Search Results (1,711)

To address the problems of uneven stubble height and high missed-cutting rate caused by the insufficient profiling capability of traditional forage harvesters in complex hilly terrain, this paper designs a three-degrees-of-freedom (DOF) profiling header primarily for typical hilly terrain with gentle slopes of 8–15°. Through pitch, roll, and height adjustments, it stably maintains stubble height at 150 mm. Subsequently, geometric analysis and structural optimization achieved kinematic decoupling among all degrees of freedom, thereby overcoming the inherent limitations of the two-DOF header, such as poor adaptability to longitudinal slope and strong adjustment coupling. Three-dimensional modeling was completed in SolidWorks, multibody dynamics simulation was performed in ADAMS, and a profiling control system incorporating a hydraulic system, multi-source sensor fusion, and a fuzzy PID controller was built. The dynamics simulation results show that under the working conditions of 15° longitudinal and 10° transverse slopes, the stubble height error of the header is controlled within 10%, the attitude angle adjustment error is less than 0.5°, and the dynamic response is excellent. Prototype field tests showed that, compared with the two-DOF header, the three-DOF profiling header improved the stubble height stability by about 35%, reduced the missed-cutting rate by about 5%, and increased the operating efficiency by about 15%. No cutting blade contact with the soil occurred, verifying the rationality of the mechanism design and its adaptability to terrain. This study provides an effective technical solution for improving the mechanization level of forage harvesting in hilly and mountainous areas. Full article

Background/Objectives: This study aimed to develop and evaluate deep learning models for the detection, localization, and classification of impacted maxillary canines, and to compare their performance with cone-beam computed tomography (CBCT) as the reference standard. Methods: This cross-sectional diagnostic accuracy study was conducted at King Abdulaziz University Dental Hospital to develop and validate artificial intelligence (AI) models for detecting and localizing maxillary canine impactions using panoramic and cone-beam computed tomography (CBCT) imaging data. A total of 641 panoramic ra and 158 CBCT scans were collected, of which 158 cases had matched panoramic–CBCT pairs for localization analysis. Images were annotated and validated by expert radiologists and orthodontists, with consensus review ensuring labeling reliability. Data augmentation expanded each panoramic and CBCT category to 500 samples for panoramic and 1000 samples for CBCT, resulting in 1935 panoramic and 5703 CBCT images after preprocessing and normalization. The datasets were divided into (training + validation) (80%) and testing (20%) subsets. MobileNetV2 architectures were used for classification, and whdiographsile, a ResNet-50–based Few-Shot Learning framework, enabled spatial localization of impacted canines. Models were trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ and evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Cohen’s kappa and 95% confidence intervals were used to assess agreement between AI predictions and expert annotations. Results: Panoramic classification achieved 94% accuracy, demonstrating the highest performance in normal cases and reduced recall for bilateral impactions. The CBCT classifier achieved 98% accuracy across positional categories. Cross-modality prediction reached 93.5% accuracy, with strong agreement compared to CBCT (Cohen’s kappa = 0.91). Expert review confirmed reliable localization of impacted canines on both imaging modalities. Conclusions: Artificial intelligence applied to panoramic radiographs supports the detection, localization, and characterization of impacted maxillary canines with performance comparable to CBCT. This approach may enable lower-radiation decision support for clinical triage. Full article

This study focuses on the use of Physics-Informed Neural Networks (PINNs) to solve the 1D Advection–Diffusion–Reaction (ADR) equation. The performance of the PINN model is evaluated in comparison with the classical Crank–Nicolson Finite Difference Method (CNFDM) and validated against analytical solutions to assess improvements in accuracy, robustness, and flexibility. Quantitative analysis reveals that the PINN achieved a high level of accuracy with absolute errors ranging from approximately $2.13\times {10}^{-4}$ to $1.17\times {10}^{-3}$ across the spatial domain. The study utilizes a neural network architecture with two hidden layers of 80 neurons each, optimized through a two-stage training process involving Adam and L-BFGS optimizers. This work contributes to the growing field of physics-informed machine learning by demonstrating the strengths and quantitative reliability of the PINN technique for solving complex partial differential equations in transport phenomena. Full article

This paper focuses on the dynamics modeling and control methods for an underactuated Dual-Unmanned Aerial Helicopter Slung Load System (DUH-SLS), which consists of two Unmanned Aerial Helicopters (UAHs) connected to the suspended load via two sling cables. The DUH-SLS is a multi-body coupled system with internal ideal constraint forces and has seven motion degrees of freedom (DOFs) in the longitudinal plane. In this paper, a set of independent and complete generalized coordinates is selected to describe the system’s motion. The dynamics model of DUH-SLS is established using Lagrange analytical mechanics. This approach, which avoids system internal forces, greatly improves modeling efficiency. Finally, the correctness of this dynamics model is validated using a virtual prototype of the DUH-SLS developed in the multi-body dynamics simulation software ADAMS. The DUH-SLS is a complex nonlinear controlled object, and the iterative Linear Quadratic Regulator (iLQR) method is introduced to design an integrated optimal controller to achieve trajectory tracking and swing suppression for the DUH-SLS. This method transforms the quadratic optimal control problem of nonlinear systems into a series of linear quadratic optimal control (LQR) problems through iterative optimization in function space, thus obtaining an optimal solution. The iLQR optimal controller requires offline iterative computation, but the optimal control obtained has a state feedback closed-loop form, which ensures robustness during online control. Numerical simulation results demonstrate that the proposed iLQR optimal controller exhibits excellent control performance in complex multi-task scenarios. Particularly in trajectory tracking tasks, the maximum average position tracking error of the iLQR controller is only 0.14 m, compared to 3.57 m and 3.11 m for the LQR and LMC (Lyapunov Method Controller) controllers, respectively. Furthermore, the controller demonstrates strong robustness against internal parameter perturbations and external complex wind disturbances, fully validating the effectiveness and superiority of the proposed approach. Full article

Colorectal cancer (CRC) remains a major global health challenge, primarily due to late-stage diagnosis and high metastatic potential. Effective management requires novel diagnostic and prognostic strategies, with a growing focus on molecular biomarkers. A Disintegrin and Metalloproteinase (ADAM) proteins, characterized by unique proteolytic activity, play a fundamental role in tumorigenesis by regulating tumor growth, epithelial–mesenchymal transition (EMT), and metastasis. Based on recent investigations, among all ADAMs, ADAM8, ADAM9, ADAM12, ADAM15, and ADAM17 have been proved to play an important role in the CRC pathogenesis. Thus, this review underscores the potential of selected ADAM family members as promising candidates for biomarkers of CRC. Elevated ADAM8, ADAM9, ADAM12 and ADAM17 levels were observed in CRC tissues and correlated with more advanced tumor stage, while increased serum ADAM15 concentrations associated with the presence distant metastases. Moreover, ADAM9, ADAM12, ADAM15 and ADAM17 levels were associated with poorer survival, whereas ADAM8 overexpression was found to be independent prognostic factor for CRC patients’ survival. In addition, the measurement of serum ADAM15 concentrations, especially in combination with well-established tumor marker–CEA improved the diagnosis of patients with this malignancy. In conclusion, selected ADAM are critical contributors to the development and progression of CRC, affecting tumor growth, EMT, and metastasis. ADAM8, ADAM9, ADAM12, ADAM15 and ADAM17 were identified as promising biomarkers for the assessment of CRC progression and proved to be prognostic indicators for patients’ survival. Further validation through large prospective studies and standardized assays is necessary to establish their potential in clinical practice. Full article

Social capital is one of the most influential yet fragmented concepts in the social sciences. To gain insight into its substantive use within specific domains, this review explores how social capital (SC) has been applied in occupational studies, with particular attention to regional perspectives. Building on Adams and Fitch’s bibliometric mapping, it applies abstract-based topic modeling with citation data to identify thematic clusters and theoretical foundations within a corpus spanning over four decades. The results show that SC remains widely used across diverse themes. Citation patterns vary sharply across topics, with few sharing unified theoretical anchors. A closer look at studies on the topic of regional perspective reveals that SC is more employed as a background concept rather than through theorization or explicit operationalization. These findings refine Adams and Fitch’s conclusions on the fragmentation of SC research and highlight research opportunities for connecting SC mechanisms to regional perspectives. Full article

Background: Alzheimer’s disease (AD) remains an incurable disorder with severe clinical consequences. The type 3 diabetes hypothesis posits that AD may constitute a neuroendocrine disorder driven by disrupted insulin and insulin-like growth factor signaling. Amyloid pathogenesis in AD is characterized by the accumulation of beta-amyloid (Aβ) monomers, their subsequent oligomerization, and amyloid deposition. One of the causes of Aβ accumulation is disruption of amyloid precursor protein (APP) processing due to imbalance in ADAM10 and BACE1 expression. In recent years, increasing attention has been devoted to investigating the role of environmental factors in AD pathogenesis. The receptor for advanced glycation end products (RAGE) serves as a key molecular link between environmental exposure and neuroinflammatory pathology. Formaldehyde (FA) is one of the most widespread environmental pollutants. Its involvement in amyloid plaque formation has been previously reported; however, the molecular mechanisms underlying this process remain insufficiently understood. Moreover, most available data are based on prolonged FA exposure, whereas industrial FA emissions are often short-term. The objective of this study was to determine whether brief intranasal administration of FA, modeling episodic industrial pollution, induces RAGE-mediated neuroinflammation and amyloid deposition in CD1 mice. Methods: Mice received intranasal FA at environmentally relevant 0.02 mg/day or 0.2 mg/day doses for seven days; an additional group was co-treated with insulin. Cognitive function was assessed using passive avoidance (PA) and radial arm maze (RAM) tests, and synaptic plasticity was evaluated by electrophysiology. Hippocampal tissue was analyzed for RAGE expression, ADAM10/BACE1 gene balance, Aβ42 monomer levels, and amyloid deposits using optimized Thioflavin-S (Th-S) staining. Results: We observed cognitive decline in mice receiving intranasal FA administration. Elevated blood glucose levels were also observed following intranasal FA exposure. Sustained impairment of glucose metabolism led to overexpression of the RAGE in the hippocampus. There was also an imbalance of ADAM10 and BACE1 expression in the hippocampus. This was caused by overexpression of RAGE, as the enhanced interaction of the ligand and RAGE is a key factor disrupting this balance. Finally, Th-S staining confirmed amyloid deposition in mice subjected to intranasal FA exposure. Conclusions: This study provides new insights into the RAGE-mediated mechanisms by which FA contributes to the pathogenesis of AD. Full article

Background and Objectives: Physiotherapeutic Scoliosis-Specific Exercises (PSSE) are recognized treatment methods for idiopathic scoliosis, focused on correcting three-dimensional postural abnormalities. Objective indices such as Angle of Trunk Rotation (ATR), Anterior Trunk Symmetry Index (ATSI), and Posterior Trunk Symmetry Index (POTSI) enable precise assessment of clinical changes, while the Trunk Appearance Perception Scale (TAPS) reflects the patient’s subjective perception of their posture. Combining these data allows for a comprehensive assessment of the effects of therapy after intensive 5-day inpatient rehabilitation. We aimed to assess the improvement in the patients’ clinical appearance and compare objective and subjective trunk assessment indicators after intensive 5-day inpatient rehabilitation, treated by PSSE, according to the Functional Individual Therapy of Scoliosis (FITS) Method. Materials and Methods: This retrospective study included 75 patients with idiopathic scoliosis who participated in a 5-day inpatient rehabilitation, treated by FITS Method. The average age was 13.5 years, and 63% of the girls were after menarche. The mean Cobb angle was 27.41° in single-curve scoliosis and 31.03° in double-curve scoliosis (31.24° in the thoracic spine, 30.82° in the lumbar spine), Risser test 2, and ATR was 7.1° in the thoracic spine and 4.6° in the lumbar spine. Forty-nine patients wore a brace. At the beginning and end of inpatient care, objective assessments were performed, including ATR at the peak of the scoliosis using the Adams test and photoregistration of the trunk in the front and back standing positions—ATSI and POTSI. A subjective assessment was also performed using the TAPS. Results: A statistically significant difference was demonstrated after therapy in the ATSI (p < 0.001) and POTSI (p = 0.008) values. A reduction in the ATR in the thoracic spine was observed (p < 0.001). The TAPS questionnaire demonstrated a statistically significant difference in the values of all indicators measured before and after therapy: in the frontal plane SET 1 (p = 0.002), in the transverse plane SET 2 (p = 0.042), and in the frontal plane SET 3 (p = 0.028). A statistically significant negative correlation was demonstrated between objective and subjective indicators after therapy: ATR Th vs. TAPS-SET 2 (−0.45) (p < 0.001) and ATSI vs. SET 3 (−0.29) (p = 0.011). Conclusions: The subjective assessment of trunk appearance correlates with the objective assessment, except for SET 1 vs. POTSI. Patients who noticed a change in their posture can expect confirmation in objective clinical tests. FITS Method positively influences the improvement of subjective and objective assessments of idiopathic scoliosis patients during the short term of intensive care. Full article

Lower-limb rehabilitation exoskeletons must balance biomechanical compatibility, structural safety, and low mass to enable practical, repeatable gait assistance. This paper proposes a planar pantograph-derived exoskeleton leg driven by a Chebyshev Lambda linkage and develops an integrated workflow from mechanism synthesis to manufacturable optimization and experimental verification. A mannequin-coupled multibody model was built in MSC ADAMS to evaluate joint kinematics, end-point (foot) trajectories, and joint reaction forces under multiple scenarios (fixed-frame, ramp, stair ascent, and inclined-plane walking). The extracted joint loads were transferred to a parametric finite element model in ANSYS Workbench 2019, where response surface surrogates and a multi-objective genetic algorithm (MOGA) were used to minimize mass under stiffness and strength constraints. For the optimized load-bearing link, the selected minimum-mass design reached a component mass of 0.542 kg while respecting the imposed structural limits, i.e., a maximum total deformation below 0.2 mm and a maximum equivalent (von Mises) stress below 50 MPa (e.g., ~0.188 mm deformation and ~39 MPa stress in the optimal candidate). A rapid prototype was manufactured by 3D printing and experimentally evaluated using CONTEMPLAS high-speed video tracking, providing measured X_M(t) and Y_M(t) trajectories and joint-angle histories for quantitative comparison with simulations via RMSE metrics. Full article

(1) Background: Idiopathic scoliosis is a three-dimensional deformity, yet clinical and research decision-making still relies largely on radiographic Cobb angle measurements. As a radiation-free alternative, clinical assessment of transverse and sagittal plane deformities has gained importance. This study evaluated the concurrent validity and intra- and interrater reproducibility of continuous measurements of rib hump, thoracic kyphosis, and lumbar lordosis obtained using a smartphone application in adolescents with spinal deformities. (2) Methods: Adolescents aged 10–17 years with scoliosis (>10° Cobb) or hyperkyphosis (>50° Cobb) were recruited. Continuous measurements of angle of trunk rotation (ATR) during the Adams forward bend test and in standing position, as well as sagittal profile, were collected using the ISICO app mounted on a standardized plastic tool. Concurrent validity was assessed against a scoliometer using Spearman correlation, root mean square error, and Bland–Altman analysis, while reproducibility was evaluated using intraclass correlation coefficients, standard error of measurement, and minimal detectable change. (3) Results: Thirty-two adolescents were included for validation and intrarater analyses and 34 for interrater analyses. ATR measured during the Adams test showed very high correlation with the scoliometer and minimal bias, while standing ATR showed moderate correlation. Reliability was excellent for rib hump during forward bending and moderate for sagittal parameters, with the lowest values observed for lumbar lordosis. (4) Conclusions: These findings support the clinical use of continuous app-based ATR assessment and suggest that sagittal measurements may be useful with appropriate examiner training. Full article

Glaucoma affects a significant proportion of people worldwide, and if it progresses to a severe stage, it can lead to blindness. Furthermore, screening and accurately diagnosing glaucoma present a challenge for ophthalmologists. Early detection of glaucoma is crucial because it allows for timely treatment, potentially preventing severe complications that could lead to blindness. Typically, ophthalmologists diagnose glaucoma by analyzing eye fundus photographs to assess the ratio of the optic cup and optic disc (CDR). Machine learning algorithms can assist in glaucoma detection by classifying fundus images. This study introduces image preprocessing techniques for optic disc localization, combined with an integrating multi-view network for accurate glaucoma classification. The dataset used in this research was obtained from Naresuan University Hospital. The study found that EfficientNet underwent training using the Adam optimizer at a fixed learning rate of 0.0001. The multi-view network achieved Accuracy 90.48%, AUC 95.14%, Precision 81.95%, Recall 75.90%, and F1-score 78.72%. This study presents an effective approach to assist ophthalmologists in detecting early-stage glaucoma and glaucoma, thereby improving diagnostic efficiency. Full article

EGFR signaling, which requires ligand shedding by ADAM proteases, drives the progression of a variety of cancers, including breast, ovarian and lung. We previously reported the generation and characterization of a fully human, affinity-matured anti-ADAM17 monoclonal antibody, D8P1C1, which inhibits both the proliferation of an array of cancer cell lines in vitro as well as breast cancer growth in a mouse xenograft model. Here, we show that the mAb inhibits the shedding of EGFR ligands and EGFR phosphorylation in cancer cell lines, thus explaining its anti-tumor effects. In a xenograft model with a high-grade serous ovarian cancer (HGSOC) cell line, D8P1C1 showed only modest therapeutic effect, without any discernible toxicity. These results suggest that ovarian cancers are less susceptible than breast cancers to therapeutic targeting of ADAM17- or EGFR-dependent signaling. Radioimmuno PET imaging with ⁸⁹Zr-DFO-D8P1C1 confirmed tumoral accumulation of the mAb in high-grade and non-high-grade serous ovarian tumor xenografts. Furthermore, we report the generation and preliminary characterization of a bispecific T cell engager derivative of D8P1C1 with improved anti-tumor efficacy in vitro. Full article

A proposed study based on an artificial neural network (ANN) model will be used to predict microhardness (VHN) and tensile strength (TS) of Friction Stir Additive Manufacturing (FSAM) of AA8090 alloy. The process parameters taken into consideration were rotational speed (1000, 1500, 2000 rpm), traverse speed (45, 65, 85 mm/min) and tilt angle (0°, 1°, 2°). We performed 90 physical experiments (74 + 7 + 6 + 3), in which 74 experiments were generated with the help of the Central Composite Design of ANN modeling, seven independent experiments were used to validate the results, six repeat experiments were taken, and three mid-level interpolation experiments were performed. Out of 74 modeling runs, 60 samples were trained, 14 were internally tested, and seven separate modeling runs were exclusively tested externally. An ANN model was created based on the Adam optimizer, where the loss was taken to be Mean Squared Error (MSE). The level of model robustness was assessed employing 5-fold cross-validation and grouped validation (LOPCO, LOFLO-RPM, and LOFLO-TA). Under 5-fold cross-validation, the ANN had mean R² values equal to 0.940 (VHN), 0.920 (TS). In normalized training, the model achieves MAE = 0.26 and R² = 0.97, whereas testing in physical units has developed MAE values of 1.0 and 2.0, respectively (VHN and TS). These results correspond with the high predictive ability and generalization of the ANN model, as indicated by the uniform performance of the ANN model on training, cross-validation, internal testing, and independent validation. The importance analysis of features revealed that rotational speed was the most significant factor that influenced the tensile strength and microhardness. The constructed ANN model is a credible and sound system for optimizing and replicating processes from other friction-stir processing methods on AA8090 alloy. Full article

In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state–space models, a method employing the Runge–Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams–Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone. Full article

Multi-point array thin-film thermocouples have strong potential for high-precision, wide-range temperature monitoring in applications such as aircraft engine thermal condition assessment and industrial process control. However, conventional single-point thin-film thermocouples cannot satisfy the distributed measurement requirements of large-area temperature fields, and the accuracy of multi-point arrays is often degraded by coupling effects among sensing nodes, which hinders their engineering deployment. In this work, a multi-point array thin-film thermocouple is fabricated via precision welding, and an insulating layer is deposited on the sensor surface using electrospray atomization to establish a multi-point temperature-sensing hardware system. To compensate for coupling-induced deviations, a deep learning–based calibration method is developed: measurements from the array and reference thermocouples are synchronously collected to build the dataset, outliers are removed using the interquartile range (IQR) method, and a three-hidden-layer multilayer perceptron (MLP) is trained for each node independently using the Adam optimizer (learning rate 0.001) with an 8:2 train–test split. Performance is quantified by MAE, MSE, and R², and the results show that the proposed approach markedly reduces measurement errors and improves the accuracy of the array thermocouples, demonstrating reliable performance and practical applicability for precise large-area temperature-field monitoring. Full article