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Ochratoxin A (OTA) is a highly toxic food contaminant and is harmful to human beings. Herein, a ratiometric electrochemical aptasensor based on a DNA tetrahedral nanomaterial (NTH) was developed in combination with the signal tag of a zirconium metal-organic framework (UiO-66) for the detection of OTA. In the sensor, UiO-66 and a [Fe(CN)

This study aimed to develop and validate a nomogram to forecast severe kidney disease (SKD) outcomes for hospitalized Henoch–Schönlein purpura (HSP) children. The predictive model was built based on a primary cohort that included 2,019 patients with HSP who were diagnosed between January 2009 and December 2013. Another cohort consisting of 461 patients between January 2014 and December 2016 was recruited for independent validation. Patients were followed up for 24 months in development/training and validation cohorts. The data were gathered at multiple time points after HSP (at 3, 6, 12, and 24 months) covering severe kidney disease as the severe outcome after HSP. The least absolute shrinkage and selection operator (LASSO) regression model was utilized to decrease data dimension and choose potentially relevant features, which included socioeconomic factors, clinical features, and treatments. Multivariate Cox proportional hazards analysis was employed to establish a novel nomogram. The performance of the nomogram was assessed on the aspects of its calibration, discrimination, and clinical usefulness. The nomogram comprised serious skin rash or digestive tract purpura, severe gastrointestinal (GI) manifestations, recurrent symptoms, and renal involvement as predictors of SKD, providing favorable calibration and discrimination in the training dataset with a C-index of 0.751 (95% CI, 0.734–0.769). Furthermore, it demonstrated receivable discrimination in the validation cohort, with a C-index of 0.714 (95% CI, 0.678–0.750). With the use of decision curve analysis, the nomogram was proven to be clinically useful. The nomogram independently predicted SKD in HSP and displayed favorable discrimination and calibration values. It could be convenient to promote the individualized prediction of SKD in patients with HSP.

ObjectiveTo explore the application values of deep-learning based artificial intelligence (AI) automatic classification system, on the differential diagnosis of non-lactating mastitis (NLM) and malignant breast tumors, via its comparation with traditional ultrasound interpretations and the following interpretation conclusions made by the sonographers with various seniorities.MethodsA total of 707 patients suffering from breast lesions (475 malignant breast tumors and 232 NLM), were selected from the following three medical centers, including Zhejiang Cancer Hospital, Hebei Province Hospital of Traditional Chinese Medicine, and Yantai Affiliated Hospital of Binzhou Medical University, and the time period was set from April 2020 to September 2021. All selected cases firstly accepted the routine breast ultrasound diagnosis, followed by the interpretations from a senior sonographer with more than 15 years of work experience, and an intermediate-aged sonographer with more than 5 years of work experience, independently. Meanwhile, a third physician also interpreted the same ultrasound images by deep learning–based AI automatic classification system, independent of the interpretation results from the previous two physicians. The kappa test was performed to evaluate the consistency between the conventional ultrasound interpretation results and pathological results interpreted from physicians with different working experiences.ResultsIn total, 475 cases of malignant breast tumors (512 nodules) and 232 cases of NLM (255 nodules) were pathologically diagnosed. The accuracy, sensitivity, and specificity of conventional ultrasound interpretations vary from different sonographers with different working experiences. The accuracy, sensitivity, and specificity for intermediate-aged sonographers and senior sonographers were 76.92% (590/767), 84.71% (216/255), and 73.95% (374/512) and 87.35% (670/767), 86.27% (220/255), and 87.89% (450/512), respectively (PConclusionsThe deep learning–based AI automatic classification system is expected to become a reliable auxiliary way to distinguish NLM and malignant breast tumors due to its high sensitivity, accuracy, and specificity.

Objectives An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. Methods In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. Results The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2–93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0–92.9%) and the general AUC value was 0.955 (p < 0.001). Conclusions A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription–polymerase chain reaction (RT-PCR) test. Key Points • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07797-x.

Summary Tidal current turbine is the core equipment to convert the tidal current energy. In this paper, the performance of the designed tidal current turbine is tested in the laboratory, then the impeller is used to conduct an experiment on the desalination of sea water. The sea test of the novel equipment has been done in BoHai Sea. The experimental results indicate that the efficiency of the newly developed turbine could reach up to 47.6%; the corresponding tip speed ratio is from 3.5 to 6. Under the condition of big yaw angle, the turbine could still remain high working efficiency. When the tidal current velocity exceeds 1.0 m/s, the pressure may increase to 3.5 Mpa, and under this condition, sea water can be desalinated through reverse osmic membrane. The electrical conductivity of desalinated water is around 540 μS/cm. For the experiment on hydrodynamic performance, the self-starting flow velocity of the turbine is 0.745 m/s, the staring torque is 1 Nm while about 0.97–1.0 m/s self-starting flow velocity and around 10–15 Nm starting torque for the experiment on desalination of sea water. It is believed a good attempt by using tidal current turbine in the desalination of sea water, providing the practice basis on expanding the utilization of ocean energy. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, numerical and experimental investigations are presented on the hydrodynamic performance of a horizontal tidal current turbine (TCT) designed and made by our Dalian University of Technology (DUT) research group. Thus, it is given the acronym: DUTTCT. An open-source computational fluid dynamics (CFD) solver, called pimpledymfoam, is employed to perform numerical simulations for design analysis, while experimental tests are conducted in a DUT towing tank. The important factors, including self-starting velocity, tip speed ratio (TSR), and yaw angle, which play important roles in the turbine output power, are studied in the investigations. Results obtained show that the maximum power efficiency of the newly developed turbine (DUTTCT) could reach up to 47.6%, and all its power efficiency is over 40% in the TSR range from 3.5 to 6; the self-starting velocity of DUTTCT is about 0.745 m/s; and the yaw angle has negligible influence on its efficiency as it is less than 10 deg.

A multireference configuration interaction (MRCI) study has been carried out on the LiCl molecule. The potential energy has been calculated over a wide range of internuclear separation for the 21 low-lying electronic states of the LiCl molecule dissociating into Li (2S, 2P, 3S)+Cl (2P). The (4)1Σ+, (3)1Π, 1–33Σ+, 1–33Π, 1,3Δ, 1,3Σ−, (5)1Σ+, (4)3Σ+, (4)1Π, (4)3Π excited states are studied for the first time in theory. Molecular spectroscopic constants (Re,De, ωe, ωeχe, Be and αe) have been derived for the 9 bound states (X1Σ+, (3)1Σ+, (2)3Σ+, 1,3Δ, 1,3Σ−, (4)1Π, (4)3Π) with a regular shape, and the spectroscopic constants of ground states X1Σ+ are in good agreement with available experimental and theoretical values. The relative differences between experimental values and our values for Re, De,ωe, ωeχe, Be and αe are 1.02%, 0.60%, 1.72%, 9.46%, 2.0%, and 0.75%, respectively. Moreover, vibrational levels of 9 bound states, which have not been investigated experimentally, are computed.

A novel procedure for calculating the dynamic response of elastic solid structures is presented. The ultimate aim of this study is to develop a consistent set of finite volume (FV) methods on unstructured meshes for the analysis of dynamic fluidstructure interaction (FSI). This paper describes a two-dimensional (2D) FV cell-vertex based method for dynamic solid mechanics. A novel matrix-free implicit scheme was developed using the Newmark method and dual time step algorithm and the model is validated with a 2D cantilever test case as well as a 2D plate one.

In this paper, numerical and experimental investigations are presented on the hydrodynamic performance of a horizontal tidal current turbine (TCT) designed and made by our Dalian University of Technology (DUT) research group. Thus it is given the acronym: DUTTCT. An open source CFD solver, called PimpleDyMFoam, is employed to perform numerical simulations for design analysis, while experimental tests are conducted in a DUT towing tank. The important factors, including self-starting velocity, tip speed ratio (TSR) and yaw angle, which play important roles in the turbine output power, are studied in the investigations. Results obtained show that the maximum power efficiency of the newly developed turbine (DUTTCT) could reach up to 47.6% and all its power efficiency is over 40% in the TSR range from 3.5 to 6; the self-starting velocity of DUTTCT is about 0.745m/s; the yaw angle has negligible influence on its efficiency as it is less than 10°.

Classical Contract-Net protocol among the multi-Agent bidding can realize the cooperation of open heterogeneous environment. But MAS (Multiple Agent System) is a typical complex system. MAS's open, distributed and dynamic characteristics make the MAS environment filled with a lot of indeterminate factors, lead to surge in network traffic and negotiation deadlock. To solve these problems, this paper introduces the trust level domain and reputation mechanism to establish trust and reputation of the Contract-Net model. The model based on the history of cooperation experience in multi-Agent can effectively reduce network traffic, improve the efficiency and cooperation real-time of MAS.

Quickly finding the physical locations of one or more replicas of desired data based on logical replica name is the foundation to implementing grid data access efficiently. A replica location method based on direct index linked lists is proposed based on the analysis of recent condition in replica location. The distributed replica location can be quickly implemented by using the direct index location node, the main replica linked lists and local location information in the method. It can achieve good scalability and ease of implementation.

In this paper, Complex Network theory is applied to macroscopic research of C2 system, and based on fitness degree model of scale-free network, the topological properties and robustness of C2 Complex Network are discussed. In view of the cascading failures of Complex Network, the node dependency degree parameter and the adjustable increasing parameter are added to the load-capacity model, and the fundamental research approach to the cascading failures of C2 system, which opens a new way for the research of macroscopic law of C2 system.

Wireless sensor network coverage control commonly makes nodes dormancy and arousal by algorithm based on 0–1 apperceiving model. The algorithm doesn't reflect practical conditions. Aiming at the problem, this paper adopts the probability perception model, puts forward a new distributed k-coverage control algorithm, the node exchange information with neighbor-nodes and finds k advantage nodes on energy and ability of perception of neighbor nodes, and then sends corresponding awakening information. By the ways, every node is k-coverage controlled. It realizes working nodes decrease and working time of network increase. The simulation results have validated its effectiveness.