In complex industrial processes, it is difficult to measure the key quality variables online. It takes a long time to obtain quality variables through offline testing, which makes it difficult to get ...timely information to guide the production process. Therefore, this article proposes a novel dynamic time feature expanding and extracting framework for the sinter quality prediction. First, the original data are differentiated, compensated for time delay, expanded, and serialized by using time characteristics, and the input time series is reconstructed. Second, the integrated time features extractor is used to obtain the process information. Then, the recurrent neural network regression is applied to obtain the prediction of key quality variables. Finally, the effectiveness of the proposed method is verified by a numerical example, the actual data of sintering process and various comparative experiments, and the prediction effect of FeO content in sintering process is improved.
Quantum computational advantage using photons Zhong, Han-Sen; Wang, Hui; Deng, Yu-Hao ...
Science (American Association for the Advancement of Science),
12/2020, Letnik:
370, Številka:
6523
Journal Article
Recenzirano
Odprti dostop
Quantum computers promise to perform certain tasks that are believed to be intractable to classical computers. Boson sampling is such a task and is considered a strong candidate to demonstrate the ...quantum computational advantage. We performed Gaussian boson sampling by sending 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix-the whole optical setup is phase-locked-and sampling the output using 100 high-efficiency single-photon detectors. The obtained samples were validated against plausible hypotheses exploiting thermal states, distinguishable photons, and uniform distribution. The photonic quantum computer,
, generates up to 76 output photon clicks, which yields an output state-space dimension of 10
and a sampling rate that is faster than using the state-of-the-art simulation strategy and supercomputers by a factor of ~10
.
We report phase-programmable Gaussian boson sampling (GBS) which produces up to 113 photon detection events out of a 144-mode photonic circuit. A new high-brightness and scalable quantum light source ...is developed, exploring the idea of stimulated emission of squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. The obtained samples are efficiently validated by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our GBS experiment passes a nonclassicality test based on inequality constraints, and we reveal nontrivial genuine high-order correlations in the GBS samples, which are evidence of robustness against possible classical simulation schemes. This photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to ∼ 1043, and a sampling rate ∼ 1024 faster than using brute-force simulation on classical supercomputers.
The development of highly efficient bifunctional catalysts for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is crucial for improving the efficiency of the Zn–air battery. ...Herein, we report porous NiO/CoN interface nanowire arrays (PINWs) with both oxygen vacancies and a strongly interconnected nanointerface between NiO and CoN domains for promoting the electrocatalytic performance and stability for OER and ORR. Extended X-ray absorption fine structure spectroscopy, electron spin resonance, and high-resolution transmission electron microscopy investigations demonstrate that the decrease of the coordination number for cobalt, the enhanced oxygen vacancies on the NiO/CoN nanointerface, and strongly coupled nanointerface between NiO and CoN domains are responsible for the good bifunctional electrocatalytic performance of NiO/CoN PINWs. The primary Zn–air batteries, using NiO/CoN PINWs as an air–cathode, display an open-circuit potential of 1.46 V, a high power density of 79.6 mW cm–2, and an energy density of 945 Wh kg–1. The three-series solid batteries fabricated by NiO/CoN PINWs can support a timer to work for more than 12 h. This work demonstrates the importance of interface coupling and oxygen vacancies in the development of high-performance Zn–air batteries.
in recent years, more attention has been paid to the fuzzy relationship between skeletal muscle components and renal cell carcinoma (RCC). This study attempts to conduct a meta-analysis using all ...relevant research evidence to explore the impact of sarcopenia on the final survival and recurrence outcome of RCC patients and the change process of this impact after treatment.
This systematic review and Meta-analysis study took "sarcopenia", "kidney" and "tumor" and their synonyms as the main search terms, and comprehensively searched all relevant literatures published in PubMed, web of science, SpringerLink, EMBASE, Cochrane Library, Ovid (Lww oup), Wiley, ScienceDirect and Scopus databases since February 2, 2022. Multivariate hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), cancer specific survival (CSS), and progression free survival (PFS), as well as rough data of Kaplan-Meier survival curve, were combined as the main analysis results. Subgroup analyses based on cohort characteristics (treatment, ethnicity, and BMI factors) for each study were used as secondary outcomes. The combined effect was estimated by random effect model or fixed effect model, and the heterogeneity was evaluated by I2 value. Because this study belongs to secondary literature, the medical ethics committee of the First Affiliated Hospital of Xinjiang Medical University considers that ethical review is unnecessary.
Eighteen retrospective studies involving 3591 patients with RCC were analyzed, of which 71.5% were men and the median age of the cohort was 61.6. The prevalence of sarcopenia was 43% (38-48%). Sarcopenia is an independent predictor of OS (HR: 1.83, 95% CI = 1.41, 2.37), and this prognostic value can also be reflected in Asian populations (HR: 2.59, 95% CI = 1.90, 3.54) and drug treated patients (HR: 2.07, 95% CI = 1.07, 4.04). Sarcopenia can also be used as an independent predictor of CSS (HR: 1.78, 95% CI = 1.34, 2.36) and PFS (HR: 1.98, 95% CI = 1.34, 2.92). The effect of low skeletal muscle mass on OS and CSS increased slowly from 1 to 5 years.
Sarcopenia can be used as a comprehensive prognostic factor in RCC population, but the detailed effects from ethnic characteristics and treatment mechanism need to be further studied.
Layered materials have attracted tremendous interest for accessing two-dimensional structures. Materials such as graphite or transition metal dichalcogenides, in which the layers are held together by ...van der Waals interactions, can be exfoliated through a variety of processes in a manner that retains the structure and composition of the monolayers, but this has proven difficult for solids with stronger interlayer interactions. Here, we demonstrate the exfoliation of AgCrS2, a member of the AMX2 family (where A is a monovalent metal, M is a trivalent metal and X is a chalcogen), through intercalation with tetraalkylammonium cations, chosen for their suitable redox potential. The as-exfoliated nanosheets consist of Ag layers sandwiched between two CrS2 layers, similar to their structure in the bulk. They show superionic behaviour at room temperature, with an ionic conductivity of 33.2 mS cm−1 at 298 K that originates from Ag+ ions rapidly hopping between neighbouring tetrahedral interstices; in the bulk, this behaviour is only observed above 673 K.Layered materials held together by weak interactions can be exfoliated into monolayers that retain the structure and composition of their bulk counterpart, but this has remained challenging to achieve for non-van der Waals materials. Now, AgCrS2 has been exfoliated into such CrS2AgCrS2 nanosheets through intercalation with tetraalkylammonium cations chosen for their suitable redox potential. The nanosheets show superionic behaviour at room temperature.
Ultraviolet (UV) organic emitters that can open up applications for future organic light‐emitting diodes (OLEDs) are of great value but rarely developed. Here, we report a high‐quality UV emitter ...with hybridized local and charge‐transfer (HLCT) excited state and its application in UV OLEDs. The UV emitter, 2BuCz‐CNCz, shows the features of low‐lying locally excited (LE) emissive state and high‐lying reverse intersystem crossing (hRISC) process, which helps to balance the color purity and exciton utilization of UV OLED. Consequently, the OLED based on 2BuCz‐CNCz exhibits not only a desired narrowband UV electroluminescent (EL) at 396 nm with satisfactory color purity (CIEx, y=0.161, 0.031), but also a record‐high maximum external quantum efficiency (EQE) of 10.79 % with small efficiency roll‐off. The state‐of‐the‐art device performance can inspire the design of UV emitters, and pave a way for the further development of high‐performance UV OLEDs.
Through the proposed long‐short axis molecular design, an HLCT‐type ultraviolet emitter is reported. Benefiting from the features of the low‐lying LE emissive state and high‐lying reverse intersystem crossing process, the ultraviolet OLED furnishes a record‐high EQE of 10.79 %, accompanied by a satisfactory color purity and a small efficiency roll‐off.
In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to ...the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.
In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data ...are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.
Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality ...disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences.
We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA.
Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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