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Abstract Maximizing materials utilization efficiency via enhancing their reconfigurability and multifunctionality offers a promising avenue in addressing the global challenges in sustainability. To this end, significant efforts have been made in developing reconfigurable multifunctional smart materials, which can exhibit remarkable behaviors such as morphing and self-healing. However, the difficulty in efficiently manipulating and controlling matter at the building block level with manageable cost and complexity, which is crucial to achieving superior responsiveness to environmental clues and stimuli, has significantly hindered the further development of such smart materials. Here we introduce a concept of Robo-Matter, which can be activated and controlled through external information exchange at the building block level, to enable a high-level of controllability, mutability and versatility for reconfigurable multifunctional smart materials. Using specially designed micro-robot building blocks with symmetry-breaking active motion modes, tunable anisotropic interactions, and interactive coupling with a programmable spatial-temporal dynamic light field, we demonstrate an emergent Robot-Matter duality, which enables a spectrum of desirable behaviors spanning from matter-like properties such as ultra-fast self-assembly and adaptivity, to robot-like properties including active force output, smart healing, smart morphing and infiltration. Our work demonstrates a promising direction for designing next-generation smart materials and large-scale robotic swarms.

Abstract Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions.

Abstract Inertial underdamped collisions preserve the memory of physical parameters that existed before the collision, leading to phenomena usually unseen in overdamped systems. Here we probe the less studied inertial chiral matter with spinners on an air table. We show here the emergence of high levels of spin for a minority of (+) handed spinners in the presence of a majority of (−) handed spinners (vice versa). This deep violation of equipartition occurs due to the inertial (memory preserving) nature of elastic collisions between underdamped translating and rotating objects. Underdamped spinners of the same spin sign annihilate their spins when they elastically collide, transferring their spin angular momentum into orbital angular momentum, while oppositely handed spinners tend to preserve their individual spin levels, leading to the pumping of minority spinners to high spin levels. Entropy production and the flow of entropy in this underdamped system are also counter-intuitive, showing dramatic oscillations in time.

Abstract Background Osteoarthritis (OA) is a common degenerative joint disease characterized by persistent articular cartilage degeneration and synovitis. Oxymatrine (OMT) is a quinzolazine alkaloid extracted from the traditional Chinese medicine, matrine, and possesses anti-inflammatory properties that may help regulate the pathogenesis of OA; however, its mechanism has not been elucidated. This study aimed to investigate the effects of OMT on interleukin-1β (IL-1β)-induced damage and the potential mechanisms of action. Methods Chondrocytes were isolated from Sprague-Dawley rats. Toluidine blue and Collagen II immunofluorescence staining were used to determine the purity of the chondrocytes. Thereafter, the chondrocytes were subjected to IL-1β stimulation, both in the presence and absence of OMT, or the autophagy inhibitor 3-methyladenine (3-MA). Cell viability was assessed using the MTT assay and SYTOX Green staining. Additionally, flow cytometry was used to determine cell apoptosis rate and reactive oxygen species (ROS) levels. The protein levels of AKT, mTOR, LC3, P62, matrix metalloproteinase-13, and collagen II were quantitatively analyzed using western blotting. Immunofluorescence was used to assess LC3 expression. Results OMT alleviated IL-1β-induced damage in chondrocytes, by increasing the survival rate, reducing the apoptosis rates of chondrocytes, and preventing the degradation of the cartilage matrix. In addition, OMT decreased the ROS levels and inhibited the AKT/mTOR signaling pathway while promoting autophagy in IL-1β treated chondrocytes. However, the effectiveness of OMT in improving chondrocyte viability under IL-1β treatment was limited when autophagy was inhibited by 3-MA. Conclusions OMT decreases oxidative stress and inhibits the AKT/mTOR signaling pathway to enhance autophagy, thus inhibiting IL-1β-induced damage. Therefore, OMT may be a novel and effective therapeutic agent for the clinical treatment of OA.

Full-field optical angiography (FFOA)—a real-time non-invasive imaging technique for extracting biological blood microcirculation information—contributes to an in-depth understanding of the functional and pathological changes of biological tissues. However, owing to the limitation of the depth-of-field (DOF) of optical lenses, existing FFOA imaging methods cannot capture an image containing every blood-flow information. To address this problem, this study develops a long-DOF full-field optical angiography imaging system and proposes a novel multi-focus image fusion scheme to expand the DOF. First, FFOA images with different focal lengths are acquired by the absorption intensity fluctuation modulation effect. Second, an image fusion scheme based on gradient feature detection in a nonsubsampled contourlet transform domain is developed to capture focus features from FFOA images and synthesize an all-focused image. Specifically, FFOA images are decomposed by NSCT into coefficients and low-frequency difference images; thereafter, two gradient feature detection-based fusion rules are used to select the pre-fused coefficients. The experimental results of both phantom and animal cases show that the proposed fusion method can effectively extend the DOF and address practical FFOA image defocusing problems. The fused FFOA image can provide a more comprehensive description of blood information than a single FFOA image.

Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments.

Abstract Background Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM). Results Our simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage. Conclusions The unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF $$\ge$$ ≥ 0.001 and Rsq $$\ge$$ ≥ 0.3.

Developing a pleasant and supportive emotional bond with teachers might help students feel more at home at school. However, research on the relationship between positive teacher–student relationships and students’ feelings of belonging in university settings is scarce. Using data from 426 student surveys, this study estimated the moderated mediation model which is generated based on attachment theory and reciprocity theory. As a result, teacher–student reciprocity increases students’ sense of belonging to the university, and teacher–student attachment moderates this effect. The effects of teacher-student reciprocity and teacher-student attachment on students’ sense of belonging to the institution are positively moderated by social group attachment. This work contributes to the study of the sense of belonging by expanding on the applications of attachment theory and reciprocity theory. This research has a practical application.

Abstract Background Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software. Results We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory. Conclusion Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data.

The ability to capture and record high-resolution images over long distances is essential for a wide range of applications, including connected and autonomous vehicles, defense and security operations, as well as agriculture and mining industries. Here, we demonstrate a self-assembled bistatic long-range imaging LiDAR system. Importantly, to achieve high signal-to-noise ratio (SNR) data, we employed a comprehensive suite of denoising methods including temporal, spatial, spectral, and polarization filtering. With the aid of these denoising technologies, our system has been validated to possess the capability of imaging under various complex usage conditions. In terms of distance performance, the test results achieved ranges of over 4000 m during daylight with clear weather, 19,200 m at night, 6700 m during daylight with haze, and 2000 m during daylight with rain. Additionally, it offers an angular resolution of 0.01 mrad. These findings demonstrate the potential to offer comprehensive construction strategies and operational methodologies to individuals seeking long-range LiDAR data.

Background: Asthma, type 2 diabetes (T2D), and anthropometric measures are correlated complex traits that all have a major genetic component.Objective: To investigate the overlap in genetic variants associated with these complex traits.Methods: Using United Kingdom Biobank data, we performed univariate association analysis, fine-mapping, and mediation analysis to identify and dissect shared genomic regions associated with asthma, T2D, height, weight, body mass index (BMI), and waist circumference (WC).Results: We found several genome-wide significant variants in and around the JAZF1 gene that are associated with asthma, T2D, or height with two of these variants shared by the three phenotypes. We also observed an association in this region with WC when adjusted for BMI. However, there was no association with WC when it was not adjusted for BMI or weight. Additionally, only suggestive associations between variants in this region and BMI were observed. Fine-mapping analyses suggested that within JAZF1 there are non-overlapping regions harboring causal susceptibility variants for asthma, T2D, and height. Mediation analyses supported the conclusion that these are independent associations.Conclusion: Our findings indicate that variants in the JAZF1 are associated with asthma, T2D, and height, but the associated causal variant(s) are different for each of the three phenotypes.

To optimize the output of SBS sub-nanosecond pulse compression, two kinds of compact double-cell structures are carried out and compared experimentally. The beam parameters of the compact double-cell structure are calculated theoretically, which provides the selection guidance of structural parameters such as lens focal length and SBS cell size. The dependence of lens parameters and medium parameters on SBS output parameters are experimentally studied. Results show that SBS pulse compression enters the saturation region at a low injection energy with a long focal length lens or a large gain coefficient medium. For compact double-cell setup with one lens, it is easy to obtain narrow pulses for the medium FC40 with a short phonon lifetime. While in setup with two lenses, it is easy to obtain SBS output with a high energy efficiency and narrow pulse width for HT110 medium with a large gain coefficient. The pulse width compression ratio is up to 16 times after optimization. These experimental results can provide references for the experimental design of SBS pulse compression.

Abstract Background The aim of this study is to identify the genetic defects in a Chinese family with fundus albipunctatus. Methods Complete ophthalmic examinations, including slit-lamp biomicroscopy, dilated indirect ophthalmoscopy, fundus photography, autofluorescence, swept source optical coherence tomography (SS-OCT) and full-field electroretinography (ffERG) were performed. Genomic DNA was extracted from blood samples and whole genome sequencing was performed. Variants were validated with Sanger sequencing. Results Six members in this Chinese family, including three affected individuals and three controls, were recruited in this study. The ophthalmic examination of three recruited patients was consistent with fundus albipunctatus. Three variants, a novel frameshift deletion c.39delA [p.(Val14CysfsX47] and a haplotype of two rare missense variants, c.683G > A [p.(Arg228Gln)] along with c.710A > G [p.(Tyr237Cys], within the retinal dehydrogenase 5 (RDH5) gene were found to segregate with fundus albipunctatus in this family in an autosomal recessive matter. Conclusion We identified novel compound heterozygous variants in RDH5 responsible for fundus albipunctatus in a large Chinese family. The results of our study further broaden the genetic defects of RDH5 associated with fundus albipunctatus.

Brain–computer interfaces are enabling a range of new possibilities and routes for augmenting human capability. Here, we propose brain–computer interfaces as a route towards forms of computation, i.e., computational imaging, that blend the brain with external silicon processing. We demonstrate ghost imaging of a hidden scene using the human visual system that is combined with an adaptive computational imaging scheme. This is achieved through a projection pattern “carving” technique that relies on real-time feedback from the brain to modify patterns at the light projector, thus enabling more efficient and higher-resolution imaging. This brain–computer connectivity demonstrates a form of augmented human computation that could, in the future, extend the sensing range of human vision and provide new approaches to the study of the neurophysics of human perception. As an example, we illustrate a simple experiment whereby image reconstruction quality is affected by simultaneous conscious processing and readout of the perceived light intensities.

Ghost imaging (GI) retrieves an image from the correlation between a sequence of illumination patterns on the object and their corresponding bucket detections. Traditionally, GI requires the precise information of the illumination patterns, which raises technology barriers on building a high-speed illumination source, limiting the scope of its application. In this study, we propose a high-speed GI system, which implements a self-correlation with a purely optical operation without determining illumination patterns. The light source is an optical phased array (OPA), built of a set of waveguide-type electro-optic phase modulators. The OPA is driven to randomly change the phases in every 200 ns, generating speckle patterns at a rate of 5 MHz. Although the speckle patterns are not predictable or post-determinable, with the help of the naked-eye GI scheme, the system in real time optically generates the images of the object at a frame rate of more than 1 kHz, which can be directly observed by eyes or using a camera. This method avoids acquiring the information of the illumination, thus providing a simple and easy way to realize high-speed GI. It also inspires a different way of applying OPAs to high-speed imaging.

Abstract Background The aim of this study is to identify the genetic defect in a Chinese family with congenital aniridia combined with cataract and nystagmus. Methods Complete ophthalmic examinations, including slit-lamp biomicroscopy, dilated indirect ophthalmoscopy, anterior segment photography, and anterior segment optical coherence tomography (OCT) were performed. Blood samples were collected from all family members and genomic DNA was extracted. Genome sequencing was performed in all family members and Sanger sequencing was used to verify variant breakpoints. Results All the thirteen members in this Chinese family, including seven patients and six normal people, were recruited in this study. The ophthalmic examination of affected patients in this family was consistent with congenital aniridia combined with cataract and nystagmus. A novel heterozygous deletion (NC_000011.10:g.31802307_31806556del) containing the 5′ region of PAX6 gene was detected that segregated with the disease. Conclusion We detected a novel deletion in PAX6 responsible for congenital aniridia in the affected individuals of this Chinese family. The novel 4.25 kb deletion in PAX6 gene of our study would further broaden the genetic defects of PAX6 associated with congenital aniridia.

Summary: 3′ UTR alternative polyadenylation (APA) quantitative trait loci (3′aQTL) can explain approximately 16.1% of trait-associated non-coding variants and is largely distinct from other molecular QTLs. Here, we describe a bioinformatic protocol for identifying 3′aQTLs through standard RNA-seq and matched genomic data. This protocol allows users to analyze dynamic APA events, identify common genetic variants associated with differential 3′ UTR usage, and predict the potential causal variants that affect APA.For complete details on the use and execution of this protocol, please refer to Li et al. (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.

In order to accurately detect the temperature of molten aluminum and overcome the adverse influence of high temperature and corrosiveness on the sensing results, a temperature detection system based on a multi-node sapphire fiber sensor was proposed and developed. Through the structural parameter design of the fiber sensor, the scheme of utilizing the 0.7 mm diameter fiber and 0.5 mm groove was formulated. Simulation and analysis were carried out to determine the ultrasonic response distribution of the signal passing through the whole fiber sensor. The results indicate that the system is capable of distinguishing test signals from various positions and temperatures. Following the completion of the static calibration, the temperature of the molten aluminum was observed in real-time, and the data of the temperature measurements conducted at the two groove locations were compared. According to the obtained results, the test accuracy was greater than 1 degree Celsius and the temperature test stability was good, laying a solid foundation for the potential development of temperature measurement devices.

Abstract The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

To handle the limitations from two types of methods, a hybrid prognostics approach is put forward to predict the remaining useful life (RUL) of wind turbine bearings in this paper. Firstly, the features of the signal are extracted from the time domain and the frequency domain. Secondly, features with better monotonicity are selected through the Spearman rank correlation analysis method and the hierarchical clustering method. Thirdly, a feature fusion method is applied to fuse the selected features into health indicator (HI) based on the percentage of the total variance. Finally, the RUL is achieved by the exponential degradation model. Moreover, the position of the first prediction is detected by applying the T-test. Implementation results indicate that the proposed approach is practical and effective in the RUL prognostics of wind turbine bearings.

Abstract The precise genetic diagnosis of dystrophinopathies can be challenging, largely due to rare deep intronic variants and more complex structural variants (SVs). We report on the genetic characterization of a dystrophinopathy patient. He remained without a genetic diagnosis after routine genetic testing, dystrophin protein and mRNA analysis, and short‐ and long‐read whole DMD gene sequencing. We finally identified a novel complex SV in DMD via long‐read whole‐genome sequencing. The variant consists of a large‐scale (~1Mb) inversion/deletion‐insertion rearrangement mediated by LINE‐1s. Our study shows that long‐read whole‐genome sequencing can serve as a clinical diagnostic tool for genetically unsolved dystrophinopathies.

The conception of the equivalent silent chain plate is proposed，the meshing relations between the inner-outer compound meshing silent chain and involute sprocket are analyzed. The correct form of silent chain plate inner profile for meshing with the sprocket involute profile is pointed out，the optimization design method of silent chain plate inner profile based on the secondary variation is proposed. The multi-body dynamics models of silent chain transmission system are established，and the silent chain transmission system based on different design methods is compared and analyzed. The results show that the optimization design of inner-outer compound meshing silent chain plate inner profile based on the secondary variation could effectively reduce the polygon effect and meshing impact of silent chain transmission system.

Power management systems are urgently needed for maximally utilizing the energy generated from triboelectric energy devices. Here, the authors report a fractal-designed switched-capacitor-convertor as a high efficiency power management strategy for triboelectric nanogenerators.

A guidance and control algorithm for the heading control of a large underwater vehicle in hover state is proposed. The complexity of the flow field makes it difficult to build an accurate mathematical model for heading control under hover condition. Furthermore, the turning motion in the hover state causes additional vertical hydrodynamic and pitching moments which may affect the control performance of depth and pitching, and even bring safety problems. An H∞ synthesis method is proposed, in which guidance weighting function is designed for the heading control constraints, sensitivity weighting function for the tracking performance requirements, and uncertainty weighting function for modeling error. Firstly, the hydrodynamic characteristics and constraints of hover and steering are analyzed, and the mathematical model is established. Secondly, based on the analysis of the affection of steering motion on depth and trim state, a control strategy is proposed to suppress interference by setting steering speed and limiting acceleration, which improves the safety of the control algorithm. And then, a H∞ robust control algorithm based on three kinds of weighting function is designed. Finally, the method is simulated by using submarine model and validated by underwater vehicle experiment.The research in this article has potential application value for safety and low noise performance for the control of large underwater platform and manned submarines.

The EX16+22Y system is a polymerase chain reaction (PCR)-based amplification kit that enables typing of 15 autosomal short tandem repeat (STR) loci (i.e., D3S1358, D13S317, D7S820, D16S539, TPOX, TH01, D2S1338, CSF1PO, D19S433, vWA, D18S51, D21S11, D8S1179, D5S818, and FGA) and 22 widely used Y chromosome STR (Y-STR) loci (DYS391, DYS527a/b, DYS635, DYS458, DYS456, DYS385a/b, DYS438, DYS448, DYS437, DYS19, DYS576, DYS533, DYS393, DYS389I/II, DYS439, DYS392, Y_GATA_H4, DYS390, and DYS481) which contains 20 core Y-STR recommended by the Ministry of Public Security and amelogenin. This multiplex system was designed for the simultaneous analysis of amelogenin-Y allele mutation, single-source searches, kinship (including familial searching), mixture profiles, international data sharing, and other forensic applications. In this study, the multiplex system was validated for sensitivity of detection, species specificity, DNA mixtures, stability, sizing precision, stutter, reproducibility, and PCR-based conditions according to the Scientific Working Group on DNA Analysis Methods developmental validation guidelines and Chinese criteria for the human fluorescent STR multiplex PCR reagent. The results show that the EX16+22Y system is a robust and reliable amplification kit which can be used for human identification testing.

The fault detection of the suspension system in a maglev train is of great importance for its operational safety and reliability. However, in random matrix theory (RMT), the size of the random matrix direct impacts the result of the mean spectral radius (MSR). In this article, a state-of-the-art fault detection method for suspension systems is proposed using optimized RMT. The random matrix with the largest number of eigenvalues is obtained by reshaping the original data, with the help of the auto-correlation length from the correlation analysis. Finally, the optimized MSR is applied to detect the fault. The results of the experiment illustrate that the proposed method is applicable and effective.

Dense mapping is an important part of mobile robot navigation and environmental understanding. Aiming to address the problem that Dense Surfel Mapping relies on the input of a common-view relationship, we propose a local map extraction strategy based on spatiotemporal consistency. The local map is extracted through the inter-frame pose observability and temporal continuity. To reduce the blurring of map fusion caused by the different viewing angles, a normal constraint is added to the map fusion and weight initialization. To achieve continuous and stable time efficiency, we dynamically adjust the parameters of superpixel extraction. The experimental results on the ICL-NUIM and KITTI datasets show that the partial reconstruction accuracy is improved by approximately 27–43%. In addition, the system achieves a greater than 15 Hz real-time performance using only CPU computation, which is improved by approximately 13%.

Suspended-core fibers (SCFs) are considered the best candidates for enhancing fiber nonlinearity in mid-infrared applications. Accurate modeling and optimization of its structure is a key part of the SCF structure design process. Due to the drawbacks of traditional numerical simulation methods, such as low speed and large errors, the deep learning-based inverse design of SCFs has become mainstream. However, the advantage of deep learning models over traditional optimization methods relies heavily on large-scale a priori datasets to train the models, a common bottleneck of data-driven methods. This paper presents a comprehensive deep learning model for the efficient inverse design of SCFs. A semi-supervised learning strategy is introduced to alleviate the burden of data acquisition. Taking SCF’s three key optical properties (effective mode area, nonlinear coefficient, and dispersion) as examples, we demonstrate that satisfactory computational results can be obtained based on small-scale training data. The proposed scheme can provide a new and effective platform for data-limited physical computing tasks.

Triboelectric nanogenerators may benefit Internet-of-Things era energy demands, but application is hindered by low charge density. Here the authors maximize charge density in ambient conditions and achieve stable power generation in a triboelectric nanogenerator that can realize external and self-excitation.

This paper mainly studies the model design of a thin-film heat-flux sensor (TFHFS), and focuses on the comparison of three dynamic calibration methods. The primary motivation for studying this came from the urgent need for heat-flux dynamic measurements in extreme environments, and the one-sidedness of the dynamic performance evaluation of the corresponding TFHFS. The dynamic theoretical model of the TFHFS was originally established on the principle of a temperature gradient on the basis of a thermal radiation boundary. Then, a novel TFHFS sensor was developed, which can be used at temperatures above 880 °C and has a high sensitivity of 2.0 × 10−5 mV/(W/m2). It can function stably for long durations under a heat-flux density of 3 MW/m2. The steady-state, transient, and frequency calibration of a TFHFS were compared to comprehensively analyze the dynamic characteristics of the TFHFS. The steady-state response time measured by the step excitation method was found to be 0.978 s. The QR decomposition method was applied to the steady-state response experimental model construction, and the fitting degree of a second-order transfer function model obtained was 98.61%. Secondly, the transient response time of the TFHFS was 0.31 ms based on the pulse-excitation method. The transient relationship between the surface temperature and the heat flux, and the pulse-width dependence of the TFHFS transient response time were established. Surprisingly, the response frequency of the TFHFS, about 3000 Hz, was efficiently tested using the frequency response function (FRF), which benefitted from the harmonic characteristics of a periodic square-wave excitation signal. Finally, a comprehensive evaluation of the dynamic performance of the TFHFS was realized.

Smart factories have real-time demands for the statistics of productivity to meet the needs of quick reaction capabilities. To solve this problem, a counting method based on our decomposition strategy of actions was proposed for complex actions. Our method needs to decompose complex actions into several essential actions and define a label string for each complex action according to the sequence of the essential actions. While counting, we firstly employ an online action recognition algorithm to transform video frames into label numbers, which will be stored in a result queue. Then, the label strings are searched for their results in queue. If the search succeeds, a complex action will be considered to have occurred. Meanwhile, the corresponding counter should be updated to accomplish counting. The comparison test results in a video dataset of workers’ repetitive movements in package printing production lines and illustrate that our method has a lower counting errors, MAE (mean absolute error) less than 5% as well as an OBOA (off-by-one accuracy) more than 90%. Moreover, to enhance the adaptability of the action recognition model to deal with the change of action duration, we propose an adaptive parameter module based on the Kalman filter, which improves counting performances to a certain extent. The conclusions are that our method can achieve high counting performance, and the adaptive parameter module can further improve performances.

To solve the problem that the time-consuming optimization process of Genetic Algorithm (GA) can erode the expected time-saving brought by the algorithm, time-optimal trajectory planning based on cubic spline was used, after the modification to classical fitness sharing function of NGA, a dual-threaded method utilizing elite strategy characteristic was designed which was based on Niche Genetic Algorithm (NGA) with the fitness sharing technique. The simulation results show that the proposed method can mitigate the contradiction of the long term the optimization algorithm takes but a short running time the trajectory gets, demonstrating the effectiveness of the proposed method. Besides, the improved fitness sharing technique has reduced the subjective process of determining relevant parameters and the optimized trajectory results met performance constraints of the robot joints.

The colour and the characteristics of colour change curves of winter jujube slices at different temperatures (55, 60, 65, 70, and 75°C) by the single factor experiment during pulsed air-jet impingement drying were stidied. The experimental data of colour difference (ΔE*) were fitted and verified by classic colour change model of thin fruit and vegetable. The results showed that L*(whiteness/darkness) and -a* (redness/greenness) of winter jujube slices decreased with the increase of drying time at different drying temperatures, while the change in the early stage was rapid, the latter stage was slow. The b* (yellowness/blueness) increased with the increase of drying time at different drying temperatures. Under different drying temperatures, the colour difference (ΔE*) of winter jujube slices increased with the increase of drying time during the drying process, the change in the early stage was rapid, the latter stage was slow. When the drying temperatures were 55, 60, 65 and 75°C, the first order fractional model of the colour difference (ΔE*) obtained the best fitting results with experimental data. Meanwhile, when the drying temperature was 70°C, the first of the first order mode of the total colour difference (ΔE*) was the best.

The drying curves, moisture effective diffusivity and drying activation energy of winter jujube slices were here investigated at different drying temperatures (55, 60, 65, 70, 75, and 80°C), wind speeds (6, 7, 8, 9, and 10 m/s) and pulsation rates (rotatational speed of material disk: 4, 5.5, and 7 rpm) in a single-factor experiment design. A mathematical model of pulsed air-jet impingement drying for winter jujube slices was fitted and verified. The results showed that the entire drying process could be described as falling rate drying; the moisture effective diffusivity was in the range 1.52-4.93 × 10-9 m2/s and increased with increasing drying temperature, wind speed and pulsation rate. The drying activation energy was 43.9 kJ/mol as determined using the Arrhenius equation. According to the statistical parameters of the correlation coefficient (R2), root mean square error (RMSE) and the sum of squared errors (SSE), the modified Page model was selected as best for representing the correlation between moisture ratio and drying time.

To study the law that governs the complex movements of the mechanism in the process of automatic weapon operation, the velocity tracking test technology of photon Doppler velocimetry is introduced to accurately measure velocity, displacement and acceleration, on the condition that there are long displacement and rapid velocity change. In the traditional way, out of interference signal time-frequency (TF) transformation draws TF distribution, and then by modulus maxima frequency extraction, comes to the law of velocity change. Due to the influence resulting from the change of fundamental signal as well as that of light intensity signal in the test, based on the TF distribution obtained by TF transformation, the traditional modulus maxima frequency extraction can extract frequency signals, but they show abnormal sudden changes at some moments, making the velocity discontinuous, unsmooth and unreal, which brings obvious errors to the subsequent calculation of acceleration and accurate displacement. Addressing the above-mentioned problems, this paper proposes a ridge extracting correction algorithm based on modulus maxima frequency extraction; this method, based on a large number of experiments where rodless cylinders are used to simulate the motion of a gun automatic mechanism, conducts a detailed calculation and analysis of the experimental results. A comparison of the two algorithms’ processing results, in terms of the speed, displacement and acceleration, suggests that the ridge extracting correction algorithm successfully corrects the frequency selection error, which draws a more continuous and, therefore, effective curve of the velocity change, and by so doing, the error of the displacement test (within 1.36 m displacement) is reduced from more than 3.6% to less than 0.58%, and the uncertainty dropped 97.07%. All these show that the accurate measurement of velocity, displacement and acceleration, with sudden and rapid velocity changes considered, is realized successfully.

Computationally intensive disciplines such as computational biology often require use of a variety of tools implemented in different scripting languages and analysis of large data sets using high-performance computing systems. Although scientific workflow systems can powerfully organize and execute large-scale data-analysis processes, creating and maintaining such workflows usually comes with nontrivial learning curves and engineering overhead, making them cumbersome to use for everyday data exploration and prototyping. To bridge the gap between interactive analysis and workflow systems, we developed Script of Scripts (SoS), an interactive data-analysis platform and workflow system with a strong emphasis on readability, practicality, and reproducibility in daily computational research. For exploratory analysis, SoS has a multilanguage scripting format that centralizes otherwise-scattered scripts and creates dynamic reports for publication and sharing. As a workflow engine, SoS provides an intuitive syntax for creating workflows in process-oriented, outcome-oriented, and mixed styles, as well as a unified interface for executing and managing tasks on a variety of computing platforms with automatic synchronization of files among isolated file systems. As illustrated herein by real-world examples, SoS is both an interactive analysis tool and pipeline platform suitable for different stages of method development and data-analysis projects. In particular, SoS can be easily adopted in existing data analysis routines to substantially improve organization, readability, and cross-platform computation management of research projects.

Based on the land use data of 2000, 2010 and 2020, using GIS technology along with landscape ecology methods, this paper monitored the changes in land use and landscape pattern in Wuhan. The findings are as follows: (1) the main features of land use change in Wuhan were the expansion of urban area and the decline of cropland, forest, wetland and water in recent 20 years; (2) forest, wetland and water kept a transfer-out trend while urban kept a transfer-in trend; (3) the fragmentation degree of forest, grassland and urban landscapes decreased from 2000 to 2020; (4) the patch shapes of almost all landscapes tended to be more regular under the human interventions. It is thereby worth reducing the interference intensity of human activities on landscape pattern in the process of urban growth.

In order to promote the sustainable development of sports facilities, the statistical data of football fields in 13 districts of Wuhan city were selected. Based on geographic information system (GIS) and exploratory spatial data analysis (ESDA) technology, this paper analyzed the global and local autocorrelation of the number of football fields per capita in Wuhan city. The results indicate that: (1) Comparing to the new districts in Wuhan, the number of football fields per capita in the central districts was relatively few. (2) In the global scale, the number of football fields per capita in Wuhan presented a spatial aggregation pattern. (3) In the local scale, the regional discrepancy and spatial clusters were confirmed in the local spatial autocorrelation analysis for football fields in Wuhan. At the same time, the research results could provide reasonable references for the planning of football field facilities.

We theoretically show that isolated agents that locally and symmetrically consume resources and sense positive resource gradients can generate constant motion via bootstrapped resource gradients in the absence of any externally imposed gradients, and we show a realization of this motion using robots. This self-generated agent motion can be coupled with neighboring agents to act as a spontaneously broken symmetry seed for emergent collective dynamics. We also show that in a sufficiently weak externally imposed gradient, it is possible for an agent to move against an external resource gradient due to the local resource depression on the landscape created by an agent. This counter-intuitive boot-strapped motion against an external gradient is demonstrated with a simple robot system on an light-emitting diode (LED) light-board.

Three-dimensional (3D) imaging under the condition of weak light and low signal-to-noise ratio is a challenging task. In this paper, a 3D imaging scheme based on time-correlated single-photon counting technology is proposed and demonstrated. The 3D imaging scheme, which is composed of a pulsed laser, a scanning mirror, single-photon detectors, and a time-correlated single-photon counting module, employs time-correlated single-photon counting technology for 3D LiDAR (Light Detection and Ranging). Aided by the range-gated technology, experiments show that the proposed scheme can image the object when the signal-to-noise ratio is decreased to −13 dB and improve the structural similarity index of imaging results by 10 times. Then we prove the proposed scheme can image the object in three dimensions with a lateral imaging resolution of 512 × 512 and an axial resolution of 4.2 mm in 6.7 s. At last, a high-resolution 3D reconstruction of an object is also achieved by using the photometric stereo algorithm.

Intermittent wind power, photovoltaic and other renewable energies have been paralleled, which makes the phenomena of high-order harmonics and simple harmonics more and more serious in the power system, showing a wide-frequency trend. The existing measurement algorithms mainly aim at signals in midfrequency and low-frequency. Besides, they are lack of a uniform high-precision algorithm for widefrequency measurement. To solve this problem, we propose a high-precision algorithm based on Z-ADALINE. Firstly, Zoom FFT algorithm is used to analyze original sampled signals. This step enables the refinement of its frequency spectrum, and obtains accurate frequency measurement results. At this time, the number of frequencies can also be determined. Secondly, the result of Zoom FFT is used as the input of the adaptive linear neural network(ADALINE). ADALINE can estimate amplitude and phase with high precision. The simulation results show that the proposed algorithm can realize high-precision measurement of frequency, amplitude and phase of wide-frequency signal effectively. Among them, the frequency resolution can be up to 0.3 Hz. The amplitude error is within 1V. Phase error is less than 0.6°. The results may provide some significant references for practical wide-frequency signal measurement in power electronic power grid.

Robot manipulators should be able to quickly detect collisions to limit damage due to physical contact. Traditional model-based detection methods in robotics are mainly concentrated on the difference between the estimated and actual applied torque. In this paper, a model independent collision detection method is presented, based on the vibration features generated by collisions. Firstly, the natural frequencies and vibration modal features of the manipulator under collisions are extracted with illustrative examples. Then, a peak frequency based method is developed for the estimation of the vibration modal along the manipulator structure. The vibration modal features are utilized for the construction and training of the artificial neural network for the collision detection task. Furthermore, the proposed networks also generate the location and direction information about contact. The experimental results show the validity of the collision detection and identification scheme, and that it can achieve considerable accuracy.

Background : Fatty acid synthase (FASN) is overexpressed in a variety of human cancers, and may be involved in cancer metastasis. Hence, the strategies targeted on FASN may have therapeutic potential for treating cancer metastasis. Objectives : The aim of this study is to investigate the correlation of FASN expression with metastasis in human osteosarcoma. Materials and Methods : Human osteosarcoma cell lines U2-OS and osteosarcoma biopsy specimens were employed in this study. The expression of FASN protein in osteosarcoma specimens was detected by IHC (immunohistochemistry) and the relationship with metastasis was analyzed. We performed the cerulenin, an inhibitor of FASN, to inhibit FASN expression in U2-OS cells. Western blot and RT-PCR were performed to investigate the expression of FASN in U2-OS cells. Cells mobility was detected by wound healing and Transwell assays. Results : Results showed that the FASN expression level in the cases with pulmonary metastases was significantly higher than in those without metastasis. In vitro, the invasion and migration of U2-OS cells were suppressed by inhibiting FASN. Our findings suggested that FASN may be involved in osteosarcoma metastasis