Search Results (780)

Advanced glycation end products (AGEs) are important biomarkers associated with diabetes and metabolic disorders; yet existing detection methods are invasive and unsuitable for frequent monitoring. This study aimed to develop a non-invasive and portable AGEs detection device, optimize strategies for mitigating pigmentation-related interference, and evaluate its feasibility for metabolic assessment. The proposed system employs a 365 nm ultraviolet LED excitation source, an optical filter assembly integrated into an ergonomic dark chamber, and an eyelid-signal-based algorithm to suppress ambient light and skin pigmentation interference. Simulation experiments were conducted to evaluate the influence of different pigment colors and skin tones on fluorescence measurements. A clinical study was performed in 200 participants, among whom 42 underwent concurrent serum AGEs measurement as the reference standard. Predictive models combining corneal fluorescence signals and body mass index (BMI) were constructed and evaluated. The results indicated that purple and blue pigments introduced greater interference, whereas green and pink pigments had minimal effects. Device-derived AGEs estimates demonstrated good agreement with serum AGEs, with a mean error below 8%. A hybrid model incorporating BMI achieved improved predictive accuracy compared with single-parameter models. Participants with high-AGE dietary habits exhibited elevated fluorescence signals and BMI. These findings suggest that the proposed device enables stable and accurate non-invasive AGEs assessment, with potential utility for metabolic monitoring. Incorporating lifestyle-related parameters may further enhance predictive performance and expand clinical applicability. Full article

Safe and sample-conscious controller synthesis for nonlinear dynamics benefits from reinforcement learning that exploits an explicit plant model. A nonlinear mass–spring–damper with hardening effects and hard stops is studied, and off-plant Q-learning is enabled using two data-driven surrogates: (i) a piecewise linear model assembled from operating region transfer function estimates and blended by triangular memberships and (ii) a global nonlinear autoregressive model with exogenous input constructed from past inputs and outputs. In unit step reference tracking on the true plant, the piecewise linear route yields lower error and reduced steady-state bias (MAE = $0.03$; SSE = $3\%$) compared with the NLARX route (MAE = $0.31$; SSE = $30\%$) in the reported configuration. The improved regulation is obtained at a higher identification cost (60,000 samples versus 12,000 samples), reflecting a fidelity–knowledge–data trade-off between localized linearization and global nonlinear regression. All reported performance metrics correspond to deterministic validation runs using fixed surrogate models and trained policies and are intended to support methodological comparison rather than statistical performance characterization. These results indicate that model-based Q-learning with identified surrogates enables off-plant policy training while containing experimental risk and that performance depends on modeling choices, state discretization, and reward shaping. Full article

Prader–Willi (PWS) and Angelman (AS) syndromes were the first examples in humans with errors in genomic imprinting, usually from de novo 15q11-q13 deletions of different parent origin (paternal in PWS and maternal in AS). Dozens of genes and transcripts are found in the 15q11-q13 region, and may play a role in PWS, specifically paternally expressed SNURF-SNRPN and MAGEL2 genes, while AS is due to the maternally expressed UBE3A gene. These three causative genes, including their encoding proteins, were targeted. This review article summarizes and illustrates the current understanding and cause of both PWS and AS using strategies to include the literature sources of key words and searchable web-based programs with databases for integrated gene and protein interactions, biological processes, and molecular mechanisms available for the two imprinting disorders. The SNURF-SNRPN gene is key in developing complex spliceosomal snRNP assemblies required for mRNA processing, cellular events, splicing, and binding required for detailed protein production and variation, neurodevelopment, immunodeficiency, and cell migration. The MAGEL2 gene is involved with the regulation of retrograde transport and promotion of endosomal assembly, oxytocin and reproduction, as well as circadian rhythm, transcriptional activity control, and appetite. The UBE3A gene encodes a key enzyme for the ubiquitin protein degradation system, apoptosis, tumor suppression, cell adhesion, and targeting proteins for degradation, autophagy, signaling pathways, and circadian rhythm. PWS is characterized early with infantile hypotonia, a poor suck, and failure to thrive with hypogenitalism/hypogonadism. Later, growth and other hormone deficiencies, developmental delays, and behavioral problems are noted with hyperphagia and morbid obesity, if not externally controlled. AS is characterized by seizures, lack of speech, severe learning disabilities, inappropriate laughter, and ataxia. This review captures the clinical presentation, natural history, causes with genetics, mechanisms, and description of established laboratory testing for genetic confirmation of each disorder. Three separate searchable web-based programs and databases that included information from the updated literature and other sources were used to identify and examine integrated genetic findings with predicted gene and protein interactions, molecular mechanisms and functions, biological processes, pathways, and gene-disease associations for candidate or causative genes per disorder. The natural history, review of pathophysiology, clinical presentation, genetics, and genetic-phenotypic findings were described along with computational biology, molecular mechanisms, genetic testing approaches, and status for each disorder, management and treatment options, clinical trial experiences, and future strategies. Conclusions and limitations were discussed to improve understanding, clinical care, genetics, diagnostic protocols, therapeutic agents, and genetic counseling for those with these genomic imprinting disorders. Full article

Hot-forming, as a typical representative forming technology of high-strength steel (HSS), is one of the most effective ways to manufacture structural components for achieving automotive lightweighting goal. In this paper, a newly-developed commercial microalloyed hot-formed steel is selected and its hot-forming is studied by experiments and simulations. The new steel has a wide undercooled austenite region, providing more suitable condition for the manufacturing of one-piece large-sized integrated parts. The high-temperature mechanical behaviors of the investigated steel show that the flow stress obviously decreases with the increase in deformation temperature, and it increases with the increasing strain rate. An integrated component assembly of the rear floor and longitudinal beam is selected as a typical one-piece integrated part when performing the hot-forming simulation to evaluate the formability. The influences of the key process parameters, namely forming velocity and frictional coefficient, on formability are further analyzed. Finally, the Latin Hypercube Sampling (LHS) method is used to generate the parameter combination and the Response Surface Method (RSM) is adopted in optimization. As a result, an optimal process parameter combination is obtained and its predicted result matches the simulated one very well, with a relative error of only 2.57%. The research results of this paper are favorable for understanding the mechanical behaviors of the hot-formed steel at elevated temperatures, improving the formability and providing a reference for the development of large-sized integrated hot-formed parts. Full article

Against the backdrop of intensifying global population aging, lower-limb exoskeleton robots serve as core devices for rehabilitation and power assistance. Their control accuracy and motion smoothness rely on precise dynamic models. However, parameter uncertainties caused by variations in human lower limbs, assembly errors, and wear pose a critical bottleneck for accurate modeling. Aiming to achieve high-precision dynamic modeling for a two-degree-of-freedom lower-limb exoskeleton, this paper proposes a parameter identification method named Tent-GA-GWO. A dynamic model incorporating joint friction and link inertia was constructed and linearized. An excitation trajectory based on Fourier series, conforming to human physiological constraints, was designed. To enhance algorithm performance, Tent chaotic mapping was employed to optimize population initialization, a nonlinear control parameter was used to balance search behavior, and genetic algorithm operators were integrated to increase population diversity. Simulation results show that, compared to the traditional GWO algorithm, Tent-GA-GWO improved convergence efficiency by 32.1% and reduced the fitness value by 0.26%, demonstrating superior identification accuracy over algorithms such as GA and LIL-GWO. Validation on a physical prototype indicated a close agreement between the computed torque based on the identified parameters and the actual output torque, confirming the method’s effectiveness and engineering feasibility. This work provides support for precise control of exoskeletons. Full article

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article

Engineering laboratory courses are essential for developing conceptual understanding and practical skills; however, the time students spend assembling prototypes and troubleshooting wiring issues often reduces opportunities for analysis, programming, and reflective learning. To address this limitation, this study designed and evaluated an integrated STM32-based educational laboratory that consolidates the main peripherals required in a microcontroller course into a single Printed Circuit Board (PCB) platform. A quasi-experimental intervention was implemented with 40 engineering students divided into a control group using traditional STM32 Blue Pill and breadboard connections and an experimental group using the integrated platform. Throughout ten laboratory sessions, data were collected through pre- and post-tests, laboratory logs, and the Motivated Strategies for Learning Questionnaire Short Form (MSLQ-SF). Results showed that the experimental group achieved a Hake normalized learning gain of 40.09% compared with 16.22% in the control group, also showing that it completed the sessions an average of 27 min faster and facilitated a substantial reduction in hardware- and connection-related errors. Significant improvements were also observed in metacognitive and improved motivational and self-regulated learning scores. Overall, the findings indicate that reducing operational barriers in laboratory work enhances both cognitive and motivational learning processes, supporting the adoption of integrated educational hardware to optimize learning outcomes in engineering laboratory courses. Full article

Thin-walled parts are widely used in the automotive manufacturing industry due to their lightweight characteristics and high structural efficiency. However, it is difficult to accurately measure machining errors in key regions due to the feature deformation. To improve the online measurement accuracy of complex thin-walled parts, a machining error measurement approach based on multi-constraint point cloud registration is proposed. To address the low overlap and complex geometric features among multi-segment measured point clouds, a point cloud stitching method based on hole boundary features is developed to acquire complete measured point clouds. Meanwhile, a point cloud surface extraction method based on normal neighborhood searching is developed to acquire model point clouds. Since different regions of thin-walled parts require different geometric tolerances, a registration model integrating multiple locating and assembly constraints is proposed to satisfy the requirements for optimal point cloud registration. A measurement system composed of a line-structured light sensor and a six-axis robotic arm is developed to validate the proposed method. Experimental results show that the proposed approach reduces the overall dimensional error of point cloud stitching by approximately 70–86% and decreases the point number deviation between upper and lower surfaces by more than 98%. Furthermore, the measurement accuracy in locating holes and key assembly regions is improved to 0.05 mm and 2 mm, representing improvements of approximately 96.3% and 23.9% compared with registration methods without multi-constraint conditions, and approximately 95.3% and 14.5% compared with commonly used multi-constraint registration methods. Full article

Overshooting defects in shipbuilding subassemblies are essential to ensure the final product’s overall integrity and safety. In this work, we focus on the automatic detection of overshooting defects in simple and T-shaped sub-assemblies by employing reconstruction-based unsupervised learning on 3D point clouds. To this purpose, we implemented and compared four state-of-the-art architectures, including a Variational Autoencoder (VAE), FoldingNet, a Dynamic Graph CNN (DGCNN) autoencoder, and a PointNet++ autoencoder. These architectures were trained exclusively on defect-free samples, anticipating the possibility of overshooting defects occurring in different locations and with varying geometric patterns that are difficult to characterize explicitly in advance. Those defects are then identified by applying an Isolation Forest to the reconstruction error features, enabling fully unsupervised anomaly detection and allowing us to study how the detection performance changes with the contamination parameter. The results show that reconstruction-based anomaly detection on point clouds is a viable strategy for identifying defects in an industrial environment and the importance of choosing architectures that balance detection performance, stability across different geometries, and computational cost. Full article

Partial differential equations (PDEs) govern a wide range of physical phenomena, but their numerical solution remains computationally demanding, especially when repeated simulations are required across many parameter settings. Recent Scientific Foundation Models (SFMs) aim to alleviate this cost by learning universal surrogates from large collections of simulated systems, yet they typically rely on monolithic architectures with limited interpretability and high pretraining expense. In this work, we introduce Compositional Neural Operators (CompNO), a compositional neural operator framework for parametric PDEs. Instead of pretraining a single large model on heterogeneous data, CompNO first learns a library of Foundation Blocks, where each block is a parametric Fourier neural operator specialized to a fundamental differential operator (e.g., convection, diffusion, nonlinear convection). These blocks are then assembled, via lightweight Adaptation Blocks, into task-specific solvers that approximate the temporal evolution operator for target PDEs. A dedicated boundary-condition operator further enforces Dirichlet constraints exactly at inference time. We validate CompNO on one-dimensional convection, diffusion, convection–diffusion and Burgers’ equations from the PDEBench suite. The proposed framework achieves lower relative $L^2$ error than strong baselines (PFNO, PDEFormer and in-context learning-based models) on linear parametric systems, while remaining competitive on nonlinear Burgers’ flows. The model maintains exact boundary satisfaction with zero loss at domain boundaries, and exhibits robust generalization across a broad range of Péclet and Reynolds numbers. These results demonstrate that Compositional Neural Operators provide a scalable and physically interpretable pathway towards foundation models for PDEs. Full article

During batch soldering, assembly of micro image sensor modules, initial random pose, and feature partially occlude target micro-component image, leading to issues of missed and erroneous detection, and low 3D spatial positioning accuracy due to cross-depth-of-field detection errors in microscopic vision. This paper proposes Small object-YOLO11-Cross-Depth-of-field Positioning (SO-YOLO11-CDP), an instance segmentation-based approach for precision cross-depth-of-field positioning micro-component. First, an improved Small object-YOLO11 (SO-YOLO11) image segmentation algorithm is designed. By incorporating a coordinate attention mechanism (CA) into segmentation head to enhance localization of micro-targets, the backbone uses non-stride convolution to preserve fine-grained feature, while target regression performance is boosted via Efficient-IoU (EIoU) loss combined with normalized Wasserstein distance (NWD). Subsequently, to further improve spatial position detection accuracy in cross-depth-of-field detection, a calibration error compensation model for image Jacobian matrix is established based on pinhole imaging principles. Experimental results indicate that SO-YOLO11 achieves 16.1% increase in precision, 4.0% increase in recall, and 9.9% increase in mean average precision (mAP0.5) over baseline YOLO11. Furthermore, it accomplishes spatial detection accuracy superior to 6.5 μm for target micro-components. The method presented in this paper holds significant engineering application value for high-precision spatial position detection of micro image sensor components. Full article

Time delays are intrinsic to mitotic regulation, particularly within the spindle assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). These delays emerge from multi-step protein activation, molecular transport, force-dependent conformational transitions, and spatial redistribution of regulatory complexes. They span seconds to minutes and strongly influence checkpoint activation, maintenance, and silencing. Increasing evidence shows that such delayed processes shape mitotic timing, checkpoint robustness, and cell-fate decisions. While classical ordinary differential equation (ODE) models assume instantaneous biochemical responses, delay differential equations (DDEs) provide a natural framework for representing these finite timescales by explicitly incorporating system history. Recent DDE-based studies have revealed how delayed signaling contributes to bistability, oscillatory responses, prolonged mitotic arrest, and variability in checkpoint outputs. This review summarizes the biological origins of delays in SAC and SPOC, including Mad2 activation, MCC assembly and turnover, APC/C reactivation, tension maturation at kinetochores, and Bfa1–Bub2 regulation of Tem1. The article further discusses how mechanistic models with explicit delays improve our understanding of SAC–SPOC ordering, error-correction dynamics, and mitotic exit control. Finally, open challenges and future directions are outlined for integrative delay-aware modeling that unifies biochemical, mechanical, and spatial processes to better explain checkpoint function and chromosomal stability. Full article

Automated precision drilling is essential for aircraft skin manufacturing, yet current robotic systems face dual challenges: chatter-induced inaccuracies in hole quality and limited access to confined spaces such as air inlets. To overcome these limitations, this paper develops a compact drilling robot for drilling large-curvature skins of aircraft air inlets. Targeting the precision drilling requirements for complex-curvature aircraft air inlets, we present the robot’s overall design scheme, detailing each module’s composition to ensure precision drilling. In-depth analysis of the robot’s large-curvature adaptability precisely calculates the wheel assembly dimensions. To ensure high-precision drilling bit entry into guide mechanisms, a flexible drilling spindle mechanism is designed, with calculated and verified elastic ranges. An integrated intelligent control system is developed, combining vision recognition, real-time pose adjustment, and automated drilling workflow planning. Finally, traversability and drilling capabilities are validated using a simplified air inlet model. Test results confirm successful traversal on R200 mm curvature skins and automated drilling of Carbon Fiber-Reinforced Polymer (CFRP)/7075 aluminum stacks with a diameter of Φ4–Φ6 mm, achieving dimensional errors of less than 0.05 mm and normal direction errors of less than 0.65°. Full article

Errors in de novo synthesized DNA can originate from the oligonucleotides used during assembly. Oligonucleotides may contain substitutions, deletions, and insertions resulting from either incomplete reactions at individual steps of the phosphoramidite synthetic cycle or various side reactions. In this study, we quantitatively assessed errors in both gene constructs assembled from synthetic oligonucleotides by Sanger sequencing and in synthetic oligonucleotides by NGS. Our data demonstrate that side reactions involving carboxylic acid anhydrides during the capping step of oligonucleotide synthesis lead to the modification of guanine residues. This guanine modification subsequently results in the accumulation of G to A substitutions in the final gene constructs. We show that the error rate can be reduced by replacing the standard acetic anhydride-based capping mixture with anhydrides of carboxylic acids weaker than acetic acid. Furthermore, a more significant reduction in errors is achievable by using capping reagents based on phosphoramidite chemistry. Full article