Search Results (205,401)

Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements are often found. Limited soil moisture measurements can be employed to develop a numerical model for long-term and accurate soil moisture estimations. A key input variable to the model is precipitation, which is also not easily accessible, particularly at a finer spatial resolution; hence, publicly available remote sensing data can be used as an alternative. This study, therefore, aims to develop a numerical model HYDRUS-1D to estimate soil moisture in the data-scarce state of the Northern Territory, Australia, with a land cover of shrubland and a Tropical-Savannah type climate. The HDYRUS-1D is based on the numerical solution of Richards’ equation of variably saturated flow that relies on information about the soil water retention characteristics. This study utilized the van Genuchten model parameters, which were optimized (against measured soil moisture) through parameter optimization with initial estimates obtained from the HYDRUS catalogue. Initial estimates from different sources can differ for the same soil texture (e.g., loamy sand) and can induce uncertainties in the calibrated model. Therefore, a comprehensive uncertainty analysis was conducted to address potential uncertainties in the calibration process. The HYDRUS-1D was calibrated for a period between March 2012 and February 2013 and was independently validated against three different periods between March 2013 and October 2016. Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), and Mean Absolute Error (MAE) were used to assess the efficiency of the model in simulating the measured soil moisture. The model exhibited good performance in replicating measured soil moisture during calibration (RMSE = 0.00 m³/m³, MAE = 0.005 m³/m³, and R = 0.70), during validation period 1 (RMSE = 0.035 m³/m³ and MAE = 0.023 m³/m³, and R = 0.72), validation period 2 (RMSE = 0.054 m³/m³ and MAE = 0.039 m³/m³, and R = 0.51), and validation period 3 (RMSE = 0.046 m³/m³ and MAE = 0.032 m³/m³, and R = 0.61), respectively. Remotely sensed precipitation data were used from the CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now to assess their capabilities in estimating soil moisture. Efficiency evaluation metrics and visual assessment revealed that these products underestimated the soil moisture. The CHRS-CCS outperformed other products in terms of overall efficiency (average RMSE of 0.040 m³/m³, average MAE of 0.023 m³/m³, and an average R of 0.68, respectively). An integrated approach based on numerical modelling and remote sensing employed in this study can help understand the long-term dynamics of soil moisture and soil water balance in the Northern Territory, Australia. Full article

The satellite Internet of Things (SatIoT) enables real-time acquisition and large-scale coverage of hyperspectral imagery, providing essential data support for decision-making in domains such as geological exploration, environmental monitoring, and urban management. Hyperspectral remote sensing classification constitutes a critical component of intelligent applications driven by the SatIoT, yet it faces two major challenges: the massive data volume imposes heavy storage and processing burdens on conventional satellite systems, while dimensionality reduction often compromises classification accuracy; furthermore, mainstream neural network models are constrained by insufficient labeled data and spectral shifts, frequently leading to misclassification of unknown categories and degradation of cross-regional performance. To address these issues, this study proposes an open-set hyperspectral classification method with dual branches of reconstruction and prototype-based classification. Specifically, we build upon an autoencoder. We design a spectral–spatial attention module and an information residual connection module. These modules accurately capture spectral–spatial features. This improves the reconstruction accuracy of known classes. It also adapts to the high-dimensional characteristics of satellite data. Prototype representations of unknown classes are constructed by incorporating classification confidence, enabling effective separation in the feature space and targeted recognition of unknown categories in complex scenarios. By jointly leveraging prototype distance and reconstruction error, the proposed method achieves synergistic improvement in both accurate classification of known classes and reliable detection of unknown ones. Comparative experiments and visualization analyses on three publicly available datasets: Salinas-A, PaviaU, and Dioni-demonstrate that the proposed approach significantly outperforms baseline methods such as MDL4OW and IADMRN in terms of unknown detection rate (UDR), open-set overall accuracy (OpenOA), and open-set F1 score, while on the Salinas-A dataset, the performance gap between closed-set and open-set classification is as small as 1.82%, highlighting superior robustness. Full article

Growth regulatory factors (GRFs) are sequence-specific DNA-binding transcription factors that play pivotal roles in regulating plant growth and development, and in enhancing plant tolerance to biotic and abiotic stresses. Although genome-wide structural and evolutionary studies have mapped and analyzed GRF genes in different plant species, knowledge of their characteristics and functions in sunflower (Helianthus annuus) remains limited. In this study, we used bioinformatics analyses and transgenic experiments to systematically analyze the structure and function of these genes. A total of 17 HaGRF genes were identified and classified into four distinct clades, with members of the same clade sharing conserved exon-intron structures and domain architectures. All HaGRFs were predicted to localize to the nucleus, which was experimentally verified for HaGRF2c, HaGRF3, and HaGRF8c. Transcriptome analysis demonstrated tissue-specific expression and stress-responsive profiles among the HaGRF genes. Quantitative real-time PCR revealed that several HaGRF genes were significantly induced under polyethylene glycol and NaCl stress. Additionally, ectopic expression of HaGRF2c in Arabidopsis enhanced growth and conferred greater drought tolerance, supporting its dual functions in regulating growth and in adapting to stress. In summary, this research elucidates the evolutionary relationships, conserved structural characteristics, expression patterns, and roles of the HaGRF gene family in sunflowers. These findings not only deepen our understanding of the biological functions of GRF transcription factors in sunflowers but also provide valuable candidate genes for improving yield and stress resistance in H. annuus. Full article

Porosity characterization in metallic alloys is a fundamental aspect of materials engineering due to its influence on mechanical and structural properties. This study presents a method based on digital image processing for detecting and analyzing porosity in the AlSi10Mg aluminum alloy, additively manufactured using laser powder bed fusion (L-PBF). X-ray computed tomography, segmentation algorithms and filtering techniques were employed to identify and quantify the porosity present in the material’s microstructure. The research demonstrates that combining numerical methods with qualitative analysis provides a comprehensive understanding of porosity characteristics. Notably, the effectiveness of the proposed image processing methods was validated by comparing the results with actual material density measurements. However, challenges such as the need for proper calibration and potential imaging artifacts affecting accuracy were identified. This study represents a significant advancement in materials engineering, offering a detailed methodology for porosity analysis in aluminum alloys that not only enhances quality control and process optimization, but also improves segmentation accuracy and facilitates the reliable detection of small and interconnected pores in complex additively manufactured geometries. Full article

This study presents a systematic analysis of the stability and roll characteristics of an Oscillating Water Column (OWC) wave energy buoy. By integrating theoretical derivation and AQWA simulation, the research identifies thirteen possible heeling states of OWC buoy, focusing on five representative states applicable to the current design. A novel segmented-integration model is proposed to compute the centre of buoyancy and righting moment for the hollow-annular OWC buoy, accurately capturing the evolution of static and dynamic stability across heel angles from 0° to 90°. Results show that the buoy has an initial metacentric height of 0.33 m, a maximum righting arm of 0.713 m, a limiting static heel angle of 77°, and a minimum capsizing moment of 22,887 N·m—all significantly exceeding regulatory requirements. The roll natural period ranges from 5.8 to 7.7 s, with a tuning factor above 1.3, effectively avoiding resonance with typical wave periods in the target sea area. The buoy demonstrates excellent dynamic stability and capsize resistance. This study fills a gap in OWC buoy stability analysis and provides a practical guidance for the safe design of wave energy devices. Full article

This paper reports the spinning and wear comfort properties of polyethylene terephthalate (PET)/copolymer-PET (Co-PET) hollow yarns and their fabrics, as well as the effect of the wt.% of inorganic particles embedded in the core of the bicomponent yarns. The results are discussed in terms of the types and amounts of inorganic particles (titanium dioxide (TiO₂) and calcium carbonate (CaCO₃)) embedded in the sheath of the bi-component yarns (Kolon semi-dull (KSD), Kolon full-dull (KFD), and Kolon calcium carbonate (KCC) PET/Co-PET yarns). The three sheath/3-core bicomponent yarns developed in this study exhibited good spinnability and weavability with relatively strong tenacity and breaking strain. Their optimal spinning conditions were determined. The KCC PET/Co-PET fabric showed the greatest hollowness ratio, followed by the KFD PET/Co-PET and KSD PET/Co-PET fabrics. This might be attributed to the higher wt.% (2.5 wt.%) of CaCO₃ particles embedded in the sheath of the KCC PET/Co-PET yarns and to the larger particle size (0.8 μm) of CaCO₃. Regarding the wear comfort, the moisture management system (MMT) test indicated that the KFD PET/Co-PET fabric is suitable for market applications because of its good moisture absorption and rapid drying. The KFD PET/Co-PET fabric is useful for winter clothing applications because of its relatively high heat retention rate and lack of durability issues with washing. An examination of the wearing performance for fitness with a tactile hand feel showed that KFD and KCC/Co-PET fabrics imparted a softer tactile hand feel than the KSD PET/Co-PET fabric. On the other hand, the KCC PET/Co-PET fabric was assumed to have some issues with wearing durability. Full article

Cellulose is the primary component of plant cell walls, and its content is linked to the strength of plant stems. The cellulose synthase genes (CesA) are crucial for regulating cellulose biosynthesis. To examine the characteristics and functions of CesA genes in sugarcane, our study conducted a genome-wide analysis of the Saccharum officinarum LA-Purple genome. The results identified 10 CesA genes in the S. officinarum genome, which could be grouped into six categories. SoCesA10, SoCesA11, and SoCesA12 are clustered within the same subclass as genes involved in secondary cell wall synthesis in rice and Arabidopsis. Further transcriptome analysis of stems at different stages and sections showed that SoCesA10, SoCesA11, and SoCesA12 were highly expressed during mature stages. Among these, SoCesA10 and SoCesA11 showed differences in expression between species and organs. Their gene functions were also validated in rice, revealing that the expression of SoCesA10 and SoCesA11 was positively correlated with cellulose content. In summary, this study identified key cellulose biosynthesis genes, SoCesA10 and SoCesA11, in sugarcane and preliminarily confirmed their functions in rice, providing a foundation for breeding sugarcane with improved lodging resistance. Full article

Dried banana slices can be nutritious snacks that meet consumers’ needs. However, preserving their color, texture, and antioxidant properties is challenging during convective drying. The new approach aimed to produce high-quality dried banana slices with higher antioxidant activity and lower browning. In this paper, the simultaneous application of ultrasound (at three levels: 0 W, 500 W, and 1000 W) and carboxymethyl cellulose (CMC) coating (the ratio of banana slice mass to the coating solution mass (BS:CS) at three levels: 1:2, 1:3, and 1:4) pretreatments, and their combined effects on various characteristics of the finally obtained dried banana slices were examined. The convective drying of banana slices was carried out at 80 °C and 3 m/s air velocity to achieve a consistent moisture content of roughly 10% (kg water/kg dry matter). As the power of ultrasound was increased from 0 W to 1000 W and with changing the BS:CS ratio from 1:2 to 1:4, the results demonstrated that the effective water diffusion coefficient (D_eff), water absorption capacity (WAC), and antioxidant activity (AA) of the dried banana slices were enhanced; however, their browning index (BI) decreased. Consequently, prior to convective drying, CMC coating using an ultrasonic system can be used as a practical strategy to produce fruit chips with desirable qualitative and nutritional properties. Full article

Background: Firefighters are at elevated risk of low back pain (LBP), yet predictors, mechanisms, and interventions for LBP in this occupation remain poorly defined. The purpose of this study was to systematically review the literature and synthesize the evidence on functional biomarkers associated with the risk of LBP in firefighters. Methods: PubMed, EMBASE, CINAHL, and PEDro were searched for studies evaluating functional biomarkers in firefighters with or without LBP, including aerobic capacity, anthropometric measures, disability/kinesiophobia, functional work tasks/capacity, imaging/structural/morphological characteristics, kinematics, movement quality/range of motion, muscular fitness, overall physical fitness, physical activity. Empirical evidence statements were generated for each biomarker domain, under Protocol Registration PROSPERO (CRD420251010061). Results: Eighteen studies (n = 32,977) met inclusion criteria and were predominantly cross-sectional (14/18) with fair quality (13/18), which suggests a substantial risk of bias. Higher disability/kinesiophobia and poorer functional work task performance were linked to increased risk of LBP, although causal relationships cannot be determined. Associations for the eight other biomarkers were inconsistent. Two interventional studies demonstrated benefits from trunk-focused exercise. Conclusions: The literature examining functional biomarkers and LBP in firefighters is fragmented, which precludes making robust and broad clinical recommendations for evidence-based implementation. Findings of future research may ultimately lead to approaches to improve the safety and health of firefighters with LBP through patient-centered and tailored programs addressing integrated functional biomarkers across the continuum of prevention, clinical care, and resilience development. Full article

For effective water purification, the combination of membrane separation and catalytic degradation technologies not only permits continuous pollutant degradation but also successfully reduces membrane fouling. In recent years, catalytic membranes (CMs) have garnered a lot of interest in the water treatment industry. The main benefits of CMs are methodically explained in this review, emphasizing the synergistic effect of membrane separation and catalysis. These benefits include stable catalyst loading achieved through membrane structure manipulation, nanoconfinement, and effective degradation of organic pollutants. The application of catalytic membranes in water treatment is then thoroughly summarized, and they are separated into five main groups based on their unique catalytic reaction mechanisms: ozone catalytic membranes, photocatalytic membranes, electrocatalytic membranes, Fenton-type catalytic membranes, and persulfate catalytic membranes. The mechanisms and performance characteristics of each kind of CM are looked at in greater detail. Finally, research directions and future prospects for water treatment using catalytic membranes are proposed. This review provides recommendations for future research and development to ensure the effective use of catalytic membranes in water treatment, in addition to providing a thorough examination of the advancements made in their application in the treatment of various wastewaters. Full article

The technology of 3D printing has become one of the most effective methods of creating various parts, such as those used for fast prototyping. The most important aspect of 3D printing is the selection and application of the appropriate material, also known as filament. The current review concerns mainly the description of the mechanical and physical properties of the different filaments and the possibilities of improving those properties. The review begins with a short description of the development of 3D printing technology. Next, the basic characteristics of thermoplastics used in the fused filament fabrication (FFF) are discussed, namely polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PETG). According to modern con-cepts, the printed parts can be reinforced with the use of different kinds of fibers, namely synthetic fibers (carbon, glass, aramid) or natural fibers (wood, flax, hemp, jute). Thus, the impact of such a reinforcement on the performance of FFF composites is also presented. The current review, unlike other works, primarily addresses the problem of the aging of parts made from the thermoplastics above. Environmental conditions, including UV radiation, can drastically reduce the physical and mechanical properties of printed elements. Moreover, the current review contains a detailed discussion about the influence of the different fibers on the final mechanical properties of the printed elements. Generally, the synthetic fibers improve the mechanical performance, with documented increases in tensile modulus reaching, for instance, 700% for carbon-fiber-reinforced ABS or over 15-fold for continuous aramid composites, enabling their use in functional, load-bearing components. In contrast, the natural ones could even decrease the stiffness and strength (e.g., wood–plastic composites), or, as in the case of flax, significantly increase stiffness (by 88–121%) while offering a sustainable, lightweight alternative for non-structural applications. Full article

The rotor is a crucial component in rotating machinery, where its stability directly impacts performance and safety. Imbalance-induced vibrations can cause severe component wear, resonance instability, and even catastrophic failures, especially in high-speed systems like aero-engines. While the squeeze film damper (SFD) is widely used for vibration suppression, the effects of imbalance (manifested as SFD eccentricity) on its dynamic performance are not well understood. Additionally, the combined impact of imbalance and acceleration on rotor–SFD system stability has not been systematically investigated. This study uses numerical simulations to explore the influence of SFD eccentricity, caused by imbalance, on its dynamic characteristics. Experimental tests are conducted to examine the effects of imbalance and acceleration on rotor–SFD dynamics. Results show that increasing imbalance raises SFD eccentricity, reducing the effective oil film bearing area. This results in a rapid increase in the oil film’s stiffness and slower growth in damping, enhancing nonlinearity and reducing stability. Under small imbalance conditions, increasing acceleration improves stability by facilitating critical speed crossing and reducing vibration amplitude. However, excessive imbalance renders acceleration control ineffective, exacerbating system instability. This study provides valuable insights into the interaction between imbalance, acceleration, and SFD performance, offering guidance for optimizing rotor–SFD system parameters and ensuring stable operation. Full article

Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergence—reducing the number of communication rounds by approximately 20%—while maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios. Full article

This study investigates the combined resonance phenomenon in magneto-electro-elastic (MEE) cylindrical shells under longitudinal and lateral excitations with thermal factors, addressing the complex interaction between mechanical, electrical, and magnetic fields in smart structures. The research aims to establish a theoretical framework for predicting resonance behaviors in energy harvesting and sensing applications. Using Maxwell’s equations and Hamilton’s principle, the governing equations for combined resonance are derived. The method of varying amplitude (MVA) is employed to acquire the combined resonance response across varying parameters. Furthermore, the Runge–Kutta method is applied to investigate the bifurcation and chaotic motion characteristics under different longitudinal and lateral excitation conditions. Key findings reveal the coupling effects of multi-physical fields on resonance frequencies, demonstrating quantitative agreement with prior studies. The results provide fundamental insights into the dynamic characteristics of MEE materials, offering theoretical support for optimizing their performance in adaptive engineering systems. Full article

Background: Despite the high mortality associated with angiosarcoma, its low prevalence has limited sample sizes in prior studies. To address these gaps, we analyzed the AACR Project GENIE registry, a large, multi-institutional database. Methods: 359 tumor samples from 346 patients with angiosarcoma were identified from the AACR Project GENIE v18.0-public database using cBioPortal. Somatic mutations and copy number alterations were assessed. Statistical significance was assessed by t-test for continuous variables and a chi-squared test for categorical data, with significance set at p < 0.05. Results: Recurrent mutations included TP53 (20.6%), KDR (13.6%), and PIK3CA (10.6%). Copy number alterations occurred in MYC (27.3%), CRKL (10.4%), FLT4 (5.5%), and KDR (4.8%). Homozygous deletions occurred in CDKN2A (6.6%), CDKN2B (6.56%), and MTAP (3.81%). Significant co-occurrence included FAT1-NOTCH2, TP53-ATRX, and NOTCH1-ARID1A. Mutual exclusivity was seen with KDR-FLT4 and KDR-ATRX. Females exhibited enrichment in MYC and HRAS, while males exhibited enrichment in POT1, NTRK2, and FAT1. Compared with primary tumors, metastatic tumors more often displayed ZFHX4, FGFR1, MSI2, HIST1H1C, and TOP1 mutations, while MAPK7 mutations occurred only in primary tumors. Conclusions: In one of the largest genomic analyses of angiosarcoma to date, we identified recurrent alterations, suggesting potential future therapeutic targets. Full article