Search

SnO2 has been the most commonly used electron transport layer (ETL) in perovskite solar cells (PSCs) due to its excellent electron mobility and stability. To meet the applications of SnO2 ETL in la...

Limited by the number of available photons collected by a pixel and the stability of the imaging system, images captured under poor illumination conditions are often degraded by heavy shot noise and low contrast. In this letter, we propose a novel low-light image enhancement algorithm using the Poisson noise-aware Retinex model, in which the Poisson distribution is considered to formulate the fidelity term in the Retinex model for the first time. Furthermore, the space-variant weight maps for total variation regularization terms are calculated according to the piecewise smooth prior of illumination component and the Poisson noise distribution prior, which contributes to noise suppression in different noise intensity while preserving image details and structures. We get the optimal solution of the Poisson noise-aware Retinex model by an iterative optimization algorithm. Finally, the enhanced image is obtained by gamma correcting the estimated illumination map. Experimental results demonstrate that the proposed model performs better than state-of-the-art methods in low-light image enhancement and noise suppression.

Demand-side management (DSM) could realize “peak cutting and valley filling” of power load and improve the stability and efficiency of power system. With the development of information systems, more smart devices are being deployed on the demand side. It has become a challenge for DSM service providers to take full use of the edge computing capacity and demand-side information to improve the accuracy of DSM decision-making, without revealing user privacy. This paper proposes a distributed DSM pricing method for service provider, based on asynchronous advantage actor-critic (A3C) algorithm and long short-term memory (LSTM) network under cloud-edge environment. The on-site utilization of user information is realized through distributed training and centralized decision-making structure of A3C algorithm. The training process is accelerated by LSTM based virtual environment, which greatly reduces the training cost of the algorithm. Case study results shows that the proposed method is able to make pricing decision for DSM service provider under cloud-edge environment. Moreover, through the combination of LSTM based virtual environment and A3C algorithm, the proposed method requires less historical data than other methods and improves the profit of service providers.

BackgroundThe involvement of gastrointestinal (GI) symptoms in the progression of illness in COVID-19 patients has not been illustrated, with the association between GI symptoms and illness severity remaining controversial. The present study aimed to evaluate the association between GI symptoms and the illness progression, severity, and prognosis in COVID-19 patients.MethodsThis study retrospectively recruited consecutive patients with laboratory-confirmed COVID-19 from three hospitals in Wuhan. The severity of illness was classified as non-severe and severe for analyses. The primary outcome was the association between GI symptoms and progression from non-severe to severe illness (PNTS) in COVID-19 patients. ResultsOf the 934 COVID-19 patients (mean age 59.3 years; 43.7% males), the prevalence of overall and specific GI symptoms at/prior to admission were 59.9% and 13.0%, respectively. Patients with GI symptoms were associated with increased risk of fever (56.1% vs. 48.1%; P=0.02), increased IL-6 (18.2% vs. 11.7%; P=0.04), ground-glass opacity (56.8% vs. 43.1%; PConclusions The occurrence of GI symptoms is proved to be an independent risk factor for PNTS, which might be a predicting indicator in the prevention of illness deterioration at an early stage.

Background. Reprogramming of lipid metabolism is closely associated with tumor development, serving as a common and critical metabolic feature that emerges during tumor evolution. Meanwhile, immune cells in the tumor microenvironment also undergo aberrant lipid metabolism, and altered lipid metabolism also has an impact on the function and status of immune cells, further promoting malignant biological behavior. Consequently, we focused on lipid metabolism-related genes for constructing a novel prognostic marker and evaluating immune status in prostate cancer. Methods. Information about prostate cancer patients was obtained from TCGA and GEO databases. The NMF algorithm was conducted to identify the molecular subtypes. The least absolute shrinkage and selection operator (Lasso) regression analysis was applied to establish a prognostic risk signature. CIBERSORT algorithm was used to calculate immune cell infiltration levels in prostate cancer. External clinical validation data were used to validate the results. Results. Prostate cancer samples were divided into two subtypes according to the NMF algorithm. A six-gene risk signature (PTGS2, SGPP2, ALB, PLA2G2A, SRD5A2, and SLC2A4) was independent of prognosis and showed good stability. There were significant differences between risk groups of patients with respect to the infiltration of immune cells and clinical variables. Response to immunotherapy also differed between different risk groups. Furthermore, the mRNA expression levels of the signature genes were verified in tissue samples by qRT-PCR. Conclusion. We constructed a six-gene signature with lipid metabolism in prostate cancer to effectively predict prognosis and reflect immune microenvironment status.

In order to improve the reliability and continuity of the power distribution system, power Internet of Things analysis monitoring system was constructed in this article, and the system was used for comprehensive applications such as data acquisition and fault analysis. Fault location methods considering distribution phasor measurement unit (D-PMU) information and network symmetry characteristics are proposed. With the measurement information provided by the D-PMU, the fault sections and the fault types are determined by the phase difference between the positive and negative sequence currents. Different location algorithms are obtained based on the load symmetry of the distribution network. The fault location method based on the line distribution parameters is used for the three-phase symmetric system, and the fault location method based on line impedance is proposed for the three-phase asymmetric system. Finally, the effect of different nodes with a D-PMU on the fault location accuracy is analyzed, and the configuration principle of the D-PMU was proposed. The PSCAD/MATLAB simulation software is used to analyze the example and verify the effectiveness of the proposed method. The simulation results demonstrate that high-precision fault location could be realized and that the practicality and economy are improved.

The new reform of power system promotes the market-oriented operation of microgrids. The ubiquitous power internet of things provide support in information, data, and computation to microgrids in market operation, energy management, and coordination interaction. This study takes the park microgrid with multi-stakeholder as the object, establishes a two-level optimisation model of microgrid bidding transaction based on multi-agent system. In the lower level optimisation, considering the deviation penalty of power generation and the previous round bidding results, the optimal bidding strategy model is established by the bidding unit agent to maximise its benefit. In the upper-level model, bidding strategies of distributed energy resources (DERs) as constraints, a multiple objective mixed-integer linear programming model was built to optimise the overall objectives of clearing price and imbalanced deviation, searching for the optimal clearing price and the generation plan of DERs. The results were fed back to the DERs to help them form the next round of strategies until the result reaches equilibrium. The proposed optimised operation mode is compared with the traditional operation mode in the case study, verifying that the proposed method can realise the optimal operation of the microgrid and the coordinated interaction with the main grid, increasing the benefit of stakeholders.

Endoscopic submucosal dissection (ESD) is accepted as a standard therapeutic technique for superficial esophageal neoplasms (SENs). However, esophageal refractory stricture is a serious adverse event secondary to extensive ESD (≥ 3/4 of the luminal circumference). This retrospective study aimed to investigate the risk factors for refractory postoperative stricture after extensive ESD.The data of patients who underwent esophageal ESD at the Endoscopy Center of Changhai Hospital were reviewed between January 2011 and September 2019. Risk factors for postoperative refractory stricture [≥ 6 sessions of endoscopic balloon dilation (EBD)] after extensive ESD were then identified using univariate analysis and multivariate logistic regression analysis.A total of 69 SENs in 67 patients treated by extensive ESD were enrolled in this study. The refractory stricture incidence was 62% (43/69). Significant differences between non-refractory stricture group and refractory stricture group were observed in depth of infiltration (m1or m2/m3 or sm1:20/6 vs. 17/26, P = 0.003), longitudinal resection length (50 mm/ ≥ 50 mm:19/7 vs. 10/33, P0.001), circumferential range (3/4~1/1:20/6 vs. 19/24, P = 0.008), muscular injury (NO/YES:18/8 vs. 19/24, P = 0.043), and clip number (≤ 5/5:15/11 vs. 12/31, P = 0.014). Multivariate analysis revealed that longitudinal resection length ≥ 50 mm (odds ratio [OR] 11.099, 95% confidence interval [CI] 2.620-47.019), depth of infiltration above m2 (OR 5.716, 95%CI 1.324-24.672) and muscular injury happened (OR 4.431, 95%CI 1.052-18.659) were independent risk factors for refractory stricture. In addition, the EBD sessions for treatment of refractory stricture was related to longitudinal resection length (relation coefficient γ = 0.528; P 0.05).The longitudinal resection length, depth of tumor infiltration and muscular injury are the reliable risk factors for esophageal refractory stricture after extensive ESD.

In this paper, we consider a one-dimensional laminated beam with structural damping and an infinite memory acting on the effective rotation angle. Under appropriate assumptions imposed on the relaxation function, we show that the system is well-posed by using the Hille–Yosida theorem, and then we establish general decay results, from which exponential and polynomial decays are only special cases, in the case of equal speeds of wave propagation as well as that of nonequal speeds. In the particular case when the wave propagation speeds are different and the relaxation function decays exponentially, we show the lack of exponential stability.

Active Object Tracking (AOT) is crucial to many vision-based applications, e.g., mobile robot, intelligent surveillance. However, there are a number of challenges when deploying active tracking in complex scenarios, e.g., target is frequently occluded by obstacles. In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. To achieve effective collaboration among cameras, we propose a novel Pose-Assisted Multi-Camera Collaboration System, which enables a camera to cooperate with the others by sharing camera poses for active object tracking. In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images. The pose-based controller moves the camera in accordance to the poses of the other cameras. At each step, the switcher decides which action to take from the two controllers according to the visibility of the target. The experimental results demonstrate that our system outperforms all the baselines and is capable of generalizing to unseen environments. The code and demo videos are available on our website https://sites.google.com/view/pose-assisted-collaboration.

Although circulating tumor cells (CTCs) have shown promise as potential biomarkers for diagnostic and prognostic assessment in gastric cancer (GC), determining the predictive and prognostic value of programmed death‐ligand 1 (PD‐L1)‐positive CTCs in patients with GC is a challenge. Here, we identified that the expression of total vimentin (VIM) protein was positively correlated with PD‐L1 and inhibited CD8+ T‐cell activation in patients with GC according to bioinformatics analysis. Notably, coexpression of PD‐L1 and cell‐surface VIM (CSV) was detected by immunofluorescence and immunohistochemistry assay in locally advanced GC tumor specimens and metastatic lymph nodes. Likewise, CSV expression level was significantly decreased after transiently knocking down PD‐L1 in GC cell lines. Based on our established CTC detection platform, CTCs were isolated from peripheral blood samples collected from 70 patients (38 resectable and 32 unresectable) with GC using magnetic positive selection and a CSV‐specific monoclonal antibody, 84‐1. CSV+PD‐L1+CTCs were observed in 50 of 70 (71%) GC patient samples, ranging from 0 to 261 mL−1. A higher number of CSV+PD‐L1+CTCs were significantly associated with a short survival duration and poor therapeutic response. This study demonstrated that detection of PD‐L1+CTCs using a CSV‐enrichment method has promising value as a clinically relevant prognostic marker for GC., CSV+PD‐L1+CTC exhibits epithelial mesenchymal transition subtype, which can be observed in GC patient’s peripheral blood samples. A higher number of CSV+PD‐L1+CTCs are significantly associated with a short survival duration and poor therapeutic response.

Demand-side management (DSM) increases the complexity of forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from the data set. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to convert the continuity feature of input data into binomial distribution feature. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on the genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods.

The recently theoretical and experimental researches on $${\mathcal{PT}}$$ PT -symmetric system have attracted unprecedented attention because of the various interesting features and potential applications in extending canonical quantum mechanics. However, as the counterpart of $${\mathcal{PT}}$$ PT -symmetry, there are few experimental researches on the anti-$${\mathcal{PT}}$$ PT -symmetric quantum system because of the challenges in simulating anti-$${\mathcal{PT}}$$ PT -symmetry in experiment. Here, we propose an algorithm for simulating the generalized anti-$${\mathcal{PT}}$$ PT -symmetric system in the quantum circuit model. Utilizing the algorithm, we demonstrated the quantum simulation of anti-$${\mathcal{PT}}$$ PT system experimentally, and an oscillation of information flow is observed in a nuclear magnetic resonance quantum simulator. The experiment showed that the information could recover from the environment completely when the anti-$${\mathcal{PT}}$$ PT -symmetry is broken, whereas no information can be retrieved in the symmetry-unbroken phase. Our work opens the gate for the practical quantum simulation and experimental investigation of universal anti-$${\mathcal{PT}}$$ PT -symmetric system in a quantum computer.

Thermostatically controlled loads (TCLs) have become a major tool for the demand response (DR) program when air conditioners cause peak loads in a day during the winter or summer. To solve the problem of a direct load control with TCL usually affecting user comfort and hardly considering responsiveness, a power retailer air-conditioning load aggregation operation control and demand response method was proposed in this research. From the perspective of a power retailer, a compensation mechanism for TCL was constructed, which was composed of a basic incentive program and an additional incentive program. The basic incentive program aimed to encourage users with a low response degree to increase the response capacity in order to participate in DR. An auxiliary service market control strategy based on a new compensation mechanism of the electricity retailer was detailed, which fully considered the enthusiasm of the user in mobilizing the response and reducing the load reduction fluctuation when using the state-queuing (SQ) model. Case studies were provided to verify the effectiveness of the proposed method. Compared with other compensation schemes, the simulation results showed that the compensation mechanism provided in this research was more reasonable, and it could smooth the load and reduce fluctuations. The compensation distribution among the user groups could effectively control the uniform distribution in the user groups in the temperature range, and it could mobilize users at different temperatures to participate in DR.

Polymeric hole-transporting materials (HTMs) with low production cost and versatile wet processing procedures are important for efficient and stable perovskite solar cells. In this study, three F-substituted benzodithiophene copolymers were introduced in perovskite solar cells as dopant-free efficient HTMs. Among them, perovskite solar cells based on dopant-free PBDT[2F]T achieved a comparable power conversion efficiency (PCE) (17.52%) to that of the well-known p-type doped spiro-OMeTAD (∼20.2%). More importantly, the solar cells with pristine PBDT[2F]T HTMs presented impressive stability in contrast to that with doped spiro-OMeTAD as the HTM in ambient atmosphere and even in high humidity. The detailed study showed that the dopant-free PBDT[2F]T HTM presents exceptional ability to extract holes from the perovskite layer, and simultaneously the capability to reduce trap-assisted recombination at the interface of perovskite/PBDT[2F]T. These findings pave the way for the development of dopant-free polymeric HTMs for the fabrication of cost-effective and stable perovskite solar cells.

Rotavirus A (RVA) G3P[8] is sporadically detected in China, although G9P[8] predominates. To evaluate their genetic composition at the whole-genome level, 24 G3P[8] RVA strains isolated from children under five years were sequenced and characterized. The 24 strains were genotyped as G3-P[8]-I1-R1-C1-M1-A1-N1-T1-E1-H1, indicating the Wa-like genotype constellation. A maximum clade credibility (MCC) tree for VP7 indicated that G3 had an estimated mean evolutionary rate of 7.279 × 10

SHP1 is a non-receptor protein tyrosine phosphatase that is widely expressed in hematopoietic cells such as white blood cells, neutrophils, and immune cells. SHP1 can regulate the occurrence and differentiation of immune cells and plays an important role as a tumor suppressor. Previous studies have suggested that SHP2, the homologous protein of phosphatase SHP1, can undergo liquid-liquid phase separation (LLPS). Therefore, in this study, we investigated if SHP1 is also capable of LLPS. To the best of our knowledge, our study is the first to reveal that SHP1 has the ability to undergo LLPS. In addition, we identified an important residue, SHP1-R360E, that can completely inhibit the LLPS ability of SHP1, but this mutation has no remarkable effect on SHP1's enzymatic activity. This allows us to explore the phosphatase activity and phase separation ability of SHP1 separately, providing a basis for future exploration of the phase separation mechanism of phosphatases.

Multiple myeloma (MM) is a monoclonal malignancy characterized by abnormal proliferation of plasma cells. The disease clinically manifests as anemia, hypercalcemia, renal insufficiencies, and osteolytic damage. Osteolytic damage goes with severe bone pain, spinal instability, and pathological fracture, symptoms that are collectively referred to as multiple myeloma bone disease (MMBD). Polymethylmethacrylate (PMMA) bone cement is widely used for bone repair after MMBD surgery, owing to its excellent biomechanical properties and fast curing. To date, however, efficacy of drug-loading PMMA in inhibition of tumor growth and angiogenesis remains unknown. Here, we report that 17-AAG-loaded PMMA bone cement inhibits MM growth in vivo and suppresses tumor diffusion to peripheral tissues. In addition, 17-AAG-loaded PMMA promotes MM apoptosis by downregulating Bax and active Caspase-3.

A novel dicyanoisophorone-based red-emissive fluorescence probe (YT) with large Stokes shift (230 nm) was synthesized for rapid (20 s) and selective detection of hypochlorite ions in nearly 100% aqueous medium. YT responded to hypochlorite ions via the ClO

Background: Several published studies have shown the significance of neuroinflammation in the pathophysiological development of cerebral palsy (CP). However, the underlying molecular mechanisms by which neuroinflammation is associated with brain injury in CP remain poorly understood. Spastic CP is the most common form of CP, comprising 80% of all cases. Therefore, identifying patterns of inflammatory and related biomarkers would serve to understand the etiology of spastic CP.Methods: Related clinical parameters were assessed in 18 children with spastic CP and 20 age-matched healthy individuals. Blood samples of children with spastic CP and controls were analyzed with integrated transcriptomics and proteomics profiling to detect common differentially expressed mRNAs and proteins. Hypoxic ischemic encephalopathy was induced in postnatal day 7 rat pups. Behavioral testing was performed postinjury, and then, the differentially expressed markers and inflammatory cytokines were verified in the peripheral blood and cerebral cortex of the CP model rats by ELISA and Western blot. Independent sample t-tests, one-way analysis of variance and the Pearson correlation were used for statistical analysis.Results: Through proteomic and transcriptome analysis, we identified common differentially expressed genes. Among them, ALPL, encoding tissue nonspecific alkaline phosphatase (TNAP), was downregulated in spastic CP. In addition, compared with those of the corresponding controls, significantly lower mRNA and protein levels of TNAP were found in children with CP and the CP model rats. In contrast, compared with the control rats, the model rats demonstrated a significant increase in osteopontin and proinflammatory biomarkers, such as interleukin (IL)-6, IL-17, and C-reactive protein (CRP), in both the plasma and cerebral cortex, while serum 25 hydroxyvitamin D and anti-inflammatory cytokines such as IL-10 were significantly decreased. Additionally, the level of serum TNAP was correlated with the content of serum CRP and IL-10 in model rats.Conclusion: These results suggest that TNAP, the gene expression product of ALPL, is closely related to the pathogenesis and development of spastic CP, particularly in the upregulation of proinflammatory cytokines and the downregulation of anti-inflammatory cytokines, which contribute to the occurrence of inflammation in the progression of spastic CP.

Amino-thiol bifunctional polysilsesquioxane/carbon nanotubes (PSQ/CNTs) magnetic composites were prepared by sol-gel method with two types of functional siloxanes coating on carboxyl CNTs simultaneously. The composites were served as efficient adsorbents for removing Hg(II) in aqueous solution and the adsorption properties were investigated systematically. The optimal pH of bifunctional composites for Hg(II) removal is at pH 4.5. The thermodynamic fitting curves are more consistent with the Langmuir model and the adsorption capacities of the bifunctional composites for Hg(II) varied from 1.63 to 1.94 mmol g−1 at 25°C according to the Langmuir model. The kinetics curves are more fitted to the pseudo-second-order model and the composites could selectively adsorb Hg(II) in a series of binary metal ions solution. The elution regeneration tests showed that the adsorption rate could still reach 78% after repeat cycle three times. It is expected that the bifunctional PSQ/CNTs magnetic composites can be potentially applied to remove low concentration Hg(II) from waste water.

Research on improving the reliability of oxygen analyzers is of great significance to industrial production and environmental monitoring. In order to improve the reliability of the oxygen analyzer, based on the analysis of the common redundant system, combined with the actual situation, considering the maximum cost saving and the effective improvement of the reliability, this paper analyzes the common redundant system, and selects the best redundant system and the number of redundancy. Finally, the Markov model is used to calculate the specific reliability of the designed oxygen analyzer and compared with the reliability of the traditional oxygen analyzer, which proves that the reliability of the oxygen analyzer designed in this paper is better than that of the traditional oxygen analyzer. The great improvement lays a theoretical foundation for the oxygen analyzer in improving the reliability.

Multiple myeloma (MM) is a monoclonal malignancy characterized by abnormal proliferation of plasma cells. Its main clinical symptoms are osteolytic damage, severe bone pain, spinal instability, and pathological fracture and are collectively referred to as multiple myeloma bone disease (MMBD). Due to its good biomechanical properties and fast curing, polymethylmethacrylate (PMMA) bone cement is widely used for bone repair after MMBD surgery. However, whether drug-loading PMMA can inhibit tumor growth and angiogenesis has not been reported. Here, we report that 17-AAG-loaded PMMA bone cement inhibits MM growth in vivo and suppresses tumor diffusion to peripheral tissues. 17-AAG-loaded PMMA promotes MM apoptosis by downregulating Bax and active Caspase-3.

Modulation classification or recognition of underwater acoustic (UWA) communication signals faces great difficulties due to the unpredictable characteristics of underwater acoustic channels as well as lack of training data. As traditional feature-based methods work poorly in such scenarios, resurgent artificial neural networks are gaining more attentions in the area of automatic modulation classification. Deep learning, however, demands large amount of training data for good recognition performance. To address the problem of insufficient training data, we propose to adopt the channel replay technique. With this technique, channel realizations with the same statistical characteristics as the measured underwater acoustic channel, can be generated as much as one needs. Training and validation data sets can then be generated with the replayed channels. A residual network (ResNet) is adopted to achieve simultaneously feature extraction and modulation recognition. It is shown the proposed ResNet not only classifies artificial test data but also experimental data collected in an at-sea UWA communication trail.

With the development of energy internet and power market, the operation regulation and pricing mechanism of traditional virtual power plants are improved to adapt to the new environment. In this paper, a bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant is proposed to provide a framework for solving the interest distribution between operators and optimal scheduling problems of multiple-operator virtual power plant. An operator power allocation and internal electricity price formation method based on bidding equilibrium is proposed in the upper level, which introduces the fluctuation cost coefficient to express the influence of the uncertainty of renewable energy power generation on the bidding process. A multi-time scale optimal scheduling method combining scheduling model and adjustment strategy is established in the lower level. A default penalty mechanism in the scheduling model is used to ensure that operators provide the electricity allocated from the bidding process and considering the influence of demand response based on internal electricity price on adjustment strategy. Simulation results show that the proposed method can realize the optimal distribution of operators’ power generation and form the internal electricity price that reflects the internal supply and demand level of virtual power plant. Besides, it can reduce the impact of uncertainty on dispatching results and improve the application range of virtual power plant to enhance the competitiveness of virtual power plant in market transactions.