Exploring the core knowledge of business intelligence

With the rapid development of data analysis, there is few research on the core knowledge of business intelligence system (BIS). In order to fill this research gap, this paper collected the 1003 articles and 31345 references from the Web of Science database, and then applied co-citation analysis and factor analysis, to analyze their core knowledge. We identified 52 highly cited articles and obtained 9 core knowledge categories in the field of BI: BI success, IT acceptance and measurement, big data analysis,dataanalysis anddecision making,business strategy, BIS,consumer behavior, knowledge management, business adoption. Research shows that BISs are still in the growing trend and core knowledge helps researchers and managers better understand the core concepts and relevance of BI, so as to quickly discover possible research directionsinthisresearchfieldandusefulapplicationsintheenterprise

Determination of Optimal Opening Scheme for Electromagnetic Loop Networks Based on Fuzzy Analytic Hierarchy Process

Studying optimization and decision for opening electromagnetic loop networks plays an important role in planning and operation of power grids. First, the basic principle of fuzzy analytic hierarchy process (FAHP) is introduced, and then an improved FAHP-based scheme evaluation method is proposed for decoupling electromagnetic loop networks based on a set of indicators reflecting the performance of the candidate schemes. The proposed method combines the advantages of analytic hierarchy process (AHP) and fuzzy comprehensive evaluation. On the one hand, AHP effectively combines qualitative and quantitative analysis to ensure the rationality of the evaluation model; on the other hand, the judgment matrix and qualitative indicators are expressed with trapezoidal fuzzy numbers to make decision-making more realistic. The effectiveness of the proposed method is validated by the application results on the real power system of Liaoning province of China

Nonlinear System Dynamic Reliability Analysis Using Equivalent Duffing System Method

Equivalent linearization method is the main approach for nonlinear structural system random response analysis. But it will generate big error that using the random response results of equivalent linearization method to analyze the structural dynamic reliability. In order to improve the analysis precision of dynamic reliability of nonlinear system, an equivalent nonlinear system method is presented in this paper. In this method general nonlinear systems are converted to equivalent Duffing nonlinear system according to minimum mean square error principle, whose exact analytic solution of steady state of random responses can be worked out by Fokker Planck Kolmogorov equation (FPK equation). Then the exact results of stochastic response processes are used for the analysis of structural dynamic reliability. So it is not only convenient for calculation but also with high degree of accuracy for the results that using the equivalent nonlinear system method to analyze structural dynamic reliability. In addition, the equivalent nonlinear system adopted in this work has a parameterεwhich controls the degree of nonlinear. Thus we can obtain conveniently the analysis results of converting the original system to equivalent nonlinear systems with different degree of nonlinear by changing the value of the parameterε. In particular, when the parameter ε is equal to zero we can obtain the analysis results of equivalent linearization method. It is shown from the example analysis that the analysis results of equivalent nonlinear system method presented in this paper is reliable and the calculation accuracy is higher than equivalent linear system method apparently

Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence

Open-source software (OSS) supply chain enlarges the attack surface, which makes package registries attractive targets for attacks. Recently, package registries NPM and PyPI have been flooded with malicious packages. The effectiveness of existing malicious NPM and PyPI package detection approaches is hindered by two challenges. The first challenge is how to leverage the knowledge of malicious packages from different ecosystems in a unified way such that multi-lingual malicious package detection can be feasible. The second challenge is how to model malicious behavior in a sequential way such that maliciousness can be precisely captured. To address the two challenges, we propose and implement Cerebro to detect malicious packages in NPM and PyPI. We curate a feature set based on a high-level abstraction of malicious behavior to enable multi-lingual knowledge fusing. We organize extracted features into a behavior sequence to model sequential malicious behavior. We fine-tune the BERT model to understand the semantics of malicious behavior. Extensive evaluation has demonstrated the effectiveness of Cerebro over the state-of-the-art as well as the practically acceptable efficiency. Cerebro has successfully detected 306 and 196 new malicious packages in PyPI and NPM, and received 385 thank letters from the official PyPI and NPM teams

Sustained efficacy of chimeric antigen receptor T-cell therapy in central nervous system lymphoma: a systematic review and meta-analysis of individual data

Background: Central nervous system lymphoma (CNSL) is considered an aggressive lymphoma with a poor prognosis. Studies investigating CNSL have shown that chimeric antigen receptor (CAR) T-cell therapy has demonstrated an effective response in limited sample sizes. Therefore, we conducted this systematic review and meta-analysis to clarify the sustained efficacy and factors associated with the sustained efficacy of CAR T-cell therapy in the treatment of CNSL.Methods: We searched studies from PubMed, Embase, Medline, and the Cochrane Center Register of Controlled Trials up to July 2023. Studies that included individual data on the duration of response (DoR) after receiving CAR T-cell therapy were enrolled. Pooled response rates were calculated using fixed-effects or random-effects models. Subgroup analysis was performed to analyze the heterogeneity, and a Cox regression model was performed to identify the factors associated with sustained efficacy.Results: In total, 12 studies including 69 patients were identified and included in this meta-analysis. The pooled relapse rate was 45% [95% CI 35, 56]. Subgroup analyses of relapse rates revealed that CAR T-cells using the CD28/4-1BB domain (CD28/4-1BB vs. CD28 vs. 4-1BB, p = 0.0151), parenchymal or leptomeningeal involvement (parenchymal or leptomeningeal vs. both parenchymal and leptomeningeal, p < 0.0001), and combined treatment with CAR T-cell therapy [Autologous stem cell transplantation (ASCT) plus CAR T-cell therapy vs. CAR T cells with maintenance therapy vs. CAR T-cell therapy alone, p = 0.003] were associated with lower relapse rates in patients. Time-to-event endpoints were assessed using reconstructed individual patient survival data to explore key modulators of DoR. Partial response status at CAR-T infusion and the use of ASCT plus CAR T-cell therapy were associated with longer DoR at the multivariate level, with hazard ratios of 0.25 and 0.26, respectively.Conclusion: CAR T-cell therapy shows promising and sustained efficacy in CNSL patients. However, further prospective large-scale studies are needed to assess these effect modifiers to optimize patient selection and improve the sustained efficacy of CAR T-cell therapy in the treatment of CNSL.Systematic review registration:https://clinicaltrials.gov/, identifier PROSPERO CRD42023451856