A new triboelectric nanogenerator based on a multi-material stacking structure achieves efficient power conversion from discrete mechanical movement.

Due to its invaluable potential in discrete mechanical energy collection, TENG (triboelectric nanogenerator) is considered to satisfy the power requirements of intelligent electronic devices and drive the development of the Internet of Things (IoT). Nowadays, the promotion of TENGs has been hindered due to the limitation of their output performance and service life. Herein, a brand new triboelectric nanogenerator based on a multi-material stacking structure is proposed. By stacking various triboelectric materials in a specific order, a special charge balance state could be achieved inside the system such that the conductive layer generates more induced charges, and the output performance is significantly enhanced. Besides, due to the usage of the electropositive elastomer PU (polyurethane sponge), the design also effectively alleviates abrasion on the contact surface and adjusts its own output according to different compression environments. The experimental results show that the stacked PTFE/FKM/PU TENG (PFP-TENG) presents a more than 50% increase in transferred charge and almost 5 times the current output compared with the general contact-separation type TENG. When connected to the application circuit, the maximum output power reached 10.2 W m-2 and 145.2 W m-3, and more than 1400 LEDs could be easily lit. Finally, the PFP-TENG was also used to collect mechanical energy from simple motion and realize considerable power generation. This study not only provides new ideas for the design of TENGs by reasoning the theoretical model but also presents improved output performance, thus exemplifying the strong potential of this design in developing a power-generation device that can collect discrete mechanical energy.

Large aperture and defect-free liquid crystal planar optics enabled by high-throughput pulsed polarization patterning.

The liquid crystal (LC) geometrical phase optics, which is realized by the high-resolution control of the optical axis orientation in transparent micrometer-thin polymer films, is emerging as a next generation of planar optics. It features pronounced optical properties and stimuli-responsive behaviors, which could introduce appealing and new possibilities for photonic purposes. The development of fabrication techniques producing elements with large aperture sizes and arbitrarily varying molecular orientation is of significance in terms of practical utility. Here, we propose the pulsed polarization patterning technique to create large-aperture and defect-free LC geometrical phase elements. We investigated the capability of the azo photo-alignment material responding to nanosecond laser pulses and the corresponding anchoring behaviors to LCs. The threshold was reduced to one fourth of that under the continuous wave recording. The patterning resolution was found to be enhanced to around 0.71 µm, due to the ultra-fast interaction nature of the photo-alignment material with the polarized light field. We proposed the flying exposure mode to deliver high frequency modulated polarized laser pulses (8 kHz), with the precision stage moving in a uniform velocity for light-field stitching and the servo auto-focusing in the sample normal, enabling the stable and reliable polarization patterning for large aperture sizes. We further report on representative fabrication of LC polarization gratings with an aperture of 4 inch and 99.2% average diffraction efficiency.

Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

Abnormal Behavior Monitoring Method of Larimichthys crocea in Recirculating Aquaculture System Based on Computer Vision.

It is crucial to monitor the status of aquaculture objects in recirculating aquaculture systems (RASs). Due to their high density and a high degree of intensification, aquaculture objects in such systems need to be monitored for a long time period to prevent losses caused by various factors. Object detection algorithms are gradually being used in the aquaculture industry, but it is difficult to achieve good results for scenes with high density and complex environments. This paper proposes a monitoring method for Larimichthys crocea in a RAS, which includes the detection and tracking of abnormal behavior. The improved YOLOX-S is used to detect Larimichthys crocea with abnormal behavior in real time. Aiming to solve the problems of stacking, deformation, occlusion, and too-small objects in a fishpond, the object detection algorithm used is improved by modifying the CSP module, adding coordinate attention, and modifying the part of the structure of the neck. After improvement, the AP50 reaches 98.4% and AP50:95 is also 16.2% higher than the original algorithm. In terms of tracking, due to the similarity in the fish's appearance, Bytetrack is used to track the detected objects, avoiding the ID switching caused by re-identification using appearance features. In the actual RAS environment, both MOTA and IDF1 can reach more than 95% under the premise of fully meeting real-time tracking, and the ID of the tracked Larimichthys crocea with abnormal behavior can be maintained stably. Our work can identify and track the abnormal behavior of fish efficiently, and this will provide data support for subsequent automatic treatment, thus avoiding loss expansion and improving the production efficiency of RASs.

Radiologists with assistance of deep learning can achieve overall accuracy of benign-malignant differentiation of musculoskeletal tumors comparable with that of pre-surgical biopsies in the literature.

PURPOSE: The purpose of this study was to assess if radiologists assisted by deep learning (DL) algorithms can achieve diagnostic accuracy comparable to that of pre-surgical biopsies in benign-malignant differentiation of musculoskeletal tumors (MST). METHODS: We first conducted a systematic review of literature to get the respective overall diagnostic accuracies of fine-needle aspiration biopsy (FNAB) and core needle biopsy (CNB) in differentiating between benign and malignant MST, by synthesizing data from the articles meeting our inclusion criteria. To compared against the accuracies reported in literature, we then invited 4 radiologists, respectively with 2 (A), 6 (B), 7 (C), and 33 (D) years of experience in interpreting musculoskeletal MRI to perform diagnostic tests on our own dataset (n = 62), with and without assistance of a previously developed DL algorithm. The gold standard for benign-malignant differentiation was histopathologic confirmation or clinical/radiographic follow-up. RESULTS: For FNAB, a meta-analysis containing 4604 samples met the inclusion criteria, with the overall diagnostic accuracy reported to be 0.77. For CNB, an overall accuracy of 0.86 was derived by synthesizing results from 7 original research articles containing a total of 587 samples. On our internal MST dataset, the invited radiologists, respectively, achieved diagnostic accuracies of 0.84 (A), 0.89 (B), 0.87 (C), and 0.90 (D), with the assistance of DL. CONCLUSION: Use of DL algorithms on musculoskeletal dynamic contrast-enhanced MRI improved the benign-malignant differentiation accuracy of radiologists to a level comparable to that of pre-surgical biopsies. The developed DL algorithms have a potential to lower the risk of miss-diagnosing malignancy in radiological practice.

A quantitative study of bone defects in displaced femoral neck fractures based on virtual reduction techniques.

BACKGROUND: Bone defects in femoral neck fractures are strongly associated with the prognosis after internal fixation. However, qualitative analysis of bone defects in femoral neck fractures has already been performed, quantitative studies have not been reported. In this study, we aimed to systematically analyse the morphological characteristics of bone defects in patients with femoral neck fractures using computed tomography (CT) images combined with computer image analysis techniques. METHODS: Four hundred and sixty-nine patients with femoral neck fractures from January 2014 to December 2018 at two grade A tertiary hospitals were included. Models were created in Mimics software based on CT images collected within 1 week after injury and then imported into 3-matic software for virtual reduction. The volume of the bone defect (VBD), maximum defect thickness (MDT), extent of the bone defect region (EBDR) , main defect quadrant (MDQ), collapse type and fracture classification were calculated and recorded. RESULTS: The EBDR, collapse type and MDT all had a significant positive effect on the VBD (P <0.05), with a more significant effect at higher quantiles. Age also had a significant positive effect on the VBD (P < 0.05), but its effect was more pronounced at lower quantiles. Compared to non-subcapital fractures, subcapital fractures had a positive effect on the VBD only at the 50 and 75% quantiles (P < 0.01). The female sex had a significant negative effect on the VBD compared to the male sex (P < 0.05). CONCLUSION: This study established a reliable computer image processing method for quantitative analysis of the VBD in femoral neck fractures and revealed that all patients with femoral neck fractures had bone defects, which can occur at any part of the femoral neck. The EBDR, MDT, collapse type, and patient age and sex were all important risk factors for the extent of the defect and should be taken into account in surgical planning.

Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging.

BACKGROUND: Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death. PURPOSE: To improve the diagnostic efficacy of musculoskeletal tumors by developing deep learning (DL) models based on contrast-enhanced magnetic resonance imaging and to quantify the improvement in diagnostic performance obtained by using these models. STUDY TYPE: Retrospective. POPULATION: Three hundreds and four musculoskeletal tumors, including 212 malignant and 92 benign lesions, were randomized into the training (n = 180), validation (n = 62) and testing cohort (n = 62). FIELD STRENGTH/SEQUENCE: A 3 T/T1 -weighted (T1 -w), T2 -weighted (T2 -w), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted (CET1 -w) images. ASSESSMENT: Three DL models based, respectively, on the sagittal, coronal, and axial MR images were constructed to predict the malignancy of tumors. Blinded to the prediction results, a group of specialists made independent initial diagnoses for each patient by reading all image sequences. One month after the initial diagnoses, the same group of doctors made another round of diagnoses knowing the malignancy of each tumor predicted by the three models. The reference standard was the pathological diagnosis of malignancy. STATISTICAL TESTS: Sensitivity, specificity, and accuracy (all with 95% confidential intervals [CI]) corresponding to each diagnostic test were computed. Chi-square tests were used to assess the differences in those parameters with and without DL models. A P value < 0.05 was considered statistically significant. RESULTS: The developed models significantly improved the diagnostic sensitivities of two oncologists by 0.15 (95% CI: 0.06-0.24) and 0.36 (95% CI: 0.24-0.28), one radiologist by 0.12 (95% CI: 0.04-0.20), and three of the four orthopedists, respectively, by 0.12 (95% CI: 0.04-0.20), 0.29 (95% CI: 0.18-0.40), and 0.23 (95% CI: 0.13-0.33), without impairing any of their diagnostic specificities (all P > 0.128). DATA CONCLUSION: The DL models developed can significantly improve the performance of doctors with different training and experience in diagnosing musculoskeletal tumors. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Malignant fibrous histiocytoma of the bone in a traumatic amputation stump: A case report and review of the literature.

BACKGROUND: Malignant fibrous histiocytoma (MFH) is one of the most common soft tissue sarcomas among adults. It is characterized by large size, high grade, and biological aggressiveness. There are many reports of MFH after local stimulation, such as bone fracture, implants, and chronic osteomyelitis. In this paper, we report a patient who developed MFH 6 years after amputation, suggesting that wound healing and mechanical force play a role in the local stimulation of this disease. CASE SUMMARY: A 66-year-old man complained of persistent pain in his residual mid-thigh. He had undergone amputation surgery due to a traffic accident 6 years prior. Physical examination showed tenderness but no abnormalities in appearance. X-ray radiographs and magnetic resonance imaging supported the diagnosis of a tumor, and a biopsy confirmed that the lesion was MFH. The patient received neoadjuvant chemotherapy and left hip disarticulation. During the 6-mo follow-up, there were no symptoms of recurrence. CONCLUSION: Postsurgery MFH has been reported before, and many studies have attributed it to the biological effects of implants. Our case report shows that this disease can develop without an implant and thus highlights the importance of local stimulation. The wound-healing process and mechanical force can both promote this tumor, but whether they directly cause MFH needs further investigation.

Insufficient blood supply of fovea capitis femoris, a risk factor of femoral head osteonecrosis.

BACKGROUND: A defective nutrient foramen in the fovea capitis femoris was hypothesized to reflect the blood circulation pattern of the femoral head, leading to insufficient blood supply and causing osteonecrosis of the femoral head. METHODS: Normal and necrotic femoral head specimens were collected. The necrotic femoral head group was divided into a non-traumatic and traumatic subgroup. 3D scanning was applied to read the number, the diameter, and the total cross-sectional area of the nutrient foramina in the fovea capitis femoris. Chi-squared tests and independent t-tests were used to detect any differences in the categorical and continuous demographic variables. Logistic regression models were used to estimate the odds ratio (OR) for non-traumatic and traumatic osteonecrosis in different characteristic comparisons. RESULTS: A total of 249 femoral head specimens were collected, including 100 normal femoral heads and 149 necrotic femoral heads. The necrotic femoral head group revealed a significantly higher percentage of no nutrient foramen (p < 0.001), a smaller total area of nutrient foramina (p < 0.001), a smaller mean area of nutrient foramina (p = 0.014), a lower maximum diameter of the nutrient foramen (p < 0.001), and a lower minimum diameter of the nutrient foramen (p < 0.001) than the normal femoral head group. The logistic regression model demonstrated an increasing number of nutrient foramina (crude OR, 0.51; p < 0.001), a larger total area of nutrient foramina (crude OR, 0.58; p < 0.001), a larger mean area of nutrient foramina (crude OR, 0.52; p = 0.023), a greater maximum diameter of the nutrient foramen (crude OR, 0.26; p < 0.001), and greater minimum diameter of the nutrient foramen (crude OR, 0.20; p < 0.001) significantly associated with reduced odds of osteonecrosis of the femoral head (ONFH). The necrotic femoral head group was further divided into 118 non-traumatic and 31 traumatic necrotic subgroups, and no significant difference was observed in any characteristics between them. CONCLUSIONS: Characteristics of the nutrient foramen in the fovea capitis femoris showed a significant defect of necrotic than normal femoral heads, and significantly reduced odds were associated with the higher abundance of the nutrient foramen in ONFH. Therefore, the condition of the nutrient foramen might be the indicator of ONFH.

High Responsivity and External Quantum Efficiency Photodetectors Based on Solution-Processed Ni-Doped CuO Films.

Photodetectors based on p-type metal oxides are still a challenge for optoelectronic device applications. Many effects have been paid to improve their performance and expand their detection range. Here, high-quality Cu1-xNixO (x = 0, 0.2, and 0.4) film photodetectors were prepared by a solution process. The crystal quality, morphology, and grain size of Cu1-xNixO films can be modulated by Ni doping. Among the photodetectors, the Cu0.8Ni0.2O photodetector shows the maximum photocurrent value (6 × 10-7 A) under a 635 nm laser illumination. High responsivity (26.46 A/W) and external quantum efficiency (5176%) are also achieved for the Cu0.8Ni0.2O photodetector. This is because the Cu0.8Ni0.2O photosensitive layer exhibits high photoconductivity, low surface states, and high crystallization after 20% Ni doping. Compared to the other photodetectors, the Cu0.8Ni0.2O photodetector exhibits the optimal response in the near-infrared region, owing to the high absorption coefficient. These findings provide a route to fabricate high-performance and wide-detection range p-type metal oxide photodetectors.