High Precision Research Articles

Advances in 3-D printing: polymers, fabrication mechanisms, mass balance models and applications

AbstractIn Science and Engineering, additive manufacturing, which is synonymously known as 3-D printing is a layer-by-layer computer-aided approach that is used to produce high precision commercial industrial products. So far, 3-D printing techniques, which translate computer-aided virtual 3-D models into physical objects, are gaining traction globally. However, the current evolution from conventional molding and machining to rapid prototyping followed by rapid additive manufacturing prompts new challenges for engineers and scientists alike. This review is particularly prompted by novel innovations in 3-D printing with respect to emerging fabrication designs and mechanisms, printable polymeric materials, and current applications. Central to all spotlights is the discussion on 3-D printing techniques discussed under 3 broad categories; (i) extrusion-based methods, (ii) powder binding technologies and (iii) photopolymerization methods and corresponding polymer design aspects, processing parameters and mass balance models. Aspects of mass-balance models addressed in this review include models of multimaterial 3-D printing of viscoelastic inks, Newtonian fluid in extrusion, the Gaussian Beam model and multiscale computational simulations for prediction of macroscale properties of various polymeric materials. This review also highlights selected applications that demonstrate how polymer-based 3-D printing is being exploited in industry, and future perspectives.

An Analysis of Dynamic Recrystallization During the Reduction Pretreatment Process Using a Multiscale Model

In this study, a multiscale model is developed through secondary development (UMAT and UEXTERNALDB) in Abaqus with the objective of simulating the thermal deformation process with dynamic recrystallization behavior. The model couples the finite element method (FEM) with the multiphase field model (MPFM), thereby establishing bidirectional coupling between macroscopic mechanical behavior and microstructural evolution. A comparison between the single-element hot compression simulation and experimental results demonstrates that the model accurately simulates both the macroscopic mechanical behavior and microstructural evolution during the thermal deformation process, thereby exhibiting high precision. Simulations of the reduction pretreatment (RP) process under different reduction amounts and billet surface temperatures demonstrate that increasing the reduction amount and billet surface temperature significantly enhances both plastic deformation and the volume fraction of dynamic recrystallization in the billet core. This results in the closure of core voids and the refinement of the core microstructure, thereby providing valuable guidance for the development of optimal reduction pretreatment (RP) processes.

Co-Phase Error Detection for Segmented Mirrors Based on Far-Field Information and Transfer Learning

The resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equivalent aperture, provided that the segments are co-phased correctly. This paper proposes a method for high-precision detection of piston errors in segmented mirror telescope systems, based on far-field information and transfer learning. By training a ResNet-18 network model, this method can predict piston errors with high precision within 10 ms of a single-frame far-field diffraction image. Simulation results demonstrate that the method is robust to tip-tilt errors, wavefront aberrations, and noise. This approach is simple, fast, highly accurate in detection, and resistant to noise, providing a new solution for piston error detection in segmented mirror systems.

An Optimized Approach for Detection and Classification of Spam Email’s Using Ensemble Methods

AbstractSince the advent of email services, spam emails have been a major concern because users’ security depends on the classification of emails as ham or spam. It’s a malware attack that has been used for spear phishing, whaling, clone phishing, website forgery, and other harmful activities. However, various ensemble Machine Learning (ML) algorithms used for the detection and filtering of spam emails have been less explored. In this research, we offer a ML-based optimized algorithm for detecting spam emails that have been enhanced using Hyper-parameter tuning approaches. The proposed approach uses two feature extraction modules, namely Count-Vectorizer and TFIDF-Vectorizer that provide the most effective classification results when we apply them to three different publicly available email data sets: Ling Spam, UCI SMS Spam, and the Proposed dataset. Moreover, to extend the performance of classifiers we used various ML methods such as Naive Bayes (NB), Logistic Regression (LR), Extra Tree, Stochastic Gradient Descent (SGD), XG-Boost, Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perception (MLP), and parameter optimization approaches such as Manual search, Random search, Grid search, and Genetic algorithm. For all three data sets, the SGD outperformed other algorithms. All of the other ensembles (Extra Tree, RF), linear models (LR, Linear-SVC), and MLP performed admirably, with relatively high precision, recall, accuracies, and F1-score.

Height reconstructions from geometric wavefronts using vision ray metrology

A recently reported vision ray metrology technique [Opt. Express 29, 43480 (2021)OPEXFF1094-408710.1364/OE.443550] measures geometric wavefronts with high precision. This paper introduces a method to convert these wavefront data into height information, focusing on the impact of back surface flatness and telecentricity errors on measurement accuracy. Systematic errors from these factors significantly affect height measurements. Using ray trace simulations, we estimate reconstruction errors with various plano-concave and plano-convex elements. We also developed a calibration technique to mitigate telecentricity errors, achieving submicron accuracy in surface reconstruction. This study provides practical insights into vision ray metrology systems, highlighting validity limits, emphasizing the importance of calibration for larger samples, and establishing system alignment tolerances. The reported technique for the conversion of geometric wavefronts to surface topography employs a direct non-iterative ray-tracing-free method. It is ideally suited for reference-free metrology with application to freeform optics manufacturing.

Abstract B066: Analytical validation of a multiplexed CTC-mRNA assay for androgen receptor pathway inhibitors

Abstract Introduction: Androgen receptor pathway inhibitors (ARPIs) have been shown to improve survival for men with metastatic prostate cancer, however, the use of 2nd line ARPIs has limited efficacy. There is a critical need to develop biomarker-driven treatment strategies for this clinical need. We have previously reported on a novel circulating tumor cell (CTC) multiplexed mRNA biomarker that has prognostic and predictive capability in clinical utility trials to detect AR- driven resistance and Neuroendocrine Prostate Cancer (NEPC). However, before these biomarkers can be tested in prospective clinical validation trials and clinical care, they must undergo thorough analytical validation. We report a novel approach to the analytical validation of rare cell assays for gene expression analysis and the testing of this CTC mRNA assay. Methods: Analytical validation for assays with single-cell level detection is particularly challenging in that results may be categorical as well as quantitative: sometimes measured at higher or lower levels, and other times resulting in no detectable signal, precluding the use of standard measures of precision. The inter-metastatic cellular heterogeneity composition of a liquid biopsy further complicates efforts to demonstrate clinical-grade precision at the single-cell level. To address this, we applied logistic regression to replicates of patient samples, cell lines and unique DNA and mRNA oligonucleotides to define the minimum amount of analyte to achieve high precision for the underlying PCR and exclusion-based sample preparation (ESP) mRNA extraction technologies. Results: We demonstrated clinical-grade precision at the single-cell level, comparable to the sensitivity previously demonstrated for qPCR, and analyte quantities of 15 total transcripts. Equivalent analyte yield was obtained from the extraction of single-cell quantities of cell line cells with or without a maximum of 10,000 background cells, demonstrating robustness to a range of sample purities. Extractions from 10 healthy donors demonstrate analytical specificity of the assay, with a synthetic mRNA spike-in control used to confirm successful extractions. Replicates of cell line extractions at different concentrations were compared to 44 replicate patient samples to delineate technical precision from biological heterogeneity. Discussion: The automated CTC-mRNA extraction technology demonstrates high precision equivalent to PCR with single-cell sensitivity. We can further reproducibly detect as low as 15 transcripts. We have achieved a semi-qualitative assay read-out that maximizes the analytical sensitivity of the assay, allowing the clinical reporting of multiplexed results for clinical utility in prostate cancer. This validation approach also serves as a model for clinical implementation of other single-cell liquid biopsy assays. Currently, this assay is being prospectively evaluated in the ARCTIC trial for patients with prostate cancer treated with an ARPI (NCT06141993). Citation Format: Jennifer L Schehr, Rory M Bade, Anupama Singh, Jacob C Caceres, Nasya A Ramli, Matthew C Mannino, Diego Eyzaguirre, Alyssa M Hintz, Manushi N Vatani, Serena K Wolfe, Kayo Miyazaki, Elizabeth Haas, Joshua Faulkes, Dana E Rathkopf, Susan Halabi, Andrew J Armstrong, Shuang G Zhao, Joshua M Lang. Analytical validation of a multiplexed CTC-mRNA assay for androgen receptor pathway inhibitors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B066.

Weld Seam Recognition and Tracking With Omnidirectional Wall-Climbing Robot for Spherical Tank Inspection

Abstract Weld seams of in-service pressure storage equipment, such as spherical tanks, require regular inspection to ensure safe operation. Wall-climbing robots can replace manual operations, increasing inspection efficiency and reducing maintenance costs. High precision and fast weld seam identification and tracking are beneficial for improving the automated navigation and spatial positioning of wall-climbing robots. This study proposes a weld seam recognition and tracking method with the omnidirectional wall-climbing robot for spherical tank inspection. Based on deep learning networks, the robot has a front-mounted camera to recognize weld seams and extract weld paths. Weld seam deviation data (drift angle and offset distance) were used in real time to provide feedback on the robot's relative position. For the robot to quickly correct deviations and track weld seams, a seam path-tracking controller based on sliding mode control was designed and simulated. Weld recognition experiments revealed that the robot can accurately recognize and extract weld paths, and the recognition time for each image was approximately 0.25 s. In the weld seam tracking experiments, the robot could successfully track longitudinal and transverse weld seams at different speeds (from 0.05 to 0.2 m/s). During the process of weld seam tracking, the robot angle error was kept within ±3 deg, and the maximum offset distance was less than ±35 mm. Field tests on a 3000-m3 spherical tank were conducted to verify the practicability and effectiveness of the weld seam tracking system. This robotic system can autonomously complete weld seam identification and tracking, which promotes the automation of spherical tank inspection and maintenance.

A mode shape sensitivity-based wavelet feature extraction method for interface debonding detection in concrete-filled steel tubes

Abstract The use of concrete-filled steel tube (CFST) composite columns is increasingly prevalent in the construction industry, particularly in high-rise structures. A common issue in CFST columns is interface debonding between the concrete core and the steel tube. If this debonding progresses both superficially and deeply, it can lead to instability and buckling of the column, posing a serious threat to the overall structural integrity. This study presents an innovative and effective method for extracting damage-sensitive features using horizontal, vertical, and diagonal detail coefficients derived from the wavelet analysis of corrected modal signals. The study introduces the total normalized irregularity detection index (NIDIT) as a damage detection metric. The results indicate that NIDIT is highly effective in identifying and detecting debonding regions. NIDIT quantifies the accumulation of irregularities and disturbances in the affected areas, allowing for the detection of concrete surface debonding from the steel tube. The findings show that NIDIT can accurately and efficiently detect damage in middle and end-edge regions, addressing a significant challenge in structural health monitoring with high precision.

INVESTIGATION ON ELECTRODE WEAR IN MICRO-EDM DRILLING

Micro-EDM drilling is extensively utilized in various industrial sectors due to its capability to machine all types of conductive materials, irrespective of their mechanical properties. This technology is particularly advantageous for drilling applications requiring high aspect ratios and precision. However, electrode wear, both frontal and radial, poses significant challenges, especially in blind hole drilling. This study proposes a mechanical method to measure electrode geometry through touch operations on a sharp target, varying the spindle rotation angle and Z depth. The method's effectiveness was validated through extensive experiments on Ti6Al4V plates using cylindrical and tubular electrodes of different diameters. The study varied energy types and tested new and steady-state electrodes. Results demonstrated the method's ability to detect changes in electrode tip shape under different conditions, revealing phenomena such as the correlation between electrode runout and wear, and the impact of internal washing on tip shape.

Abstract A012: Analytical validation of a cfDNA-based fragmentomic profiling assay for the detection and quantification of 14 HPV types in cervical cancer

Abstract Purpose: Human papillomavirus (HPV) is the primary cause of cervical cancer, one of the most common cancers affecting women globally. Despite advances in prevention and screening, there remains a critical need for reliable, non-invasive assays to detect and monitor HPV-driven cervical cancers. Liquid biopsy offers a promising solution to meet this need. Here, we report the analytical validation of a plasma-based fragmentomic profiling assay designed to detect and characterize circulating tumor tissue modified viral (TTMV)-HPV DNA from 14 HPV types associated with cervical cancer. Methods: A high-complexity, laboratory-developed test was developed that employs 68 primers and 34 probes to target 14 HPV types via droplet digital PCR (ddPCR). A quantitative algorithm was applied to integrate fragment counts to generate a TTMV-HPV DNA Score, reflective of the quantity of circulating tumor-derived HPV DNA. Analytical validation was performed by determining the limit of blank (LOB), limit of quantitation (LOQ), and limit of detection (LOD) for each HPV type. To measure the limits of assay performance, engineered samples were used for reference interval studies. Synthesized target-specific DNA regions of HPV16, HPV18, HPV31, HPV33, HPV35, HPV39, HPV45, HPV51, HPV52, HPV56, HPV58, HPV59, HPV66, and HPV68 were diluted to varying concentrations to perform assay validation. Results: The assay demonstrated excellent specificity, with LOBs ranging from 0 to 0.07 copies/µL across the 14 HPV types. LODs ranged from 0.20 to 1.02 copies/µL, indicating a high level of sensitivity. The assay also showed high accuracy and precision, with coefficients of variation (CV) below 20% for all HPV types across both intra- and inter-assay precision studies. Regression analysis confirmed strong linearity (R2 > 0.99) across a wide range of analyte concentrations. Conclusion: These results demonstrate that TTMV-HPV DNA assay accurately and reproducibly detects circulating HPV DNA for 14 HPV types commonly associated with cervical cancer, offering a valuable tool for both diagnosis and ongoing surveillance in clinical settings. Future studies will present the clinical validation in order to establish the prognostic utility of the assay in predicting the likelihood of HPV-driven cervical cancer in patients. Citation Format: Sunil Kumar, Joshua Hutcheson, David Conway, Michael Horan, Syandan Chakraborty, Chloe Wiseman, Alicia Gunning, Evgeny Skyrypkin, Cassin Williams, Catherine Del Vecchio Fitz, Piyush B Gupta. Analytical validation of a cfDNA-based fragmentomic profiling assay for the detection and quantification of 14 HPV types in cervical cancer [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A012.

Temperature stability analysis of the all-fiber current sensor with a loop structure

Abstract All-fiber optical current sensor (AFOCS) is a perfect product formed by the combination of fiber-optic sensing technology and the Faraday effect. It boasts significant advantages such as resistance to electromagnetic interference, a high measurement dynamic range and precision, low power consumption, low cost, and insulation. Additionally, it enables long-distance transmission, making it one of the main devices for current monitoring in smart grids. However, the birefringence within the sensing fiber is highly susceptible to changes in temperature, causing the polarization state of light to be extremely sensitive to temperature variations. This sensitivity significantly impacts the accuracy of current measurements. A novel loop structure AFOCS with a coupled fiber polarization rotator (FPR) is introduced in this paper. The operating principle of this structure is theoretically analyzed, and the Jones matrix is used to analyze the output light intensity signal and derive the error formula. After comparing the measurement accuracy of the basic AFOCS and the loop structure AFOCS, it is demonstrated that this novel structure can improve current sensitivity by enhancing the temperature robustness of the system. Additionally, the error generated by the FPR is controlled below 1%, meeting the requirements for stable system operation. Therefore, this novel structure effectively improves the accuracy of current measurement and exhibits strong temperature stability.

Associations between sex and lifestyle activities with cognitive reserve in mid-life adults with genetic risk for Alzheimer's disease.

Females have a higher age-adjusted incidence of Alzheimer's Disease (AD) than males, even when accounting for longer lifespan and, therefore, stand to benefit the most from dementia prevention efforts. As exposure to many modifiable risk factors for dementia begins in mid-life, interventions must be implemented from middle-age. Building cognitive reserve, particularly through stimulating avocational activities and occupational attainment presents a crucial, underexplored, dementia prevention approach for mid-life. It is currently unknown, however, whether modifiable lifestyle factors can protect against AD processes, from mid-life, differentially for females and males who carry inherited risk for late-life dementia. To address this gap, this study investigated the impact of biological sex and APOE4 carrier status on the relationship between stimulating activities, occupational attainment, and cognition in mid-life. We leveraged the PREVENT-Dementia program, the world's largest study investigating the origins and early diagnosis of dementia in mid-life at-risk individuals (N = 700; 40-59 years). Cognitive performance was measured using the Cognito Battery and the Visual Short Term Memory Binding task. Mid-life specific reserve contributors were assessed via the Lifetime of Experiences Questionnaire. Females had significantly better episodic and relational memory (p < 0.001), and lower occupational attainment than males (p < 0.001). Engagement in stimulating activities was positively associated with episodic and relational memory, regardless of sex and APOE4 status (β = 0.05, CI 0.03-0.07, p < 0.001). APOE4 carriers showed significant sex differences in the association between occupational attainment and episodic and relational memory (β = 0.38, CI 0.12-0.63, p = 0.003). APOE4 carrier females with higher occupational attainment showed better cognition (β = 0.16, CI -0.002-0.32, p = 0.053), whereas APOE4 carrier males showed the opposite effect (β = -0.20, CI -0.40 - -0.001, p = 0.049). Our findings suggest that occupational attainment in mid-life contributes to cognitive reserve against inherited risk of dementia in females, but not males. They highlight the need for high precision approaches that consider biological sex and APOE4 carrier status to inform Alzheimer's disease prevention strategies and clinical trials.

Challenges in the extraction of physics beyond the Standard Model from electron scattering

Abstract Precise measurements of electron and positron scattering, including parity violation, offer great promise in the search for physics beyond the Standard Model. In this context it is crucial to understand the corrections which might arise from charge symmetry violation, as well as the less well known strange and charm quark distributions. Our analysis, using state of the art parton distributions, suggests that these contributions lead to corrections in the extraction of the weak couplings $g^{eq}_{AV}$ and $g^{eq}_{VA}$ of the order $(1-2)\%$, while they are as large as $4\%$ for $g^{eq}_{AA}$, at a typical scale of $Q^2 = 10\ {\rm GeV}^2$. These results underline the importance of carrying out high precision measurements, which will not only provide information on physics beyond the Standard Model but also reduce the current uncertainties on our knowledge of the strange and charm quark distributions in the proton.

Ultrasensitive Detection of Blood-Based Alzheimer's Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning.

Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in their early stages, leading to delayed treatment outcomes. Early diagnosis using blood derivatives as a source for biomarkers is particularly important for managing Alzheimer's disease (AD). This study introduces a novel approach for the precise and ultrasensitive detection of multiple core AD biomarkers (Aβ40, Aβ42, p-tau, and t-tau) using surface-enhanced Raman spectroscopy (SERS) combined with machine-learning algorithms. Our method employs an antibody-immobilized aluminum SERS substrate, which offers high precision, sensitivity, and accuracy. The platform achieves an impressive detection limit in the attomolar (aM) range and spans a wide dynamic range from aM to micromolar (μM) concentrations. This ultrasensitive and specific SERS immunoassay platform shows promise for identifying mild cognitive impairment (MCI), a potential precursor to AD, from blood plasma. Machine-learning algorithms applied to the spectral data enhance the differentiation of MCI from AD and healthy controls, yielding excellent sensitivity and specificity. Our integrated SERS-machine-learning approach, with its interpretability, advances AD research and underscores the effectiveness of a cost-efficient, easy-to-prepare Al-SERS substrate for clinical AD detection.

Molecular dynamics-based study of the effects of grain size and temperature on the nanoscratching groove characteristics of grating polycrystalline layered Al films

Abstract As a special diffraction grating, the echelle grating has excellent beam splitting ability with the periodic groove pattern on it, which is characterized by low number of lines per millimeter and large flash angle. Al films with a layered structure determined by the coating technology are the first blanks for the echelle grating in mechanical scratching. However, the special interlayer structure of the layered Al films and the need for large depth and high precision triangular diffraction grooves have increased the difficulty of controlling the scratching process, and the traditional mechanical scratching “trial and error” process is still the main means. Therefore, in the study, molecular dynamics (MD) is utilized to simulate the mechanical response and deformation mechanism of polycrystalline layered Al films during the scratching process at different grain sizes and temperatures. The results represent that the friction force and normal force show a decreasing trend with increasing temperature, and the decrease in grain size leads to smaller friction and normal force; The number of removed atoms of polycrystalline layered Al films shows an increasing trend with increasing temperature, which improves the material removal rate and facilitates precise shape control of the groove; In addition, the presence of layered grain boundary interface hinders the downward propagation of defects such as dislocations and can accommodate dislocations. At the same time, the total length of dislocation lines decreases with increasing temperature, the high temperature promotes the transformation of crystals to an amorphous structure, and the grain boundaries between grains and the interlayer grain boundaries at the delamination limit the movement of dislocations. The deformation behavior indicates that grain boundaries play a key role in inhibiting the propagation of strain and stress, and a gradient distribution is formed at the layered grain boundary interface structure, which further hinders the downward transfer.

Diffusion Behavior and Kinetics for the Vapor Phase Infiltration of Trimethylaluminum in Poly(ethylene oxide): An In Situ Quartz Crystal Microgravimetry Study.

Vapor phase infiltration (VPI) facilitates the incorporation of inorganic components into organic polymers, emerging as an effective technique for fabricating organic-inorganic hybrid materials. However, the complexity of diffusion behavior during the VPI process presents challenges in studying diffusion kinetics, particularly for highly reactive precursor-polymer systems such as trimethylaluminum (TMA) and poly(ethylene oxide) (PEO). In this study, we investigate the VPI process of TMA in PEO using in situ quartz crystal microgravimetry (QCM), which enables measurement of diffusion behavior and kinetics with high precision due to its high temporal resolution. Our results indicate that the VPI process consists of two main regions: a rapid diffusion process, corresponding to the initial penetration of the precursor into the film, followed by a slower relaxation process, attributed to the ongoing chemical reaction. The equivalent diffusion coefficient (De) was estimated to be on the order of 10-9 cm2/s and decreased with increasing aluminum content. Using energy application as a proof-of-concept, when optimized, VPI-modified PEO films were successfully utilized as solid polymer electrolytes (SPEs) for lithium metal batteries (LMBs), showcasing superior performance in mitigating lithium dendrite growth. This study offers valuable insights into the VPI process for PEO-TMA systems and provides guidance for optimizing VPI conditions to enhance the performance of advanced materials.

Overview of High-Performance Timing and Position-Sensitive MCP Detectors Utilizing Secondary Electron Emission for Mass Measurements of Exotic Nuclei at Nuclear Physics Facilities

Timing and/or position-sensitive MCP detectors, which detect secondary electrons (SEs) emitted from a conversion foil during ion passage, are widely utilized in nuclear physics and nuclear astrophysics experiments. This review covers high-performance timing and/or position-sensitive MCP detectors that use SE emission for mass measurements of exotic nuclei at nuclear physics facilities, along with their applications in new measurement schemes. The design, principles, performance, and applications of these detectors with different arrangements of electromagnetic fields are summarized. To achieve high precision and accuracy in mass measurements of exotic nuclei using time-of-flight (TOF) and/or position (imaging) measurement methods, such as high-resolution beam-line magnetic-rigidity time-of-flight (Bρ-TOF) and in-ring isochronous mass spectrometry (IMS), foil-MCP detectors with high position and timing resolution have been introduced and simulated. Beyond TOF mass measurements, these new detector systems are also described for use in heavy ion beam trajectory monitoring and momentum measurements for both beam-line and in-ring applications. Additionally, the use of position-sensitive timing foil-MCP detectors for Penning trap mass spectrometers and multi-reflection time-of-flight (MR-TOF) mass spectrometers is proposed and discussed to improve efficiency and enhance precision.

Motion-induced phase shift for dynamic structured light measurement.

Structured light 3D shape measurement is extensively utilized in semiconductor inspection, smart manufacturing, and biomedical imaging due to its rapid measurement speed, high precision, and versatile applicability to different objects. However, the traditional implementations of this method often require that the object remains static while recording the phase-shifting structured light images, which limits the adaptability of dynamic measurement. Here, we propose a dynamic 3D shape measurement using structured light based on a motion-induced phase shift (MIPS). As the object moves, the surface features distort the fringe pattern, resulting in a phase-shifting effect. By employing the MIPS method, we can determine the phase even in the situations where the knowledge of phase-shifting conditions is not accurate. This enables the acquisition of the 3D topography of the object surface with a high level of precision. Experimental results demonstrate that the MIPS method can accurately measure the 3D shape of objects moving as fast as 100 mm/s, with a relative discrepancy of less than 0.23%.

Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques.

The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject to variability and may lack precision. Current methodologies leverage conventional deep learning models that, while effective to an extent, often suffer from overfitting and generalization issues on medical image datasets due to the intricate and subtle variations in disease manifestations. These models typically do not fully utilize the potential of transfer learning or advanced data augmentation, leading to less-than-optimal performance, especially in diverse real-world scenarios where data variability is high. This study introduces a robust model using the EfficientNetB5 architecture combined with a sophisticated data augmentation strategy. The model is tailored for the high variability and intricate details present in gastrointestinal tract disease images. By integrating transfer learning with maximal pooling and extensive regularization, the model aims to enhance diagnostic accuracy and reduce overfitting. The proposed model achieved a test accuracy of 98.89%, surpassing traditional methods by incorporating advanced regularization and augmentation techniques. The application of horizontal flipping and dynamic scaling during training significantly improved the model's ability to generalize, evidenced by a low-test loss of 0.230 and high precision metrics across all classes. The proposed deep learning framework demonstrates superior performance in the automated classification of gastrointestinal diseases from image data. By addressing key limitations of existing models through innovative techniques, this study contributes to the enhancement of diagnostic processes in medical imaging, potentially leading to more accurate and timely disease interventions.

Design and Optimization of Electromagnetically Driven End-Effector

The excellent flexibility and plasticity of a flexible robotic hand enable it to grip objects of various shapes and sizes. Currently, most of the flexible robotic hand fingers on the market use pneumatic drives. However, the unstable power of the pneumatic drive can cause the object gripped by the finger to slip. Additionally, the pneumatic pump can create noise pollution. To address these issues, a flexible end-effector is designed using an electromagnetic coil as the power source. The electromagnetic-driven end-effector has high precision and fast response, without the need for external devices. It only requires controlling the current to control the magnetic force of the electromagnet, thereby controlling the degree of end-effector bending. The finite element simulation is used to analyze the impact of the structural parameters of the end-effector on bending degree. And the optimal basic parameters of the end-effector are determined. Under the same intensity of the current, when the bottom layer thickness is 5 mm, end-effector knuckle spacing is 4 mm, the maximum angle of end-effector bending is reached.