A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution

Effective aggregation of temporal information of consecutive frames is the core of achieving video super-resolution. Many scholars have utilized structures such as sliding windows and recurrent to gather spatio-temporal information of frames. However, although the performance of the constructed VSR models is improving, the size of the models is also increasing, exacerbating the demand on the equipment. Thus, to reduce the stress on the device, we propose a novel lightweight recurrent grouping attention network. The parameters of this model are only 0.878M, which is much lower than the current mainstream model for studying video super-resolution. We design forward feature extraction module and backward feature extraction module to collect temporal information between consecutive frames from two directions. Moreover, a new grouping mechanism is proposed to efficiently collect spatio-temporal information of the reference frame and its neighboring frames. The attention supplementation module is presented to further enhance the information gathering range of the model. The feature reconstruction module aims to aggregate information from different directions to reconstruct high-resolution features. Experiments demonstrate that our model achieves state-of-the-art performance on multiple datasets

Network Supplier Credit Management: Models Based on Petri Net

In current credit evaluation methods, the credit condition of the network supplier and the credit degree of each index cannot be described well, and the credit evaluation data only source of the transaction platform have much limitation. This research proposes the method of calculating the importance and the value of the credit evaluation indexes, and proposes to put credit evaluation into big data environment. This research uses the transaction process of B2C as the case, and constructs multiple attribute weighted Petri net credit index subnet (CWPSN) for realizing the credit evaluation of the network supplier, and for presenting the correlations among the evaluation results of the credit evaluation indexes, and for presenting the importance of the indexes and the credit degree of each index, and describes the cost optimization process with credit cost optimization investment process Petri net (CCOIPPN). By the case to verify the credit evaluation method based on Petri net and the cost optimization method based on Petri net. The researches have provided methods for clearly and concretely describing the process of credit evaluation and cost optimization of network supplier, and have guidance significance for similar other researches

Covariant tensor formalism for partial wave analyses of ψ→ΔΔˉ\psi \rightarrow\Delta\bar{\Delta}

The covariant tensor formulae for partial wave analysis of $\psi \rightarrow\Delta\bar{\Delta}$, $\Delta \to p \pi^\pm$, $\bar{\Delta} \to \bar{p} \pi^\mp$ are derived, as well as the formulae for the decay sequence of $\psi \rightarrow N^* \bar{p}$, $N^* \to \Delta \pi^\mp$, $\Delta \to p \pi^\pm$. These formulae are practical for the experiments measuring $\psi$ decaying into $p \bar{p} \pi^+ \pi^-$ final states, such as BESIII with its recently collected huge $J/\psi$ and $\psi(2S)$ data samples.Comment: 10 page

Operation Mechanism for G2B System Based on Blockchain

The characteristics of blockchain as decentralization, transparency, business activity undeniable proof mechanism etc. have achieved extensive attention from the academic circles and industrial circles. In view of the current deficiency of poor data sharing in G2B system, data authenticity, data security, and transaction subject identity’s confidentiality cannot be effectively guaranteed, and the lack of authentication for government management departments (organization institutions) providing service or implementing management to enterprise businesses, this paper proposed to construct G2B system based on blockchain. Based on maintaining the architecture of traditional G2B system and the serviced or managed characteristics of enterprise businesses, was constructed respective G2B system based on blockchain for each section of enterprise business process. Each G2B system was based on blockchain correlated by the virtual links of enterprises and serviced or managed data for enterprises’ business, and constituted blockchain interconnection network. The protocol was designed and the characteristics of G2B system analyzed based on blockchain. Application mode for G2B system was designed based on blockchain. A case based on blockchain was designed, including business operation principle, consensus mechanism, and supervision to government (organization)

CPS Information Security Risk Evaluation Based on Blockchain and Big Data

CPS (Cyber Physical Systems) have got wide application and research, and information security risk evaluation became the key for CPS greatly developing. In view of the physical structure and business characteristics of CPS, this paper constructs an information security risk evaluation system for CPS. In the process of risk evaluation, colligating the analysis results from experts and the analysis results of external data sources’ related big data for information security risk evaluation of CPS, by experts confirming the index system and indexes’ weight values for CPS information security risk evaluation, further through using evaluation model to realize the quantitative calculation to CPS information security risks. This paper proposes using blockchain technology to construct the data’s authenticity and reliability guarantee system for CPS and CPS related external systems, and constructing blockchain’s layered model structure based on CPS. In the part of case analysis, comparing and analysing the evaluation system based on blockchain and big data and the evaluation system based on traditional mode, to confirm the research value of this paper

Comprehensive comparative analysis of the prognostic impact of systemic inflammation biomarkers for patients underwent cardiac surgery

BackgroundInflammation plays an integral role in the development of cardiovascular disease, and few studies have identified different biomarkers to predict the prognosis of cardiac surgery. But there is a lack of reliable and valid evidence to determine the optimal systemic inflammatory biomarkers to predict prognosis.MethodsFrom December 2015 and March 2021, we collected 10 systemic inflammation biomarkers among 820 patients who underwent cardiac surgery. Time-dependent receiver operating characteristic curves (ROC) curve at different time points and C-index was compared at different time points. Kaplan–Meier method was performed to analyze overall survival (OS). Cox proportional hazard regression analyses were used to assess independent risk factors for OS. A random internal validation was conducted to confirm the effectiveness of the biomarkers.ResultsThe area under the ROC of lymphocyte-to-C-reactive protein ratio (LCR) was 0.655, 0.620 and 0.613 at 1-, 2- and 3-year respectively, and C-index of LCR for OS after cardiac surgery was 0.611, suggesting that LCR may serve as a favorable indicator for predicting the prognosis of cardiac surgery. Patients with low LCR had a higher risk of postoperative complications. Besides, Cox proportional hazard regression analyses indicated that LCR was considered as an independent risk factor of OS after cardiac surgery.ConclusionLCR shows promise as a noteworthy representative among the systemic inflammation biomarkers in predicting the prognosis of cardiac surgery. Screening for low LCR levels may help surgeons identify high-risk patients and guide perioperative management strategies

Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan

Effects of pure oxygen and reduced oxygen modified atmosphere packaging on the quality and microbial characteristics of fresh-cut pineapple

Introduction. Modified atmosphere packaging (MAP) is a preservation technique currently used by the fresh-cut fruit industry. Fruit quality may vary according to the concentration of oxygen (O2) in the packaging. However, there is no published research on the effects of a pure O2 modified atmosphere in the packaging of fresh-cut pineapple. There are also no comparative studies of the differences between pure O2 and conventional low O2 MAP on the quality of fresh-cut pineapple. Materials and methods. Pineapple slices were sealed with a tray sealer using a polyethylene (PE) / polypropylene (PP) composite film and one of the following atmosphere treatments: (4% O2 + 5% CO2), (100% O2), and ambient air (control). We evaluated the effects on quality and microbial spoilage of fresh-cut pineapple. Results and discussion. Both modified atmosphere treatments delayed decreases in firmness, soluble solid contents (SSC), reducing sugar, and ascorbic acid. Pineapple slices packaged in pure O2 contained lower amounts of sugar and ascorbic acid and displayed more browning than the slices in the low O2 concentration. Additionally, both modified atmosphere treatments strongly delayed the growth of microorganisms. Aerobic bacteria, yeast and mold levels in pineapple slices packaged in pure O2 were higher than those packaged with the low O2 atmosphere during long-term storage. Conclusion. Modified atmosphere packaging using low O2 concentration (4% O2 + 5% CO2) was better able to maintain the quality of fresh-cut pineapple than packaging with pure O2 atmosphere