High Robustness Research Articles

Autonomous Scanning Tunneling Microscopy Imaging via Deep Learning.

Scanning tunneling microscopy (STM) is a powerful technique that provides the ability to manipulate and characterize individual atoms and molecules with atomic-level precision. However, the processes of scanning samples, operating the probe, and analyzing data are typically labor-intensive and subjective. Deep learning (DL) techniques have shown immense potential in automating complex tasks and solving high-dimensional problems. In this study, we developed an autonomous STM framework powered by DL to enable autonomous operations of the STM without human interventions. Our framework employs a convolutional neural network (CNN) for real-time evaluation of STM image quality, a U-net model for identifying bare surfaces, and a deep Q-learning network (DQN) agent for autonomous probe conditioning. Additionally, we integrated an object recognition model for the automated recognition of different adsorbates. This autonomous framework enables the acquisition of space-averaging information using STM techniques without compromising the high-resolution molecular imaging. We achieved measuring an area of approximately 1.9 μm2 within 48 h of continuous measurement and automatedly generated the statistics on the molecular species present within the mesoscopic area. We demonstrate the high robustness of the framework by conducting measurements at the liquid nitrogen temperature (∼78 K). We envision that the integration of DL techniques and high-resolution microscopy will not only extend the functionality and capability of scanning probe microscopes but also accelerate the understanding and discovery of new materials.

Realization of Secure Robotic Brain Via Programmable Metasurface with Robust High‐Order BIC

AbstractTerahertz waves can be widely used for short‐range communication in complex indoor environments and non‐destructive object detection applications. Metasurfaces are widely used in terahertz sensing and communication devices because they can modulate terahertz waves in multiple dimensions. Metamaterial robot brain can utilize metasurfaces' powerful direct modulation ability to achieve sensing and communication functions. The metasurface devices realized based on Dynamic Heterogeneous Redundancy (DHR) architecture can improve the confidentiality and security of terahertz wave wireless communication. While the intrinsic ohmic loss and quality factor of usual metallic metamaterials are usually low, the concept of bound states in the continuum (BIC) has been proposed for stronger terahertz‐matter interactions. Among them, high‐order BICs are of interest because of their strong robustness to structural defects. Therefore, an aluminium‐graphene hybrid metasurface with high‐order BIC is proposed. We have the principle of excitation of high‐order BICs is investigated and creatively proposed with high robustness realized using the magnetical EIT effect. The robustness of the high‐order BIC is also utilized to design security hardware based on DHR architecture. The designed secure hardware can satisfy the demand for an intelligent robotic brain to the internal terahertz wave confidential wireless communication.

Effects of Bulgarian bag training on bio-motor fitness, physiological, haematological, and performance in young adult male Kabaddi players

Kabaddi demands high bio-motor fitness and physiological robustness in players. The Bulgarian Bag, known for its dynamic training potential, offers multi-planar movements to enhance strength, endurance, and coordination. However, its impact on Kabaddi players still needs to be explored. To assess the effects of Bulgarian Bag training (BBT) on bio-motor fitness, physiological, hematological, and performance measures in young adult Kabaddi players. A total of 30 participants were randomly assigned to either the BBT Group (BBTG) (n = 15; age = 20.73 ± 1.83 years; weight = 72.56 ± 7.27 kg; height = 175.20 ± 6.03 cm) or the active Control Group (CG) (n = 15; age = 20.80 ± 1.69 years; weight = 71.86 ± 4.75 kg; height = 171.46 ± 6.06 cm). The training intervention lasted 12 weeks and was conducted during the in-season period. All outcome parameters tests were conducted before and after the intervention, including agility (AG), explosive strength (ES), shoulder strength (SS), muscular strength (MS), resting heart rate (RHR), VO2max, red blood cells (RBC), white blood cells (WBC), hemoglobin (HG), and playing ability (PA). The ANCOVA analysis revealed significant between-group differences in all physical fitness measures at post-test. Specifically, the BBTG showed significant improvements in AG, ES, SS, MS, RHR, VO2Max, RBC, WBC, HG, and PA (d = 0.75 to 3.28; Δ0.50% to 16.37%). Moreover, the BBTG group significantly reduced resting heart rate (d = 0.75; Δ-3.31%). In contrast, no significant changes were reported in the Control Group (CG) between pre- and post-tests (d = 0.00 to 0.78; Δ-0.08% to 4.59%). Integrating BBTG into the training program of young adult male Kabaddi players yielded beneficial effects across multiple parameters of bio-motor fitness, physiological, hematological, and playing ability. The findings reveal the potential of BBTG as a valuable training method for enhancing Kabaddi performance and overall athletic conditioning. Keywords: Kabaddi Player, Bulgarian bag training, bio motor, physiological, haematological

Image-Based Fitness Yoga Pose Recognition: Using Ensemble Learning and Multi-head Attention

With the increasing awareness of fitness, more and more people are choosing to participate in fitness activities. Yoga, as a form of exercise that improves both physical and mental health, is becoming increasingly popular worldwide. In order to assist yoga practitioners in more effective training through automated or semi automated systems, improve training effectiveness, assist professional athletes in training through intelligent recognition systems, correct movements, and improve athletic performance. This paper proposes a method that addresses the low accuracy issue of current yoga pose recognition algorithms by integrating multi-head attention mechanism and ensemble learning. Firstly, the Mixup algorithm is used to enhance yoga movement images. Subsequently, convolutional features are extracted from the images using the ResNet101 and VGGNet19 transfer learning models. Finally, the extracted convolutional features are combined and stacked using a multi-head attention mechanism. Model training, validation, and testing are performed using the Soft target cross-entropy loss function. Experimental results demonstrate that the proposed method achieves a training accuracy of 100%, a validation accuracy of 89.94%, a testing accuracy of 93.79%, and a detection speed of 297 frames per second. Overall, this method demonstrates high stability and robustness, providing a technological foundation for intelligent recognition of yoga poses.

Improved Multi-Size, Multi-Target and 3D Position Detection Network for Flowering Chinese Cabbage Based on YOLOv8.

Accurately detecting the maturity and 3D position of flowering Chinese cabbage (Brassica rapa var. chinensis) in natural environments is vital for autonomous robot harvesting in unstructured farms. The challenge lies in dense planting, small flower buds, similar colors and occlusions. This study proposes a YOLOv8-Improved network integrated with the ByteTrack tracking algorithm to achieve multi-object detection and 3D positioning of flowering Chinese cabbage plants in fields. In this study, C2F-MLCA is created by adding a lightweight Mixed Local Channel Attention (MLCA) with spatial awareness capability to the C2F module of YOLOv8, which improves the extraction of spatial feature information in the backbone network. In addition, a P2 detection layer is added to the neck network, and BiFPN is used instead of PAN to enhance multi-scale feature fusion and small target detection. Wise-IoU in combination with Inner-IoU is adopted as a new loss function to optimize the network for different quality samples and different size bounding boxes. Lastly, ByteTrack is integrated for video tracking, and RGB-D camera depth data are used to estimate cabbage positions. The experimental results show that YOLOv8-Improve achieves a precision (P) of 86.5% and a recall (R) of 86.0% in detecting the maturity of flowering Chinese cabbage. Among them, mAP50 and mAP75 reach 91.8% and 61.6%, respectively, representing an improvement of 2.9% and 4.7% over the original network. Additionally, the number of parameters is reduced by 25.43%. In summary, the improved YOLOv8 algorithm demonstrates high robustness and real-time detection performance, thereby providing strong technical support for automated harvesting management.

Blind image authentication using singular value decomposition with enhanced robustness by augmenting hamming code

Singular value decomposition (SVD) based image authentication has widespread applications for digital media protection including forensic and medical domains due to the high stability and robustness of singular values under small perturbations. However, most of these techniques are non-blind and have high redundancies for the extraction of secret data. The proposed scheme employs a blind extraction strategy by utilizing the intrinsic relationship of coefficient pairs of the U matrix of the host image blocks. This relationship is utilized to hide a secret binary bit after computing a threshold value in constant time. The host image underwent block-wise decomposition and hash-based randomization of blocks, followed by block-wise SVD for embedding secret bits in the U matrix. Hamming coded watermark is embedded for high precision extraction under active attacks. High imperceptibility and strong robustness are observed under attacks when tested across general purpose as well as image datasets and the system outperforms many existing works. Experimental observation shows the average PSNR between the host and marked image and average NCC between embedded and extracted watermark to be 36.98 dB and 0.999 respectively for the SIPI dataset whereas 52.7 dB and 1 respectively for the X-Ray dataset. The system exhibited high robustness under Salt-and-pepper noise (SAPN), Gaussian Noise (GAUN), Cropping (CRP), Median Filtering (MF), Histogram Equalization (HEQ), and Jpeg Compression (JPC) with average NCC values of 0.998, 1, 0.932, 0.999 and 0.967 respectively for X-Ray dataset. The time complexity O (M × N) is observed for both embedding and extraction algorithms for a host image of size M × N.

A robust force identification method based on L1+2-norm approximation theory

Obtaining the real force of the structure is the basic condition for efficient vibration reduction design. However, the uneasiness and instability of the inverse problem make the force identification still challenging. On the basis of L2-norm regularization, we propose a new method for force identification based on L1+2-norm approximation theory. By introducing the L1-norm penalty operator, a joint norm approximation optimization model is constructed to limit the non-zero force and punish the force value which approaching to zero, so that it can adapt to the identification of various types of forces. Then the optimal force solution is obtained by using the Primal-dual interior point method. Finally, the effectiveness and accuracy of the proposed method are verified by numerical simulation and experiment of asymmetric plate and double-layer plate structures. Different from previous studies, the L1+2-norm method in this work has high robustness, considers the characteristics of sparse regularization and L2-norm regularization, and can adapt to the identification of continuous forces and sparse forces at the same time, which has potential engineering application value.

Enhancing leachate management with antibacterial nanocomposites incorporating plant-based carbon dots and Satureja Khuzestanica essential oils

Landfill leachate, a complex mixture of pollutants, poses a significant environmental hazard. This study reports the synthesis and characterization of superabsorbent nanocomposites (SANs) designed for enhanced performance in waste management applications. SANs were prepared using carboxymethyl cellulose (CMC) and sodium polyacrylate (SPA) as the main components, carbon dots (CDs) to improve absorption, and Satureja Khuzestanica essential oil (SEO) for antibacterial performance. The results demonstrated that the addition of CDs significantly increased the absorption capacity and liquid retention of the samples, with a water absorption capacity reaching up to 8621 %. Furthermore, the samples exhibited high mechanical strength, with tensile strength improving by over 100 % in the presence of CDs. The inclusion of SEO provided strong antibacterial activity against Escherichia coli and Staphylococcus aureus, with inhibition zones measuring up to 26 mm. These SANs, with their high absorption capacity, mechanical robustness, and antibacterial properties, show great potential for improving waste management practices, particularly in leachate absorption strategies.

Evaluation of the performance of a machine learning based atrial fibrillation screening algorithm using an oscillometric blood pressure monitor

Blood pressure monitors (BPMs) with atrial fibrillation (AFib) detection function can be used to detect AFib early. However, conventional algorithms require multiple BP measurements. Here, the feasibility of a machine-learning-based approach for AFib detection through single BP measurement was evaluated. First, a custom AdaBoost-based software, which analyzes the pulse-to-pulse interval (PPI) pattern and classifies it based on AFib detection, was created. Then, its classification performance was validated. For the validation study, PPI and standard 12-lead electrocardiogram (ECG) datasets were collected from 79 and 92 Japanese participants with and without AFib, respectively. PPI data were obtained using two different BPMs. All ECG results were interpreted by cardiologists. The custom software output for the PPI dataset and ECG interpreted results was compared, and the sensitivity and specificity were calculated. A sensitivity and specificity for PPI from main device were 97.5% (95% confidence interval [CI] 91.2–99.3%) and 98.9 (95% CI 94.1–99.8), respectively. No significant differences in sensitivity and specificity were observed in the subgroup analysis between different devices, age groups, and arm size groups. These results reflect the high accuracy and robustness of this AFib algorithm using a single BP measurement and supports its use for widespread AFib screening.

Smartphone-deployable and all-in-one machine vision for visual quantification analysis based on distance readout of electrophoresis titration biosensor

As a class of point-of-care (POC) assays with visible distance readout (thermometer style), the electrophoresis titration (ET) biosensor affords high robustness, versatility, and simplicity for point-of-care quantification. However, naked-eye observation of the distance readout is unreliable in POC settings and manual processing of distance readout is time-consuming. Herein, we developed a smartphone-deployable and all-in-one machine vision for four ET biosensors (bovine serum albumin, melamine, uric acid, glutathione) to classify and quantify the samples simultaneously. To ensure accurate and rapid quantification on the smartphone, we customized the decolorization methods and edge detection operators to balance the region of interest (ROI) extraction performance and processing speed. We then established a dataset of 180 distance readout images to endow our machine vision with the ability to classify four sample types. Consequently, our machine vision demonstrated high accuracy in determining the sample type (>97.2%) and concentration (>97.3%). Moreover, expanding its applications to other targets was readily achieved by including distance readout images of other ET biosensors (e.g., hemoglobin A1c) in the dataset. Therefore, our strategy of constructing machine vision is compatible with the versatile ET biosensor technique, suggesting that the same strategy can be used for other thermometer-style POC assays.

B-146 Unlocking the potential of large-cohort proteomics studies with the Orbitrap Astral mass platform

Abstract Background Large-cohort proteomics analysis using mass spectrometry is a powerful approach to discover and validate new biomarkers. In combination with clinical data and computational analysis, large-cohort proteomics study brings opportunities in improving early diagnosis, refining patient stratification, and predicting/monitoring treatment response. Yet, to achieve meaningful biological insights in large-cohort studies, robust, reproducible, and comprehensive proteome profiling in a high-throughput manner still remains challenging. Here, we use a high-resolution accurate mass (HRAM) Oribtrap Astral platform to enable high-quality and robust protein quantification across thousands of LC-MS/MS analyses. With a throughput of 100 samples/day, we can reproducibly profile ∼9000 proteins from human cell line and ∼800 proteins from undepleted plasma across multiple instruments and more than 10 consecutive days in a 24/7 operation mode. Methods Experiments were performed on multiple Orbitrap Astral mass spectrometers with and without FAIMS Pro interface. Chromatographic separations were performed using a trap/elute method on Vanquish Neo system at a 11-minute gradient and a flow rate of 1ul/min, resulting in a throughput of 100 samples/day. Undepleted plasma digest was analyzed in a 24/7 mode for > 1000 injections on each LC-MS/MS setup. In addition, HeLa digest as quality control (QC) was analyzed every 12hours. The Oribtrap Astral platform was operated in data-independent acquisition (DIA) mode using a full scan with m/z 380-980, and DIA MS/MS scans with an isolation window of 2Th. Resulting DIA raw files were automatically transferred and processed using Chimerys in a beta version of Proteome Discoverer software. Results Multiple LC-MS/MS systems were operated in DIA mode either with or without FAIMS Pro device in a 24/7 operation mode at a throughput of 100SPD. Undepleted plasma digest was analyzed with >1000 injections on each LC-MS/MS setup. To monitor the baseline performance, HeLa digest served as QC were analyzed periodically every 12 hours with 3 technical replicates each time. To effectively manage and analyze these thousands of data files generated, we developed an automated data transfer and analysis pipeline. Automatically, the resulting DIA raw data files were immediately transferred to a server, then processed by state-of-the-art intelligent search algorithm Chimerys. Benefiting from the ultra-high scan speed of up to 200Hz on the Orbitrap Astral mass spectrometer, a much narrower isolation window width of 2Th was applied in the DIA method, comparing to 10-20Th on the classic DIA method. As a result, ∼9000 proteins from HeLa digest and ∼800 proteins from undepleted plasma digest were identified within only a 11-minutes gradient, respectively. More than 80% of the plasma proteins were reproducibly identified and quantified from all the runs on each LC-MS/MS setup, indicating a great reproducibility from run-to-run longitudinally. Stable and robust peptide quantitation was observed by extracting peptides with high, medium, and low abundant across the runs. Importantly, QC showed no performance degradation throughout the entire study, indicating high robustness of the entire LC-MS/MS setup. Conclusions Orbitrap Astral mass platform can comprehensively analyze the proteome of >1000s of sample robustly and reproducibly in a high-throughput manner, addressing the needs in large-cohort studies.

Integration of a fully automated flow cytometry system with high robustness into a Screening Station

In recent years, there has been an increasing demand for the detection of rare cells in drug discovery research, such as cells that have differentiated off-purpose or are required for immunogenicity evaluation. Since detection and quantification limits depend on the robustness of the experiment, inter-human differences in technique have a significant impact on the performance of the assay system. Here, we integrated flow cytometry into a cell experiment platform, Screening Station, to construct a robust assay system, examined each step of the flow cytometric pretreatment using Jurkat cells, and finally evaluated the overall assay performance. Cell detection rate when the experiment was performed manually was 48.8% ± 5.7% (CV=11.6%) versus 73.7%±2.0% (CV=2.8%) with the automated method. To further clarify the analytical performance of the automated method, 1-100 PD-1 expressing Jurkat cells were spiked with 1 × 105 Jurkat cells, and the lower limit of detection, linearity, and CV% were evaluated. Average detection rate was 69%, decision count was 0.985, and lower limit of detection was 4 cells (0.004%). We evaluated the CV% value of the number of detected cells per spiked cell and found our system to be highly robust, approximating a binomial distribution with a 69% recovery rate. In conclusion, we have integrated the Novocyte flow cytometry system into an automated experimental platform, Screening Station, to create a fully automated flow cytometric assay system with high robustness. Our platform can fulfill the technology needs of drug discovery for rare cell detection, which have intensified in recent years.

Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis

Varying components and operating conditions in industrial machines lead to different distribution characteristics and fault states of monitoring data for critical rotating machinery. Adequate and accurate labelling of monitoring data is challenging to achieve. Existing data-driven unsupervised domain adaptation methods exhibit poor interpretability and low confidence in the target domain’s pseudo-labelling. Therefore, a physics-informed unsupervised domain adaptation framework is proposed. Data and physical information are combined to enhance the reliability and interpretability of the model while ensuring its generalization capability. First, physical information is extracted by merging the knowledge of the mechanical component fault characteristics with indicators like the signal spectrum’s energy value. Subsequently, the physical label and loss are created. The physical loss is then embedded in the loss function of the source domain model. Next, by aligning the physical labels and the clustering results of the spectral clustering, the target domain samples’ hard pseudo-labels are obtained. The probability values of the sample belonging to each category as determined by the Wasserstein criterion comprise the soft pseudo-label of the sample. Furthermore, the confidence of hard pseudo-labels is continually increased by iteratively updating the probability values of soft pseudo-labels using the multi-label as a guide and the inverse improved sigmoid function as a gradient. Finally, unsupervised cross-machine diagnostic models with high robustness can be obtained. Three datasets are employed in the experimental section, and five sub-datasets are created and crossed for cross-machine experiments to confirm the suggested approach’s efficacy and superiority.

Efficient and Robust Point Cloud Registration via Heuristics-Guided Parameter Search.

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (>95% is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost.

PE Gas Pipeline Defect Detection Algorithm based on Improved YOLO v5

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects,improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%,which can achieve the detection and identification of defect types of effects in PE gas pipes.

Simultaneous estimation of prasugrel and aspirin in bulk drugs by chemometric methods

A UV–Vis spectrophotometric method enhanced by chemometric techniques, specifically Principal Component Regression (PCR) and Partial Least Squares (PLS) regression, was developed and validated for the simultaneous quantification of prasugrel (PRA) and aspirin (ASP) in bulk drugs and pharmaceutical formulations. The method demonstrated high accuracy, precision, and robustness, achieving mean recoveries of 100.63% for PRA and 100.08% for ASP with relative standard deviations (RSD) below 3%. Both PCR and PLS models showed excellent predictive capabilities, with RMSEP values of 0.45–0.48 for PRA and 0.78–1.13 for ASP, indicating the models’ reliability. In line with green and white chemistry principles, the method minimizes environmental impact by reducing solvent consumption and waste generation compared to traditional chromatographic methods. The Analytical Eco-Scale score was 84, reflecting excellent compliance with green chemistry standards. The method’s simplicity, low energy consumption, and reduced chemical waste further support its alignment with sustainability goals. However, acetonitrile, a hazardous solvent, was still used in small quantities, and solvent recycling was not implemented, slightly affecting the eco-score. To evaluate the method’s greenness, the RGB12 algorithm was applied, achieving a high score of 94.4%, with the majority of parameters related to reagent consumption, waste production, energy efficiency, and safety scoring optimally. The method’s safety, cost-effectiveness, and minimal environmental footprint make it suitable for routine pharmaceutical analysis, particularly in quality control environments where resource efficiency and sustainability are prioritized. Thus, the developed method offers a sustainable, efficient, and environmentally friendly solution for the simultaneous analysis of prasugrel and aspirin in pharmaceutical formulations, making it a valuable tool for routine analysis in the pharmaceutical industry.

Characterization of Serum Biochemical Changes in Response to Alcohol Consumption Using FT‐Raman Spectroscopy

ABSTRACTAlcohol is one of the most widely used stimulants globally, with increasing consumption rates annually. Prolonged alcohol consumption leads to significant physiological and neurological changes, potentially resulting in various diseases, which could be detect in serum. Therefore, FT‐Raman spectroscopy was used to measure chemical changes in the serum of heavy drinkers, and these results were compared with the serum composition of a control group of non‐drinkers. Obtained results showed a significant increase in lipids and a decrease in the amide/lipid ratio after alcohol administration. Additionally, higher amide III/amide II and lower amide II/amide I ratios were observed in the serum of alcohol‐addicted patients. These shifts of 892, 966, 1286, 1459 and 2940 cm−1 peaks indicate alterations in the protein‐lipid balance due to alcohol consumption. Principal component analysis (PCA) was employed to further analyze the spectral data, with the first three principal components accounting for 95.36% of total data variability. A Bayesian‐optimized k‐nearest neighbor (BO‐KNN) model was applied for classification. The optimal hyperparameters—two neighbors, correlation distance metric, and squared inverse distance weight—were determined after seven iterations, resulting in a remarkably low classification error of 0.0384. The BO‐KNN model achieved training and test accuracies of 95.92% and 90.48%, respectively. Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 1.00 for the training set and 0.90 for the test set, demonstrating the model's high precision and robustness. In conclusion, FT‐Raman spectroscopy effectively revealed significant chemical changes in the serum of alcohol‐addicted individuals, providing valuable insights into the biochemical impact of alcohol consumption.

Two-Degree-of-Freedom Discrete-Time Decoupled Current Control of IPMSM With High Parameter Robustness

Two-Degree-of-Freedom Discrete-Time Decoupled Current Control of IPMSM With High Parameter Robustness

Vision-based multi-view reconstruction for high-precision part positioning in industrial robot machining

Traditional industrial robot machining typically involves offline programming of machining trajectories, with part positioning accomplished through robotic tools. However, this method has limitations, particularly when dealing with complex workpieces and dynamic environments. To tackle this problem, this paper introduces a vision-based multi-view reconstruction approach for part positioning in industrial robot machining. Multiple cameras capture multi-view images of the part, reconstruct its 3D point cloud, and match it with the CAD model of the part to achieve high-precision positioning. This method effectively translates offline programmed robot trajectories to real-world scenarios, improving the flexibility and adaptability of robot machining. Experimental results confirm the high accuracy and robustness of the proposed vision-based reconstruction method, achieving a relative error of less than 2 mm for 85.7% of points, with an overall root mean square error of 1.09 mm.

Graphene quantum dots as eco-friendly fluorescent probes for sensitive and selective determination of lumateperone in pharmaceutical preparation: Greenness and blueness assessment

This study investigates the potential of graphene quantum dots (GQDs) as fluorescent probes for the determination of the antipsychotic drug lumateperone. The spectral characteristics and sensing mechanism of the fluorescent probes were examined, revealing a static quenching mechanism as indicated by the Stern-Volmer analysis. Different factors affecting the quenching process, such as pH, concentration of QDs, and incubation time, were carefully tuned. The developed method was validated according to ICH guidelines, demonstrating excellent linearity in the range of 0.5–2.5 μg/mL, limits of detection and quantification of 0.1226 μg/mL and 0.3714 μg/mL, respectively, and high accuracy, precision, robustness and selectivity. Furthermore, the greenness and blueness of the proposed method were assessed using GAPI, AGREE, and BAGI tools, yielding an AGREE score of 0.78 and a BAGI score of 75 confirming its environmentally benign nature as well as analytical practicality. The method was successfully applied to determine lumateperone in a pharmaceutical formulation, highlighting its potential for routine quality control analysis. This study demonstrates the promising analytical capabilities of GQDs for the sensitive and selective detection of antipsychotic drug lumateperone, offering a simple, rapid, and green alternative to existing analytical techniques.