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Robot Learning from humans is a promising paradigm for directly transferring human skills to robots. This learning allows robots to encapsulate task constraints and motion patterns from human demonstrations as well as acquire skills that can be adapted to unseen scenarios. Even though many state-of-the-art skill-learning successes have been achieved, simultaneously addressing variability from a complex and long-horizon manipulation task and generalizing it to external uncertainty remains a challenge. This efficient skill learning has to allow for handling large-scale, high-dimensional demonstrations, adapting to environmental changes (starting, via and end points and obstacles), generalizing to task constraints (trajectory precision, stiffness), and measuring uncertainty in the reproduction. To this effect, we present a novel robot skill-learning framework called SVGP-CoGP that will implement all the aforementioned properties by encoding task variability from multiple demonstrations using Sparse Variational Gaussian Processes (SVGP) and adapting to additional constraints via a coregionalized multi-output GP (CoGP) based on SVGP. The proposed method can significantly reduce the computational complexity of model fitting by making use of the variational inference of GP models, which makes it possible for robots to learn skills from complex and long-horizon tasks. We evaluated and compared the effectiveness and strengths of our framework with existing probabilistic methods on a Kinova robot that performed emergency button-pressing tasks. The results indicated that our framework allowed the robot to learn skills from complex and long-horizon manipulation tasks that outperformed baselines both in quantitative evaluation and in an online test.

Predicting the nature of each urban functional region based on the transfer rate of empty cars plays a crucial role in constructing smart cities and urban planning. The transfer rate of empty cars describes the probability of a taxi driving from one region to another without any passengers. It can reflect the main driving directions of taxies and the main flow directions of people among different urban functional regions. Although current researches have focused on the functional regions divided by remote sensing satellite images, there is almost no discussion on determining the nature of the region through taxi behaviour. The authors consider using taxi behaviour to classify urban functional regions. Besides, the attentional spatio‐temporal model (Attentional Gated Recurrent Unit, AGRU) is introduced in the work. The AGRU consists of three modules, which are the spatial feature extraction module, the temporal feature extraction module, and the attentional pooling mechanism. The model has been evaluated on the data set provided by Didi Chuxing and it has been compared with some typical models. The experimental results show that the AGRU can reflect spatio‐temporal information, and its attentional pooling mechanism can distinguish whether a region is the place of departure or the destination.

Accurate position-force control is a core and challenging problem in robotics, especially for manipulators with redundant degrees of freedom (DOFs). For example, trajectory tracking-based control usually fails for grinding robots due to intolerable impact forces imposed onto the end effectors. The main difficulties lie in the coupling of motion and contact force, redundancy resolution, physical constraints, etc. In this article, we propose a novel motion-force control strategy in the framework of projection recurrent neural networks (RNN). Tracking error and contact force are described in orthogonal spaces, respectively, and by selecting minimizing joint torque as secondary task, the control problem is formulated as a quadratic-programming (QP) problem under multiple constraints. In order to obtain real-time optimization of joint toque, which is nonconvex relative to joint angles, the original QP is reconstructed in the velocity level, where the original objective function is replaced by its time derivative. Then, a dynamic neural network, which is convergence provable is established to solve the modified QP problem online. This work generalizes projection RNN-based position control of manipulators to that of position-force control, which opens a new avenue to shift position-force control of manipulators from pure control perspective to cross design with both convergence and optimality consideration. Numerical and experimental results show that the proposed scheme achieves accurate position-force control, and is capable of handling inequality constraints such as joint angular, velocity, and torque limitations, simultaneously, consumption of joint torque can be decreased effectively.

Islet transplantation is a potential treatment option for certain patients with type 1 diabetes; however, it still faces barriers to widespread use, including the lack of tools to monitor islet grafts post-transplantation. This study investigates whether labeling neonatal porcine islets (NPI) with polyvinylpyrrolidone-coated superparamagnetic iron oxide nanoparticles (PVP-SPIO) affects their function, and whether this nanoparticle can be utilized to monitor NPI xenografts with magnetic resonance imaging (MRI) in a mouse model. In vitro, PVP-SPIO-labeled NPI in an agarose gel was visualized clearly by MRI. PVP-SPIO-labeled islets were then transplanted under the kidney capsules of immunodeficient nondiabetic and diabetic mice. All diabetic mice that received transplantation of PVP-SPIO-labeled islets reached normoglycemia. Grafts appeared as hypo-intense areas on MRI and were distinguishable from the surrounding tissues. Following injection of spleen cells from immunocompetent mice, normoglycemic recipient mice became diabetic and islet grafts showed an increase in volume, accompanied by a mixed signal on MRI. Overall, this study demonstrates that PVP-SPIO did not affect the function of NPI that PVP-SPIO-labeled islets were easily seen on MRI, and changes in MRI signals following rejection suggest a potential use of PVP-SPIO-labeled islets to monitor graft viability.

When terahertz imaging technology is used for the nondestructive testing of composite materials, the signal is often affected by the experimental environment and internal noise of the system, as well as the absorption and scattering effect of the tested materials. The obtained image has degradation phenomena such as low contrast, poor resolution of small targets and blurred details. In order to improve the image quality, this paper proposes a novel method for the enhancement of composite materials’ terahertz image by using unsharp masking and guided filtering technology. The method includes the processing steps of hard threshold shrinkage denoising based on discrete wavelet transform, amplitude imaging, unsharp masking, guided filtering, contrast stretching, and pseudo-color mapping. In this paper, these steps are reasonably combined and optimized to obtain the final resulting image. To verify the effectiveness of the proposed method, a 150–220 GHz high frequency terahertz frequency modulated radar imaging system was used to image three commonly used sandwich structure composites, and the enhancement processing were carried out. The resulting images with significantly enhanced contrast, detail resolution and edge information were obtained, and the prefabricated defects were all detected; Five objective evaluation indexes including standard deviation, mean gradient, information entropy, energy gradient and local contrast were used to compare and analyze the processing results of different image enhancement methods. The subjective and objective evaluation results showed that the proposed method can effectively suppress the noise in terahertz detection signals, enhance the ability of defect detection and positioning, and improve the accuracy of detection. The proposed method in this paper is expected to play a positive role in improving the practicability of terahertz imaging detection technology and expanding its application fields.

Terahertz (THz) testing, by the frequency modulated continuous wave (FMCW) system, is often hampered by the limited range resolution. Thus, in this paper, a software method is proposed to reconstruct the range profile, yielding significant improvements in range resolution and in signal parameter extraction. Specifically, the multiple signal characterization algorithm is first introduced to indicate a higher-resolution range profile qualitatively and to acquire the echoes’ initial values. Then, multiple Gaussian functions are applied to fit those echoes in the whole original range profile and to accurately extract the parameters, including the amplitude, range, and width, of each echo. Finally, with these parameters, the range profile can be more precisely and flexibly reconstructed, facilitating the following detection procedure to a great extent. Both the simulated and the real THz data, acquired by a 150–220 GHz FMCW imager, have been used to demonstrate the superiority and effectiveness of our method, qualitatively and quantitatively.

Robots are prone to making anomalies when performing manipulation tasks in unstructured environments, it is often desirable that robots can detect, diagnose them, and then effectively provides prior experiences for rapidly adapting the robot anomalous behaviors. The traditional methods on anomaly diagnosis are to focus on univariate time series and ignore the modality correlation and time-dependent, which can't be applied in a Human-robot collaborative (HRC) system that is integrated with multiple sensors for improving autonomy and safety. The variability and temporal consistency of multimodal sensory data exacerbate anomaly diagnosis still an open problem in robotics. Here we propose a novel method of multimodal anomaly diagnosis by learning the time-lagged dynamics of anomalies detected during an HRC task. Specifically, a time-lagged variational auto-encoder model ( $tlVAE$ ) is first proposed to compress complex multivariate dynamics into simpler manifolds, and the manifolds are used to fitting a dynamic time warping-based K-nearest neighbors model for anomaly diagnosis in a multi-classes classification scheme. A real-robot anomaly dataset is presented to evaluate the significance and effectiveness in extracting underlying time-dependent patterns, results indicate that the efficiency and precision of diagnosis can be improved by introducing a sparse representation of the multivariate sample. Meanwhile, we compare the accuracy of anomaly diagnosis with several commonly used sparse representation methods, including Principal Component Analysis (PCA), Time-lagged Independent Component Analysis (TICA), Autoencoder (AE) as well as Variational Autoencoder (VAE). The resulting anomaly diagnosis accuracy of 97.6% across 7 kinds of anomalies, which outperformed all the baselines.

Pulmonary fibrosis (PF), a progressive and life-threatening pulmonary disease, is the main pathological basis of interstitial lung disease (ILD) which includes the idiopathic pulmonary fibrosis (IPF). No effective therapeutic strategy for pulmonary fibrosis has been established. TGF-β signaling has emerged as the vital regulator of PF; however, the detailed molecular mechanisms of TGF-β in PF were uncertain. In the present study, we proved that inhibition of MTORC2 suppresses the expression of P27 in MRC5 and HLF cells. And in bleomycin-induced PF model, the expression of α-SMA and P27 was upregulated. Moreover, TGF-β application increased the level of α-SMA, vimentin, and P27 in MRC5 and HLF cells. Furthermore, P27 overexpression advanced the cell cycle process and promoted the proliferation of MRC5 and HLF cells. Finally, the rescue experiment showed that MTORC2 knockdown reversed P27 overexpression-induced cell cycle acceleration and proliferation. Thus, our results suggest that P27 is involved in TGF-β-mediated PF, which was regulated by MTORC2, providing a novel insight into the development of PF.

Objective The purpose of this study was to explore the insulin level in the serum of chronic obstructive pulmonary disease (COPD) patients during acute exacerbation (AE). Methods The study population consisted of 22 acute exacerbation chronic obstructive pulmonary disease (AECOPD) patients, 20 COPD patients and 20 healthy controls. Fasting blood glucose, insulin and serum lipid levels were measured. After the patients recovered from AE, the insulin and glucose levels were also analyzed. Results Insulin level, glucose level and homeostasis model assessment of insulin resistance (HOMA-IR) of AECOPD patients were higher than healthy controls (7.19±6.02 vs 3.28±1.09 μIU/mL, P 0.05). Conclusion A high insulin level and a high HOMA-IR status in COPD patients were demonstrated. Downregulated levels of insulin during AE compared with the convalescent state were detected, while the variation in the glucose level was not as great as expected, indicating a potentially important role for insulin in AECOPD.

Terahertz (THz) radiation is able to penetrate many different types of nonpolar and nonmetallic materials. THz imaging can be combined with computed tomography (CT) to form THz CT, which is an effective method to visualize the sample’s internal structure. Here, we report the continuous-wave (CW) THz CT imaging results using Bessel beam generated by use of axicon. In this method, the sample cross section is scanned in all translation and rotation directions. Due to the non-diffracting property of the Bessel beam, two-dimensional (2-D) cross-sectional images of the internal structure of the plastic pipe at different distances from axicon can be full reconstructed by using filtered back projection algorithm. Compared with conventional Gaussian beam, Bessel beam can extend the depth of field in CW THz CT system. Using this system, we obtained 2-D cross-sectional image of the internal structure of the clogged plastic pipe and the obstruction was accurately detected by comparing 2-D cross-sectional images at different sections. Our experimental data demonstrated a feasible application of the CW THz CT system with Bessel beam in non-destructive testing.

Deep learning parallel plays an important role in accelerating model training and improving prediction accuracy. In order to fully consider the authenticity of the simulation application scenario of model, the development of deep learning model is becoming more complex and deeper. However, a more complex and deeper model requires a larger amount of computation compared to common spatio-temporal model. In order to speed up the calculation speed and accuracy of the deep learning model, this work optimizes the common spatial-temporal model in deep learning from three aspects: data parallel, model parallel and gradient accumulation algorithm. Firstly, the data parallel slicing algorithm proposed in this work achieves parallel GPUs load balancing. Secondly, this work independently parallelizes the components of the deep spatio-temporal. Finally, this work proposes a gradient accumulation algorithm based on deep reinforcement learning. This work uses two data sets (GeoLife and Chengdu Taxi) to train and evaluate multiple parallel modes. The parallel mode combining data parallel and gradient accumulation algorithm is determined. The experimental effect has been greatly improved compared with the original model.

Blind people have intelligent tools to rely on for travel with the development of navigation technology. The GPS navigation, blind track, etc., are tools that blind people often use when traveling outdoors. However, indoor navigation tools and technology for blind people are lacking. We propose an obstacle avoidance algorithm and a spatial-temporal model of trajectory prediction for the indoor travel task of the blind. The focus of this work is that it enables the blind to accurately avoid obstacles and achieve high accuracy trajectory prediction aiming at the unique movement characteristics of the blind. We set up a variety of baselines to conduct an experimental evaluation on a dataset of blind trajectories in a multistorey shopping mall. The experimental results show the advantages of the data model and predictive model of this work.

In the attentional bias and cognitive processing relating to the abstinent heroin addicts (AHAs), there were considerable studies about event related potentials (ERP) and behavioral data. However, the large amount of data lead to longer data processing time, and few studies were done on single channel data about AHA. This study investigated whether single channel’s data can be used to identify AHAs from healthy controls (HCs) accurately. Two groups of age-, education-, and gender-matched adults (22 AHAs, 21 HCs) performed on the gambling task. ERP features and behavior features were used to classify. For discriminating AHAs and HCs, ReliefF and SVM-RFE were applied for feature selection, and Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were used to search the optimal classification model of Support Vector Machine (SVM). We analyzed the statistical significance of all the features and obtained the classification result of different stimulation conditions. In statistics, we found that AHAs were significantly different from HCs in the amplitude of P300, ERP’s mean value and ERP’s variance under the monetary stimulation. For large money stimulation, P300 power in delta band and N100 power in delta band had significant difference between AHAs and HCs. Combining feature sorting algorithms and optimization algorithms, the results indicated that optimal performance was achieved by using ReliefF and GA. Use the above method, the best accuracy is 86.04% in four kind (+99, +9, -9, -99) of stimulation. This is the first study that used single channel’s ERP data to identify AHAs with HCs, our study provided a new insight and objective method for the rapid diagnosis of AHAs.

A quasi-optical antenna system is designed for terahertz active standoff personnel screening in this paper. The design principle of the quasi-optical antenna is described in detail according to actual needs. The results from GRASP software shows that the system can achieve 10 mm imaging resolution over a 28 × 24 cm field of view at 2 m with a center frequency 180 GHz. it has the potential to be used in active terahertz standoff personnel screening.

The spatial resolution of terahertz (THz) image is often degraded by many factors during the imaging process. Therefore, a “Zero-Shot” super-resolution CNN framework, which is trained only by the input image itself, is proposed to cope with those degradation factors. With this unsupervised framework, the restoration level of the CNN can be flexibly adjusted based on the input THz image in order to achieve the best restoration effect. Experimental results on both the simulated data and real tested THz data have proved the effectiveness of our method.

Aging is an inevitable and complex natural phenomenon due to the increase in age. Cellular senescence means a non-proliferative but viable cellular physiological state. It is the basis of aging, and it exists in the body at any time point. Idiopathic pulmonary fibrosis (IPF) is an interstitial fibrous lung disease with unknown etiology, characterized by irreversible destruction of lung structure and function. Aging is one of the most critical risk factors for IPF, and extensive epidemiological data confirms IPF as an aging-related disease. Senescent fibroblasts in IPF show abnormal activation, telomere shortening, metabolic reprogramming, mitochondrial dysfunction, apoptosis resistance, autophagy deficiency, and senescence-associated secretory phenotypes (SASP). These characteristics of senescent fibroblasts establish a close link between cellular senescence and IPF. The treatment of senescence-related molecules and pathways is continually emerging, and using senolytics eliminating senescent fibroblasts is also actively tried as a new therapy for IPF. In this review, we discuss the roles of aging and cellular senescence in IPF. In particular, we summarize the signaling pathways through which senescent fibroblasts influence the occurrence and development of IPF. On this basis, we further talk about the current treatment ideas, hoping this paper can be used as a helpful reference for future researches.

Ovarian clear cell carcinoma (OCCC) is a special pathological type of epithelial ovarian carcinoma (EOC) and has a high prevalence in Asia without specific molecular subtype classification. Endometriosis is a recognized precancerous lesion that carries 3-fold increased risk of OCCC. Ovarian endometrioid carcinoma, which also originates from endometriosis, shares several features with OCCC, including platinum resistance and younger age at diagnosis. Patients with OCCC have about a 2.5 to 4 times greater risk of having a venous thromboembolism (VTE) compared with other EOC, and OCCC tends to metastasize through lymphatic vesicular and peritoneal spread as opposed to hematogenous metastasis. There is only mild elevation of the conventional biomarker CA125. Staging surgery or optimal cytoreduction combined with chemotherapy is a common therapeutic strategy for OCCC. However, platinum resistance commonly portends a poor prognosis, so novel treatments are urgently needed. Targeted therapy and immunotherapy are currently being studied, including PARP, EZH2, and ATR inhibitors combined with the synthetic lethality of ARID1A-dificiency, and MAPK/PI3K/HER2, VEGF/bFGF/PDGF, HNF1β, and PD-1/PD-L1 inhibitors. Advanced stage, suboptimal cytoreduction, platinum resistance, lymph node metastasis, and VTE are major prognostic predictors for OCCC. We focus on update pathogenesis, diagnostic methods and therapeutic approaches to provide future directions for clinical diagnosis and treatment of OCCC.

Background Patients with coronavirus disease 2019 (COVID‐19) often suffer sudden deterioration of disease around 1‐2 weeks after onset. Once the disease progressed to severe phase, clinical prognosis of patients will significantly deteriorate. Methods This was a multicenter retrospective study on patients of all adult inpatients (≥18 years old) from Tianyou Hospital (Wuhan, China) and the Fourth Affiliated Hospital, Zhejiang University School of Medicine. All 139 patients had laboratory‐confirmed COVID‐19 in their early stage, which is defined as within 7 days of clinical symptoms. Univariate and multivariate logistic regression models were used to determine the predictive factors in the early detection of patients who may subsequently develop into severe cases. Results Multivariable logistic regression analysis showed that the higher level of hypersensitivity C‐reactive protein (OR = 4.77, 95% CI:1.92‐11.87, P = .001), elevated alanine aminotransferase (OR = 6.87, 95%CI:1.56‐30.21, P = .011), and chronic comorbidities (OR = 11.48, 95% CI:4.44‐29.66, P, Early COVID‐19 patients with chronic comorbidities, elevated hs‐CRP or increased ALT are significantly more likely to develop severe pneumonia as disease progresses

During the real-aperture-scanning imaging process, terahertz (THz) images are often plagued with the problem of low spatial resolution. Therefore, an accommodative super-resolution framework for THz images is proposed. Specifically, the 3D degradation model for the imaging system is firstly proposed by incorporating the focused THz beam distribution, which determines the relationship between the imaging range and the corresponding image restoration level. Secondly, an adjustable CNN is introduced to cope with this range dependent super-resolution problem. By simply tuning an interpolation parameter, the network can be adjusted to produce arbitrary restoration levels between the trained fixed levels without extra training. Finally, by selecting the appropriate interpolation coefficient according to the measured imaging range, each THz image can be coped with its matched network and reach the outstanding super-resolution effect. Both the simulated and real tested data, acquired by a 160 ∼ 220 GHz imager, have been used to demonstrate the superiority of our method.

Background: Patients with coronavirus disease 2019 (COVID-19) often suffer sudden deterioration of disease around 1 to 2 weeks after onset. Once the disease progressed to severe phase, clinical prognosis of patients will significantly deteriorate.Methods: This was a multicenter retrospective study on patients of all adult inpatients (≥18 years old) from Tianyou Hospital (Wuhan, China) and the Fourth Affiliated Hospital, Zhejiang University School of Medicine. All 139 patients had laboratory-confirmed COVID-19 in their early stage, which is defined as within 7 days of clinical symptoms or within 7 days of positive viral nucleic acid test for asymptomatic patients. Univariate and multivariate logistic regression models were used to determine the predictive factors in the early detection of patients who may subsequently develop into severe cases.Results: Multivariable logistic regression analysis showed that the higher level of hypersensitivity C-reactive protein (OR=4.77, 95% CI:1.92-11.87, P=0.001), elevated alanine aminotransferase (OR=6.87, 95%CI:1.56-30.21, P=0.011) and chronic comorbidities (OR=11.48, 95% CI:4.44-29.66, PConclusion: Early COVID-19 patients with chronic comorbidities, elevated hs-CRP or elevated ALT are significantly more likely to develop severe pneumonia as the disease progresses. These risk factors may facilitate the early diagnosis of critical patients in clinical practice.

Background Transbronchial needle aspiration (TBNA) is a minimally invasive procedure performed to diagnose lymph node (LN) adenopathy. TBNA with and without endobronchial ultrasound (EBUS) guidance has a high diagnostic yield for malignant LN enlargement, but the value for diagnosing benign LN enlargement has been less thoroughly investigated. Methods We retrospectively evaluated 3540 patients with mediastinal LN enlargement who received TBNA. One hundred sixty-six patients with benign mediastinal lymphadenopathy were included and 293 LNs were biopsied. A positive result was defined as a specific histological abnormality. Conventional TBNA (cTBNA) and EBUS-TBNA, as well as cTBNA and transbronchial forceps biopsy (TBFB), were compared. The subgroup analysis was stratified by disease type and LN size. Results A diagnosis was made in 76.84% of the EBUS-TBNA and 61.31% of the cTBNA (P P P P 20 mm the difference was marginal. These findings were confirmed in a group of independent patients who received cTBNA plus EBUS-TBNA. The diagnostic yield did not differ between cTBNA and TBFB, but significantly increased to 76.67% when both modalities were employed. Conclusions EBUS-TBNA is the preferred minimally invasive diagnostic method for benign mediastinal LN disease. Combined cTBNA and TBFB is a safe and feasible alternative when EBUS is unavailable.

Robotic bin-picking is a common process in modern manufacturing, logistics, and warehousing that aims to pick-up known or unknown objects with random poses out of a bin by using a robot-camera system. Rapid and accurate object pose estimation pipelines have become an escalating issue for robot picking in recent years. In this paper, a fast 6-DoF (degrees of freedom) pose estimation pipeline for random bin-picking is proposed in which the pipeline is capable of recognizing different types of objects in various cluttered scenarios and uses an adaptive threshold segment strategy to accelerate estimation and matching for the robot picking task. Particularly, our proposed method can be effectively trained with fewer samples by introducing the geometric properties of objects such as contour, normal distribution, and curvature. An experimental setup is designed with a Kinova 6-Dof robot and an Ensenso industrial 3D camera for evaluating our proposed methods with respect to four different objects. The results indicate that our proposed method achieves a 91.25% average success rate and a 0.265s average estimation time, which sufficiently demonstrates that our approach provides competitive results for fast objects pose estimation and can be applied to robotic random bin-picking tasks.

In this chapter, we introduce an anomaly monitoring pipeline using the Bayesian nonparametric hidden Markov models after the task representation and skill identification in previous chapter, which divided into three categories according to different thresholds definition, including (i) log-likelihood-based threshold, (ii) threshold based on the gradient of log-likelihood, and (iii) computing the threshold by mapping latent state to log-likelihood. Those method are effectively implement the anomaly monitoring during robot manipulation task. We also evaluate and analyse the performance and results for each method, respectively.

In this chapter, we introduce two novel anomaly diagnose methods using the Bayesian nonparametric hidden Markov models when anomaly triggered, including i)multi-class classifier based on nonparametric models, ii) sparse representation by statistical feature extraction for anomaly diagnose. Additionally, the detail procedure for anomaly sample definition, the supervised learning dataset collection as well as the data augmentation of insufficient samples are also declared. We evaluated the proposed methods with a multi-step human-robot collaboration objects kitting task on Baxter robot, the performance and results are presented of each method respectively.

In this chapter, we present a novel method for incremental learning robot complex task representation, identifying repeated skills, and generalizing to new environment by heuristically segmenting the unstructured demonstrations into movement primitives that modelled with a dynamical system. The proposed method combines the advantages of recent task representation methods for learning from demonstration in into an integrated framework. In particular, we use the combination of finite state machine and dynamical movement primitives for complex task representation, and investigate the Bayesian nonparametric hidden Markov model for repeated skill identification. To this end, a robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of skills, which helps a robot understand what it is doing at all times. Two complex, multi-step robot tasks are designed to evaluate the feasibility and effectiveness of proposed methods. We not only present the results in task representation, but also analyzing the performance of skill identification by various nonparametric models with various modality combinations.

Inter-basin water transfer (IBWT) projects offer us a long-term means to minimize the mismatch between water demand and water availability. Climate change may impose significant vulnerability to IBWT projects through perturbations in water availability. However, previous studies of climate change’s impacts on IBWT’s vulnerability are mainly based on a top-down framework, i.e. forecasting the climate change via a wide range of GCMs, which may underestimate the uncertainty of climate change. In order to address this problem, a bottom-up vulnerability assessment framework is developed to evaluate the vulnerability of IBWT. In this framework, an IBWT vulnerability indicator is proposed based on three dimensions of vulnerability including exposure, sensitivity and adaptive capacity. The framework also highlights the deep uncertainty of climate change by adopting a probabilistic Budyko model, which can estimate the water availability over a broad range of climate futures. The South-to-North Water Transfer Project (SNWTP) in China is adopted as a case study to illustrate the effectiveness of the proposed framework. It shows that the framework is a useful tool for identifying the detrimental climate condition scope for the IBWT’s vulnerability, and is valuable to guide long-term water resources management and planning for policymakers.

A large-range three-coil coaxial optical fiber displacement sensor for measuring the air gap of a direct-drive wind turbine is designed in this paper so as to overcome the problem that the traditional reflective optical fiber sensor has a small measuring range. The mathematical model of the modulation function of the three-coil coaxial large-range fiber displacement sensor is established by using the simplified geometric optical reflection spot model, so that the ratio compensation mechanism under different combinations is analyzed and compared, and the parameters affecting the characteristics of the sensor, including the fiber core radius R and the numerical aperture NA of the transmitting fiber, and the number of receiving fiber loops in the second, third and fourth layers of the sensor probe are analyzed by simulation. The results show: As the number of laps of the receiving fiber turns, the sensor range is significantly improved, and its sensitivity is improved, but the corresponding initial dead zone is also increased; the smaller the numerical aperture of the transmitting fiber, the larger the linear range of the output characteristic curve; the larger the radius of the fiber core, the larger the linear range of the output characteristic curve, but the corresponding initial dead zone becomes larger and the sensitivity is reduced, so that the final selected design parameters of the system are given by analysis. Finally, through the sensor characteristics experiment, the actual measurement range of the sensor probe is consistent with the theoretical simulation results, and its measuring range can reach 3.5mm- 8.5mm.

Parameter uncertainty inherent in reservoir operation affects operation model robustness and has been considered in conventional operation focusing on improving hydropower generation. With more attention paid to ecological environment protection recently, riverine ecosystem protection requires environmental flow (e-flow) management to sustain a near-natural flow regime. Whether there is e-flow management in reservoir operation has an impact on the uncertainty of reservoir operation, but parameter uncertainty was rarely considered in reservoir operation with e-flow management. In this study, a framework is proposed for performing parameter uncertainty analysis in reservoir operation associated with e-flow management. Both e-flow requirements and hydropower generation are considered in reservoir operation to sustain the harmonious development between ecological environment and human society. To compare the effect of different e-flow managements on the uncertainty of reservoir operation, three e-flow management scenarios are set. The Metropolis-Hastings algorithm of Markov Chain Monte Carlo (MCMC) sampling approach was applied for parameter estimation and uncertainty quantification. We used this framework in a case study of Nuozhadu hydropower station on the Lancang River in southern China to test its effectiveness. The results demonstrated that parameter uncertainty greatly affects the robustness of reservoir operation model. The comparison of reservoir operation under different e-flow management scenarios shows that more detailed e-flow management can effectively reduce uncertainty in reservoir operation and sustain the near-natural flow regime in a river.

Sub-100-nm channel length ZnSe film/graphene hybrid-based photodetectors have been achieved using ZnS nanowire with an average diameter of 100 nm. The as-fabricated 70 nm-channel ZnSe film/graphene hybrid photodetector shows a fast response speed of 50 ms and an ultrahigh responsivity of $1.2\times 10^{9}$ A/W, which is three orders of magnitude higher than that of $5~\mu \text{m}$ channel ZnSe film/graphene photodetector. Obviously, the channel length has a huge impact on the responsivity enhancement as the channel length decreases from micron scale to sub-100-nm scale.

The power consumption readings of sub meter and total meter of distribution transformer of low-voltage users follow the law of conservation of energy. The meter power loss rate of abnormal low-voltage users must also be abnormal. This paper studies the solution of the meter power loss rate under the four abnormal power consumption scenarios of single (multi) user and full (partial) period. The traditional linear solution method has accurate identification effect for the abnormal power consumption scenario of full period, but it cannot identify the abnormal power consumption scenario of partial period. In this paper, an improved artificial fish swarm algorithm is proposed. By adjusting the fixed step to the adaptive step, the power loss rate of each sub meter is obtained, and the abnormal power users are pinpointed. The research results are verified by simulation examples on IEEE European Low Voltage Test Feeder. The results show that the improved artificial fish swarm algorithm in this paper can identify abnormal power users for the above four abnormal electric field scenarios. The algorithm provides a new alternative for the identification of abnormal low voltage users.

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial–temporal model and graph spatial–temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks).

Layered ReS2 with direct bandgap and strong in-plane anisotropy shows great potential to develop high-performance angle-resolved photodetectors and optoelectronic devices. However, systematic characterizations of the angle-dependent photoresponse of ReS2 are still very limited. Here, we studied the anisotropic photoresponse of layered ReS2 phototransistors in depth. Angel-resolved Raman spectrum and field-effect mobility are tested to confirm the inconsistency between its electrical and optical anisotropies, which are along 120° and 90°, respectively. We further measured the angle-resolved photoresponse of a ReS2 transistor with 6 diagonally paired electrodes. The maximum photoresponsivity exceeds 0.515 A⋅W−1 along b-axis, which is around 3.8 times larger than that along the direction perpendicular to b axis, which is consistent with the optical anisotropic directions. The incident wavelength- and power-dependent photoresponse measurement along two anisotropic axes further demonstrates that b axis has stronger light–ReS2 interaction, which explains the anisotropic photoresponse. We also observed angle-dependent photoresistive switching behavior of the ReS2 transistor, which leads to the formation of angle-resolved phototransistor memory. It has simplified structure to create dynamic optoelectronic resistive random access memory controlled spatially through polarized light. This capability has great potential for real-time pattern recognition and photoconfiguration of artificial neural networks (ANN) in a wide spectral range of sensitivity provided by polarized light.

BaSi 2 films were fabricated on textured Si(0 0 1) substrates that consisted of {1 1 1} facets using molecular beam epitaxy. The light-trapping effect of these films and their performance when incorporated into solar cells were measured. X-ray diffraction and reflectivity measurements showed that the BaSi 2 films were grown epitaxially on the textured Si(0 0 1) substrate and confirmed the light-trapping effect. The critical thickness over which BaSi 2 relaxes increased from approximately 50 to 100 nm when comparing the BaSi 2 films on a flat Si(1 1 1) substrate and the textured substrate, respectively. p-BaSi 2 /n-Si solar cells were fabricated with varying BaSi 2 layer thickness and with hole concentrations in the range between 2.0 × 10 18 and 4.6 × 10 18 cm −3 . These cells exhibited a maximum energy conversion efficiency of 4.62% with an open-circuit voltage of 0.30 V and a short-circuit current density of 27.6 mA/cm 2 when the p-BaSi 2 layer was 75 nm-thick. These results indicated that the use of BaSi 2 films on textured Si(0 0 1) substrates in solar cells shows great promise.

The recently explored FeNb11O29 is an advanced anode material for lithium-ion batteries due to its large specific capacity and high safety. However, its poor electronic conductivity significantly limits its rate capability. To tackle this issue, a Cr3+ doping is employed, and CrxFe1-xNb11O29 (x = 0 and 0.2) materials were fabricated using a solid-state reaction. X-ray diffraction analyses combined with Rietveld refinements demonstrate that the Cr3+ doping does not destroy the orthorhombic shear ReO3 crystal structure (Amma space group) of FeNb11O29 or obviously change the unit cell volume. Scanning electron microscopy images combined with specific surface area tests reveal the similar particle size after the Cr3+ doping. Due to the free 3d electrons in the Cr3+ ions, the electronic conductivity of Cr0.2Fe0.8Nb11O29 is enhanced by three orders of magnitude comparing with FeNb11O29. Consequently, Cr0.2Fe0.8Nb11O29 exhibits improved electrochemical properties. At 0.1C, it delivers a large reversible capacity of 254 mAh g−1. At 10C, it still provides a large capacity of 123 mAh g−1 with large retention of 86.9% after 500 cycles. In contrast, FeNb11O29 shows a small capacity of 57 mAh g−1 and small retention of 41.6%. These results reveal that Cr0.2Fe0.8Nb11O29 can be a promising anode material for high-performance lithium-ion batteries.

Graphene/semiconductor film hybrid-based photodetectors have attracted extensive attention because of their ultra-high performance. However, only the photoconductive behavior of binary semiconductors with fixed bandgap was studied since achieving tunable optoelectronic properties is significant for the practical applications of semiconductors. Herein, alloyed ZnxCd1−xS (0 ≤ x ≤ 1) films with complete compositional tunability were successfully prepared by an e-beam evaporation method via changing the molar ratio of ZnS to CdS in the target. UV-visible optical absorption measurements showed that the composition-dependent band-edge absorption is generated in the as-prepared alloyed ZnxCd1−xS films, with the energy gaps continuously shifted from 2.41 eV (x = 0, CdS) to 3.67 eV (x = 1, ZnS). After graphene is transferred on the alloyed ZnxCd1−xS films, band-tunable photodetectors are achieved based on graphene/alloyed ZnxCd1−xS film hybrids with the cut-off wavelength in the spectral response gradually changing from 410 nm to 580 nm. Obviously, the method introduced in this study can be extended to fabricate other graphene/alloyed ternary film hybrid-based photodetectors with band tunability in the visible and infrared region.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) such as tungsten diselenide (WSe2) have spead many interesting physical properties, which may become ideal candidates to develop new generation electronic and optoelectronic devices. In order to reveal essential features of 2D TMDCs, it is necessary to fabricate high-quality devices with reliable electrical contact. We systematically analyze the effect of graphene and metal contacts on performance of multi-layered WSe2 field effect transistors (FETs). The temperature-dependent transport characteristics of both devices are tested. Only graphene-contacted WSe2 FETs are observed with the metal-insulator transition phenomenon which mainly attributes to the ultra-clean contact interface and lowered contact barrier. Further characterization on contact barrier demonstrates that graphene contact enables lower contact barrier with WSe2 than metal contact, since the Fermi level of graphene can be modulated by the gate bias to match the Fermi level of the channel material. We also analyze the carrier mobility of both devices under different temperatures, revealing that graphene contact can reduce the charge scattering of the device caused by ionized impurities and phonon vibrations in low and room temperature regions, respectively. This work is expected to provide reference for fabricating 2D material devices with decent performances.

Dam impoundment causes significant phosphorus (P) retention in reservoirs, leading to the emergence of legacy P release as a water quality threat under external pollution load reduction measures. Reservoir operations impact sediment P release through their complex influence on hydrodynamic and biogeochemical processes, but applicatory reservoir operation strategies for legacy P management have rarely been proposed. This study introduces a reservoir operation optimization approach for balancing economic objectives (i.e., water supply and hydropower generation) and P release mitigation. A coupled hydrodynamic-eutrophication-sediment model based on the Environmental Fluid Dynamics Code platform is used to simulate sediment P release under multiple reservoir operation scenarios. The resultant simulation data are used to train a dynamic artificial neural network (ANN) to model the response of P release to reservoir operations. By coupling the ANN with a reservoir operation model, we develop a multi-objective optimization model to refine reservoir operations for a three-tier tradeoff (i.e., water supply, hydropower generation and P release mitigation). China's Danjiangkou Reservoir is chosen as a case study to generate optimal operation rules. The optimal operation scheme decreases multi-year averaged annual P release by 19% (from 6688 tons to 5436 tons) while inversely increasing annual economic loss caused by water supply shortages by 14% and decreasing hydropower generation by 5%. Pareto-optimal solutions are subsequently generated to analyze tradeoffs between water supply/hydropower generation and P release mitigation. This study demonstrates the efficiency of using reservoir operations for legacy P management and offers an optimization tool for reservoir operation decision-making processes.

Along with globalization, countries consume a large amount of goods and services from both domestic and international markets. As one of the world's largest agricultural countries, China is faced with serious water scarcity and has to reduce the water consumption from both domestic and global aspects, of which the crop planting structure optimization (CPSO) between regions based on the virtual water perspective could be a solution for efficient agricultural water consumption. In this article, three scenarios of Chinese agriculture, including agricultural water consumption restrictions relaxed scenario, agricultural water consumption limited scenario, and net utilization ratio of water resources limited scenario, were designed to minimize the national agricultural water consumption under various water, land, and crop planting constraints in individual provinces and analyze the impacts of the CPSO. The results showed that compared with the historical situation of crop planting in 2007, 53.3%, 51.4%, and 47.3% of the agricultural water consumption and more than 10% of the sown area of China was saved under the three scenarios, respectively. Because of the reduction of agricultural water consumption and land use, it brought about the expansion of crops production in China. CPSO is found to have notable effects on water saving and food security considering the dependence of the crops by international trade.

Graphene is a promising candidate as transparent electrode owing to its high optical transmittance and conductivity. Graphene transfer is still one of critical issues for achieving the high-performance transparent graphene electrodes. Here, we report a crackless transfer method without the removal of polymer, which is usually utilized as the carrier for graphene transfer. This method enables the successful transfer of single-layer graphene onto polymethyl-methacrylate (PMMA)/AB-glue/polyethylene-terephthalate (PET) up to 14-inch with a sheet resistance of 219 Ω sq−1 at 96.5% transmittance. The conductivity is comparable with that of the doped graphene films, and is 5 times higher than that of undoped graphene films obtained by the transfer with polymer removal. Our experiment results show that the sheet resistance is proportional to the area of graphene crack, which is unavoidably caused during removing the polymer carrier. So the high conductivity is attributed to crackless graphene films produced by our new transfer method.

Purpose – This paper aims to use the crankshaft-bearing system of a four-cylinder internal combustion engine as the studying object, and develop a multi-objective optimization design of the crankshaft-bearing. In the current optimization design of engine crankshaft-bearing, only the crankshaft-bearing was considered as the studying object. However, the corresponding relations of major structure dimensions exist between the crankshaft and the crankshaft-bearing in internal combustion engine, and there are the interaction effects between the crankshaft and the crankshaft-bearing during the operation of internal combustion engine. Design/methodology/approach – The crankshaft mass and the total frictional power loss of crankshaft-bearing s are selected as the objective functions in the optimization design of crankshaft-bearing. The Particle Swarm Optimization algorithm based on the idea of decreasing strategy of inertia weight with the exponential type is used in the optimization calculation. Findings – The total frictional power loss of crankshaft-bearing and the crankshaft mass are decreased, respectively, by 26.2 and 5.3 per cent by the multi-objective optimization design of crankshaft-bearing, which are more reasonable than the ones of single-objective optimization design in which only the crankshaft-bearing is considered as the studying object. Originality/value – The crankshaft-bearing system of a four-cylinder internal combustion engine is taken as the studying object, and the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system is developed. The results of this paper are helpful to the design of the crankshaft-bearing for engine. There is universal significance to research the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system. The research method of the multi-objective optimization design of crankshaft-bearing based on the crankshaft-bearing system can be used to the optimization design of the bearing in the shaft-bearing system of ordinary machinery.

For a given photoactive semiconductor, all the light with photon energy larger than the semiconductor band gap makes a contribution to the detected signal, and therefore the fine light wavelength cannot be identified by the detected signal. Herein, we design an intelligent photodetector based on graphene/ZnS/CdS hybrids to distinguish the detected light wavelength by means of response time. On one hand, the inserted 30 nm thick ZnS films act as barriers for the charge transfer of the visible-light-generated carriers from CdS to graphene and thereby prolong the response time up to 110 ms in contrast to 40 ms of a graphene/CdS based photodetector. On the other hand, ZnS films are used to replace CdS films for the detection of ultraviolet (UV) light with a response time of 50 ms similar to that of graphene/CdS film based devices. Moreover, the amount of response time variation between visible light and UV light detection increases with the increase of inserted ZnS film thickness for a graphene/ZnS/CdS hybrid based photodetector (50 vs. 270 ms, 50 vs. 680 ms and 50 vs. 690 ms corresponding to ZnS film thicknesses of 60 nm, 90 nm and 120 nm).

With the rapid development of measurement technology, measurement plays an important role in economic construction, scientific research and social life. With the development of intelligent and digital technology, there are more and more intelligent flow meters based on different measurement principles. With the improvement of industrial intelligence and industrial production, the demand for centralized monitoring of flowmeter and the formation of flow monitoring system is also growing. Based on the computer monitoring system technology of single chip microcomputer, can bus and configuration software, this paper designs a vortex flow system with open, reliable and communication functions, which improves the detection level of common vortex flow meter through the computer monitoring system technology.

Barium disilicide (BaSi2) in thin-film solar cell applications has drawn considerable interest owing to its promising optical and electrical properties. We have achieved an efficiency of 9.9% in p-BaSi2/n-Si heterojunction solar cells, which is the highest performance reported among semiconducting silicide devices; however, this value remains much lower than the theoretical limit as defined by the material's band gap. In this paper, we performed numerical simulations based on Silvaco ATLAS to investigate the effects of defect parameters on the performance of heterojunction solar cells. The defects were modeled by introducing two tail bands around the edge of the conduction and valence bands, and an acceptor-like Gaussian distributed localized energy level within the band gap of BaSi2. The influence of the band tail density of states and the parameters of the localized energy levels on the short-circuit current density, open-circuit voltage, fill factor, and efficiency were evaluated. These results enabled us to reproduce the measured J-V characteristics by simulation. We propose a defect model of p-BaSi2 films to reproduce experimentally obtained current-voltage characteristics of p-BaSi2/n-Si heterojunction solar cell under AM1.5 illumination. Two band tails and one acceptor-like localized energy level reproduce the experimental result.

Hydropower has a damaging effect on aquatic biodiversity globally. Extensive research has demonstrated the significance of plant diversity in sustaining wetland services and functions. However, wetland plant diversity conservation has rarely been considered in hydropower operation optimization, and the relationship between hydropower generation and plant diversity indicators has rarely been quantified. In this study, a new optimization approach is proposed for reservoir operation to balance hydropower generation and plant diversity conservation in downstream wetlands. We identify key hydrological indicators affecting wetland plant diversity, accounting for the richness and abundance of plants as well as the evenness of multiple plant species. An artificial neural network (ANN) is subsequently adopted to quantify the complex relationship between hydrological and plant diversity indicators. Using the ANN model for plant diversity prediction, we develop a multi-objective optimization model for reservoir operation to maximize hydropower generation and wetland plant diversity indicators. This approach is applied to a large plant-dominated wetland (Baiyangdian) in China as a case study. Using a genetic algorithm to solve the optimization model, we suggest a reservoir operation scheme which can increase multi-year averaged plant diversity indicator in this wetland by 20% while decreasing reservoir hydropower generation by 10%. We also generate a set of Pareto-optimal solutions from the optimization model, which quantifies the tradeoff relationship between hydropower generation and plant diversity conservation. This relationship provides a reference for managers in determining reservoir operation rules depending on the demands on these two conflicting objectives.

Lake eutrophication is a serious environmental problem worldwide, caused by both natural processes and anthropogenic influences. For effective lake eutrophication management, a variety of models have been designed to investigate the complex interrelationship between physical, chemical and biological factors and processes in lakes. However, these models are inconvenient for predictions of lake eutrophication in practical application. To date, very few studies have focused on lake eutrophication in terms of anthropogenic influences (such as sewage emissions and agricultural practices), which are more readily regulated than natural processes. Effective lake eutrophication prediction models should base primarily on facile controlled or predictable indicators. Therefore, to meet this requirement, we designed simple predictive models to determine the interrelationship between the trophic states of lakes and nutrient inputs, which is a direct measurement to characterize anthropogenic influences. Lake Taihu (China) was used as a representative eutrophic water body to assess the accuracy of the proposed predictive models. Firstly, to comprehensively understand the role of nutrient input indicators (NIIs) during eutrophication, Pearson correlation coefficient analysis was conducted on 7 NIIs and 38 types of algae. Secondly, based on the NIIs identified with high correlation coefficients, we modified three commonly used growth models (Gompertz, logistic and Richards) to describe the lake's trophic state. A particle swarm optimization algorithm (PSOA) was used to optimize model variables. Results showed that the mean absolute percentage errors of the optimized Gompertz, logistic and the Richards models were 2.95%, 2.88% and 2.17%, respectively. Finally, these optimized models were used to predict lake eutrophication under several different nutrient input scenarios. Two adjustment scenarios revealed remarkably satisfied trophic states (including roughly oligotrophic and even ultra-oligotrophic) by the 2030s. Our results show that these established models are a simple way to support lake restoration projects by setting realistic and effective targets.