introduces a predictive model based on artificial neural networks (ANNs) for estimating the dynamic performance of commercially available sensible thermal energy storage (STES) systems. The model was trained and validated using high-resolution experimental data measured from two vertical cylindrical tanks (0.3 m³ each) including internal heat exchangers and operating under both heating and cooling modes. A comprehensive sensitivity analysis was conducted on 28 ANN architectures by varying the number of hidden neurons and input delays. The optimal configuration, designated as ANN5 (12 neurons, delay = 1), demonstrated superior accuracy in predicting temperature profiles and energy exchange. Validation against an independent dataset confirmed the model’s robustness, achieving normalized root mean square errors (NRMSEs) between 0.0022 and 0.0061 for the hot tank and between 0.0057 and 0.0283 for the cold tank. Energy prediction errors were within −3.87% for charging and 0.09% for discharging in heating mode, and 7.08% for charging and 0.13% discharging in cooling mode, respectively. These results highlight the potential of ANN-based approaches for real-time control, forecasting, and digital twin applications in STES systems. Full article

Mediterranean biogeography is characterized as a global “hotspot” for climate change; understanding the impacts of these changes on local agricultural systems through high-resolution analyses has thus become a critical need. This study addresses this gap by evaluating the holistic effects of climate change on site-specific agriculture systems, focusing on the Eğirdir–Karacaören (EKB) and Beyşehir (BB) lake basins in the Lakes Region of Türkiye. This study employed machine learning modeling techniques to forecast changes in the yields of key crops, such as wheat, maize, apple, alfalfa, and sugar beet. Detailed spatial analyses of changes in agro-climatic conditions (heat stress, chilling requirement, frost days, and growing degree days for key crops) between the reference period (1995–2014) and two decadal periods projected for 2040–2049 and 2070–2079 were conducted under the Shared Socioeconomic Pathways (SSP3-7.0). Daily temperature, precipitation, relative humidity, and solar radiation data, derived from high-resolution climate simulations, were aggregated into annual summaries. These datasets were then spatially matched with district-level yield statistics obtained from the official data providers to construct crop-specific data matrices. For each crop, Random Forest (RF) regression models were fitted, and a Leave-One-Site-Out (LOSOCV) cross-validation method was used to evaluate model performance during the reference period. Yield prediction models were evaluated using the mean absolute error (MAE). The models achieved low MAE values for wheat (33.95 kg da⁻¹ in EKB and 75.04 kg da⁻¹ in BB), whereas the MAE values for maize and alfalfa were considerably higher, ranging from 658 to 986 kg da⁻¹. Projections for future periods indicate declines in relative yield across both basins. For 2070–2079, wheat and maize yields are projected to decrease by 10–20%, accompanied by wide uncertainty intervals. Both basins are expected to experience a substantial increase in heat stress days (>35 °C), a reduction in frost days, and an overall acceleration of plant phenology. Results provided insights to inform region-specific, evidence-based adaptation options, such as selecting heat-tolerant varieties, optimizing planting calendars, and integrating precision agriculture practices to improve resource efficiency under changing climatic conditions. Overall, this study establishes a scientific basis for enhancing the resilience of agricultural systems to climate change in two lake basins within the Mediterranean biogeography. Full article

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article