Multidisciplinary Digital Publishing Institute (Switzerland)
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Using Species Distribution Modeling to Guide Surveys for a Rare Plant (Cymopterus sessiliflorus): Climate and Landscape Variables Predict Potential Distribution
No full textRare species at the edge of their range often persist after range contractions, yet basic information is typically lacking. We created species distribution models (SDMs) to guide field surveys for a disjunct population of Sessileflower Indian parsley (Cymopterus sessiliflorus; Apiaceae). We used historical observations to produce an initial model that guided field surveys in 2023. We refined the model using new observations from these surveys and the best predictors were shrubs, rock outcrops, mean monthly precipitation of the warmest quarter and rock type (area under the curve = 0.97). Suitable habitat (moderate-high and high classes) was predicted in <2% of Wyoming. We discovered 11 new populations over 2 summers. We collected 17 bee genera (n = 272 individuals) during C. sessiliflorus flowering suggesting diverse potential pollinators may transport pollen. Our model highlighted other areas predicted suitable and surveys in these areas may reveal new populations of this rare plant. The SDMs demonstrated how sparse historical data on rare species can be used to direct surveys in an efficient and effective manner. The information we gathered provided basic data for a rare plant at the periphery of its range where the most robust populations may occur making them critical for conservation efforts
Dual-Stream STGCN with Motion-Aware Grouping for Rehabilitation Action Quality Assessment
No full textAction quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification
Comparative Elemental Distribution in Sunflower, Wheat, and Maize Grown in Soil with a Distinct Geochemical Profile
No full textDocumenting baseline elemental distribution patterns in crops under non-contaminated conditions provides a physiological reference for understanding constitutive metal homeostasis. This study compared the internal allocation of elements in sunflower (Helianthus annuus), wheat (Triticum aestivum), and maize (Zea mays) grown in soil with a specific geochemical profile. Soil was characterized using X-ray Fluorescence Spectroscopy (XRF) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Plants were grown under controlled conditions, and elemental concentrations in roots and shoots were quantified to calculate Bioaccumulation (BCF) and Translocation (TF) Factors. Soil analysis confirmed nickel (42.6 mg kg−1) and copper (32.8 mg kg−1) concentrations within typical global ranges for uncontaminated soils. Species exhibited different distribution tendencies: sunflower showed balanced root–shoot allocation for nickel (TF = 1.00); wheat demonstrated pronounced root retention of nickel and copper (TF < 0.5); and maize exhibited preferential translocation of copper (TF = 0.76) alongside root retention of nickel. Concentrations of lead, selenium, and silver were minimal across all species. The study delineates different species-specific tendencies in internal elemental allocation under given growth conditions. These patterns represent baseline physiological behaviors rather than responses to contamination, providing a comparative dataset that contributes to the understanding of crop ionomics and informs the interpretation of tissue metal concentrations in relation to soil conditions
A Novel Graph Neural Network Method for Traffic State Estimation with Directional Wave Awareness
No full textTraffic state estimation (TSE) is crucial for intelligent transportation systems, as it provides unobserved parameters for traffic management and control. In this paper, we propose a novel physics-guided graph neural network for TSE that integrates traffic flow theory into an estimation framework. First, we constructed wave-informed anisotropic temporal graphs to capture the time-delayed correlations across the road network, which were then merged with spatial graphs into a unified spatiotemporal structure for subsequent graph convolution operations. Then, we designed a four-layer diffusion graph convolutional network. Each layer is enhanced with squeeze-and-excitation attention mechanism to adaptively capture dynamic directional correlations. Furthermore, we introduced the fundamental diagram equation into the loss function, which guided the model toward physically consistent estimations. Experimental evaluations on a real-world highway dataset demonstrated that the proposed model achieved a higher accuracy than benchmark methods, confirming its effectiveness in capturing complex traffic dynamics
Sensor-Detected Differences in Behaviors of Older Drivers with Pre-MCI and Mild Cognitive Impairment vs. Unimpaired Drivers
No full textBackground: Research to identify changes in driving behavior that occur with the onset of Pre-MCI and MCI is an emerging area with many gaps still to be addressed. These gaps include limited use of objective, continuous measurement of driver behavior in real-life traffic conditions and comprehensive, biomarker-validated, cognitive evaluation based upon both testing and clinical ratings. Using these strategies, the questions addressed in this exploratory study are whether or not differences in driving behavior are indicative of Pre-MCI/MCI and which behaviors are most predictive of Pre-MCI/MCI. Methods: As part of a naturalistic longitudinal study, older drivers with a Montreal Cognitive Assessment score ≥ 19 had telematic sensors installed in their vehicles and underwent comprehensive cognitive assessment quarterly for three years. Thirty-six participants were classified as Unimpaired (n = 23) or Pre-MCI/MCI (n = 10/3) based upon a neuropsychological battery and diagnostic algorithm. A penalized generalized linear mixed-effects model (GLMM) with a logistic link and LASSO regularization was used to model Pre-MCI/MCI group membership vs. unimpaired as a function of ten trip-level telematic features (trip distance, hard acceleration, hard braking, hard turns, speed average, maximum speed, RPM average, fuel level, throttle average, and throttle variability) at the end of their first 12 months in the study. Results: Higher RPM, shorter average trips, and greater throttle variability predicted higher odds of Pre-MCI/MCI, while more frequent hard braking, hard turns, higher mean speed, and lower average throttle (steadier pedal control) predicted lower odds of Pre-MCI/MCI. Conclusions: The model clearly distinguished unimpaired older drivers from those with MCI or Pre-MCI, suggesting that distinct patterns of driver behavior may be related to levels of cognitive function
An Improved Red-Billed Blue Magpie Optimization for Function Optimization and Engineering Problems
No full textThe Red-Billed Blue Magpie Optimization (RBMO) algorithm is an emerging metaheuristic with strong potential applications in solving function optimization and various engineering problems, but it is often hampered by limitations such as premature convergence and an imbalanced exploration–exploitation mechanism. To overcome these deficiencies, an Improved Red-Billed Blue Magpie Optimization (IRBMO) algorithm is introduced in this paper. The IRBMO integrates three synergistic strategies within a multi-population cooperative framework: (1) an enhanced RBMO search with elite guidance to accelerate convergence; (2) an adaptive differential evolution operator to bolster local search and escape local optima; and (3) a mechanism for boosting global exploration and enhancing population diversity through quasi-opposition-based learning. The performance of IRBMO was rigorously evaluated on 26 classical benchmark functions and several real-world engineering design problems. As demonstrated by the experimental results, IRBMO significantly exceeds the performance of the original RBMO and other leading algorithms across the metrics of solution accuracy, convergence speed, and stability
Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns
No full textThis study aims to investigate the performance of the combined machine learning (ML) models and tiering technique for predicting the compressive strength of FRP-strengthened circular concrete columns. A dataset consisting of 725 experimental results has been assembled from available research studies to evaluate the prediction models. Pearson’s correlation analysis has been carried out to investigate the relationship between seven input parameters and the target parameter. The Taylor diagram has been plotted to deter-mine the best design-oriented strength model. The prediction performance of the combined ML models and tiering technique was compared with that of single ML models and ten design-oriented strength models. The research outcomes revealed that applying the tiering technique significantly improved the prediction accuracy of the ML models. It was also found that the best ML model for predicting the compressive strength of FRP-strengthened circular concrete columns was the combined random forest model and tiering technique, which outperformed single ML and design-oriented strength models
Risk Scores for Stratifying Hepatocellular Carcinoma and Optimizing Surveillance Strategies
No full textBackground: Hepatocellular carcinoma (HCC) is a major global health burden, with poor outcomes largely due to diagnosis at an advanced stage and the limited performance of current surveillance tools. Ultrasound with alpha fetoprotein (AFP) provides insufficient sensitivity for early-stage detection, highlighting the need to better identify the at-risk population. Focus of the review: Many HCC risk scores have been proposed; however, some depend on specialized laboratory data that are not widely available. This review summarizes risk scores that show reliable discrimination and rely on demographic, clinical, or molecular information that can be readily obtained in routine care. Conclusions: Advances in HCC risk scores support the move toward surveillance approaches based on individual risk. These tools can improve risk stratification, increase the likelihood of early detection, and potentially support better outcomes for people who belong to the at-risk population for HCC
Robust Synchronization of Time-Fractional Memristive Hopfield Neural Networks
No full textWe introduce and study robust synchronization of time-fractional Hopfield neural networks with memristive synapses and Hebbian learning. This novel model of artificial neural networks exhibits strong memory and long-range path dependence. By scaled group estimates and analysis of fractional differencing equations, it is proved that under rather general assumptions the solution dynamics are globally dissipative and there exists a threshold condition for achieving robust synchronization of the entire neural networks if this condition is satisfied by the interneuron coupling strength. The synchronizing threshold is explicitly expressed in terms of the original parameters in the model equations and strictly decreasing for the fractional order α∈(0,1). This result makes a breakthrough in the exploration of fractional global and longtime dynamics for AI mathematical models
INVCAM: An Inverted Compressor-Based Approximate Multiplier
No full textIn this paper, a novel 8-bit approximate multiplier, called INVCAM, is proposed in which the inverted partial products (PPs) are summed using approximate 4:2 compressors. This design allows for flexibility in applying approximations, enabling the multiplier to be tuned to the specific accuracy requirements of different applications. By adjusting the number of approximated bits, the multiplier can operate with a better balance between desirable hardware characteristics and acceptable levels of error. Our approach ensures that INVCAM is customizable for a wide range of applications. The results indicate that INVCAM reduces delay, power, and area by up to 21.5%, 70.0%, and 57.6%, respectively, compared to the state-of-the-art (SoTA) approximate multipliers within its mean relative error distance (MRED) range, and by 42.4%, 80.1%, and 68%, compared to an exact multiplier. The efficacy of INVCAM is evaluated in image processing and deep neural network (DNN) applications. The images processed by different configurations of INVCAM have PSNR and SSIM values greater than 28.9 dB and 0.81, respectively, which manifests the acceptable quality of the processed approximate images. In the DNN application, the classification accuracy of the models implemented using INVCAM(7) is within 0.6% of the original model accuracy. When the number of approximate bits is increased to nine, less than 5% accuracy reduction is observed compared to an exact model, while the power-delay-area product of the multiplier improves by 46%