Search Results (17,885)

Pod corn (Zea mays var. tunicata) bears leafy glumes that enclose kernels, resembling a partial reversion to wild-forms, yet remains poorly characterized in situ in Mexico. We evaluated Mexican accessions at two contrasting locations to quantify morphological/phenological diversity and to assess functional traits via proximate kernel composition. Standard descriptors captured variation in plant architecture, tassel/ear traits (including glume length), and reproductive timing. Accessions showed strong plasticity and significant accession × environment effects on ear morphology and maturation. Grain yield ranged from 6.32 to 10.78 t ha⁻¹, with peak values comparable to commercial hybrids and above-typical yields reported for native Mexican races (2.7–6.6 t ha⁻¹). Proximate analysis showed that milling with the tunic increased moisture/ash (up to 3.07% vs. 1.80% in dehulled grain), tended to lower fat and protein, and yielded lower crude fiber than dehulled samples (0.78–0.96% vs. 1.59–1.77%); protein varied widely (1.05–6.64%). Thus, the tunic modulates elemental composition, informing processing choices (with vs. without tunic). Our results document a spectrum of morphotypes and highlight developmental diversity and field adaptability. The observed accession × environment responses provide a practical baseline for comparisons with native and improved varieties, and help guide product development strategies. Collectively, these data underscore the high productive potential of pod corn (up to 10.78 t ha⁻¹ under optimal management) and show that including the tunic substantially alters proximate composition, establishing a quantitative foundation for genetic improvement and food applications. Overall, pod corn’s distinctive ear morphology and context-dependent composition reinforce its value for conservation, developmental genetics, and low-input systems. Full article

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article

Background: Primary fibula tumours are rare, representing approximately 0.25% of all primary bone tumours. While benign lesions are often asymptomatic, malignant ones typically present with pain and functional impairment. Most tumours arise in the proximal third of the fibula, yet the literature regarding their epidemiology and clinicopathological features remains limited. This systematic review aims to synthesise current evidence on presentation, diagnosis, management, and prognosis of primary malignant tumours of the proximal fibula. Methods: A systematic review was conducted following PRISMA guidelines. PubMed, Scopus, and the Cochrane Register were searched on 28 October 2025 for English-language case reports and case series on primary malignant tumors of the proximal fibula. Two reviewers independently performed study selection and data extraction, collecting information on demographics, tumor characteristics, diagnostic approaches, treatments, and outcomes, with disagreements resolved by a third reviewer. Results: Thirty-three papers involving 228 patients (78 females, 128 males, 22 unknown) were included. The mean age at diagnosis was 22.8 years (range 4–79). The most common symptoms were painful mass and neurological complaints. Osteosarcoma and Ewing’s sarcoma were predominant histological types. Limb-sparing surgeries were most common, although 16 patients underwent amputation. At mean follow-up of 48.9 months, local recurrence occurred in 44 cases, and 12 developed distant metastases, most commonly in the lungs. Overall, 38 patients died, 37 due to disease progression. Conclusions: Primary malignant tumours of the proximal fibula, while rare, pose significant therapeutic challenges. Accurate diagnosis, appropriate multimodal treatment, and careful surgical planning are crucial to optimise oncological control and functional outcomes. Full article

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to “complex correlation modeling” and “model-blindness” issues. Specifically, cellular traffic is generated within complex urban systems characterized by an intricate structure and human mobility. Existing approaches, often based on proximity or attributes, struggle to learn the latent correlation matrix governing traffic evolution, which limits forecasting accuracy. Furthermore, while substantial knowledge about urban systems can supplement the modeling of correlations, existing methods for integrating this knowledge—typically via loss functions or embeddings—overlook the synergistic collaboration between data and knowledge, resulting in weak model robustness. To address these challenges, we develop a data–knowledge collaborative learning framework termed the knowledge-empowered spatio-temporal neural network (KESTNN). This framework first extracts knowledge triplets representing urban structures to construct a knowledge graph. Representation learning is then conducted to learn the correlation matrix. Throughout this process, data and knowledge are integrated collaboratively via backpropagation, contrasting with the forward feature injection methods typical of existing approaches. This mechanism ensures that data and knowledge directly guide the dynamic updating of model parameters through backpropagation, rather than merely serving as a static feature prompt, thereby fundamentally alleviating the “model-blindness” issue. Finally, the optimized matrix is embedded into a forecasting module. Experiments on the Milan dataset demonstrate that the KESTNN exhibits excellent forecast performance, reducing RMSE by up to 23.91%, 16.73%, and 10.40% for 3-, 6-, and 9-step forecasts, respectively, compared to the best baseline. Full article

The rapid development of unmanned aerial vehicle (UAV) technology necessitates reliable recognition methods. Radio frequency (RF)-based recognition is promising, but conventional real-valued CNNs (RV-CNNs) typically discard phase information from RF spectrograms, leading to degraded performance under low-signal-to-noise ratio (SNR) conditions. To address this, this paper proposes a complex-valued CNN (CV-CNN) that operates on a constructed complex representation, where the real part is the logarithmic power spectral density (PSD) and the imaginary part is derived from Sobel edge detection. This enables genuine complex convolutions that fuse magnitude and structural cues, enhancing noise resilience. As complex-valued networks are known to be sensitive to architectural choices, we conduct comprehensive ablation experiments to investigate the impact of key hyperparameters on model performance, revealing critical stability constraints (e.g., performance collapse beyond 4–5 network depth). Evaluated on the 25-class DroneRFa dataset, the proposed model achieves 100.00% accuracy under noise-free conditions. Crucially, it demonstrates significantly superior robustness in low-SNR regimes: at −20 dB SNR, it attains 15.58% accuracy, over seven times higher than a dual-channel RV-CNN (2.20%) with identical inputs; at −15 dB, it reaches 45.86% versus 14.03%. These results demonstrate that the CV-CNN exhibits potentially superior robustness and interference resistance in comparison to its real-valued counterpart, maintaining high recognition accuracy even under low-SNR conditions. Full article

Background: Temporomandibular disorders (TMD) and bruxism are prevalent conditions managed by dentists. However, treatment choices—especially concerning botulinum toxin (BTX)—often lack consistency. This study aimed to develop and assess a structured clinical decision-making algorithm for BTX use in patients with TMD and bruxism. Methods: A treatment algorithm was designed through a qualitative analysis of the literature and aligned with German S3 guidelines. A total of 227 dentists assessed three clinical case vignettes reflecting routine clinical practice. Each vignette was evaluated first without and subsequently with the algorithm, focusing on typical indications for botulinum toxin treatment. Data were collected via online survey (SurveyMonkey) and analyzed using Microsoft Excel. Participants were stratified by gender and clinical experience (≤5 years vs. >5 years). Results: Of the 227 dentists contacted, 103 responded, and 56 completed the survey (57.1% male; mean age: 34.5 ± 10.6 years). BTX decision accuracy significantly improved for Case 1 (62.5% → 87.5%, p = 0.0013) and Case 2 (14.3% → 87.5%, p < 0.0001), but not for Case 3 (44.6% → 46.4%, p = 1.000). Confidence increased, and uncertainty decreased, particularly among less experienced dentists. The algorithm also significantly influenced both first- and second-line treatment choices, aligning them more closely with guideline-based therapy. Usefulness was confirmed by 78.6% of respondents, with no significant differences based on gender or experience. Conclusions: The proposed algorithm significantly improved diagnostic accuracy, treatment consistency, and confidence in the use of BTX for TMD and bruxism. It facilitates evidence-based, experience-independent decision-making and potentially represents a useful clinical tool in dental practice. Full article

In corporate management practices, human resources are among the most active and critical elements, and frequent employee turnover can impose substantial losses on firms. Accurately predicting employee turnover dynamics and identifying turnover propensity in advance is therefore of significant importance for organizational development. To improve turnover prediction performance, this study proposes an employee turnover prediction model that integrates an improved ADASYN data rebalancing algorithm with a grid-search-optimized CatBoost classifier. In practice, turnover instances typically constitute a minority class; severe class imbalance may lead to overfitting or underfitting and thus degrade predictive performance. To mitigate imbalance, we employ ADASYN oversampling to reduce skewness in the dataset. However, because ADASYN is primarily designed for continuous features, it may generate invalid or meaningless values when discrete variables are present. Accordingly, we improve ADASYN by introducing a new distance metric and an enhanced sample generation strategy, making it applicable to turnover data with mixed (continuous and discrete) features. Given CatBoost’s strong predictive capability in high-dimensional settings, we adopt CatBoost as the base learner. Nonetheless, CatBoost performance is highly sensitive to hyperparameter choices, and different parameter combinations can yield markedly different results. Therefore, we apply grid search (GS) to efficiently optimize CatBoost hyperparameters and obtain the best-performing configuration. Experimental results on three datasets demonstrate that the proposed improved-ADASYN GS-CatBoost model effectively enhances turnover prediction performance, exhibiting strong robustness and adaptability. Compared with existing models, our approach improves predictive accuracy by approximately 4.6112%. Full article

Silicon oxide/graphite (SiO_x/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase (SEI) growth as independent or unidirectionally coupled processes, neglecting their bidirectional interactions. Here, we develop an electro–thermal–mechanical–gaseous coupled model to capture the dominant degradation processes in SiO_x/Gr anodes, including SEI growth, gas generation, SEI formation on cracks, and particle fracture. Model validation shows that the proposed framework can accurately reproduce voltage responses under various currents and temperatures, as well as capacity fade under different thermal and mechanical conditions. Based on this validated model, a mechanistic analysis reveals two key findings: (1) Gas generation and SEI growth are bidirectionally coupled. SEI growth induces gas release, while accumulated gas in turn regulates subsequent SEI evolution by promoting SEI formation through hindered mass transfer and suppressing it through reduced active surface area. (2) Crack propagation within particles is jointly governed by the magnitude and duration of stress. High-rate discharges produce large but transient stresses that restrict crack growth, while prolonged stresses at low rates promote crack propagation and more severe structural degradation. This study provides new insights into the coupled degradation mechanisms of SiO_x/Gr anodes, offering guidance for performance optimization and structural design to extend battery cycle life. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing. Full article

With the rapid development of industries, hard sealing ball valves are increasingly adopted in extreme working conditions, especially for the advantage of high sealing performance. However, current research works on ball valves are lack of leakage rate prediction, which is an important issue. In this paper, a typical hard sealing ball valve is selected as the research object. Mathematical equations for sealing pressure are derived on both fixed and floating ball valves. The sealing pressure on the hard sealing side of the ball valve is analyzed, and the accuracy of the theoretical equation is verified. Meanwhile, the relationship between sealing performance factor and sealing pressure is fitted, and a prediction method of hard sealing ball valve is proposed, and also is validated experimentally. Results indicate that the sealing pressure obtained from the theoretical equation is conservative, as the actual pressure on the sealing surface exhibits a U-shaped distribution. The sealing performance factor varies with sealing pressure according to a piecewise function. It increases in the form of a power function when the pressure is less than 110 MPa and decreases in the form of a quadratic function when the pressure is higher than 110 MPa. The R² of the fitting equation is greater than 0.98%. Furthermore, the theoretical predictions are consistent with the experimental results in magnitude, confirming the reliability of the proposed prediction method. When the roughness is below 0.2, further reduction in the roughness has little effect on the sealing performance. Both roughness and sealing pressure should be considered comprehensively to enhance sealing performance. This work can benefit the leakage rate prediction and further study for the sealing performance improvement of hard sealing ball valves. Full article

Background/Objectives: this paper investigates the local vs. global visual processing preference in typically developing (TD) children, youth with Down syndrome (DS), and youth with Williams syndrome (WS). In particular, the global precedence effect (GPE) and the global interference effect (GI) have recently been described as two distinct and at least partially independent effects. Methods: in this study, 50 participants (TD = 25, DS = 13, WS = 12) completed two experiments requiring the identification of either the global or local level of hierarchical stimuli, which consisted of letters and schematic faces. For each stimulus type, two separate blocks were conducted, one with the task to focus on the local elements and the other with the task to focus on the global shape. Results: our results indicate that TD children demonstrate a global precedence effect for letters but not for schematic faces, suggesting a developmental modulation of configural processing. In contrast, both DS and WS groups showed a global processing bias for schematic faces and a significant global interference effect in both conditions, likely reflecting deficits in inhibitory control. Conclusions: these findings challenge the notion that DS and WS individuals can be classified strictly as global or local processors, respectively, emphasizing the influence of stimulus type and cognitive demands. Implications for neurodevelopmental research and clinical interventions are discussed. Full article

The growing demand for gluten-free bakery products requires the development of formulations that overcome their typical technological and nutritional limitations. Using fruit by-products as natural fiber sources, in combination with xanthan gum (XG), supports a sustainable ingredient strategy that improves gluten-free product quality. This study investigated the effect of apple pomace (AP) (5% and 10%) and XG (1%) on the technological properties, texture profile, nutritional composition, and sensory acceptance of gluten-free muffins. Six formulations were prepared by partially replacing maize flour with AP and/or adding XG. AP (5–10%) reduced muffin height and volume compared with the control, whereas 1% XG increased muffin height by 11.16% and raised volume and specific volume by 38.46% and 36.11%, respectively. XG significantly decreased hardness compared with the control, while the effect of AP on texture was concentration-dependent: 5% AP reduced hardness, whereas 10% AP did not further improve softness. Combined use of AP and XG resulted in complementary effects, improving structural properties while increasing dietary fiber content. The muffins supplemented with AP were acceptable, and their overall acceptability did not differ significantly among the tested formulations. Overall, the results demonstrate that incorporating AP together with XG enhances both the technological and nutritional quality of gluten-free muffins, supporting the valorization of fruit-processing by-products in functional bakery applications. Full article

The use of different energy sources for heating and year-round domestic water heating is driven by the European Union’s increasingly strict environmental and climate requirements. For this reason, consumers are seeking alternatives and show growing interest in implementing installations that utilize solar energy. Modern households typically employ at least two different energy sources for this purpose. In practice, these are hybrid installations that, depending on the season, can operate with one, two, or more energy sources. The system examined in this paper is of this type, comprising a pellet boiler, solar vacuum tubes, and electric heaters. Managing such a system is complex, and based on the conducted studies, process optimization can be pursued. This report presents an artificial neural network (ANN) model developed to predict the behavior of a real solar installation for domestic hot water heating during the summer season. This study aims, through the obtained model, to forecast the system’s performance during transitional periods such as autumn and spring, thereby enabling more efficient control. Full article