Search

The application of robotic adrenalectomy (RA) has been increasing. However, there is still controversy about whether RA is more feasible than laparoscopic adrenalectomy (LA) for pheochromocytoma (PHEO).To evaluate the efficacy and safety of RA vs. LA for PHEO.A literature search of the PubMed, Ovid, and Scopus databases was performed to identify eligible studies up to April 2021. All studies comparing RA versus LA for PHEO were included. Data were analysed using the Cochrane Collaboration's Review Manager (RevMan) 5.4 software.Overall, 4 studies including 386 patients (RA 155; LA 231) were included. RA might have larger tumour size (WMD = 0.72 cm, 95% CI: 0.31 to 1.13; p0.001). There were no statistically significant differences in operative time (WMD = -12.49 min, 95% CI: -29.50 to 4.52; p = 0.15), estimated blood loss (EBL) (WMD = -28.48 ml, 95% CI: -58.92, 1.95; p = 0.07), transfusion rate (OR = 0.70, 95% CI: 0.07 to 7.07; p = 0.77), or conversion rate (OR = 0.44, 95% CI: 0.07 to 2.88; p = 0.39). There were no significant differences between the 2 groups in terms of postoperative complications (OR = 1.06, 95% CI: 0.62 to 1.82; p = 0.84) and Clavien Dindo score ≥ 3 complications (OR = 1.15, 95% CI: 0.39 to 3.41; p = 0.80). Patients from the RA group could benefit from shorter length of hospital stay (WMD = -0.51 days, 95% CI -0.91 to -0.12; p = 0.01).RA is a feasible, safe, and comparable treatment option for PHEO.

Due to more complex anatomical features, right laparoscopic adrenalectomy (RLA) could be more challenging than left laparoscopic adrenalectomy (LLA). However, this opinion remains elusive.To evaluate the perioperative and postoperative outcomes of RLA versus LLA.A systematic literature research of the PubMed, Ovid, Scopus databases (up to March 2021) and citation lists were performed to identify eligible studies. All studies comparing RLA versus LLA were included. Data were analysed using RevMan 5.4 software.Overall, 5 studies including 780 patients (RLA 361; LLA 419) were included. The operative time was similar in both groups (WMD -9.38 min, 95% CI: -21.04 to 2.28; p = 0.11). Compared with LLA, RLA showed greater volume of estimated blood loss (EBL) (WMD 13.82 ml, 95% CI: 3.77, 23.88; p = 0.007) and higher conversion rate (OR = 3.45, 95% CI: 1.12 to 10.57; p = 0.03). RLA had comparable complications (OR = 0.88, 95% CI: 0.44 to 1.76; p = 0.71), Clavien Dindo score ≥ 3 complications (OR = 0.38, 95% CI: 0.09 to 1.65; p = 0.20), and length of hospital stay (WMD -0.07 days, 95% CI: -0.35 to 0.21; p = 0.61). The transperitoneal approach analysis showed consistent results.RLA is associated with a higher risk of bleeding and higher conversion rate.

Laparoendoscopic single-site adrenalectomy (LESSA) has the advantages of early recovery and better cosmetic appearance. However, there are still debates on the efficacy and safety of LESSA and conventional laparoscopic adrenalectomy (CLA).To reevaluate the efficacy and safety of LESSA vs CLA for adrenal lesions.A systematic literature research of PubMed, Ovid, Scopus (up to February 2021), and citation lists was performed to identify eligible studies. All studies comparing LESSA versus CLA were included. Data were analyzed using the RevMan 5.4 software.Overall, eighteen studies including 1307 patients (LESSA 520; CLA 787) were included. LESSA was associated with smaller mean tumor size (weighted mean difference (WMD) = 0.53 cm, 95% CI: -0.81 to -0.24; p0.001). The operative time for LESSA was longer than CLA (WMD = 13.86 min, 95% CI: 4.43 to 23.30; p = 0.004). LESSA had a better visual analog scale (VAS) score (WMD = -0.56, 95% CI: -1.01 to -0.11; p = 0.02), shorter return to diet time (WMD = -0.27 days, 95% CI: -0.52 to -0.03; p = 0.03), shorter length of hospital stay (WMD = -0.56 days, 95% CI: -1.01 to -0.11; p = 0.01), and comparable postoperative complications (OR = 0.98, 95% CI: 0.56 to 1.70; p = 0.93). The wound size of LESSA was definitely smaller (WMD = -2.72 cm, 95% CI: -3.50 to -1.94; p0.001). The subgroup analysis of studies via the transperitoneal approach showed reasonable results.LESSA is significantly better in terms of postoperative pain, time to diet, length of hospital stay and wound size, but the operative time is significantly longer.

Metastasis is the leading cause of death in breast cancer patients. Osthole, as an active compound detected in the traditional Chinese medicine Wenshen Zhuanggu Formula, has shown a promising anti-metastatic activity in human breast cancer cells, but the underlying mechanisms remain ambiguous. In this study we elucidated the anti-metastatic mechanisms of osthole in highly metastatic breast cancer cells and a zebrafish xenograft model. We showed that the expression of integrin α3 (ITGα3) and integrin β5 (ITGβ5) was upregulated in highly metastatic MDA-MB-231, MDA-MB-231BO breast cancer cell lines but was downregulated in poorly metastatic MCF-7 breast cancer cell line, which might be the key targets of osthole's anti-metastatic action. Furthermore, we showed that knockdown of ITGα3 and ITGβ5 attenuated breast cancer cell migration and invasion possibly via suppression of FAK/Src/Rac1 pathway, whereas overexpression of ITGα3 and ITGβ5 caused the opposite effects. Consistently, osthole significantly inhibited breast cancer metastasis by downregulating ITGα3/ITGβ5 signaling in vitro and in vivo. These results provide new evidence that osthole may be developed as a candidate therapeutic drug for metastatic breast cancer.

Purpose The study aims to provide a comprehensive account of the association of five MDM4 gene polymorphisms (rs1380576, rs1563828, rs10900598, rs11801299, and rs4245739) with susceptibility to cancer. Methods A literature search for eligible candidate gene studies published before 27 February 2021 was conducted in PubMed, Medline and Web of Science. The following combinations of main keywords were used: (MDM4 OR MDMX OR HDMX OR mouse double minute 4 homolog) AND (polymorphism OR mutation OR variation OR SNP OR genotype) AND (cancer OR tumor OR neoplasm OR malignancy OR carcinoma OR adenocarcinoma). Potential sources of heterogeneity were sought out via meta-regression, subgroup and sensitivity analysis. Results Overall, a total of 15 articles with 21,365 cases and 29,280 controls for five polymorphisms of the MDM4 gene were enrolled. In the stratified analysis of rs1380576, we found that Asians might have less susceptibility to cancer. We found that rs4245739 was correlated with a decreased risk of cancer for Asians and breast cancer susceptibility. However, for other polymorphisms, the results showed no significant association with cancer risk. Conclusion MDM4 rs1380576 polymorphism is negatively associated with the risk of cancer in the Asian population. MDM4 rs4245739 polymorphism is inversely associated with cancer risk for Asians and breast cancer susceptibility.

Privacy-preserving cross-modal retrieval is a significant problem in the area of multimedia analysis. As the amount of data is exploding, cross-modal data analysis and retrieval is often realized on cloud computing environment. Therefore, the privacy protection of large-scale cross-modal data has become a problem that can not be ignored. To further improve the accuracy and efficiency of privacy-preserving search, this paper proposes a novel cross-modal hashing scheme, named deep adversarial privacy-preserving cross-modal hashing (DAP $$^2$$ CMH). This method consists of a deep cross-modal hashing model termed DACMH, and a secure index structure called CMH $$^2$$ -Tree. The former is a combination of deep hashing and adversarial learning to capture intra-modal and inter-modal correlation. The latter is a hierarchical hashing index structure that can provide efficient data organization based on cross-modal hash codes. We conduct comprehensive experiments on three common used benchmarks. The results show that the proposed approach DAP $$^2$$ CMH outperforms the state-of-the-arts.

White-light emitting (WLE) polymers have attracted continuous attention for their promising applications in solid-state lighting, flexible display and related fields. However, achieving dual-emission and pure white-light emission in a single-component polymer is still challenging. In this study, a brominated single-component polymer BrOD-TFB was designed and synthesized, which shows dual-emission and white light emission properties in solution and room-temperature phosphorescence (RTP) in thin films. The dual-emission properties can be tuned by concentration, solvent polarity, and excitation energy. Spectral analyses and theoretical calculations reveal that the origin of the high-energy emission band (HEB) is intramolecular charge transfer (ICT) along the polymer chain, whilst the low-energy emission band (LEB) originates from the excited-state related to the intra-chain and inter-chain C-Br⋯π interactions as demonstrated by the single-crystal structure of the model compound. Appropriate control of the formation and the destruction of the halogen-assisted interactions can initiate white-light emission in the single-component polymer. More interestingly, by dispersing BrOD-TFB (0.1 wt%) in a non-emissive, colorless and transparent polymer, the characteristics of this white-light emission can be fully demonstrated while exhibiting unexpected RTP properties, with photoluminescence quantum efficiency (ΦPL) of up to 23% and CIE coordinates of (0.32, 0.32).

In previous research, interrelationships between green innovation and supply chain collaboration (SCC) are under-explored. This study explores the effects of SCC on green innovation performance and considers supply chain collaborative drivers as a crucial part of green innovation strategy. We construct a conceptual model containing a set of 17 drivers on the implementation of green innovation and analyze the contextual relationships between these drivers using an interpretive structural modeling (ISM) method to identify the driving and dependence powers in green innovation within the context of SCC. The model is validated on a case study involving an automobile firm in southwestern China. The study reveals that the relationships between supply chain partners have a very high influence on green innovation performance. SCC should be deemed as a core component in green innovation strategies. In addition, environmental regulations, top management commitment and social recognition are among high driving and dependence powers. We have confirmed the significance of SCC enablers for green innovation and identified several specific attributes through research. To further improve the value of this study, theoretical and managerial implications are discussed.

The role of aromatic π bridges (called π-linkers), which are used to connect multiple D and A groups in asymmetric thermally activated delayed fluorescence (TADF) emitters, remains a critical issue for the development of high-efficiency organic light-emitting diodes (OLEDs). In this work, based on our previous asymmetric TADF emitter (SCP) with electron donors carbazole (D) and phenothiazine (D′), we introduced different numbers of phenyl rings as π-linkers on both sides of SCP, leading to four D–A–D′ TADF emitters (PCz, 2PCz, PSz, and 2PSz) with different levels of conjugation. Experimental and theoretical results reveal that the π-linker distance has a significant effect on excited state regulation and thereby the optoelectronic properties. In comparison, the TADF emitters (PCz, and PSz) with shorter π-linkers exhibit higher photoluminescence quantum yields, due to effective suppression of conformation changes between ground state and excited state, and consequently higher device performance with external quantum efficiencies (EQEs) ranging from 20% to 30% achieved. This work provides an insight into the relationship between molecular design and optoelectronic properties of asymmetric TADF emitters.

Persistent luminescence from metal-free organic materials is attractive for their ultralong exciton lifetimes. Color-tunable persistent luminescence from single-component organic materials is fascinating but still challenging. By utilizing an efficient approach of "self"-interface energy transfer (IET), the persistent luminescence color of an organic phosphor (CTXO) can be reversibly and continuously tuned by external physical stimuli. Its color circularly changes between green (lifetime = 0.24 s) and deep-yellow (lifetime = 0.10 s) when CTXO is repeatedly triggered with thermal annealing and mechanical grinding. Self-IET from the crystalline part (donor), which exhibits persistent room-temperature phosphorescence, to the amorphous part (acceptor) inside its semicrystal during these treatments is found to be the key exciton process for such novel color modulation. This also provides opportunity for designing stimuli-responsive smart materials with controlled persistent luminescence.

Background Previous studies have suggested that the relative heterogeneity of frailty declines with increases in age and the level of the frailty index (FI). In this study, we investigated the sex difference in the relative heterogeneity of frailty and its response to health‐protective factors, in a Chinese community sample. Methods Data used for this secondary analysis were obtained from the Beijing Longitudinal Study of Aging that involved 3257 community‐dwelling Chinese people aged 55 years and older at baseline. An FI was constructed for each indicial using 35 variables assessing health‐related problems. A protection index (PI) consisting of 27 variables assessing lifestyle and social engagement was also built. The relative heterogeneity of frailty, as measured by the coefficient of variation (CV) of the FI, was calculated as the ratio of the standard deviation to the mean FI for different age, FI, and PI groups, and for the five‐year survival status. Results The CV decreased with the increase in age (F = 20.60, P = .006) and the FI (F = 57.59, P = .001), consistent in both sexes. In each age group, the CV was higher in men than in women (t = 3.25, P = .018). A great level of protection was associated with a significantly reduced mortality, and an increased CV (t = 2.91, P = .027). Conclusions Our data demonstrate that a gender difference exists in the relative heterogeneity of frailty, which is negatively related to age and frailty as well as positively associated with health protection and the five‐year survival.

Many vision–language models that output natural language, such as image-captioning models, usually use image features merely for grounding the captions and most of the good performance of the model can be attributed to the language model, which does all the heavy lifting, a phenomenon that has persisted even with the emergence of transformer-based architectures as the preferred base architecture of recent state-of-the-art vision–language models. In this paper, we make the images matter more by using fast Fourier transforms to further breakdown the input features and extract more of their intrinsic salient information, resulting in more detailed yet concise captions. This is achieved by performing a 1D Fourier transformation on the image features first in the hidden dimension and then in the sequence dimension. These extracted features alongside the region proposal image features result in a richer image representation that can then be queried to produce the associated captions, which showcase a deeper understanding of image–object–location relationships than similar models. Extensive experiments performed on the MSCOCO dataset demonstrate a CIDER-D, BLEU-1, and BLEU-4 score of 130, 80.5, and 39, respectively, on the MSCOCO benchmark dataset.

The process of operating a micro-nano robotic manipulator in a scanning electron microscope (SEM) to place carbon nanotube (CNT) on the surface of a nanodevice is very difficult. Not to mention allowing the micro-nano robotic manipulator to learn its motor skills from the demonstration of the placement task. In this paper, this complex assembly task is divided into multiple simple meta-tasks, from each of which the motor skills for the placement operation can be more easily learned. We identified the AFM cantilever in each frame by template matching and calculated its position coordinates to obtain the motion trajectory of the cantilever during the whole manipulation. The sharpness of some regions of the cantilever is evaluated using the Tenengrad gradient function. And based on the specificity of the working environment of the micro-nano robotic manipulator, we established applicable division criteria, analyzed and calculated the required division parameters from the captured video of the manipulation process, and successfully divided multiple simple meta-tasks based on the information.

The task of picking-up carbon nanotube (CNT) by the atomic force microscope (AFM) cantilever in the scanning electron microscope (SEM) was divided into several meta-tasks in this paper, and the motor skills of the complete task were learned from the simple meta-tasks, so as to learn the motor skills of the micro-nano robotic manipulator. Firstly, according to the motion characteristics and working environment of the manipulator, a segmentation criterion was established to divide the manipulation tasks into several different meta-tasks. Secondly, the motion trajectories of the same meta-tasks divided by multiple demonstration was filtered by moving average filter, and procrustes dynamic time warping (pDTW) was used for timing alignment. Then, Gaussian mixture model (GMM) was used to characterize the motion characteristics of the meta-tasks, and the optimal motion trajectories of the meta-tasks were generated by Gaussian mixture regression (GMR). Finally, according to the optimal meta-task trajectories, Dynamic Movement Primitive (DMP) was used to learn the motor skills of meta-tasks, and the meta-tasks library was recorded and created. We learned the motor skills of micro-nano robotic manipulator working in micro-nano environment, which laid a foundation for autonomous movement to complete tasks in the future.

The application value of repeated intra-articular pulsed radiofrequency for the treatment of knee joint pain has remained to be determined. To investigate this, a total of 64 patients with chronic knee joint pain admitted to Caoxian People's Hospital (Caoxian, Chine) between October 2016 and May 2018 were enrolled in the present study and analyzed prospectively. The patients were randomly divided into a control group, receiving treatment with a single intra-articular pulsed radiofrequency through the knee joint (n=32), and an experimental group, receiving multiple intra-articular pulsed radiofrequency treatments through the knee joint (n=32). The visual analog scale score (VAS), clinical efficacy and adverse reactions prior to and after treatment were compared between the two treatments. Synovial fluid cytokines were measured using ELISA prior to and after treatment. After the treatment, the control group and the experimental group both had a lower VAS (P

In order to perform nano-manipulation more accurately, it is necessary to study the relationship between the scanning electron microscope (SEM) image and the actual length. In this paper, based on the secondary electron image, the displacement of 1.81nm in SEM was measured the actual displacement was obtained according to the change of the pixels, so as to realize the nanometer displacement calibration and real-time measurement of the nano robot manipulator in SEM. According to the magnification, resolution and scale bar of SEM image, the mapping relationship between pixels and actual length was established. The obvious feature point and area in the SEM image were selected to be marked. The driving signal was applied to the manipulator of the nano robot, and the template matching algorithm was used to track the feature point and area in real time. The change of the feature point in the SEM image was obtained, and the actual displacement of the manipulator was calculated according to the mapping relationship between the pixel points and the actual displacement. We calibrated the displacement of the nanorobots and measured the actual displacement in real time, which laid a foundation for the precise control of the nanorobots in the future.

ObjectiveOur goal was to analyze the demographic and pathologic characteristics as well as prognosis in nonsmoking and nondrinking (NSND) oral squamous cell carcinoma (SCC) patients compared with typical oral SCC patients.Patients and MethodsA total of 353 patients were retrospectively enrolled and divided into two groups: the NSND group and the current smoking/current drinking (CSCD) group. Demographic, pathologic, and molecular data were compared between the two groups. The main research endpoints were locoregional control (LRC) and disease-specific survival (DSS).ResultsIn the NSND group, 16.3%, 41.9%, and 53.5% of patients were aged no more than 40 years, were female, and had an educational background of high school or above compared to 3.7%, 6.0%, and 38.2% of patients in the CSCD group, respectively. A total of 15.1% of the NSND patients had SCC of the lower gingiva and floor of the mouth, which was lower than the 35.6% of patients in the CSCD group. CSCD patients were likely to have an advanced disease stage (48.7% vs 32.5%, p=0.042) and poorly differentiated cancer (26.6% vs 16.3%, p=0.042). The NSND patients had a mean Ki-67 index of 24.5%, which was lower than the mean of 35.7% in the CSCD patients. The two groups had no HPV infection and similar p16 expression (4.7% vs 10.1%, p=0.132), but there was higher expression of p53 (38.6% vs 17.4%, pConclusionNSND oral SCC patients are a different entity. HPV infection has a limited role in carcinogenesis in NSND patients, and p16 expression is associated with worse locoregional control.

Summary This article proposed a high-performance triboelectric-electromagnetic hybrid wind energy harvester (WEH). By adopting the revolution and rotation movements of tapered rollers, which serve as both the rotor of the electromagnetic generator (EMG) part and freestanding layers of the triboelectric nanogenerator (TENG) part, the WEH can work as a sustainable power source and a self-powered wind speed sensor. When the wind speed is 12 m/s, super-high open-circuit voltage peaks of 47.4 and 683 V can be achieved by the EMG and TENG, respectively, corresponding to the high-power outputs of 62 and 1.8 mW. It was demonstrated that the WEH can easily light up over 600 red light-emitting diodes and even a 5-W globe light. A self-powered wireless temperature and humidity sensing network was also systematically demonstrated. In summary, the proposed WEH exhibits bright future toward IoT applications, such as in border detection, smart buildings, and so on., Graphical abstract, Highlights • A triboelectric-electromagnetic hybrid generator based on tapered rollers is proposed • The high-performance WEH has been systematically optimized and tested • A self-powered anemometer system has been successfully established • The WEH shows bright prospect in future IoT applications, Electromagnetics ; Energy Resources ; Energy Systems ; Device

Background Community-acquired lower respiratory tract infections (CA-LRTIs) are the primary cause of hospitalization among children globally. A better understanding of the role of atypical pathogen infections in native conditions is essential to improve clinical management and preventive measures. The main objective of this study was to detect the presence of 7 respiratory viruses and 2 atypical pathogens among hospitalized infants and children with community-acquired lower respiratory tract infections in Luzhou via an IgM test. Methods Overall, 6623 cases of local hospitalized children with 9 pathogen-IgM results from 1st July 2013 to 31st Dec 2016 were included; multidimensional analysis was performed. Results 1) Out of 19,467 hospitalized children with lower respiratory tract infections, 6623 samples were collected, for a submission ratio of 33.96% (6623 /19467). Of the total 6623 serum samples tested, 5784 IgM stains were positive, for a ratio of 87.33% (5784 /6623). Mycoplasma pneumoniae (MP) was the dominant pathogen (2548 /6623, 38.47%), with influenza B (INFB) (1606 /6623, 24.25%), Legionella pneumophila serogroup 1 (LP1) (485 /6623, 7.32%) and parainfluenza 1, 2 and 3(PIVs) (416 /6623, 6.28%) ranking second, third and fourth, respectively. 2) The distribution of various pathogen-IgM by age group was significantly different (χ 2 = 455.039, P

The near-field enhancement underneath the AFM tip irradiated by a laser beam is widely utilized in various nanofabrication techniques, such as nano-welding, nano-manipulation, nano-etching and so on. In this paper, a numerical model of near-field enhancement on AFM tip irradiated by annular laser beam is established using COMSOL Multiphysics. The results show that when the AFM tip is irradiated by annular laser beam, strong near-field enhancement is induced at the apex of the tip. The field distribution curve with the substrate presents a saddle shape, which demonstrates that the annular beam irradiated the AFM tip produce an extremely high near-field enhancement between the particle and the tip. In addition, the results indicate that when the AFM tip is irradiated by the annular laser with higher frequency, the near-field enhancement underneath the apex of the tip increases correspondingly. Moreover, when the irradiation angle is 60 Deg and the irradiation distance is 65 nm, the near-field enhancement reaches the peak. Based on the numerical simulation of the near-field enhancement, the system of the AFM tip irradiated by annular laser beam can be anticipated for further application in the field of nanofabrication.

Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as \textbf{\underline{A}}symmetric \textbf{\underline{D}}eep \textbf{\underline{S}}emantic \textbf{\underline{Q}}uantization (\textbf{ADSQ}). \textbf{ADSQ} is implemented using three stream frameworks, which consist of one \emph{LabelNet} and two \emph{ImgNets}. The \emph{LabelNet} leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two \emph{ImgNets}, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two \emph{ImgNets} are used to generate discriminative compact hash codes. Specifically, the function of the \emph{LabelNet} is to capture rich semantic information that is used to guide the two \emph{ImgNets} in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, \textbf{ADSQ} can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed \textbf{ADSQ} can outperforms current state-of-the-art methods., Accepted to IEEE ACCESS. arXiv admin note: text overlap with arXiv:1812.01404

Thermally activated delayed fluorescence (TADF) emitters integrated with aggregation-induced emission (AIE) property are expected to enable nondoped organic light-emitting diodes (OLEDs) with merits of high efficiency and simple fabrication process. We previously proposed a novel design strategy of combining AIE and TADF features to develop AIE–TADF emitters with asymmetric structure, leading to an AIE–TADF emitter 4,4-CzSPz exhibiting mechanoluminescence. Herein, photophysical, thermal, and electroluminescence properties of neat 4,4-CzSPz films have been systematically investigated. It was found that 4,4-CzSPz film exhibits a high photoluminescence quantum efficiency of 97.3% and excellent thermal properties. Significantly, 4,4-CzSPz afforded its doped and nondoped OLEDs with very high external quantum efficiencies of 26.2% and 20.7%, respectively, which are rarely reported among TADF-based OLEDs. The results demonstrate universal applications of 4,4-CzSPz in OLEDs with different device configurations fo...

With the booming development of flexible pressure sensors, the need for multifunctional and high-performance pressure sensor has become increasingly important. Although great progress has been made in the novel structure and sensing mechanism of the pressure sensor, the trade-off between the sensitivity and the wide-detection range has prevented its development, further restricting its application in wearable human–machine interfaces (WHMIs). Herein, a novel pressure sensor based on the hierarchical conductive fabric was fabricated and purposed as a WHMI. Poly(3,4-ethylenedioxythiophene) nanowires (PEDOT NWs) and cellulose nanofibers (CNF) were stacked on a conductive poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) fabric to form a special spatial multi-level hierarchical structure inside the fabric, which is a breakthrough for the improvement of the sensor's performance and makes the fabrication process of in situ polymerization suitable for large-scale production. The multi-level hierarchical structures endowed the pressure sensor with characteristics of high sensitivity (15.78 kPa−1), a wide-detection range from 30 Pa to 700 kPa, and outstanding stability toward compression and bending deformation. Benefiting from its excellent performance, a human–machine interface based on arrayed pressure sensors and signal processing system can control the illumination of the LED array and effectively capture finger motion to control the eight-direction movement of an unmanned aerial vehicle (UAV). This improved performance of the pressure sensor based on the hierarchical conductive fabric made it a widespread application in intelligent fabric, electronic skin, human–machine interfaces, and robotics.

Hashing techniques have recently been successfully applied to solve similarity search problems in the information retrieval field because of their significantly reduced storage and high-speed search capabilities. However, the hash codes learned from most recent cross-modal hashing methods lack the ability to comprehensively preserve adequate information, resulting in a less than desirable performance. To solve this limitation, we propose a novel method termed Nonlinear Robust Discrete Hashing (NRDH), for cross-modal retrieval. The main idea behind NRDH is motivated by the success of neural networks, i.e., nonlinear descriptors, in the field of representation learning, and the use of nonlinear descriptors instead of simple linear transformations is more in line with the complex relationships that exist between common latent representation and heterogeneous multimedia data in the real world. In NRDH, we first learn a common latent representation through nonlinear descriptors to encode complementary and consistent information from the features of the heterogeneous multimedia data. Moreover, an asymmetric learning scheme is proposed to correlate the learned hash codes with the common latent representation. Empirically, we demonstrate that NRDH is able to successfully generate a comprehensive common latent representation that significantly improves the quality of the learned hash codes. Then, NRDH adopts a linear learning strategy to fast learn the hash function with the learned hash codes. Extensive experiments performed on two benchmark datasets highlight the superiority of NRDH over several state-of-the-art methods.

Mechanoluminescence (ML) materials are attracting increasing interest owing to promising applications in various areas. However, to date, it remains a major challenge to develop a precise and universal route to achieving organic ML materials. Herein, we show that ML can be easily realized in organic piezophotonic host-guest crystals, under conditions in which neither the host nor the guest is ML-active. The experimental and theoretical results reveal that excitons of the host generated by piezoelectricity can be harvested effectively by the guest for light emission, owing to the restraint of intersystem crossing process. Moreover, different host-guest crystals are constructed, wherein the emission color, intensity, color purity, and emission duration of ML can be manipulated. This work deepens our understanding of organic ML generation in piezophotonic host-guest crystals and provides an inspiring principle to design more organic ML materials.

The denoising of SEM images and extracting of image information have attracted attention in the micro-nano research fields. In this paper, we analyze the noise types and characteristics of SEM images and propose the method of deep convolutional neural network to denoise SEM images. On the basis of denoising, suitable methods are used to identify the position of single, multiple nanowires and positional relationship of multiple nanowires in the depth direction. Because of special imaging principles, there are many differences between SEM images and ordinary gray images. To the SEM image, noise has more significant influences on extracting information from SEM image. Samples which have various types and morphologies were observed under different types of electron microscopy and observation conditions to produce dataset. Then the supervised deep convolutional neural network was trained by this dataset, which has a good adaptive on denoising of SEM images. After denoising, the identification method of nanowires was further proposed which could accurately recognize single, multiple nanowires by canny edge detection and Hough transform, and quickly identify their positional relationship.

In this paper, we report a novel design of a microspherical lens assembly that can realize super-resolution optical imaging in air with super-wide field of view (FOV). The microspherical lens assembly is made from a barium titanate glass (BTG) microsphere that is glued to a microspherical crown lens through optical adhesive. Experimental results show that super-resolution optical imaging in air can be achieved under a regular optical microscope when the microspherical lens assembly is brought sufficiently close to the sample surface. Super-wide FOV is also achieved when a larger BTG microsphere is used and there seems no obvious limitation on the size of the BTG microsphere as long as the curvature of the microspherical crown lens is properly tuned. Simulation study suggests that the focus point of the microlens needs to close to the bottom surface of the BTG microsphere in order to achieve the super-resolution optical imaging. The novel microspherical lens assembly will make the microsphere based super-resolution imaging a step further close to practical applications.

Graphene is widely used as the basic materials of nano optical devices and sensors on account of its special structures and excellent photoelectric properties. Graphene is considered as an ideal material for photodetectors because of its ultra-wide absorption spectrum from the ultraviolet to the terahertz band, ultrahigh carrier mobility and ultrafast photoreaction speed. In this study, a photothermal nano-device was made using graphene that was transferred to an electrode using an all-dry viscoelastic stamping method. The nano-device has the advantages of simplicity, high efficiency and instant measurement. This nano-device was used to measure the light absorption of graphene, and the calculated light absorption rate of graphene is basically consistent with previous research results. Experiments on irradiation at different wavelengths and thermal heating at different temperatures show that the nano-device has an excellent response to near-infrared and mid-infrared light. The conclusions provide an experimental basis for the research, design and fabrication of nano-devices, and this device can provide an effective method for detecting light and temperature in areas such as electronic components and solar cells.

Triboelectric nanogenerators and sensors have been widely adopted for diversified energy harvesting and sensing applications, but the demonstrations of 3D information sensing and controlling are very limited. In this paper, we present a novel self-powered virtual reality 3D-control sensor (VR-3D-CS) based on triboelectric mechanism for controlling the attitude (both the position and rotation) of object in 3D virtual space. This innovative, cost-effective, simple-designed sensor has a symmetric 3D structure with eight separated sensing electrodes and two touching spheres as the interactive interface with human fingers for 3D force information sensing and VR controlling. Based on the coupling effect of triboelectrification and electrostatic induction, the VR-3D-CS generates different electric output signals in response to different operation manner that can be used to control the attitude of objects in 3D virtual space. The symmetrical 3D configuration design of the sensor enables the detection of 3D force from both the normal direction and shear direction. By employing vector properties of force and signal analysis from the eight sensing electrodes, detection of six-axis directions in 3D space is achieved by triboelectric mechanism for the first time. The VR-3D-CS has been demonstrated to be able to detect normal force in the range of 0–18 N. It can resolve the shear force direction with step resolution of at least 15°. Besides, due to the positive output voltage and low internal impedance, the VR-3D-CS is readily compatible with commercial portable signal processing system for signal analysis and controlling. Demonstration of the VR-3D-CS as interactive interface for Augmented Reality (AR) control is successfully realized. The robust structure, stable output performance and self-powered sensing property enable the device as an ideal human machine interface towards AR interface, batteryless and energy saving applications.

Images with resolution beyond the diffraction limit (superresolution) were obtained by combining conventional microscopy with microscale microsphere, which provides a new possibility for real-time optical superresolution imaging. Here, we propose a method to manipulate the microsphere by attaching it to a probe with optical glue. The optical simulation has been performed to demonstrate the influence of probe on superresolution image. The probe was fixed on a translation stage which was regarded as a robotic manipulator with four degrees of freedom. The kinematic analysis of the robotic manipulator was studied for manipulation strategy of microsphere. The force analysis has been carried out to analyze the possibility of microsphere detach from probe. To improve imaging quality, the microsphere was immersed in liquid medium and the resolution of 130 nm (∼λ/4.5) was achieved. In addition, by scanning the microsphere at the speed of 5 μm/s over the sample surface and stitching the recorded images, the superresolution image of large continuous area was achieved. This work had important practical significance for manipulating microsphere and optical superresolution imaging.

Persistent luminescence from purely organic materials is basically triggered by light and electricity, which largely confines its practical applications. A purely organic AIEgen exhibits not only persistent photoluminescence, but also transient and persistent room-temperature mechanoluminescence. By simply turning on and off a UV lamp, tricolor emission switching between blue, white, and yellow was achieved. The data from single-crystal structure analysis and theoretical calculation suggest that mechanism of the observed persistent mechanoluminescence (pML) is correlated with the strong spin-orbit coupling of the bromine atom, as well as the formation of H-aggregates and restriction of intramolecular motions in noncentrosymmetric crystal structure. These results outline a fundamental principle for the development of new pML materials, providing an important step forward in expanding the application scope of persistent luminescence.

Thermally activated delayed fluorescence (TADF) emitters can harvest singlet and triplet excitons for light emission to afford highly efficient organic light-emitting diode (OLED) devices, especially for all-TADF white OLEDs (WOLEDs). However, the majority of TADF emitters suffer from concentration quenching effects and require complicated doping techniques for device fabrication. Herein, we demonstrate a yellow TADF emitter that enables non-doped OLED systems, which is beneficial for the simplification of the OLED device structure and fabrication. The non-doped yellow OLED achieves a maximum external quantum efficiency (EQE) of 16.7% at a high luminance of 3600 cd m−2, in which such a high magnitude is rarely reported. More significantly, by the combination of the yellow TADF emitter with a blue TADF emitter, a fully non-doped emissive layer (EML) strategy has been demonstrated for the fabrication of an all-TADF WOLED, in which the non-doped yellow and blue TADF emissive layers are closely stacked together without any interlayers. The fully non-doped WOLEDs show a maximum EQE of up to 19.8%, along with high quality white-light emission. This is the first report regarding the fully non-doped all-TADF WOLEDs, and the presented design strategy provides a universal route towards the fabrication of simple and efficient WOLEDs.

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

The walking state monitoring is indispensable during the robot-aided walking of people with lower limb dysfunctions. In this paper, the existence of human–robot coordination state is first statistically verified in the process of using a walking-aid cane-type robot during walking. Based on this coordination, a new walking state monitoring method is proposed by using the principal component analysis (PCA). The abnormal or emergency walking state is promptly detected if the new sample data are found to deviate from an off-line PCA model, which is generated from plentiful normal walking data of different subjects. Furthermore, a state diagnosis algorithm based on the contribution plot is also developed for the walking state recognition and diagnosis. In this way, typical abnormal states like the leg restrictions can be distinguished from the emergency states including falls and the stumbling. Moreover, the human–robot coordination analysis can be performed using less sensors built-in the robot without needing the posture information of full human body. The effectiveness of the proposed method is proven by experiments. Better recognition rate and real-time performance of the method are also verified by comparing with conventional center of pressure based monitoring method.

Thermally activated delayed fluorescence (TADF) emitters have aroused serious attention for their use in organic light-emitting diodes (OLEDs). However, TADF emitters providing both doped and non-doped OLEDs with high device performance are rare. In this work, we present a yellow TADF emitter (OPDPO) meeting the above requirements. The OPDPO-based doped OLEDs show a maximum external quantum efficiency (EQE) of 26.7%, which is amongst the highest ever reported for doped yellow TADF-based OLEDs. The EQE remains as high as 22.1% at a luminance of 1000 cd m−2. Meanwhile, the OPDPO-based non-doped OLEDs afford a maximum EQE of 16.6% at a high luminance of 3600 cd m−2, which is amongst the best performances of non-doped yellow TADF-based OLEDs. Moreover, simple and efficient white OLEDs are implemented by combining a blue conventional fluorescent emitter with either a doped or non-doped OPDPO emissive layer, confirming the universal applications of OPDPO in high-performance OLEDs.

Warm-white organic light-emitting diodes (WOLEDs) fabricated with hybrid fluorescence-based emitters, consisting of traditional fluorescence and thermally activated delayed fluorescence (TADF) emitters can achieve high efficiency without the use of expensive organometallic phosphors. However, most of these types of OLEDs require complicated fabrication techniques, especially host–guest doping systems and multiple interlayers. Herein, an efficient design strategy for hybrid fluorescence WOLEDs is presented, in which a traditional blue fluorescence emitter is simply stacked together with a yellow TADF emitter without any interlayers. Due to the high triplet excited state of the chosen blue emitter, its intrinsically non-radiative triplet excitons can be efficiently utilized for light emission, by exoergic transfer to the adjacent yellow TADF emitter. Following this idea, a maximum external quantum efficiency of 20.8% at a high luminance of 400 cd m−2 is achieved for a fully non-doped WOLED when using a blue fluorescent emitter with the aggregation-induced emission property. This strategy provides a universal approach for the fabrication of highly-efficient fluorescence–TADF hybrid WOLEDs based on the fully non-doped device configuration.

A carbon monoxide (CO) sensor based on a Michelson interferometer combined with α-Fe2O3/reduced graphene oxide quantum dots (rGOQDs) composite film is proposed and fabricated. First, a waist-enlarged taper is formed between the single-mode fiber (SMF) and the no-core fiber (NCF), then the other end of the NCF is spliced with a section of thin-core fiber (TCF). The end of the TCF is coated with a layer of silver film to enhance the reflection. Thus, a Michelson interferometer comprising SMF–NCF–TCF is formed. The α-Fe2O3/rGOQDs composite film is deposited on the outside surface of the TCF. The specific adsorption of CO by the composite film leads to a change in the sensor’s effective refractive index, realizing the detection of CO. The results show that the interference intensity of the monitoring valley decreases with increase in the concentration of CO. The sensitivity of the sensor is 0.057 dBm ppm−1, the detection limit of the sensor is 105 ppb and the response time and recovery time are 70 s and 100 s, respectively. The sensor has the advantages of high sensitivity, high selectivity and simple structure, and it is expected to be used for the detection of low concentrations of CO gas.

Distantly supervised relation extraction is a prevalent technique for identifying semantic relations between two entities. Most prior models cannot distinguish the local and global information in the long-range dependency among words effectively. The latent semantic information presented in existing knowledge graphs is completely neglected, as the knowledge graph information is simply used as a label to specify the class of relations instead of being treated as a graph. Moreover, previous studies only utilized a selective attention mechanism over sentences to alleviate the impact of noise; they did not consider the implicit interaction between sentences in a sentence bag. In this paper, (1) we propose a knowledge-aware framework to enhance word representations, which can highlight the importance of key words and relation clues; (2) we adopt a piecewise self-attention mechanism to model long-range dependency among words; (3) we design a heterogeneous graph structure for each sentence bag and introduce a heterogeneous graph convolutional network with knowledge attention to aggregate the implicit interaction among sentences from a local-to-global perspective. Experimental results on two popular relation extraction datasets demonstrate that our model obtains more discriminative relation representations and outperforms most of the state-of-the-art models.

Hashing has been shown to be successful in a number of Approximate Nearest Neighbor (ANN) domains, ranging from medicine, computer vision to information retrieval. However, current deep hashing methods either ignore both rich information of labels and visual linkages of image pairs, or leverage relaxation-based algorithms to address discrete problems, resulting in a large information loss. To address the aforementioned problems, in this paper, we propose an E nhanced D eep D iscrete H ashing (EDDH) method to leverage both label embedding and semantic-visual similarity to learn the compact hash codes. In EDDH, the discriminative capability of hash codes is enhanced by a distribution-based continuous semantic-visual similarity matrix, where not only the margin between the positive pairs and negative pairs is expanded, but also the visual linkages between image pairs is considered. Specifically, the semantic-visual continuous similarity matrix is constructed by analyzing the asymmetric generalized Gaussian distribution of the visual linkages between pairs with label consideration. Besides, in order to achieve an efficient hash learning framework, EDDH employs an asymmetric real-valued learning structure to learn the compact hash codes. In addition, we develop a fast discrete optimization algorithm, which can directly generate discrete binary codes in single step, and introduce an intermediate term before iterations to avoid the problems caused by directly the use of large semantic-visual similarity matrix, which results in a significant reduction in the computational overhead. Finally, we conducted extensive experiments on three datasets to show that EDDH has a significantly enhanced performance compared to the compared state-of-the-art baselines.

Thermally activated delayed fluorescence (TADF) has been explored actively in luminescent organic materials. Yet, realizing such TADF-active, multifunctional emitters with high emission efficiency still remains hugely challenging. In this context, a series of twist-conjugated organic molecules bearing diphenylsulfone and 9,9-dimethylacridine moieties are designed and prepared, and are found to show, in one molecule, TADF, room-temperature phosphorescence, triboluminescence, and aggregation-induced emission enhancement. In addition, remarkably high photoluminescence quantum efficiency, up to ≈100%, is achieved for these novel molecules. Single-crystal analysis and theoretical calculations reveal that the through-space charge transfer (TSCT) effect in these molecules is responsible for both the multifunctional emission and high emission efficiency. A maximum external quantum efficiency of 20.1% is achieved, which is among the highest recorded in a solution-processable device containing TSCT-based TADF materials. These results illustrate a new approach to achieving highly efficient TADF-active, multifunctional emitters.

Online healthcare communities (OHCs) have become producers of medical information. Solving the issue of how to effectively reuse such a large amount of medical data and discover its potential value is of the utmost importance for alleviating the shortage of medical resources. Online consultation has received widespread attention and population since its first appearance in 1999, and as a result, many diagnostic multi-turn questions and answers (Q&A) documents have become available. This type of document is formed by multiple rounds of patient questions and doctors’ diagnostic answers and contains massive medical knowledge and doctors’ diagnostic experience. Few studies concentrate on the modeling and recommendation of this type of document, yet making these documents convenient for reuse reduces the cost of medical consultation for patients and saves time addressing common diseases for doctors. In this paper, we focus on the modeling and understanding of diagnostic multi-turn Q&A records and propose a deep-learning recommendation framework based on patient medical information needs, the contents of Q&A records and doctor background information. With the evaluation based on a real dataset that contains pediatric consultation dialogues fetched from DingXiangYuan, a famous online consultation application in China, we found that the proposed model achieved a good performance on the recommendation of diagnostic multi-turn Q&A records and outperformed baseline models. In addition, we discussed a potential application scenario of the recommendation model, suggesting that the proposed model can promote the reduction of patient costs and doctors’ work pressure in countries or regions with insufficient medical resources.

Hashing technology has been widely used in approximate nearest neighbor search algorithms for large-scale cross-modal retrieval due to its significantly reduced storage and high-speed search capabilities. However, most existing supervised cross-modal hashing methods either mainly rely on binary pairwise similarity and fail to exploit the rich semantic information contained in the label matrix, or the use of single linear projections which suffer from limited information completeness. In this paper, we propose a novel method, named Nonlinear Supervised Discrete Hashing (NSDH). Specifically, NSDH consists of two components, (1) a semantic enhancement descriptor consisting of multiple linear projections that is used to extract comprehensive latent representations of heterogeneous multimedia data, which aligns the original heterogeneous features and integrates the rich semantic label matrix; (2) a fast discrete optimization module used to learn discriminative compact hash codes, which preserves the similarity information using an inner product between the real-valued embeddings of the output of the semantic enhancement descriptors. Therefore, NSDH leverages both the label matrix and similarity information in order to enhance the semantic information of the learned hash codes. In this way, the representation learning capability of the output layer of semantic enhancement descriptors can be greatly enhanced and as a result the learned hash codes are more discriminative. In addition, we present a fast discrete optimization algorithm to efficiently learn the binary hash codes. Results from our experiments on two benchmark datasets highlight the superiority of NSDH in comparison to many state-of-the-art cross-modal hashing methods.

Energy harvesting technique has been identified as an innovative and environment-friendly way for powering micro sensors and electronic devices. In this paper, a hybrid generator based on triboelectric and electromagnetic mechanisms (TMHG), which provides two electrical power outputs at the same time, was proposed. The output performance in time and frequency domains are investigated at different initial gap distances and excitation accelerations. At an acceleration of 1 g and an initial gap distance of 5 mm, the maximum power outputs of electromagnetic and triboelectric parts can be obtained as 0.28 mW and 1.09 mW, respectively. The resonant frequency of the TMHG is as low as 23.5 Hz, making it more suitable to harvest energy from low frequency vibrations of less than 30 Hz. Besides, the operating bandwidth of TMHG can be adjusted by changing the initial gap distance according to the application requirements.

Non-skewed rotors can be used to eliminate the slot harmonic field, as well as, the noise and vibration can also be weakened effectively. However, it is difficult to determine the spatial distribution of the asymmetrical rotor bars and slot combinations, which can affect the loss and air-gap force significantly. Taking a 5.5kW, 4-pole induction motor as an example, this paper studies the influence of the above factors on the loss and air-gap force systematically, by using Finite Element Method (FEM), and the experiment validation on efficiency and no-load noise are also performed on a 5.5 kW prototype.

In this paper, an impact-based frequency up-converted piezoelectric energy harvesting system (PEHS) is proposed and fabricated using MEMS piezoelectric thick film technology. The PEHS mainly consists of a high-frequency piezoelectric cantilever (PC-H) with resonate frequency of 935 Hz and a low-frequency stainless-steel cantilever (SC-L) with resonate frequency of 23 Hz. Under the external low-frequency vibration excitation, the SC-L at the bottom impacts the PC-H during the movement, in this way the low-frequency external vibration is converted to high-frequency piezoelectric cantilever vibration. When the PC-H is triggered to vibrate at 935 Hz, the maximum voltage output is 74 mV at 1.0 g. Then incorporate the PC-H with SC-L and form the PEHS, at driving frequency of 20 Hz to 34 Hz, the maximum voltage output and 3dB bandwidth of the PEHS reach 1220 mV and 4.7 Hz, respectively. The maximum voltage output of the PEHS (1220 mV) is more than 16 times of that of the single PC-H (74 mV).