Search Results (1,524)

Multiple Myeloma (MM) is an incurable malignancy that progresses from asymptomatic precursor stages—Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smouldering Multiple Myeloma (SMM)—to active disease. Despite ongoing research, the molecular mechanisms driving this progression remain poorly understood. In this study, we aimed to uncover key regulatory factors involved in MM progression by integrating single-cell RNA sequencing (scRNA-seq) data with curated a priori biological knowledge of MM. To this end, we first integrated a priori knowledge from databases in a synthetic gene network map to play the role of an MM-related backbone to project findings from scRNA analysis on CD138⁺ Plasma Cells. This was followed by stage-specific regulatory network construction and analysis using Integrated Value of Influence (IVI) metrics to identify the most influential genes across disease stages. Our findings revealed GSK3B, RELA, CDKN1A, and PCK2 as central regulators shared across multiple stages of the disease. Notably, several of these genes had not previously been included in established MM gene sets, highlighting them as prime candidates for biomarkers and drug targets. Full article

This work investigates fixed point results for mappings satisfying generalized P-type contractive conditions in the framework of b-metric spaces. Several existence and uniqueness theorems are established by employing appropriate iterative techniques adapted to the b-metric setting. Illustrative examples are provided to clarify the relationship between P-contractions and classical contractions. In addition, an application to a boundary value problem involving a second-order $(p,q)$-difference Langevin equation is presented to demonstrate the effectiveness of the theoretical results. Full article

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

Surface electromyography (sEMG) is widely used for decoding motion intent in prosthetic control and rehabilitation, yet the impact of external load on sEMG-to-kinematics mapping remains insufficiently characterized, particularly for wrist flexion-extension This pilot study investigates wrist angle estimation (0–90°) under four discrete counter-torque levels (0, 25, 50, and 75 N·cm) using a multilayer perceptron neural network (MLPNN) regressor with mean absolute value (MAV) features. Multi-channel sEMG was acquired from three healthy participants while performing isotonic wrist extension (clockwise) and flexion (counterclockwise) in a constrained single-degree-of-freedom setup with potentiometer-based ground truth. Signals were filtered and normalized, and MAV features were extracted using a 200 ms sliding window with a 20 ms step. Across all load levels, the within-subject models achieved very high accuracy (R² = 0.9946–0.9982) with test MSE of 1.23–3.75 deg²; extension yielded lower error than flexion, and the largest error was observed in flexion at 25 N·cm. Because the cohort is small (n = 3), the movement is highly constrained, and subject-independent validation and embedded implementation were not evaluated, these results should be interpreted as a best-case baseline rather than evidence of deployable rehabilitation performance. Future work should test multi-DoF wrist motion, freer movement conditions, richer feature sets, and subject-independent validation. Full article

Gamification is widely used to enhance student motivation, yet many educational design proposals remain conceptual and provide limited operational guidance for digital learning environments. This paper introduces APAR (Activities, Points, Achievements and Rewards), a content-independent structural framework for designing and implementing educational gamification in learning platforms. Grounded in motivation theories (including Self-Determination Theory and Relatedness–Autonomy–Mastery–Purpose) and reward taxonomies (Status, Access, Power and Stuff), APAR distinguishes high-level design constructs from concrete game elements (e.g., points, badges and leaderboards) and provides a systematic design loop linking learning activities, feedback, intermediate goals and reinforcement. The contribution includes (i) a mapping table relating each APAR construct to motivation models, supported dynamics and typical learning-platform implementations; (ii) an actionable design guide; and (iii) an empirical illustration implemented in Moodle in a higher-education Computer Networks course. In this setting, the proportion of enrolled students taking the final exam increased from 58% to 72% in the first year, and the proportion of enrolled students passing increased from 17% to 38%; in 2022–2023 these values were 70% and 39%, respectively (56% of exam takers passed). While the use case relies on quantitative course-level indicators and is observational, the findings support the potential of structural gamification as an integrated methodological tool and motivate further mixed-method validations. Full article

This paper discusses the assimilation of significant wave height (Hs) observations from the China France Oceanography SATellite (CFOSAT) into the Global Deterministic Wave Prediction System developed by Environment and Climate Change Canada. We focus on the quantification of background errors in an effort to address the conventional, simplified, homogeneous assumptions made in previous studies using Optimal Interpolation (OI) to generate Hs analysis. A map of Best Correlation Length, L, is generated to count for the inhomogeneity in the wave field. This map was calculated from pairs of Hs forecasts of two grid points shifted in space and time from which a look-up table is derived and used to infer the spatial extent of correlations within the wave field. The wave spectra are then updated from Hs analysis using a frequency shift scheme. Results reveal significant spatial variance in the distribution of L, with notably high values located in the eastern tropical Pacific Ocean, a pattern that is expected due to the persistent swells dominating in this region. Experiments are conducted with spatially varying correlation lengths and a set correlation length of eight grid points in the analysis step. Forecasts from these analyses are validated independently with the Global Telecommunications System buoys and the Copernicus Marine Environment Monitoring Service (CMEMS) altimetry wave height observations. It is found that the proposed statistical method generally outperforms the conventional method with lower standard deviation and bias for both Hs and peak period forecasts. The conventional method has more drastic corrections on Hs forecasts, but such corrections are not robust, particularly in regions with relatively short spatial correlation length scales. Based on the analysis of the CMEMS comparison, the globally varying correlation length produces a positive increment of the Hs forecast, which is globally associated with forecast error reduction lasting up to 24 h into the forecast. Full article

The use of deep learning radiomics to predict whether advanced prostate cancer (PCa) will progress within two years after treatment has been validated, yet there remains a lack of research on estimating time to progression. Patients were enrolled from October 2017 to March 2024. One hundred and eighty-two patients with advanced PCa diagnosed through ultrasound-guided systematic prostate biopsy were enrolled. A deep learning-based radiomics model for predicting progression was firstly developed using pretreatment MR apparent diffusion coefficient (ADC) maps, and the performance of manual (ROIref) versus AI-derived (ROIai) tumor segmentations was compared. Then, survival analysis was performed to compare ROIref-based and ROIai-based radiomics-predicted probabilities in the risk stratification. The area under the receiver operating characteristics curve (AUC) was used to estimate the model efficacy. The model achieved high AUC values for progression prediction in test sets (ROIref: 0.840, ROIai: 0.852). No significant difference was observed between ROIai-based and ROIref-based approaches (ΔAUC = 0.012, p = 0.870) in the test set. Both ROIref-predicted and ROIai-predicted probabilities independently predicted progression in multivariate Cox proportional hazard regression models (p < 0.001) and stratified patients into distinct survival groups (log-rank p < 0.001). Decision curve analysis confirmed equivalent clinical utility across thresholds (0.1–0.6), with net benefit exceeding the “treat all” and “treat none” strategies. In conclusion, deep learning-based radiomics models could effectively predict advanced PCa progression, with AI-derived tumor annotations performing equally to manual expert ones. Full article

Solid–liquid stirred tanks are widely used in multiphase processes, including bioreactors for mesenchymal stem cell (MSC) culture, yet simultaneous experimental data for both dispersed and carrier phases remain limited. Here, a refractive index-matched (RIM) suspension of PMMA microparticles ($d_p=168\mathrm{\mu m}$, ${\rho }_p/{\rho }_l\approx 0.96$) in an NH₄SCN solution is studied at an intermediate Reynolds number ($Re\approx 5000$), low Stokes number ($St=0.078$), and particle volume fractions $0.1\le {\alpha }_p\le 0.5$ v%. This system was previously established and studied for the effect of addition of particles on the carrier phase. In this work, a dual-camera PIV set-up provides simultaneous velocity fields of the liquid and particle phases in a stirred tank equipped with a three-blade down-pumping HTPGD impeller. The liquid mean flow and circulation loop remained essentially unchanged with particle loading, whereas particle mean velocities were lower than single-phase and liquid-phase values in the impeller discharge. Turbulence levels diverged between phases: liquid-phase turbulent kinetic energy (TKE) in the impeller region increased modestly with ${\alpha }_p$, while solid-phase TKE was attenuated. Slip velocity maps showed that particles lagged the fluid in the impeller jet and deviated faster from the wall in the upward flow, with slip magnitudes increasing with ${\alpha }_p$. An approximate axial force balance indicated that drag dominates over lift in the impeller and wall regions, while the balance is approximately satisfied in the tank bulk, providing an experimental benchmark for refining drag and lift models in this class of stirred tanks. Full article

This paper investigates a friction oscillator model in both its Integer-Order and Fractional-Order formulations. The lack of classical solutions for the governing differential equations with discontinuous right-hand sides is addressed by adopting a Differential Inclusion framework. Using Filippov regularization, the discontinuity is replaced by a set-valued map satisfying appropriate regularity conditions. Selection theory is then applied to construct a Lipschitz-continuous, single-valued function that approximates the set-valued map. This procedure reformulates the discontinuous initial value problem as a continuous, single-valued one, thereby providing a rigorous justification for the proposed approximation method. Numerical simulations are performed to study stick–slip attractors in both the Integer-Order and Fractional-Order cases. The results demonstrate that, in contrast to the Integer-Order system, periodic attractors cannot occur in the Fractional-Order regime. Full article

This paper is devoted to the investigation of the proper strict efficient solutions to a set optimization problem with a partial set order relation. Firstly, the notion of proper strict efficient solution defined by the Minkowski difference is introduced, and it is worth mentioning that the introduced strict efficiency is different from those in the existing literature. Secondly, a class of generalized contingent derivatives for set-valued maps is proposed, which are characterized in terms of a set criterion. Finally, the necessary and sufficient optimality conditions and a scalarization theorem for proper strict efficiency are established. Some concrete examples are given to illustrate the obtained results. Full article

Background: Burns cause about 180,000 deaths annually and lead to substantial morbidity, especially in low- and middle-income countries. Clinical assessment of burn depth and TBSA relies on visual and bedside examination and remains subjective. Convolutional neural networks (CNNs) have been proposed to improve objectivity in image-based burn assessment, but clinical generalizability and acceptance remain uncertain. Aims: To map current evidence on CNN performance for burn TBSA, burn depth and treatment-related tasks and to explore whether a large language model (LLM) can organize extracted findings into a transparent, literature-derived orientation decision tree. Methods: We performed a scoping review following PRISMA-ScR. PubMed, Web of Science and Cochrane were searched on 5 April 2025. Eligible studies reported CNN analysis of 2D burn images and quantitative performance metrics. We summarized reported values descriptively. We then provided a structured summary of extracted findings to ChatGPT to draft a one-page orientation decision tree. Two consultant burn surgeons reviewed the figure for clarity and plausibility. Results: Of 659 records, 24 studies were included. Across studies, reported performance for TBSA and depth assessment was often high, but study designs, datasets, labels, imaging modalities and validation strategies varied substantially. High reported performance does not necessarily imply clinical robustness or real-world accuracy. A single study reported high test-set accuracy for graft versus non-graft using heavily expanded data. This value should not be generalized. Conclusions: CNNs show promise for image-based burn TBSA and depth assessment, but heterogeneity, dataset limitations and limited external validation restrict interpretation and clinical transfer. The LLM-derived decision tree is a literature-synthesis orientation figure, not a clinical decision-support tool. Full article

Populus euphratica is a key constructive species in desert ecosystems and plays a vital role in maintaining their stability. However, effective automated methods for accurately delineating its distribution outlines are currently lacking. This study used the mainstream area of the Tarim River as a case study and proposed a technical solution for identifying the distribution outline of Populus euphratica using multi-source thematic classification data. First, cropland thematic data were used to optimize the accuracy of the Populus euphratica classification raster data. Discrete points were removed based on density to reduce their impact on boundary identification. Then, a hierarchical identification scheme was constructed using the alpha-shape algorithm to identify the boundaries of high- and low-density Populus euphratica distribution areas separately. Finally, the outlines of the Populus euphratica distribution polygons were smoothed, and the final distribution outline data were obtained after spatial merging. The results showed the following: (1) Applying a closing operation to the cropland thematic classification data to obtain the distribution range of shelterbelts effectively eliminated misclassified pixels. Using the kd-tree algorithm to remove sparse discrete points based on density, with a removal ratio of 5%, helped suppress the interference of outlier point sets on the Populus euphratica outline identification. (2) Constructing a hierarchical identification scheme based on differences in Populus euphratica density is critical for accurately delineating its distribution contours. Using the alpha-shape algorithm with parameters set to α = 0.02 and α = 0.006, the reconstructed geometries effectively covered both densely and sparsely distributed Populus euphratica areas. (3) In the morphological processing stage, a combination of three methods—Gaussian filtering, equidistant expansion, and gap filling—effectively ensured the accuracy of the Populus euphratica outline. Among the various smoothing algorithms, Gaussian filtering yielded the best results. The equidistant expansion method reduced the impact of elongated cavities, thereby contributing to boundary accuracy. This study enhances the automation of Populus euphratica vector data mapping and holds significant value for the scientific management and research of desert vegetation. Full article

Brain age has been widely recognized as an important biomarker for monitoring adolescent brain development and assessing dementia risk. However, existing model visualization methods primarily highlight brain regions associated with aging, making it difficult to comprehensively reveal broader brain changes. In this study, we developed a VGGNet-based brain age prediction model and proposed the Softmax-Derived Brain Age Mapping algorithm to simultaneously identify brain regions associated with both youthful and aging features. The resulting saliency maps provide explicit representations of developmental and degenerative processes across different brain regions. Brain Age Map analysis revealed that aging features in the healthy group were primarily confined to the frontal cortex, aligning with findings that the frontal lobe is the earliest region to undergo natural senescence. In contrast, the dementia group exhibited widespread aging across the frontal, temporal, parietal, and occipital lobes, as well as the ventricular regions. These results suggest that the spatial distribution of brain aging can serve as a critical biomarker for distinguishing normal aging trajectories from pathological degeneration. From an application perspective, we further explored the potential of the proposed framework in neurodegenerative diseases. The analysis reveals that dementia patients generally exhibit an advanced brain age, with cortical aging being markedly more pronounced than in age-matched healthy samples. Notably, although dementia cases were not included in the training set, the model was still able to localize abnormalities in relevant brain regions, underscoring its potential value as an assistive tool for early dementia diagnosis. Full article

As the core component in intelligent manufacturing systems, the precise control of the welding clamp’s electrode pressure plays a decisive role in ensuring the quality of spot welding. This paper proposes a novel pressure control strategy for robotic welding clamp based on partitioned adaptive RBF neural networks: (1) Deformation of the clamp body can lead to deviations in workpiece positioning. To address this issue, a deflection compensation method for robot welding clamp based on the PSO-RBF neural network is proposed. By leveraging pre-calibrated empirical data, the intrinsic mapping relationships are identified, and the derived deflection compensation value is integrated into the real-time position command of the robot end-effector. (2) During electrode motion, the system is subjected to external disturbances such as friction and gravitational forces. So, a sliding mode control strategy incorporating adaptive RBF disturbance compensation is proposed to achieve robust speed regulation. Furthermore, the electrode’s reference velocity is dynamically adjusted based on the welding force error and improved admittance control algorithm, enabling indirect regulation of the welding force to reach the desired set value. The results demonstrate that the proposed composite control strategy reduces electrode pressure overshoot to less than 5% and enhances steady-state control accuracy to ±1.5%. Full article

Soil organic carbon (SOC) plays a vital role in regional carbon cycling and ecosystem services. However, previous studies have primarily focused on spatial patterns and environmental drivers, with limited attention to long-term observations, underlying mechanisms, and large-scale modeling. In this study, we collected surface soil samples (0–20 cm) and integrated topography, soil physicochemical properties, climate, vegetation, and MODIS remote sensing data to develop 16 SOC prediction models using linear regression and machine learning approaches. SOC was significantly correlated with latitude, mean annual temperature, and precipitation and negatively associated with several remote sensing indices. The LASSO-selected variable set combined with a support vector machine (SVM) achieved the highest predictive accuracy (R² = 0.53, RMSE = 36.19). From 2001 to 2020, the mean SOC stock in the Yellow River source region was estimated at 1683.98 g C/m², showing higher values in the southeast and lower values in the northwest. Alpine meadow exhibited the highest total stock due to its extensive coverage, whereas the cold temperate wet coniferous forest had higher mean content and unit area value, indicating strong carbon sequestration potential. This study identifies key SOC drivers and mechanisms, provides quantitative estimates of regional SOC content and stock, and offers a scientific basis for grassland carbon management and large-scale digital soil mapping. Full article