Search Results (60)

The James Webb Space Telescope (JWST) has begun to revolutionize our view of the Cosmos. The discovery of Blue Monsters (i.e., ultra-compact yet very bright high-z galaxies) and the Little Red Dots (i.e., very compact dustless strong Balmer break cosmic dawn sources) pose significant challenges to pre-JWST era models of the assembly of first stars and galaxies. In addition, JWST data further strengthen the problem posed by the origin of the supermassive black holes that power the most distant quasars observed. Stars powered by Dark Matter annihilation (i.e., Dark Stars) can form out of primordial gas clouds during the cosmic dawn era and subsequently might grow via accretion and become supermassive. In this paper we argue that Supermassive Dark Stars (SMDSs) offer natural solutions to the three puzzles mentioned above. Full article

In 3D reconstruction, loss of depth data caused by highly reflective surfaces often undermines the accuracy of point cloud registration. Traditional registration methods suffer from reduced accuracy and computational efficiency under such conditions. This paper presents a novel coarse-to-fine point cloud registration approach that combines a density-aware keypoint detection method with iterative closest point optimization to enhance both precision and computational performance. The proposed keypoint detection method optimizes registration by progressively refining the initial pose estimate through multi-scale geometric feature detection. This process includes a density-aware mechanism for removing edge outliers and an adaptive threshold based on normal vector inner products. This improves both keypoint identification accuracy and matching efficiency, providing better initial registration for the iterative closest point algorithm in scenarios with significant data loss. The approach prevents the iterative closest point algorithm from converging to local optima, which improves both convergence speed and overall computational performance. Experimental results show that, under optimal conditions, the runtime is reduced by up to 78.01% across several datasets, including those from Stanford, Kinect, Queen, and ASL-LRD. Compared to other traditional methods, the proposed approach delivers higher registration accuracy, even for multi-view point clouds with severe data loss, which demonstrates its robustness and potential for engineering applications. Full article

We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Re-decomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T₁Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method. Full article

The real nature of little red dots (LRDs), a class of very compact galaxies in the early Universe recently discovered by the James Webb Space Telescope, is still poorly understood. The most popular theories competing to interpret the phenomena include active galactic nuclei and enhanced star formation in dusty galaxies. To date, however, neither model gives a completely satisfactory explanation to the population as a whole; thus, alternative theories have arisen, including tidal disruption events (TDEs). By considering observational constraints on the radio emission of LRDs, we discuss whether TDEs are adequate alternatives solving these high-redshift enigmas. We utilise radio flux density upper limits from LRD stacking analyses, TDE peak radio luminosities, and volumetric density estimates. We find that the characteristic values of flux densities and luminosities allow radio-quiet TDEs as the underlying process of LRDs in any case, while the less common radio-loud TDEs are compatible with the model under special constraints only. Considering other factors, such as volumetric density estimates, delayed and long-term radio flares of TDEs, and cosmological time dilation, TDEs appear to be a plausible explanation for LRDs from the radio point of view. Full article

Background/Objectives: Left/right discrimination (LRD) training is increasingly being used in the treatment of chronic low back pain (CLBP). However, it is unclear whether trunk LRD-performance is impaired in CLBP patients and whether clinical parameters are related to LRD-performance. Therefore, this cross-sectional study aimed to examine (1) whether LRD-performance differs between CLBP patients and pain-free individuals; (2) whether these differences depend on the low back pain (LBP) history in pain-free individuals; (3) if clinical factors are related to LRD-performance; (4) whether LRD-task difficulty influences these results. Methods: Participants included 150 pain-free persons (107 with no LBP-history; 43 with past LBP) and 150 patients with CLBP. All participants performed the LRD-task in a simple and complex condition. Outcomes were reaction time and accuracy. Results: CLBP patients were significantly slower (Cohen’s d = 0.47 to 0.50, p < 0.001) and less accurate (Cohen’s d = 0.30 to 0.55, p < 0.001) than pain-free individuals without LBP-history, but not compared to those with past LBP (Cohen’s d reaction time = 0.07 to 0.15, p = 0.55; Cohen’s d accuracy = 0.03 to 0.28, p-value = 0.28). All participant groups were slower and less accurate in the complex condition, but between-groups differences were independent of task difficulty. Linear mixed models showed that older age and lower education were independently associated with less accuracy. When controlling for demographics, pain intensity, disability, fear of movement, pain-related worry and pain duration were not related to LRD-performance in patients with CLBP. Conclusions: Patients with CLBP showed impaired trunk LRD-performance compared to pain-free persons without LBP history, but not compared to those with past LBP. When controlling for demographics, clinical parameters were not related to LRD-performance in patients with CLBP. Our findings indicate that LRD-performance may remain impaired after recovering from LBP. Full article

We analyze long-range dependence (LRD) for word time series, understood as a slower than exponential decay of the two-point Shannon mutual information. We achieve this by examining the decay of the cosine correlation, a proxy object defined in terms of the cosine similarity between word2vec embeddings of two words, computed by an analogy to the Pearson correlation. By the Pinsker inequality, the squared cosine correlation between two random vectors lower bounds the mutual information between them. Using the Standardized Project Gutenberg Corpus, we find that the cosine correlation between word2vec embeddings exhibits a readily visible stretched exponential decay for lags roughly up to 1000 words, thus corroborating the presence of LRD. By contrast, for the Human vs. LLM Text Corpus entailing texts generated by large language models, there is no systematic signal of LRD. Our findings may support the need for novel memory-rich architectures in large language models that exceed not only hidden Markov models but also Transformers. Full article

Soil active nitrogen (N) fractions are essential for plant growth and nutrient cycling in terrestrial ecosystems. While previous studies have primarily focused on the impact of vegetation restoration on soil active nitrogen in karst ecosystems, the role of microclimate variation in rocky desertification areas has not been well explored. This study investigates soil active nitrogen fractions and key biotic and abiotic factors across four grades of rocky desertification—non-rocky desertification (NRD), light rocky desertification (LRD), moderate rocky desertification (MRD), and intense rocky desertification (IRD)—within two distinct microclimates: a dry-hot valley and a humid monsoon zone in the karst region of Guizhou Province, China. We evaluate soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), soil nitrate nitrogen (NO₃⁻-N), ammonium nitrogen (NH₄⁺-N), microbial biomass nitrogen (MBN), soluble organic nitrogen (SON), and plant diversity. Results showed that SOC, TN, and TP were significantly higher in IRD areas. Soil NO₃⁻-N, MBN, and SON initially decreased before increasing, with consistent MBN growth in the dry-hot valley. NH₄⁺-N did not differ significantly under NRD but was higher in the dry-hot valley under LRD, MRD, and IRD. The dry-hot valley had higher MBN and SON across most desertification grades. Microclimate significantly influenced soil active N, with higher levels in the dry-hot valley under LRD and MRD conditions. Plant diversity and regeneration varied markedly between the microclimates. In the dry-hot valley, Artemisia dominated herbaceous regeneration, especially in MRD areas. Conversely, the humid monsoon zone showed more diverse regeneration, with Artemisia and Bidens prevalent in MRD and NRD grades. Despite declining plant diversity with desertification, the humid monsoon zone displayed greater resilience. These findings highlight the role of microclimate in influencing soil nitrogen dynamics and plant regeneration across rocky desertification gradients, offering insights for restoration strategies in karst ecosystems. Full article

In the framework of lateritic material valorization, we demonstrated how the geological environment determines the mineralogical characterizations of two laterite samples, KN and LA. KN and LA originate from the Birimian and Precambrian environments, respectively. We showed that the geological criterion alone does not determine the applicability of these laterites as potential adsorbents but must be associated with their physicochemical properties. The characterizations were carried out using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermal analysis, and Atomic Emission Spectrometry Coupled with an Inductive Plasma Source. The major mineral phases obtained by X-ray diffraction analysis coupled with infrared analysis showed that the KN and LA laterite samples were composed of quartz (33.58% to 45.77%), kaolinite (35.64% to 17.05%), hematite (13.36% to 11.43%), and goethite (7.44% to 6.31%). The anionic exchange capacity of the KN and LA laterites ranged from 86.50 ± 3.40 to 73.91 ± 9.94 cmol(-)·kg⁻¹ and from 73.59 ± 3.02 to 64.56 ± 4.08 cmol(-)·kg⁻¹, respectively, and the cation exchange capacity values are in the order of 52.3 ± 2.3 and 58.7 ± 3.4 cmol(+)/Kg for the KN and LA samples, respectively. The specific surface values determined by the BET method were 58.65 m²/g and 41.15 m²/g for the KN and LA samples, respectively. The effects of adsorbent doses on As(III,V), Pb(II), and Cu(II) adsorption were studied. At 5 mg/L As and 15 g/L adsorbent (pH 6.5–7), arsenate removal was 99.72 ± 0.35% and 99.58 ± 0.45% for KN and LA, respectively, whereas arsenite removal reached 83.52 ± 2.21% and 98.59 ± 0.64% for LA and KN, respectively. The Pb(II) and Cu(II) removal rates were 74.20 ± 0.95% for 2.4 g/L KN and 54.18 ± 0.01% for 8 g/L KN, respectively. Based on their physicochemical and mineralogical characteristics, the KN and LA laterite samples were shown to possess a high potential as adsorbent material candidates for removing heavy metals and/or anionic species from groundwater. Full article

Rice (Oryza sativa L.) feeds half the world’s population and serves as one of the most vital staple food crops globally. The brown planthopper (BPH, Nilaparvata lugens Stål), a major piercing–sucking herbivore specific to rice, accounts for large yield losses annually in rice-growing areas. Developing rice varieties with host resistance has been acknowledged as the most effective and economical approach for BPH control. Accordingly, the foremost step is to identify BPH resistance genes and elucidate the resistance mechanism of rice. More than 70 BPH resistance genes/QTLs with wide distributions on nine chromosomes have been identified from rice and wild relatives. Among them, 17 BPH resistance genes were successfully cloned and principally encoded coiled-coil nucleotide-binding leucine-rich repeat (CC-NB-LRR) protein and lectin receptor kinase (LecRK), as well as proteins containing a B3 DNA-binding domain, leucine-rich repeat domain (LRD) and short consensus repeat (SCR) domain. Multiple mechanisms contribute to rice resistance against BPH attack, including transcription factors, physical barriers, phytohormones, defense metabolites and exocytosis pathways. Plant hormones, including jasmonic acid (JA), salicylic acid (SA), ethylene (ET), abscisic acid (ABA), gibberellins (GAs), cytokinins (CKs), brassinosteroids (BRs) and indoleacetic-3-acid (IAA), play crucial roles in coordinating rice defense responses to the BPH. Here, we summarize some recent advances in the genetic mapping, cloning and biochemical mechanisms of BPH resistance genes. We also review the latest studies on our understanding of the function and crosstalk of phytohormones in the rice immune network against BPHs. Further directions for rice BPH resistance studies and management are also proposed. Full article

With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R² of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges. Full article

Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes a novel approach for generating localized, representative blemish images within substations. Firstly, to mitigate global style variations in images generated by generative adversarial networks (GANs), we developed a YOLO-LRD method focusing on local region detection within equipment. This method enables precise identification of blemish locations in substation equipment images. Secondly, we introduce a SEB-GAN model tailored specifically for generating blemish images within substations. By confining blemish generation to identified regions within equipment images, the authenticity and diversity of the generated defect data are significantly enhanced. Theexperimental results validate that the YOLO-LRD and SEB-GAN techniques effectively create precise datasets depicting flaws in substations. Full article

Burst detection in water asset management is a crucial issue in ensuring the efficient and sustainable operation of water distribution systems. For an online burst detection method based on flow time series data, the challenge arises in the variability of anomaly definitions across different datasets, rendering a one-size-fits-all anomaly detection algorithm impossible. Additionally, existing prediction-driven anomaly detection schemes, relying on single-step prediction, face accuracy issues due to susceptibility to input data contamination. In this paper, a novel scheme for burst detection is proposed to address the limitations of existing methods. The approach incorporates a multi-step forecasting model, offering multiple sources for the forecasting, and aggregates the forecasts to establish a common expectation for the data pattern. A metric termed Local Residual Discrepancy (LRD) is proposed to score deviation between predictions and observations. The effectiveness of the proposed method is evaluated through its application to both synthetic and real datasets. Experimental results reveal significant improvements in detection accuracy achieved by the LRD metric, irrespective of the underlying prediction model. This research contributes to the advancement of burst detection methodologies, offering a more robust and versatile approach applicable to varied datasets and prediction models in water distribution systems. Full article

Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R² value of 0.87 and RMSE of 6.61 g·kg⁻¹. For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R² of 0.77 and RMSE of 0.37 g·kg⁻¹. For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R² of 0.69 and RMSE of 0.04 g·kg⁻¹. For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R² of 0.83 and RMSE of 24.12 mg·kg⁻¹. The results demonstrate the high accuracy of these models in predicting soil nutrient content. Full article

Natural laterite fixed-bed columns intercalated with two types of layers (inert materials, such as fine sand and gravel, and adsorbent materials, such as activated carbon prepared from Balanites aegyptiaca (BA-AC)) were used for As(III) removal from an aqueous solution. Investigations were carried out to solve the problem of column clogging, which appears during the percolation of water through a natural laterite fixed-bed column. Experimental tests were conducted to evaluate the hydraulic conductivities of several fixed-bed column configurations and the effects of various parameters, such as the grain size, bed height, and initial As(III) concentration. The permeability data show that, among the different types of fixed-bed columns investigated, the one filled with repeating layers of laterite and activated carbon is more suitable for As(III) adsorption, in terms of performance and cost, than the others (i.e., non-intercalated laterite; non-intercalated activated carbon, repeating layers of laterite and fine sand; and repeating layers of laterite and gravel). A study was carried out to determine the most efficient column using breakthrough curves. The breakthrough increased from 15 to 85 h with an increase in the bed height from 20 to 40 cm and decreased from 247 to 32 h with an increase in the initial As(III) concentration from 0.5 to 2 mg/L. The Bohart–Adams model results show that increasing the bed height induced a decrease in the k_AB and N₀ values. The critical bed depths determined using the bed depth service time (BDST) model for As(III) removal were 15.23 and 7.98 cm for 1 and 20% breakthroughs, respectively. The results show that the new low-cost adsorptive porous system based on laterite layers with alternating BA-AC layers can be used for the treatment of arsenic-contaminated water. Full article

Roller bearing degradation features fractal characteristics such as self-similarity and long-range dependence (LRD). However, the existing remaining useful life (RUL) prediction models are memoryless or short-range dependent. To this end, we propose a RUL prediction model based on fractional Brownian motion (FBM). Bearing faults can happen in different places, and thus their degradation features are difficult to extract accurately. Through variational mode decomposition (VMD), the original degradation feature is decomposed into several components of different frequencies. The monotonicity, robustness and trends of the different components are calculated. The frequency component with the best metric values is selected as the training data. In this way, the performance of the prediction model is hugely improved. The unknown parameters in the degradation model are estimated by the maximum likelihood algorithm. The Monte Carlo method is applied to predict the RUL. A case study of a bearing is presented and the prediction performance is evaluated using multiple indicators. Full article