Explore the vast range of titles available.

Minimally invasive surgical robots have received widespread attention due to its numerous advantages. However, the lack of adequate perception capability remains a significant issue for the robots. In this work, a full‐range proximity‐tactile sensing module has been developed for safe operation of surgical robots, which performs multimodal fusion perception through ultrasonic sensor for long‐range proximity detection, capacitive sensor for close‐range proximity sensing, and triboelectric sensor for tactile sensing. In order for a minimum sensor size, the ultrasonic sensor is developed based on MEMS piezoelectric micromachined ultrasonic transducers (pMUTs), and the capacitive sensor and triboelectric sensor adopt common structures, which collaborate to achieve accurate proximity‐tactile perception. Additionally, a wireless vibration feedback wristband and digital‐twin interface are developed to provide multimodal feedback without interfering with operation. Experimental results demonstrates the safety enhancement for surgical robots by the perception and feedback system. Furthermore, the sensing module is applied in preliminary detection of subcutaneous abnormal tissues and the identification accuracy based on the ultrasound echoes and convolutional neural networks is 91.6%, which can provide an initial diagnostic reference. The full‐range proximity‐tactile sensor holds significant potential for enhancing the safety and detection capability of surgical robots, and promoting the intelligence of robot‐assisted minimally invasive surgery. A full‐range proximity‐tactile sensing module is developed for safe operation of surgical robots, which performs multimodal fusion perception through ultrasonic sensor for long‐range proximity detection, capacitive sensor for close‐range proximity sensing, and triboelectric sensor for tactile sensing. Meantime, the sensing module can be applied in preliminary detection of subcutaneous abnormal tissues, providing an initial diagnostic reference.

This study presents a highly sensitive 3D force sensor based on a piezoresistive cantilever with stress concentration. The sensor achieves precise 3D force detection by integrating a polydimethylsiloxane (PDMS) cap with pyramid microstructures and silicon chip containing four piezoresistive cantilevers with a micro-hole at their root. Finite element simulation validates the superior performance of the micro-hole in enhancing the sensitivity of the force sensor. Compared to a traditional sensor based on a piezoresistive cantilever, the microhole design increases strain in the piezoresistive region of the cantilever by 2 times and the stress by 2.72 times. The proposed sensor exhibits promising potential for applications in various fields, including medical devices, robotics, industrial automation, and others.

TiN and Ti(C,N) are the main precipitates in Ti micro-alloyed steel during the continuous casting, which directly affect the microstructure and property of the slab. However, due to the solute segregation and the different cooling rates at different locations of the slab section, it is challenging to accurately predict the precipitation behavior of carbonitride during the continuous casting process using the existing thermodynamic model. Taking into account the solute segregation laws at the solid-liquid interface during continuous casting solidification and incorporating the influence of precipitates on solute segregation, this study proposes a model for coupled carbonitride precipitation and solute segregation by introducing dynamic solute partition coefficients. Based on this, a kinetic model is established to describe growth of TiN and Ti(C,N) precipitates during continuous cooling of the slab by utilizing the micro-element superposition method. The nano carbonitride will precipitate in the solid phase upon complete solidification of the slab. The macroscopic solute distribution at different positions of the slab, ranging from the center to the surface, was calculated using a segregation model that incorporates a convective coefficient. Through this, a precipitation model for nano-carbonitrides in Ti microalloyed steel is developed by incorporating the classical nucleation theory. The models are validated by comparing the predicted values of Ti carbonitride precipitation quantity and average precipitate size with experimental data at typical locations, including the surface, 1/4 position, and internal regions of the slab. The accuracy of the predictions reaches over 90 pct. This study enhances the comprehension of precipitation and growth mechanisms of carbonitride in Ti microalloyed steel, contributing to a more comprehensive understanding of this material system.

Re-oxidation of Cr(III) in treated Cr-contaminated sites poses a considerable source of Cr(VI) pollution, necessitating stable treatment solutions for long-term control. This study explores the immobilization of Cr(VI) into chromite, the most stable and weathering-resistant Cr-bearing mineral, under ambient conditions. Batch experiments demonstrate chromite formation at pH above 7 and Fe(III)/Cr(III) ratios exceeding 1, with Fe(III) occupying all tetrahedral sites, essential for stability. A theoretical model is developed to evaluate the effects of pH and Fe(III)/Cr(III) ratios on chromite crystallinity, resulting in AI4Min-Cr, a publicly accessible platform offering real-time intelligent remediation strategies. To tackle the complexities of non-point source Cr pollution, we employ microbial methods to regulate on-site Eh and pH, optimizing chromite precipitation. Long-term stability tests confirm that chromite remained stable for over 180 days, with potential for magnetic separation recovery. This study presents a mineralogical strategy to address re-oxidation and Cr resource recovery in Cr-contaminated water and soil. Integration of mineralogy and biology leads authors to develop the Al4Min-Cr platform to stabilize chromium as chromite in contaminated soils and water for point-to-point remediation and long-term Cr pollution control.

This paper proposes a metal artifact reduction method of using MV-CBCT images to correct metal artifacts in kV-CT images, especially for the complex metal artifacts caused by multi-metal interaction of patients with head and neck tumors. The different tissue regions are segmented in the MV-CBCT images to obtain template images and the metal region is segmented in the kV-CT images. Forward projection is performed to get sinogram of the template images, kV-CT images and metal region images. Artifact images can be reconstructed through those sonograms. Corrected images is generated by subtracting the artifact images from the original kV-CT images. After the first correction, the template images are generated again and brought into the previous step for iteration to get better correction result. CT data set of 7 patients are used in this study, compared with linear interpolation metal artifact (LIMAR) and normalized metal artifact reduction method, mean relative error of CT value is reduced by 50.5% and 63.3%, noise is reduced by 56.2% and 58.9%. The Identifiability Score of the tooth, upper/lower jaw, tongue, lips, masseter muscle and cavity in the corrected images by the proposed method have significantly improved ( P  < 0.05) than original images. The artifacts correction method proposed in this paper can effectively remove the metal artifacts in the images and greatly improve the CT value accuracy, especially in the case of multi-metal and complex metal implantation.

Detecting individual gases with various sensors is a well‐established field in gas sensing. However, substantial challenges and opportunities remain in the simultaneous detection and classification of multiple gases. Artificial intelligence (AI) integrated gas sensor systems effectively enable multi‐gas detection using specialized algorithms. Nevertheless, these algorithms are prone to overfitting owing to their high model complexity; this study proposes a sensor array that engineers carbon vacancies in graphene oxide via metal ion doping and high‐temperature reduction, enabling high‐sensitivity, simultaneous detection of various gases at low temperatures (20 °C). By integrating an advanced artificial intelligence framework, the acquired electrical signals are transformed, and a multi‐task learning (MTL) approach is applied to achieve instantaneous identification of four gas types and four‐level concentrations. The proposed MTL framework demonstrates superior performance by effectively mitigating overfitting and improving generalization through feature sharing and mutual regularization between gas type classification and concentration estimation tasks. Experimental validation on vehicle exhaust gas fault diagnosis highlights the method's effectiveness and applicability in complex conditions, achieving 98.22% accuracy and 48% faster inference compared to traditional single‐task models. This study provides a basis for developing more intelligent and adaptable sensor systems capable.

This paper proposes a modeling method of maize in threshing. The static friction coefficient and rolling resistance coefficient of the maize grain were measured using the slope method. The maize grain stacking angle test was designed using the central composite design response surface test. A regression model was established based on the simulation results to find the best combination. The results suggested that the modeling method proposed in this paper was effective in improving the accuracy of maize grain simulation compared with previous methods. Furthermore, this paper presents a method to verify the feasibility and reliability of the maize grain cob discrete element model using the distribution of grain in the granary and the final removal rate as the verification method. The results of the actually simulated threshing test were analyzed using a Wilcoxon signed-rank test, heat map analysis, and the Spearman’s rank correlation coefficient. It was found that the DEM model of maize cob is suitable for simulating the maize threshing process. This can aid in further research on the subject.

With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model.

Background Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. Patients and methods This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. Results In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) ( P  = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models ( P  = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. Conclusions PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.

Tactile sensing plays a vital role in human somatosensory perception as it provides essential touch information necessary for interacting with the environment and accomplishing daily tasks. The progress in textile electronics has opened up opportunities for developing intelligent wearable devices that enable somatosensory perception and interaction. Herein, a skin‐inspired all‐textile pressure sensor (ATP) is presented that emulates the sensing and interaction functions of human skin, offering wearability, comfort, and breathability. The ATP demonstrates impressive features, including ultrahigh sensitivity (1.46 × 106 kPa−1), fast response time (1 ms), excellent stability and durability (over 2000 compression‐release cycles), a low detection limit of 10 Pa, and remarkable breathability (93.2%). The multipixel array of ATPs has been proven to facilitate static and dynamic mapping of spatial pressure, as well as pressure trajectory monitoring functions. Moreover, by integrating ATP with oscillation circuits, external force stimuli can be directly encoded into digital frequency pulses that resemble human physiological signals. The frequency of output pulses increases with the applied pressure. Consequently, an ATP‐based artificial sensory system is constructed for intelligent tactile perception. This work provides a simple and versatile strategy for practical applications of wearable electronics in the fields of robotics, sports science, and human–machine interfaces technologies. A skin‐inspired all‐textile pressure sensor (ATP) is designed to mimic the sensing and interaction functions of human skin. The ATP operates in a pressure‐triggered frequency encoding mode and delivers an ultrahigh sensitivity of 1.46 × 106 kPa−1, offering wearability, comfort, and breathability. The multipixel ATP‐based array enables static and dynamic mapping of spatial pressure while facilitating the monitoring of pressure trajectories.