Search Results (2,840)

How to rationally design composition of alloys with desired properties has always been an open and challenging question, especially for high-entropy alloy (HEA) which has huge selections of composition due to the feature of multi-principal elements. Although great efforts have been made in the past decades, such as approaches based on thermo-kinetic analysis and simulations, strategies to quick determine the optimal HEA composition remain lacking. In this study, based on the effective estimations of elastic modulus of alloys from compositions, we proposed a strategy to design intrinsically strong, ductile, and low-weight refractory HEA (RHEA) compositions. First, the Young’s moduli of three RHEAs were experimentally measured using uniaxial tensile test and impulse excitation of vibration (IEV) test. Then, the present results, combining with the data of elastic moduli of ~130 HEAs in literature, were utilized to validate the prediction of elastic moduli from compositions of HEAs. Finally, based on the property maps that containing 38,326 compositions, a novel RHEA was designed and experimentally tested, exhibiting superior strength, ductility, and low density compared to the equimolar NbMoTaVW alloy. This study provides a new strategy for developing HEAs and contributes to the development of new refractory HEAs with desired properties. Full article

Accurately predicting soil particle size fractions (PSFs) and classifying soil texture types are essential for soil resource assessment and sustainable land management. PSFs, comprising clay, silt, and sand, form a compositional dataset constrained to sum to 100%. The practical implications of incorporating compositional data characteristics into PSF mapping remain insufficiently explored. This study applies a two-point machine learning (TPML) model, integrating spatial autocorrelation and attribute similarity, to enhance both the quantitative prediction of PSFs and the categorical classification of soil texture types in the Heihe River Basin, China. TPML was compared with random forest regression kriging (RFRK), random forest (RF), XGBoost, and ordinary kriging (OK), and a novel TPML-C model was developed for multi-class classification tasks. Results show that TPML achieved R² values of 0.58, 0.55, and 0.64 for clay, silt, and sand, respectively. Among all models, the ALR_TPML predictions showed the most consistent agreement with the observed variability, with predicted ranges of 2.63–98.28% for silt, 0.26–36.16% for clay, and 0.64–96.90% for sand. Across all models, the dominant soil texture types were identified as Sandy Loam (SaLo), Loamy Sand (LoSa), and Silty Loam (SiLo). For soil texture classification, TPML with raw, ALR-, and ILR-transformed data reached right ratios of 61.09%, 55.78%, and 60.00%, correctly identifying 25, 26, and 27 types out of 43. TPML with raw data exhibited strong performance in both regression and classification, with superior ability to separate ambiguous boundaries. Log-ratio transformations, particularly ILR, further improved classification performance by addressing the constraints of compositional data. These findings demonstrate the promise of hybrid machine learning approaches for digital soil mapping and precision agriculture. Full article

Kyoho grapes are rich in nutrients, yet their susceptibility to spoilage poses a significant challenge for postharvest preservation. While light treatment can improve fruit quality and carbohydrate metabolism in postharvest grapes, the potential benefits of combining light treatment with modified atmosphere packaging (MAP) remain unexplored. A preservation method that combined red and blue light treatments with MAP has been developed to enhance postharvest fruit quality and carbohydrate metabolism in Kyoho grapes. Our study showed that this combined treatment significantly increased postharvest fruit hardness, as well as total soluble solids (TSS) and fruiting pedicel water content. It also improved the activities of superoxide dismutase (SOD) and phenylalanine ammonialyase (PAL) and increased the antioxidant, anti-browning capacity. This composite treatment slowed down sucrose decomposition by regulating the activities of key enzymes of carbohydrate metabolism (sucrose synthase (SS), sucrose phosphate synthase (SPS), neutral invertase (NI) and acid invertase (AI)). After 60 days of storage, the glucose, fructose, and sucrose contents of the RP group increased by 13.4%, 30.2%, and 18.1%, respectively, compared to the CK group (p < 0.05). In summary, light combined with modified atmosphere packaging significantly improved the physicochemical properties and sugar metabolism of postharvest grapes. The results indicated that the optimal treatment condition was continuous red-light irradiation combined with MAP. The hardness, TSS content, VC content and glucose content of Kyoho grapes in this treatment group were the best in all treatment groups. Full article

High-resolution remote sensing imagery offers detailed spatial and semantic insights into the Earth’s surface, yet its classification remains hindered by the limited availability of labeled data, primarily due to the substantial expense and time required for manual annotation. To overcome this challenge, we propose a hybrid semi-supervised tri-training framework that integrates traditional classification methods with a lightweight convolutional neural network. By combining heterogeneous learners with complementary strengths, the framework iteratively assigns pseudo-labels to unlabeled samples and collaboratively refines model performance in a co-training manner. Additionally, a landscape-metric-guided relearning module is introduced to incorporate spatial configuration and land cover composition, further enhancing the framework’s representational capacity and classification robustness. Experiments were conducted on four high-resolution multispectral datasets (QuickBird (QB), WorldView-2 (WV-2), GeoEye-1 (GE-1), and ZY-3) covering diverse land-cover types and spatial resolutions. The results demonstrate that the proposed method surpasses state-of-the-art baselines by 1.5–10% while generating more spatially coherent classification maps. Full article

The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. This extension enables the hierarchical aggregation of DTs across various abstraction levels. Traditional DT frameworks often lack mechanisms for dynamic composition at the level of entire systems. This is essential for modeling distributed systems in heterogeneous environments. UniTwin addresses this gap by grouping DTs into composite entities with an aggregation mechanism. The aggregation mechanism is demonstrated in a smart manufacturing case study, which covers the orchestration of a production line for personalized shopping cart chips. It uses modular DTs provided for each device within the production line. A System-Aggregated Digital Twin (S-ADT) is used to orchestrate the individual DTs, mapping the devices in the production line. Therefore, the production line adapts and reconfigures according to user-defined parameters. This validates the flexibility and practicality of the aggregation mechanism. This work contributes an aggregation mechanism for the UniTwin framework, paving the way for adaptable DTs for complex CPSs in domains like smart manufacturing, logistics, and infrastructure. Full article

Aquaculture effluents rich in phosphorus and nitrogen (P and N) can lead to eutrophication of aquatic ecosystems. These nutrients may contribute to harmful algal blooms, oxygen depletion, and deterioration of water quality, which poses a threat to aquatic biodiversity. In shrimp diets, environmental impacts from P and N nutrient leaching can be reduced by improving dietary P digestibility through the use of alternative ingredients. While fishmeal, with its high phosphorus content, has traditionally been a primary source, its declining use due to cost and limited availability necessitates the inclusion of inorganic P sources to meet shrimp nutritional requirements. Optimising these sources ensures adequate phosphorus availability while minimising nutrient waste. This study evaluated the effects of inorganic phosphate supplementation (monoammonium phosphate, MAP; monosodium phosphate, MSP; and sodium calcium phosphate, SCP-2%) in standard diets (35% CP) on nutrient digestibility, residue generation, and performance of Litopenaeus vannamei. Results showed that phosphorus digestibility exceeded 96% across all phosphate sources, with MSP achieving the highest values. Calcium digestibility was notably higher in diets containing monocalcium phosphate, such as SCP-2%, which demonstrated superior digestibility values. No significant differences were observed in nitrogen or phosphorus excretion; however, residue analysis revealed that SCP-2% diets generated the lowest nitrogenous waste relative to ingested nitrogen, whereas MAP diets produced the highest nitrogen residues, followed by the Control diet. For phosphorus residues, the Control diet showed the greatest proportion relative to ingested phosphorus, followed by MSP. Phosphate inclusion did not affect shrimp growth, survival, or body composition. However, phosphorus and calcium retention efficiencies were inversely proportional to their dietary content, underscoring the importance of optimising phosphate sources to enhance nutrient utilisation and minimise environmental impact. Full article

In this paper, an innovative method consisting of the construction of an artificial dune reinforced with a composite made by combining sand and seagrass wrack is presented. The performance of this reinforced dune is compared with sand-only dunes, built at the same time, through data collected during 17 field campaigns (covering a period of one year) carried out with an unmanned aerial vehicle (UAV), whose images allow digital elevation models (DEMs) to be built. The results show that, in the medium term, while the sand-only dunes lose much of their volume (up to 25% of the refilled sediment), the reinforced dune only reduces its volume by around 1.4%. In addition, the cross-shore and longitudinal profiles extracted from the DEMs of the dunes indicate that sand-only dunes greatly reduce the elevation of their crests, while the profile of the reinforced dune remains almost unchanged. This suggests that the addition of seagrass wrack can greatly contribute to increasing the resilience of restored dunes and the time between re-fillings, therefore reducing beach protection costs. However, as the results are based on a single wrack–sand dune and have not been replicated, they should be treated with caution. At the same time, this work illustrates how UAVs can acquire the data needed to map coastal restoration works in a fast and economical way. Full article

In this review paper, we briefly discuss the occurrence of oil spills and their behavior under natural sea conditions and clean-up methods, as well as their environmental and economic impacts. We discuss methodologies for oil spill modeling used to predict the fate of a spill under dynamic physical and chemical processes. Weathering processes such as evaporation, emulsification, spreading, dissolution, dispersion, biodegradation, and sedimentation are considered within easy-to-use modeling frameworks. We present simple models based on the principles of thermodynamics, mass transfer, and kinetics that under certain conditions can predict oil thickness, volume, area, composition, and the distribution of toxic compounds in water and air over time for various types of oil and their products. Modeling approaches for underwater oil jets, including applications related to the 2010 BP oil spill in the Gulf of Mexico, are reviewed. The influence of sea surface velocity and wind speed on oil spill mapping, spill location, oil spill trajectory over time, areas affected by light, medium, and heavy oil, and comparisons between satellite images and model predictions are demonstrated. Finally, we introduce several recently published articles on more recent oil spill incidents and the application of predictive models in different regions. We also discuss the challenges, advantages, and disadvantages of various models and offer recommendations at the end of the paper. Full article

Accurate delineation of fields is difficult in fragmented landscapes where single-date images provide no seasonal cues and supervised models require labels. We propose a method that explicitly represents phenology to improve zero-shot delineation. Using 22 cloud-free PlanetScope scenes over a 5 × 5 km area, a single harmonic model is fitted to the NDVI per pixel to obtain the phase, amplitude and mean. These values are then mapped into cylindrical colour spaces (Hue–Saturation–Value, Hue–Whiteness–Blackness, Luminance-Chroma-Hue). The resulting recoloured composites are segmented using the Segment Anything Model (SAM), without fine-tuning. The results are evaluated object-wise, object-wise grouped by area size, and pixel-wise. Pixel-wise evaluation achieved up to F1 = 0.898, and a mean Intersection-over-Union (mIoU) of 0.815, while object-wise performance reached F1 = 0.610. HSV achieved the strongest area match, while HWB produced the fewest fragments. The ordinal time-of-day basis provided better parcel separability than the annual radian adjustment. The main errors were over-segmentation and fragmentation. As the parcel size increased, the IoU increased, but the precision decreased. It is concluded that recolouring using harmonic NDVI time series is a simple, scalable, and interpretable basis for field delineation that can be easily improved. Full article

Objectives: Angelica sinensis is a type of traditional Chinese medicine (TCM) used primarily as a blood tonic. The chemical components that exert their efficacy are mainly bioactive metabolites, such as ferulic acid, flavonoids, and volatile oils. The resources of wild Angelica sinensis (WA) are very scarce, and almost all the market circulation of TCM formulations relies on cultivated Angelica sinensis (CA). Some studies have shown that WA and CA differ in morphological features and chemical composition, but the reasons and mechanisms behind the differences have not been studied deeply. Methods: Herein, metabolomics analysis (MA) and transcriptomics analysis (TA) were used to reveal the differences in bioactive metabolites and genes between WA and CA. Expression of key genes was verified by real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: Results showed that 12,580 differential metabolites (DMs) and 1837 differentially expressed genes (DEGs) were identified between WA and CA. Fourteen DMs (e.g., cinnamic acid, caffeic acid, ferulic acid, p-coumaroylquinic acid, and phlorizin) and 27 DEGs (e.g., cinnamic acid 4-hydroxylase (C4H), 4-coumarate-CoA ligase (4CL), shikimate O-hydroxycinnamoyltransferase (HCT), caffeic acid-O-methyltransferase (COMT), cinnamyl-alcohol dehydrogenase (CAD), flavonol synthase (FLS)) were screened in phenylpropanoid biosynthesis and flavonoid biosynthesis. A combined analysis of MA and TA was performed, and a network map of DMs regulated by DEGs was plotted. The results of real-time RT-qPCR showed that the transcriptome data were reliable. Conclusions: These findings provide a reference for further optimization of the development of WA cultivation and breeding of CA varieties. Full article

In artificial neural networks, the activation dynamics of non-trainable variables are strongly coupled to the learning dynamics of trainable variables. During the activation pass, the boundary neurons (e.g., input neurons) are mapped to the bulk neurons (e.g., hidden neurons), and during the learning pass, both bulk and boundary neurons are mapped to changes in trainable variables (e.g., weights and biases). For example, in feedforward neural networks, forward propagation is the activation pass and backward propagation is the learning pass. We show that a composition of the two maps establishes a duality map between a subspace of non-trainable boundary variables (e.g., dataset) and a tangent subspace of trainable variables (i.e., learning). In general, the dataset-learning duality is a complex nonlinear map between high-dimensional spaces. We use duality to study the emergence of criticality, or the power-law distribution of fluctuations of the trainable variables, using a toy and large models at learning equilibrium. In particular, we show that criticality can emerge in the learning system even from the dataset in a non-critical state, and that the power-law distribution can be modified by changing either the activation function or the loss function. Full article

In recent years, large multimodal models (LMMs), such as ChatGPT 4o and DeepSeek R1—artificial intelligence systems capable of multimodal (e.g., image and text) human–computer interaction—have gained traction in industrial and civil engineering applications. Concurrently, insufficient real-world drone-view data (specifically close-distance, high-resolution imagery) for civil engineering scenarios has heightened the importance of artificially generated content (AIGC) or synthetic data as supplementary inputs. AIGC is typically produced via text-to-image generative models (e.g., Stable Diffusion, DALL-E) guided by user-defined prompts. This study leverages LMMs to interpret key parameters for drone-based image generation (e.g., color, texture, scene composition, photographic style) and applies prompt engineering to systematize these parameters. The resulting LMM-generated prompts were used to synthesize training data for a You Only Look Once version 8 segmentation model (YOLOv8-seg). To address the need for detailed crack-distribution mapping in low-altitude drone-based monitoring, the trained YOLOv8-seg model was evaluated on close-distance crack benchmark datasets. The experimental results confirm that LMM-prompted AIGC is a viable supplement for low-altitude drone crack monitoring, achieving >80% classification accuracy (images with/without cracks) at a confidence threshold of 0.5. Full article

Background and Clinical Significance: Bladder neoplasms often present with coexisting thrombi and hematuria, appearing as complex intraluminal masses on imaging, and posing a key diagnostic challenge in distinguishing neoplastic tissue from thrombus, to prevent harmful overstaging. Case Presentation: An 82-year-old man with recurrent gross hematuria and urinary disturbances was evaluated by ultrasound, which identified a large endoluminal lesion in the anterior bladder wall. The patient subsequently underwent contrast-enhanced CT using a second-generation dual-layer spectral CT system, which utilizes a dual-layer detector to simultaneously acquire high- and low-energy X-ray data. Conventional CT images confirmed a multifocal, bulky hyperdense lesion along the bladder wall, protruding into the lumen and raising suspicion for a heterogeneous mass, though further characterization was not possible. Spectral imaging enabled the reconstruction of additional maps—such as iodine density, effective atomic number (Z-effective), and electron density—which were used to further characterize these findings. The combination of these techniques clearly demonstrated differences in iodine uptake and tissue composition within the parietal lesions, allowing for a reliable differentiation between neoplastic tissue and intraluminal thrombus. Conclusions: The integration of conventional CT imaging with spectral-derived maps generated in post-processing allowed for accurate and reliable tissue differentiation between bladder neoplasm and thrombus. Spectral imaging holds the potential to prevent tumor overstaging, thereby supporting more appropriate clinical management. The dual-layer technology enables the generation of these maps from every acquisition without altering the scan protocol, thereby having minimal impact on the daily clinical workflow. Full article

Background Chronic low-back pain (LBP) is the largest contributor to disability worldwide, yet many assessments still reduce a complex, spatially distributed condition to a single 0–10 score. Body-map drawings capture location and extent of pain, but manual digitization is too slow and inconsistent for large studies or real-time telehealth. Methods Paper pain drawings from 332 adults in the multicenter COMEBACK study (four University of California sites, March 2021–June 2023) were scanned to PDFs. A Python pipeline automatically (i) rasterized PDF pages with pdf2image v1.17.0; (ii) resized each scan and delineated anterior/posterior regions of interest; (iii) registered patient silhouettes to a canonical high-resolution template using ORB key-points, Brute-Force Hamming matching, RANSAC inlier selection, and 3 × 3 projective homography implemented in OpenCV; (iv) removed template outlines via adaptive Gaussian thresholding, Canny edge detection, and 3 × 3 dilation, leaving only patient-drawn strokes; (v) produced binary masks for pain, numbness, and pins-and-needles, then stacked these across subjects to create pixel-frequency matrices; and (vi) normalized matrices with min–max scaling and rendered heat maps. RGB composites assigned distinct channels to each sensation, enabling intuitive visualization of overlapping symptom distributions and for future data analyses. Results Cohort-level maps replicated classic low-back pain hotspots over lumbar paraspinals, gluteal fold, and posterior thighs, while exposing less-recognized clusters along the lateral hip and lower abdomen. Neuropathic-leaning drawings displayed broader leg involvement than purely nociceptive patterns. Conclusions Our automated workflow converts pen-on-paper pain drawings into machine-readable digitized images and heat maps at the population scale, laying practical groundwork for spatially informed, precision management of chronic LBP. Full article

Background and Objectives: Atrial fibrillation (AF) in critically ill patients (CIP) is associated with worse outcomes and increased mortality in the intensive care unit (ICU). Rhythm control strategies are often unfeasible due to underlying comorbidities, making rate control the preferred initial approach. However, conventional beta-blockers may worsen hemodynamics through negative inotropic effects and peripheral vasodilation. Landiolol, an ultra-short-acting adrenoreceptor antagonist, may offer an alternative due to its high β₁-cardioselectivity and minimal blood pressure (BP) impact. This study evaluated the efficacy and feasibility of landiolol in hemodynamically unstable CIP with tachyarrhythmia, used as add-on therapy after failure of standard treatments. Materials and Methods: Ten CIP, admitted for non-postoperative reasons, were prospectively enrolled for landiolol treatment (L-group) in the ICU of Ulm University Heart Center between July and December 2017. The control group contained 41 patients who had received standard therapy without landiolol (NL-group). The primary composite endpoint was defined as heart rate (HR) reduction while maintaining mean arterial pressure (MAP) above 65 mmHg. Results: The most frequent reason for ICU admission was hemodynamic instability related to tachyarrhythmia in patients with cardiogenic or septic shock. At therapy initiation, all patients exhibited a compromised hemodynamic status, with a median MAP of 68.0 (IQR 60.0–80.0) mmHg and a median HR of 160.0 (IQR 144.0–176.0) bpm. After a three-hour observation period, no significant differences in BP values were observed between the groups. The primary composite endpoint was achieved at comparable rates in both groups (p = 0.525). However, patients in the L-group achieved a greater reduction in HR compared to those in the NL-group (25.3% vs. 21.9%, p < 0.001). Conclusions: Landiolol achieved more effective HR control than standard therapy without adversely affecting BP stability. These findings suggest that landiolol may be a feasible and effective option for HR control in ICU CIP. Full article