Search Results (3,636)

Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R² = 0.92–0.95) in 2019 and 0.07–0.08 m (R² = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R² = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications. Full article

Improving wheat processing quality is a crucial objective in modern wheat breeding. Among various quality parameters, grain protein content (GPC) and wet gluten content (WGC) significantly influence the end-use quality of flour. These traits are controlled by multiple minor effect genes and highly influenced by environmental factors. Identifying stable and major-effect genetic loci and developing breeder-friendly molecular markers are of great significance for breeding high-quality wheat varieties. In this study, we evaluated the GPC and WGC of 310 diverse wheat varieties, mainly from China and Europe, across four environments. Genotyping was performed using the wheat 100K SNP chip, and genome-wide association analysis (GWAS) was employed to identify stable loci with substantial effects. In total, four loci for GPC were identified on chromosomes 1A, 3A, 3B, and 4B, with explained phenotypic variation (PVE) ranging from 6.0 to 8.4%. In addition, three loci for WGC were identified on chromosomes 4B, 5A, and 5D, which explained 7.0–10.0% of the PVE. Among these, three loci coincided with known genes or quantitative trait loci (QTL), whereas QGPC.zaas-3AL, QGPC.zaas-4BL, QWGC.zaas-4BL, and QWGC.zaas-5A were potentially novel. Seven candidate genes were involved in various biological pathways, including growth, development, and signal transduction. Furthermore, five kompetitive allele specific PCR (KASP) markers were developed and validated in a natural population. The newly identified loci and validated KASP markers can be utilized for quality improvement. This research provides valuable germplasm, novel loci, and validated markers for high-quality wheat breeding. Full article

This study investigates the influence of tool geometry and cutting parameters on thrust forces and process stability during the drilling of 90MnCrV8, a hard and wear-resistant tool steel. The objective was to identify the dominant and interactive effects of feed per revolution (f_rev), nominal tool diameter (D), cutting speed (v_c), and geometry angles (ε_r, α_o, ω_r) on the thrust force (F_f). Experimental data were evaluated using analysis of variance (ANOVA) to determine statistical significance and effect size (η²), supported by theoretical models by Kienzle, Merchant, Oxley and Zorev to explain observed physical trends. Feed per revolution had the most decisive influence on thrust force (η² = 0.690; p < 0.001), followed by tool diameter (D; η² = 0.188). Geometric parameters showed secondary yet significant effects, mainly on stress distribution and chip evacuation. The interaction between D and f_rev produced a multiplicative force increase, while the combination of f_rev and helix angle (ω_r) reduced friction at higher feeds. Cutting speed had a minor effect (η² = 0.007), suggesting limited thermal softening. The findings confirm that drilling hard steels is primarily governed by the energy of plastic deformation. Full article

This study proposes an artificial intelligence-integrated Six Sigma framework for reducing multiple critical defects in plastic injection molding using real industrial production data from a washing-machine control-panel manufacturing line. Predictive models were developed under severe class imbalance conditions and combined with SHAP-based interpretability to identify the most influential process parameters. A multi-objective NSGA-II optimization strategy was then employed to simultaneously minimize major defect types, including gas-trapped burn (GTB), short shot (SS), sink mark (SK), and flash (FL). The proposed framework was validated through on-site continuous trial production of 300 parts after process stabilization, demonstrating substantial and consistent defect reduction. The results indicate that the integration of data-driven modeling, explainable artificial intelligence, and evolutionary multi-objective optimization provides a practical and scalable approach for quality improvement in industrial injection molding processes. Full article

Thermal runaway (TR) of lithium-ion batteries (LIBs) represents a critical safety challenge in EV applications. This study explores the potential of data-driven safety management strategies for mitigating TR risks in EVs. To minimize the impact of external environmental factors on the degradation of LIBs, experiments were conducted using an accelerating rate calorimeter (ARC). The intrinsic thermal behavior of six nickel–cobalt–manganese (NCM) cells at different states of health (SOH) and operating temperatures has been captured in created adiabatic conditions. Multiple sensors were deployed to monitor the temperature and electrochemical and environmental parameters throughout the degradation process until TR occurred. The results show that both the thermal and electrochemical stability of LIBs have been affected, exhibiting consistent thermal patterns and early electrochemical instability. Furthermore, even under adiabatic conditions, the degradation of LIBs show synergistic effects with environmental parameters such as chamber temperature and pressure. Correlation analysis further revealed the coupling relationships between the monitored parameters. Through calculating their correlation coefficients, the results indicate advantages of combining thermal, electrochemical, and environmental parameters as being to characterize the degradation of LIBs and enhance the identification of TR precursors. These findings stress the importance of considering the battery-environment system as a whole in safety management of EVs. They also provide insights into the development of data-driven safety management strategies, highlighting the potential for achievement and integration of anomaly detection, diagnosis, and prognostics functions in current EV management frameworks. Full article

Understanding particle transport in rough-walled fractures is essential for predicting flow behavior, clogging, and permeability evolution in natural and engineered subsurface systems. This study develops a fully coupled CFD–DEM framework to investigate how self-affine fractal roughness, represented by the Joint Roughness Coefficient (JRC), governs fluid–particle interactions across multiple scales. Nine fracture geometries with controlled roughness were generated using a fractal-based surface model, enabling systematic isolation of roughness effects. The results show that increasing JRC introduces a hierarchy of geometric perturbations that reorganize the flow field, amplify shear and velocity-gradient fluctuations, and enhance particle–wall interactions. Particle migration exhibits a nonlinear response to roughness due to the competing influences of disturbance amplification and the formation of preferential high-velocity pathways. Furthermore, roughness-controlled scaling relations are identified for mean particle velocity, residence time, and energy dissipation, revealing JRC as a fundamental parameter linking geometric complexity to transport efficiency. Based on these findings, a unified mechanistic framework is established that conceptualizes fractal roughness as a multiscale geometric forcing mechanism governing hydrodynamic heterogeneity, particle dynamics, and dissipative processes. This framework provides new physical insight into transport behavior in rough fractures and offers a scientific basis for improved prediction of clogging, proppant placement, and transmissivity evolution in subsurface engineering applications. Full article

Background/Objective: Bitewing radiographs are widely used for evaluating dental caries, restorations, and periodontal status due to their low radiation dose and high image quality. While artificial intelligence–based studies are common for other dental imaging modalities, multi-class diagnostic charting on bitewing radiographs remains limited. This study aimed to simultaneously detect eight periodontal and restorative parameters using a YOLOv8x-seg–based deep learning model and to assess its diagnostic performance. Materials and Methods: A total of 1197 digital bitewing radiographs were retrospectively analyzed and annotated by experts, resulting in 7860 labels across eight conditions. Periodontal conditions included alveolar bone loss, dental calculus, and furcation defects, while restorative and dental conditions comprised caries, cervical marginal gaps, open contacts, overhanging fillings, and secondary caries. The dataset was divided on a patient basis into training (80%), validation (10%), and test (10%) sets. The YOLOv8x-seg model was trained for 800 epochs with extensive data augmentation, and performance was evaluated using precision, recall, and F1-score, along with confusion matrices. Results: The model showed the highest accuracy in the alveolar bone loss class (precision: 0.84, recall: 0.93, F1: 0.88). While moderate performance was achieved for dental calculus (F1: 0.58) and caries (F1: 0.57) detection, lower scores were recorded in low-frequency classes such as cervical marginal gap (F1: 0.23), secondary caries (F1: 0.29), overhanging filling (F1: 0.35), furcation defect (F1: 0.40), and open contact (F1: 0.41). The overall segmentation performance achieved an mAP@0.5 of 0.30 and an mAP@0.5:0.95 of 0.10, indicating an acceptable performance level for segmentation-based multi-class models. Conclusions: The obtained findings demonstrate that the YOLOv8x-seg architecture can detect and segment periodontal conditions with high success and restorative parameters with moderate success in automation processes in bitewing radiographs. Accordingly, the model presents a methodologically feasible framework for the multiple and simultaneous radiographic evaluation of periodontal and restorative findings on bitewing radiographs, with performance varying across classes and lower sensitivity observed in low-frequency conditions. Full article

Diffusion-controlled processes play a critical role in the heat treatment and microstructural homogenization of β-titanium alloys containing multiple β-stabilizing elements. Adding β-phase stabilizing elements like Cr and Nb to titanium alloys can significantly improve the high-temperature strength and creep performance of the alloy. Their diffusion coefficients can be used to predict the risk of softening and creep failure in high-temperature components caused by diffusion. However, reliable diffusion kinetic data for the β phase in the Ti–Cr–Nb ternary system remain scarce, limiting quantitative process modeling and simulation. In this study, diffusion behavior in the BCC (β) region of the Ti–Cr–Nb system was investigated using diffusion couples combined with CALPHAD-based kinetic modeling. Twelve sets of diffusion couples were prepared and annealed at 1373 K for 48 h, 1423 K for 36 h, and 1473 K for 24 h. The corresponding composition–distance profiles were measured by electron probe microanalysis. Composition-dependent interdiffusion coefficients and atomic mobility parameters were determined using the numerical inverse method. The results revealed temperature and composition dependence of the main interdiffusion coefficients, with Nb exhibiting a stronger influence than Cr. The evaluated kinetic parameters provide an effective kinetic description for diffusion-controlled process simulations. Full article

In this contribution, we propose and study long-time behaviors of a new class of N-dimensional delayed Kirchhoff–Carrier-type problems with variable transfer coefficients involving a logarithmic nonlinearity. We take into account the dependence of diffusion and damping coefficients on the position and direction, as well as the presence of different types of delays. This class of nonlocal anisotropic and nonlinear wave-type equations with multiple time-varying mixed delays and dampings, of a fairly general form, containing several arbitrary functions and free parameters, is of the following form: ${\displaystyle \frac{{\partial }^{2}u}{\partial t^2}-{div(\mathcal{K}(\Vert \sigma \nabla u\Vert }_{L^2(\Omega )}^2)A_{\sigma }{(\mathbf{x})\nabla u)+\mathcal{M}(\Vert u\Vert }_{L^2(\Omega )}^2)u-div(\zeta (t)A_{\sigma }(\mathbf{x})\nabla \frac{\partial u}{\partial t})+d_0(t)\frac{\partial u}{\partial t}+{\mathbb{D}}_r(\mathbf{x},t;\frac{\partial u}{\partial t})=\mathbb{G}(u),}$ where $u(\mathbf{x},t)$ is the state function, $\mathcal{M}$ and $\mathcal{K}$ are the nonlocal Kirchhoff operators and the nonlinear operator $\mathbb{G}(u)$ corresponds to a logarithmic source term. The symmetric tensor $A_{\sigma }$ describes the anisotropic behavior and processes of the system, and the operator ${\mathbb{D}}_r$ represents the multiple time-varying mixed delays related to velocity ${\displaystyle \frac{\partial u}{\partial t}}$. Our problem, which encompasses numerous equations already studied in the literature, is relevant to a wide range of practical and concrete applications. It not only considers anisotropy in diffusion, but it also assumes that the strong damping can be totally anisotropic (a phenomenon that has received very little mathematical attention in the literature). We begin with the reformulation of the problem into a nonlinear system coupling a nonlocal wave-type equation with ordinary differential equations, with the help of auxiliary functions. Afterward, we study the local existence and some necessary regularity results of the solutions by using the Faedo–Galerkin approximation, combining some energy estimates and the logarithmic Sobolev inequality. Next, by virtue of the potential well method combined with the Nehari manifold, conditions for global in-time existence are given. Finally, subject to certain conditions, the exponential decay of global solutions is established by applying a perturbed energy method. Many of the obtained results can be extended to the case of other nonlinear source terms. Full article

Loess subgrades are prone to significant strength reduction and deformation under cyclic traffic loads and moisture ingress. Permeable polyurethane polymer grouting has emerged as a promising non-excavation technique for rapid subgrade reinforcement. This study systematically investigated the fatigue behavior of polymer-grouted loess using laboratory fatigue tests and numerical simulations. A series of stress-controlled cyclic tests were conducted on grouted loess specimens under varying moisture contents and stress levels, revealing that fatigue life decreased with increasing moisture and stress levels, with a maximum life of 200,000 cycles achieved under optimal conditions. The failure process was categorized into three distinct stages, culminating in a “multiple-crack” mode, indicating improved stress distribution and ductility. Statistical analysis confirmed that fatigue life followed a two-parameter Weibull distribution, enabling the development of a probabilistic fatigue life prediction model. Furthermore, a 3D finite element model of the road structure was established in Abaqus and integrated with Fe-safe for fatigue life assessment. The results demonstrated that polymer grouting reduced subgrade stress by nearly one order of magnitude and increased fatigue life by approximately tenfold. The consistency between the simulation outcomes and experimentally derived fatigue equations underscores the reliability of the proposed numerical approach. This research provides a theoretical and practical foundation for the fatigue-resistant design and maintenance of loess subgrades reinforced with permeable polyurethane polymer grouting, contributing to the development of sustainable infrastructure in loess-rich regions. Full article

Background: Patient-based real-time quality control (PBRTQC) enables continuous analytical monitoring using routine patient results; however, the performance of classical statistical process control (SPC) algorithms varies across analytes, and standardized evaluation and optimization strategies remain limited. To address this gap, this study compared three SPC algorithms—moving average (MA), moving quantile (MQ), and exponentially weighted moving average (EWMA)—within a unified preprocessing framework and proposed a composite performance metric for parameter optimization. Methods: Routine patient results from six laboratory analytes were analyzed using a standardized “transform–truncate–alarm” PBRTQC workflow. Simulated systematic biases were introduced for model training, and algorithm-specific parameters were optimized using a composite metric integrating sensitivity, false-positive rate (FPR), and detection delay. Performance was subsequently evaluated on an independent validation dataset. Results: For most analytes, all three SPC algorithms demonstrated robust PBRTQC performance, achieving high sensitivity (generally ≥0.85), very low false-positive rates (<0.002), and rapid detection of systematic bias. EWMA showed more balanced performance for thyroid-stimulating hormone (TSH), with improved sensitivity and shorter detection delay compared with MA and MQ. The proposed composite metric effectively facilitated clinically meaningful parameter optimization across algorithms. Conclusions: Under a unified preprocessing framework, classical SPC algorithms provided reliable PBRTQC performance across multiple analytes, with EWMA offering advantages for more variable measurements. The proposed composite metric supports standardized, practical, and analyte-adaptive PBRTQC implementation in clinical laboratories. Full article

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article

The global aging population has led to a rising prevalence of cognitive impairment, posing a significant public health challenge. Resistance training (RT) is a non-pharmacological intervention that has been increasingly investigated for its potential to support cognitive function in older adults. Clinical evidence suggests that RT may be associated with benefits in certain cognitive domains, including memory, executive function, processing speed, and visuospatial ability. However, findings across studies remain heterogeneous, with several trials reporting neutral outcomes. Most intervention studies involve structured RT programs conducted at moderate to high intensity and performed multiple times per week. However, optimal training parameters have not yet been clearly established due to variability in study design and the absence of formal dose–response analyses. Emerging evidence suggests that the cognitive effects of RT may be mediated, at least in part, through muscle–brain axis signaling involving exercise-induced myokines. Factors such as irisin, brain-derived neurotrophic factor, interleukin-6, interleukin-15, and insulin-like growth factor-1 have been implicated in processes related to neuroplasticity, neuroinflammatory regulation, and neurovascular function, primarily based on preclinical and translational research. This review synthesizes current evidence on RT-related molecular mechanisms and clinical findings to provide an integrative perspective on the potential role of resistance training in mitigating age-related cognitive decline. Full article

The sliding window method is a commonly used data processing in time series forecasting tasks, and determining the appropriate window size is a crucial step in constructing predictive models. However, the current setting of window size parameters is often based on empirical knowledge, making the scientific determination of the optimal sliding window size highly significant. This paper proposes an algorithm for optimizing window size based on sample entropy, which is applicable not only to the original undecomposed sequences but also effectively to the decomposed sequences. The proposed algorithm has been validated using the open-source Elia grid data across multiple model architectures, including recurrent (GRU/LSTM) and attention-based (Transformer) networks. Experimental results demonstrate that the algorithm effectively determines an optimal window size of 106. The optimized window consistently leads to superior prediction performance, with the CEEMD-GRU model achieving a MAPE of 0.256, RMSE of 22.529, and MAE of 18.186—representing reductions of over 5% compared to the undecomposed benchmark. Furthermore, the enhancement is more significant for decomposed sequences, and the algorithm’s efficacy is validated across different neural network architectures (e.g., LSTM, GRU, Transformer), confirming its practical utility and generalizability. Full article

Oxidative stress contributes significantly to chronic disease burden, necessitating identification of accessible dietary antioxidant sources. Pigeon peas (Cajanus cajan L.) contain substantial bioactive compounds, yet most exist in bound forms with limited bioavailability. This study evaluated wild fermentation combined with systematic extraction optimization to enhance antioxidant recovery from pigeon peas. Seeds underwent wild fermentation in brine solution, followed by extraction under varying conditions (seven solvent systems, three temperatures, and three-time durations). Multiple complementary assays assessed antioxidant capacity (total phenolic content, DPPH radical scavenging, ferric reducing power, and ABTS activity). Fermentation substantially improved antioxidant properties across all parameters, with particularly pronounced effects on radical scavenging activities. Extraction optimization identified 70% methanol at 40 °C for 24 h as optimal, demonstrating marked improvements over conventional protocols. Strong intercorrelations among assays confirmed coordinated enhancement of multiple antioxidant mechanisms rather than isolated changes. The findings demonstrate that both biotechnological processing and analytical methodology critically influence antioxidant characterization in pigeon peas. This integrated approach offers practical guidance for developing antioxidant-rich functional foods, particularly relevant for resource-limited settings where pigeon peas serve as dietary staples. The study establishes foundation for translating fermentation technology into nutritional interventions, though further research addressing bioavailability, microbiological characterization, and bioactive compound identification remains essential. Full article