Enterprise Data Research Articles

Does Investment Climate Matter for Firm Performance? Evidence from Kenyan Manufacturing Firms

The productivity of Kenyan manufacturing firms is way lower than that of many developed economies and has generally exhibited a consistent decline over the last decade. While this productivity trend has largely been attributed to the presence of a high distortionary institutional and business regulatory environment, existing studies on the role of the investment climate in determining firm performance are ostensibly scanty. This study, thus, employed the World Bank panel enterprise data for the period 2007-2013- 2018 in assessing whether investment climate mattered for firm performance in Kenyan manufacturing firms. More particularly, the study sought to establish the role of the court system and property rights ownership in determining firm performance; a feat that remains unexplored in the Kenyan context. The random effects model was estimated while controlling for the year, industry, and firm-specific control variables. The findings revealed that while court inefficiencies significantly impeded labor productivity, property rights ownership significantly increased productivity. Further, human capital positively determines labor productivity. Concerning governance and institutional factors, ISO-certified firms were found to be significantly more productive. Conversely, business licenses and permits constrain firm productivity. Therefore, to ensure unrelenting firm productivity, speedy and just delivery of court rulings on firm-related matters is critical. Secondly, the acquisition of patents relating to product or process innovation by firms enhances product competitiveness. Thirdly, manufacturing firms should invest more in human capital. Finally, the imposition of favorable business licenses and permits by the governments globally coupled with the ISO Certification requirement by firms is integral in optimizing labor productivity.

Lightweight encryption of enterprise data and secure management of eventual graphical model construction

Abstract In this paper, we use the event map as well as the even map reaction system, combined with the lightweight network data transmission encryption method of matrix tensor product, to establish the even map model for big data security management. The transmission data is compressed and reconstructed by matrix tensor compression perception, and the lightweight AES encryption of network transmission data is encrypted using the symmetric encryption method. In the end, the effectiveness of AES encryption and decryption while transmitting plaintext data is assessed for each department of Enterprise D in the service area of Edge Server Server. The results show that the lightweight AES encryption algorithm encrypts at an average of 85.33 ms less than the AES encryption algorithm, while the decryption time is 48.17 ms less, and the operation speed is faster. The algorithm demonstrates that it ensures the security of data transmission and enhances encryption and decryption performance simultaneously. In this paper, the research realizes the enterprise data transmission encryption college transmission, which provides a reference for data encryption.

Business Performance of Small and Medium Enterprises: Evaluation of Respondents

Objective: The following were the objectives of this study: 1. analysis and measurement of the business performance of small and medium enterprises in Hanoi, Vietnam; 2. comparison of assessments of the business performance of small and medium enterprises in Hanoi, Vietnam, between respondents. Method: The data used in this study are primary, including survey data obtained from the respondents. We designed a questionnaire related to the business performance of small and medium enterprises in Hanoi. We determined the observed variables (scales) of the business performance of small and medium enterprises based on previous studies and expert interview results. We use both qualitative and quantitative research methods, including descriptive statistics, reliability analysis through the Cronbach alpha coefficient, EFA analysis, the independent T test, and ANOVA analysis. Besides, the study analyzed performance data for small and medium enterprises during the period 2022–2023. Results: According to the findings of this study, five attributes of the business performance of small and medium enterprises in Hanoi were quite high. All the attributes of the variables are statistically significant. There is no statistically significant difference in the business performance of small and medium enterprises in Hanoi between these different marital statuses, work locations, and job positions. Conclusions: It was found that the business performance of small and medium enterprises in Hanoi has many positive points, despite just going through the COVID-19 pandemic and economic growth. This article is an academic contribution that enables understanding of the research developed and focuses on the business performance of small and medium enterprises in Hanoi, considering data from several reference research databases and survey results.

Mechanical Equipment Failure Prediction Based on Machine Learning

Mechanical equipment plays a central role in the manufacturing industry. However, they often suffer from problems such as aging and wear, which pose a major challenge to maintaining the stability of the production environment and improving economic efficiency. In order to solve this problem, this article conducted an in-depth study using the usage and failure data of a certain enterprise's mechanical equipment. First, this article establishes an XGBoost classification model to determine whether mechanical equipment has malfunctioned. The model performed well, with performance metrics showing high precision, recall, and F1 scores. Furthermore, this paper used the XGBoost model to successfully predict the faults of 19 mechanical equipment, classified these devices, and accurately determined the types of faults, including heat dissipation faults, power faults and overload faults. Through the weight analysis of various types of fault data using the CRITIC weight analysis method, the main causes of various types of faults are clarified, which provides an important reference for further improvement and maintenance of this mechanical equipment.

Machine Learning-Based Decision Tree for Identifying and Grading Severity of Immune Effector Cell-Associated Neurotoxicity Syndrome (ICANS) Based on Neurological Adverse Events in Patients Treated with CD19 CAR-T

Introduction One of the major challenges associated with Chimeric Antigen Receptor T-cell (CAR-T) therapy is the development of immune effector cell-associated neurotoxicity syndrome (ICANS), which can vary in severity and requires prompt intervention. This study proposes using machine learning in combination with clinical insight to build a decision tree that accurately identifies and grades ICANS severity in patients treated with CD19 CAR-T based on the neurological adverse events (NAEs). Methods We conducted a retrospective analysis of 384 patients that exhibited NAEs after being treated with CD19-targeted CART-cell therapy in the pooled clinical trial data from the Medidata Enterprise Data Store. Of note, this dataset excludes patients without NAEs; these patients were assumed to not experience clinical ICANS. Patient NAEs were reviewed and the highest ICANS grade was assigned by a neuroimmunologist, resulting in the following patient counts: grade 0 (n=61), 1 (n=69), 2 (n=166), 3 (n=75), 4 (n=13). The clinical expert graded each patient based on the NAEs recorded in the first 28 days post infusion during the clinical trial, their CTCAE toxicity and symptom onset day. To process NAEs, we first mapped each of the 219 verbatim terms to the MedDRA hierarchy using a proprietary Medidata coding algorithm. We then aggregated the NAEs to the High Level Group Term (HLGT) level except for the MedDRA term “Neurological Disorders NEC” which was broken down to the High Level Term (HLT) level due to the large size of this group (73 out of 219 terms).The mapping of NAEs to MedDRA groups was reviewed together with the neuroimmunologist. The final dataset included the CTCAE severity of the NAEs mapped to MedDRA groups as features and the expert-labeled ICANS grades as the target variable. The data was then split into train (70%) and test (30%) sets stratifying by study and ICANS grade. We fit a decision tree while performing 5-fold hyperparameter grid search on the train set. The tree with the highest area under the Receiver Operating Characteristic curve one-vs-one (ROCAUC OVO) was selected and evaluated on the test set using an array of classification performance metrics. The model output was evaluated at the ICANS grade and severity (no/moderate/severe) level. The confusion matrices for the test set and the full decision tree were plotted. Results When binning the ICANS grade prediction by severity (grade 0 = no ICANS, grade 1-2 = moderate, grade 3-4 = severe), the model achieves a strong out-of-sample accuracy of 83.6% and ROCAUC OVO of 0.935. Regarding the individual ICANS grade prediction, the model yielded lower out-of-sample accuracy of 54.3%, with the majority of errors between grades 1 and 2. In spite of this, there was still reasonable discrimination based on the out-of-sample ROCAUC OVO values of 0.863. The training algorithm selected six MedDRA groups of NAEs to make predictions with: Headaches, Seizures (incl subtypes), Encephalopathies, Cortical dysfunction NEC, Disturbances in consciousness NEC, and Movement disorders (incl parkinsonism). We observe that patients with only headaches (a separate feature in our dataset) are all labeled as having no ICANS while those with seizures of grade 2 and higher are classified as grade 4. Refer to Figure 1 for the full decision tree. Comparing the neurologist-assigned versus predicted ICANS grades in the out-of-sample patient group (Figure 2) revealed that the majority of errors were bound to +/- one grade. Only three out of 116 patients were mislabeled by two grades. Crucially, none of the grade 1+ patients were predicted by the model to have grade 0. Conclusion Our machine learning-based decision tree accurately predicts the severity and grade of ICANS in CD19 CAR-T treated patients, aiding in clinical decision-making as well as retrospective analyses of ICANS patients. This type of clinical decision support tool has the potential to be a powerful aid to physicians by providing them with earlier physiological insights and data-driven assessments to help them triage patients accordingly. The model's performance highlights its potential clinical utility in improving patient care and outcomes. Further validation across multiple neurologists and prospective studies are warranted to assess its generalizability and refine its predictive capabilities. Acknowledgments We thank the developers of Scikit-learn and Dtreeviz Python packages for open-sourcing their code.

Low Inter-Rater Reliability of Calculating the 4T's Score for Heparin Induced Thrombocytopenia

Introduction Heparin induced thrombocytopenia (HIT) is an immune-mediated drug reaction that can cause thromboembolism in the setting of thrombocytopenia following heparin exposure. The “4T's score” has been validated to determine the pre-test probability of HIT and to assist in decision making around ordering testing for HIT. The 4T's scoring system requires an individual clinician to determine each component of the score. The objective of our study is to investigate the inter-rater reliability of calculating the 4T's score among clinicians. Methods Through retrospective query of Northwestern Enterprise Data Warehouse, we identified patients who had a HIT antibody (Ab) test ordered between 10/2019 and 10/2022 after implementation of a clinical decision support (CDS) tool that asked clinicians to calculate a 4T's score as part of HIT PF-4 Ab orders. From this cohort, an independent clinician randomly selected 15 patients. Four raters, including an attending hematologist, a hospital medicine attending, a hematology/oncology fellow, and an internal medicine resident, performed manual chart review of the randomly selected subjects to calculate a 4T's score. Data collected for each case included individual components of the 4T's score and overall 4T's score from each rater. In addition, we compared raters' scores to the 4T's score entered by the ordering clinician for the patient. We then categorized the numerical scores into validated pre-test probability categories: 0-3 as low, 4-5 as intermediate, and ≥6 as high. Inter-rater reliability for the categories was calculated using the Fleiss kappa statistic. Our study was approved by the Northwestern University Institutional Review Board. Results Of 15 cases selected, 5 each were scored as low, intermediate, and high probability 4T's scores by the ordering clinician (associated with the HIT Ab order). The overall agreement between the score categories for the 5 clinicians (4 raters and the ordering clinician) was 50.7%, with a Fleiss Kappa statistic of 0.26 (95% CI [0.07-0.45]), indicating poor inter-rater reliability (Figure 1). Excluding the ordering clinician, the overall agreement in score category between the 4 raters was 58.9%, with a Fleiss Kappa statistic of 0.38 (95% CI [0.14-0.62]). The same 4T's score pre-test probability category was calculated by the 4 raters in 5 cases, with only one case in which all four raters calculated the same numerical 4T's score. There was one case in which all 5 clinicians calculated the same 4T's score probability category, but none with the same numerical 4T's score. The hematology/oncology fellow had highest inter-rater agreement with the original clinical (Kappa 0.30 [-0.09-0.69]), whereas the internal medicine attending and resident had the lowest (both with Kappa 0.00 [-0.37-0.37]). Of the 15 4T's scores, 12/15 ordered by the hematology attending, 5/15 by the internal medicine attending, 12/15 by the hematology/oncology fellow, and 11/15 by the internal medicine resident were lower than those calculated by the original ordering clinicians. Conclusion Our study demonstrates poor inter-rater reliability of HIT 4T's score calculation, across levels of training and specialty. Importantly, poor inter-rater reliability was seen across 4T's categories, which has implications for clinical management of patients undergoing evaluation for HIT. This suggests that different strategies are necessary to help clinicians better use the 4Ts score.

The Validity of a Synthetic Control Arm Derived from Historical Multiple Myeloma Clinical Trials

Introduction While randomized phase III clinical trials (RCTs) are the gold-standard for establishing drug efficacy in the United States, external control arms (ECAs) may be valuable for both early and late phase drug development. An ECA is a collection of patients with the disease of interest who were treated outside of a clinical trial of interest (ie, target trial) and whose outcomes are compared to those of the target trial patients' to evaluate comparative drug efficacy and/or safety. ECAs created through rigorous statistical non-experimental methods such as matching patient-level characteristics may provide early estimates of comparative efficacy in advance of RCTs and offset prospective enrollment of control patients in randomized studies. We evaluate the validity of using a matched ECA to estimate the comparative efficacy of an experimental drug in relapsed refractory multiple myeloma (r/r MM). Methods From the anonymized Medidata Enterprise Data Store (MEDS), we identified historic RCTs in r/r MM containing patients exposed to an experimental drug as well as an active control drug (ie, a corticosteroid). Arbitrarily, we selected one trial to serve as the “target trial”. For the remaining trials, we collapsed all corticosteroid control patients into a single pool to construct an ECA. Using propensity score (PS) optimal full matching, we identified corticosteroid ECA patients by selecting from the candidate patients those whose baseline demographic and clinical attributes were similar to patients receiving the experimental drug in the target trial. Using the Kaplan-Meier (KM) estimator and a Cox proportional hazards regression, we first compared overall survival time (OS) of patients in the experimental drug arm vs control arm in the target trial. We then compared the OS in the experimental drug arm vs the ECA. Results We found fewer than five historic clinical trials from MEDS which were open-label RCTs conducted from 2010-2017. They enrolled r/r MM patients aged ≥18 years who had received more than two prior lines of therapy, one of which was lenalidomide and bortezomib. The target trial contained 300 patients in the investigational drug arm and 152 patients in the control arm. Using the PS matching, we identified 183 patients from the control arms in pooled trials that were matched with 290 patients in the investigational drug arm in the target trial (i.e., ECA). These ECA patients were then upweighted to be matched with 290 patients in the investigational drug arm in the target trial. Full matching successfully attenuated baseline differences between the investigational drug and ECA arms. Figure 2 shows the KM curves of OS (investigational vs control) in the target trial. Median OS was 13.25 months vs. 8.52 months (log-rank p<0.01) and the investigational drug was associated with a 36% reduction in the hazard of death (HR 0.74, 95% CI: 0.60-0.92). Figure 2 shows the KM curves of OS, after full matching, using the ECA. Median OS was 13.51 months vs. 8.75 months (log-rank p<0.01) and the investigational drug was associated with a 36% reduction in the hazard of death (HR 0.74, 95% CI: 0.58-0.94). Conclusions In this methodologic validation study, we have demonstrated that it is possible to produce an ECA from historical clinical trials data using propensity score methods which replicates the comparative efficacy estimate from a target without introducing bias. That is, the ECA, which was well balanced with the investigational arm with respect to baseline patient attributes, returned nearly identical estimates of comparative efficacy as assessed by OS of both target trial control arm and ECA against the target trial experimental arm. This work illustrates that it is possible to create a valid ECA even when historical data size is limited and to do so without excessive exclusion of non-matched patients. That the treatment effect on OS estimated in comparison to the randomized control was very closely matched by that of the ECA suggests this approach to ECAs may be used to (1) estimate comparative efficacy of therapies that have only been studied in single arm settings and/or (2) augment or replace a randomized control arm in future trials when ethically or practically challenging. Figure 1A. Kaplan-Meier OS estimates of target trial experimental (N=300) vs. control arm (N=152) Figure 1B. Kaplan-Meier OS estimates of target trial experimental (N=290) vs. ECA (N=290)

Immune Checkpoint Inhibitors and the Risk of Cardiovascular Adverse Events: A Pooled Clinical Trial Database Analysis

Introduction Immune checkpoint inhibitors (ICIs) are a highly effective class of cancer immunotherapy, but their use has been limited by immune-related adverse events (irAEs) affecting various organ systems. Recent real-world studies have shown higher incidence rates of cardiovascular (CV) events in patients with cancer who are treated with ICIs compared to those treated with conventional chemotherapy (Laenens et al, J Clin Oncol 2022). Studies suggest that the underlying mechanism involves a pro-inflammatory response causing unintended vascular compromise, but there have been limited data on ICI-related cardiotoxicity across pooled datasets. Given that ICIs have been applied to lung cancer treatment in recent years, we use it as an example indication to study clinical trial (CT) patients with advanced non-small cell lung cancer (aNSCLC) randomized to either a first-line (1L) ICI-containing regimen or to 1L chemotherapy alone (chemo) to evaluate for potential differential rates of CV events. Methods We pooled patient-level, phase 3 CT data on aNSCLC patients treated with ICI or chemo from the Medidata Enterprise Data Store. Treatment assignment was the main variable of interest. The primary outcome was a composite CV event (acute coronary syndrome, heart failure, stroke, transient ischemic attack), occurring between the start of study treatment (index date) and 30 days following the last dose of study treatment. Incidence rates and the relative hazard of a CV event by treatment assignment were estimated. Additionally, the risk of CV event with non-CV death treated as a competing risk was estimated using the Fine-Gray model. In a post-hoc subgroup analysis we explored the impact of treatment assignment on overall survival (OS) among patients with and without baseline vascular disease. OS was defined as the time from the index date until the date of death (all-cause). Patients alive at the end of CT follow-up were censored. Results A total of 1862 patients were included in this analysis, with 1128 (60.6%) randomized to ICI and 734 (39.4%) to chemo. Baseline demographics and comorbidities were balanced across treatment groups. Forty-six (2.5%) patients experienced at least 1 CV event. The incidence rate of CV events was 2.48 times (95% CI: 1.10, 6.57) higher among patients treated with ICI compared to chemo. The unadjusted hazard of composite CV event, estimated using Kaplan-Meier, differed significantly by treatment assignment (p= 0.008) ( Figure 1). After adjusting for baseline demographics and comorbidities, the hazard of a composite CV event remained higher for patients treated with ICI compared to chemo (aHR 2.68, 95% CI: 1.19-6.06) ( Table 1). Baseline vascular disease history (N=359; 19.3%) was also associated with an increased hazard of a CV event (aHR: 2.40; 95% CI: 1.25-4.60). Similar results were found in the competing risk analysis. In the subgroup analysis, patients without baseline vascular disease history had a significant OS benefit from an ICI compared to chemo (aHR 0.84, 95% CI: 0.75-0.95), whereas patients with baseline vascular disease did not (aHR 1.00, 95% CI: 0.78-1.27). Conclusion Though ICIs enhance the body's immune responses against cancer, it is important to study the associated effect of irAEs on off-target tissues. These analyses of historical CT data suggest a positive association between 1L treatment of aNSCLC with ICI and subsequent composite CV events compared with chemo alone. Given the high prevalence of morbidity among patients with lung cancer, the use of concomitant cardioprotective therapies may be an important consideration when treating cancer populations with ICIs. Pooling CT data increases the power to detect clinically important differences in rare toxicities and may inform future research and patient-physician decision-making regarding the suitability of ICI use in aNSCLC treatment. Furthermore, this research highlights the potential clinical utility of drug repurposing: expanding the use of drugs approved to treat CV events and exploring their use in patients with vascular disease who are also being treated with ICIs. As cell-mediated immune responses play a key role in the potential CV effects of ICI treatments, future studies are needed to define the underlying mechanisms of ICI-related dysregulation of the immune system to better monitor patients and improve clinical outcomes in the context of immunotherapy for aNSCLC and other cancer types.

2706. Effectiveness and Safety of Shingrix Vaccination in Solid Organ Transplant Recipients at a Large Urban Transplant Center

Abstract Background The varicella zoster virus (VZV) recombinant subunit vaccine, Shingrix, was found to be safe in solid organ transplant recipients (SOTR) and is now approved and recommended for all immunocompromised patients. Given limited efficacy data, we preferred a single center retrospective study to demonstrate safety and effectiveness of the Shingrix vaccine in solid organ transplant recipients. Methods After IRB approval, clinical data from all SOTR at Northwestern Memorial Hospital (NMH) who received the Shingrix vaccine after their transplant, between January 1, 2017 and March 1, 2022 was collected from our Enterprise Data Warehouse (EDW). Extracted data includes age, race, ethnicity, date of transplant, organ transplanted, and date(s) of vaccine administration(s). Clinical outcomes were assessed by diagnosis of acute allograft rejection and/or HZ made post-vaccine administration. Descriptive statistics were utilized. This project is supported by the IDSA G.E.R.M. program. Results 309 of 338 eligible SOTR were included in the analysis of this study (characteristics and reasons for exclusion in Table 1). Only 1 (0.32%) patient experienced a new episode of acute allograft rejection within 3 months of Shingrix administration. 18/309 (5.8%) were diagnosed with HZ after vaccination with Shingrix. This represents a reduction in VZV reactivation events compared to the 9.1% incidence found in the 2021 meta-analysis of HZ in the general solid organ transplant population [Kwon, Transpl Infect Dis., 2021].Table 1.Characteristics of 338 solid organ transplant patients who received Shingrix vaccine. Conclusion The Shingrix vaccine, when administered to SOTR post-transplant, is safe and likely effective in reducing the rate of herpes zoster. This data supports the current recommendation to vaccinate all SOTR with Shingrix. Larger, multi-center data are needed to inform the true efficacy of the vaccine in SOTRs. Disclosures All Authors: No reported disclosures

Introducing the enterprise data marketplace: a platform for democratizing company data

In this big data era, multitudes of data are generated and collected which contain the potential to gain new insights, e.g., for enhancing business models. To leverage this potential through, e.g., data science and analytics projects, the data must be made available. In this context, data marketplaces are used as platforms to facilitate the exchange and thus, the provisioning of data and data-related services. Data marketplaces are mainly studied for the exchange of data between organizations, i.e., as external data marketplaces. Yet, the data collected within a company also has the potential to provide valuable insights for this same company, for instance to optimize business processes. Studies indicate, however, that a significant amount of data within companies remains unused. In this sense, it is proposed to employ an Enterprise Data Marketplace, a platform to democratize data within a company among its employees. Specifics of the Enterprise Data Marketplace, how it can be implemented or how it makes data available throughout a variety of systems like data lakes has not been investigated in literature so far. Therefore, we present the characteristics and requirements of this kind of marketplace. We also distinguish it from other tools like data catalogs, provide a platform architecture and highlight how it integrates with the company’s system landscape. The presented concepts are demonstrated through an Enterprise Data Marketplace prototype and an experiment reveals that this marketplace significantly improves the data consumer workflows in terms of efficiency and complexity. This paper is based on several interdisciplinary works combining comprehensive research with practical experience from an industrial perspective. We therefore present the Enterprise Data Marketplace as a distinct marketplace type and provide the basis for establishing it within a company.

Multi-cluster high performance computing method based on multimodal tensor in enterprise resource planning system

The big data representation and processing method based on multimodal tensors can achieve the fusion representation of different types of data, and perform correlation analysis on the fusion data through multimodal tensor decomposition, providing accurate data services for practical applications. However, in the process of big data analysis and processing based on multimodal tensors, there are still some urgent problems to be solved, such as repeated calculations caused by streaming data, time-consuming calculations caused by large scale, ultra-high energy consumption caused by complex calculations, and poor analysis results caused by complex relationships. This article uses big data tools Hadoop and Spark to build a data mining platform to analyze and study the data of traditional manufacturing enterprises' enterprise resource planning (ERP) systems. In order to improve the accuracy of data analysis, the Stacking fusion framework was also introduced. Furthermore, We transform the data generated by the enterprise into valuable information, making more accurate decisions for enterprise personnel. This article proposes a distributed parallel multi-mode tensor chain decomposition method. In the distributed parallel multi-mode tensor chain decomposition method, a method and theoretical proof for solving tensor chain decomposition are proposed based on the original tensor chain decomposition results and tensor chain decomposition results. The multi kernel clustering algorithm based on K-Means aims to improve the performance of kernel K-clustering by integrating a set of kernels. Deep learning model is also proposed to fuse these multilinear attributes, a clustering method based on weighted average similarity matrix and a clustering method based on optional weighted Euclidean distance were proposed. The distributed parallel multimodal method proposed in this article can effectively achieve tensor chain decomposition, thereby improving the efficiency of big data processing. © 2001 Elsevier Science. All rights reserved

Countering Cybercrime Under Martial Law

To date, it is impossible to imagine your life without all kinds of gadgets, the Internet and social networks. The active using of social networks has long been carried out by all state bodies in order to cause confidence of the population to their actions, to see feedback in the format of comments, as well as to show people all topical processes. It is also important to note that Ukraine is an advanced state in the field of electronic document circulation, and therefore almost all processes, starting from the activities of banking institutions and ending with the activity of air and railway transport are carried out by automated electronic systems. Accordingly, the coordinated and uninterrupted functioning of all of the above is due to the stability of cyberspace. In connection with Russia’s attack the information space became the second battlefield. Cyberattacks and official state sites hacking was immediately started, there were active attempts to abduct electronic data of banking institutions and private enterprises. The relevance of this article is determined by it, because a detailed analysis of the characteristic features of cybercrime, the search for methods and mechanisms of countering cybercrime in the conditions of war are the primary tasks and contribution to victory. The purpose of the article is to analyze the current state of countering cybercrime and varieties of cybercrime, to identify features and characteristics of cybercrime, as well as to make proposals for improving the current legislation in this area. The study used the following methods: analysis and synthesis, legal, static, systemic and formal-legal methods, method of interpretation of law. The conclusions, which we will make, can be a basis for further improvement of legislation, research and discussions between theorists and practitioners.

Foreign financial support and corporate R&D investments: New panel data evidence

This study aims to explore empirical relationship between foreign financial support and corporate research and development investments. The statistical analysis is based upon 14 years (2006–2019) financial data of non-financial sector enterprises. We use GLS (generalized least square) and 2-step system GMM (generalized method of moments) to establish the regression among variables. The empirical findings show that FDI inflow, official aid, and remittances have a positive and statistically significant relationship while foreign trade has an inverse relationship with corporate-level R&D activities. Foreign financial support enhances the financial reserves of the government and allows to disburse of more subsidies for R&D activities. Conversely, foreign trade can reduce R&D activities by inducing unfavorable competition which further hampers innovative behavior of corporate managers. The empirical analysis suggests that government officials should do more focus on enhancing foreign financial support to accelerate the development culture in the country.

Transportation Infrastructure and Digital Economy—Evidence from Chinese Cities

In this research, the influences of railways, roads, waterways, and civil aviation on the digital economy were analyzed using traffic, urban, and enterprise data in the integrated transport system. Regression was performed through the generalized spatial least square method (GS2SLS) in the empirical section to solve the endogeneity problem. It was verified that transportation infrastructure can promote the digital economy. While the development of railways, waterways, and roads is expected to rise by 1%, the digital economy will be increased by 0.0049, 0.0048, and 0.0031, respectively, and civil aviation’s effect is not significant. The robustness test results were still remarkable. From the industry level of cities, it was found that transportation infrastructure mainly promotes the development of the digital economy by upgrading the industrial structure. At the enterprise level, promoting entrepreneurship and facilitating the digital transformation of enterprises have become the main driving forces for the development of the digital economy, and strengthening labor flow is a vital promotion mechanism at the factor level of cities. In addition, a significant single-threshold effect is observed in promoting the digital economy by transportation infrastructure. In the cities that cross the threshold of the economic development level, the progress in the digital economy increases from 0.0027 and 0.0035 to 0.0059 and 0.0061 for 1% development of railways and roads; the promotion of the digital economy by transportation infrastructure is more evident in cities with a permanent-residents population of more than 3 million. Developing the digital economy and transport infrastructure is essential for economic recovery and sustainable development.

The Influence of Carbon Finance on Roa-An Empirical Study Based on Carbon Trading Data of Domestic Enterprises

With the continuous adjustment of human living space by environmental problems, more and more people pay more attention to environmental problems, and many financial products begin to enter the market one after another, and carbon trading is one of them. This paper aims to analyze the trading data of Shanghai Environmental Energy Exchange through the actual carbon trading case, and finally draw a conclusion about how carbon trading affects ROA of enterprises, and put forward suggestions for improvement in the current carbon market trading environment.

Enterprise data and semantic modelling: conceptual model of information technology incident management

Knowledge management methods and their efficient implementation across the organization determine sound and resilient management of processes. This paper studies the semantic integration of enterprise data sources essential to service management processes. Implementing a semantic layer within the enterprise architecture uses various tools, methods, and techniques. The semantic conceptual model unifies and implements intelligent integration of multiple data sources across the enterprise, achieving consistency and more accessible interpretation. Specifically, we draw our attention to incident and problem management within enterprises. We propose an ontology - a conceptual model for the incident management process. The incident ontology presented as an intelligent data integration layer component aims to achieve operational excellence. Besides, this ontology is a fundamental part of the proactive process in problem management. An ontology as a logic-based system supports integrity validation. It infers new, no explicitly modeled facts in the problem domain, thus helping experts better analyze and understand the problem. We discuss the conducted experiment results with the proposed in this article conceptual model using the enterprise knowledge graph platform. It can be perceived as a framework for a query-answering system with components, including ontology schema, data mapping, and classification methods for data graph enrichment.

DMoiSDN: A defensive mechanism of object integrity for SDN

SummarySoftware‐defined network (SDN) technology is widely used for computer networks, especially in enterprise data centers and virtualized networking. However, SDN networks encounter severe challenges to security. One such challenge comes from third‐party applications that contain malicious logic and security vulnerabilities, resulting in controller integrity attacks. In this paper, we propose a defensive mechanism of object integrity for SDN (DMoiSDN) to mitigate the issue known as Cross‐App Poisoning (CAP). Our results contribute to increasing the integrity level of the controller's resources by conducting a potential risk analysis, which showed a decrease of 57% in the risk factor for potential attacks. We further examined the results of comparing DMoiSDN's performance with related work that uses information flow control (IFC) policies. The best results among the three conducted scenarios were as follows: we found that decreased latency in our system ranged from 12% to 90%, with an average of 59%, when encountering an increase in requests. It ranged from 78% to 49%, with an average of 63%, when receiving a variable number of total permissions for each application. DMoiSDN is expected to show a perceptible but reasonable latency and, to some extent, be able to avoid a critical impact on performance.

Research on Data Governance System for Information Sharing and Openness

Abstract In today’s informationization era, information sharing and openness represent advanced productivity, but the current information-sharing work is not being carried out as well as expected. Therefore, this paper first defines data governance and utilizes IBM software to sort out the unified process of data governance. Then, the fuzzy DEMATEL method was constructed by introducing a triangular fuzzy number and the DEMATEL method, and then the data governance capability evaluation model was established. Finally, it studies the effectiveness of enterprise data governance and analyzes the influence factors of enterprise data governance behavior based on DEMATEL. N enterprise data standard governance is highly rated, with “very good” and “good” ratings accounting for 58.6%. In terms of centrality analysis, the impacts of data standard application, kinship data and metadata collection in the third-level indicators are 6.247, 5.731 and 5.671, respectively, which need to be highly emphasized.

BIOS-01. EFFICACY AMONG THERAPEUTICALLY EFFECTIVE ABSORBED DOSE PATIENTS IN RESPECT-GBM PHASE I OF RHENIUM-186 NANOLIPOSOME (186RNL) IN RECURRENT GLIOBLASTOMA (RGBM) COMPARED TO AN EXTERNAL CONTROL ARM (ECA)

Abstract BACKGROUND ReSPECT-GBM is a Phase 1/2 study of 186RNL administered by convection enhanced delivery (CED) in patients with rGBM. In Phase I, 15 of 24 patients have achieved a therapeutic absorbed dose defined as > 100 Gy. Based on prior work establishing the comparability of a bevacizumab (Bev) comparator to inform early-phase CED study results, OS of the 15 TAD patients were compared to a Bev ECA. METHODS Recent Bev-treated rGBM clinical trial patients meeting the same eligibility criteria as patients in the ReSPECT-GBM study were identified from the Medidata Enterprise Data Store of aggregated and deidentified patient-level historical clinical trial data. Propensity score (PS) methods were used to select the ECA and align the baseline characteristics of the Bev patients to the ReSPECT TAD cohort. OS estimates were generated using the weighted Kaplan-Meier method and compared with a weighted 2-sample 2-sided log-rank test at α = 0.05 based on a data cut-off of December 2022. RESULTS Baseline characteristics of the ReSPECT TAD and ECA patients were well-aligned after PS modeling. Although not achieving statistical significance due to small sample sizes (median OS 12.2 vs 10.1 months, hazard ratio 0.63, 95% CI: 0.26, 1.52), comparison of OS between the ReSPECT TAD and ECA patients demonstrate consistently longer OS at each 6-month interval: 6 months: 85.1% vs 73.3%, 12 mos: 51.1% vs 30.3%, 18 months: 30.6% vs 25.4%, 24 months: 30.6% vs 3.5%. CONCLUSIONS Single-arm studies can be challenging to interpret. By comparing the ReSPECT-GBM outcomes to an ECA of similar patients with balanced baseline characteristics, we can better appreciate the treatment effect of 186RNL administered via CED. The current results are encouraging, and updated results reflecting current follow-up will be presented at the meeting.

Relationship Between Carbon Emissions and Economic Development of Industrial Parks Based on Decoupling Index

Industrial parks are the main carriers of industrial activities and are also key areas for carbon emissions. To deeply explore the decoupling state of carbon emissions and economic development of industrial parks and the driving forces, Zhengzhou Economic Development Zone were taken as example, based on the energy consumption data of industrial enterprises above a designated size from 2011 to 2020, the IPCC carbon emission accounting method, Tapio decoupling model, and logarithmic mean Divisia index decomposition method were used to analyze the characteristics of carbon emissions from energy consumption in the park, the relationship between carbon emissions and economic development, and the driving factors of decoupling. The results showed that:① in terms of carbon emission characteristics, the carbon emissions of energy consumption in Zhengzhou Economic Development Zone were mainly indirect carbon emissions, and the total carbon emissions showed a trend of rapid growth in the early stage, slowing down in the medium term, and negative growth in the later stage. The carbon emission intensity was decreasing annually. ② From the perspective of decoupling, the decoupling index between total carbon emissions and economic development in Zhengzhou Economic Development Zone from 2011 to 2016 was 1.021, which was in a state of growth linkage, and the decoupling index decreased to 0.089 from 2016 to 2020, turning into a weak decoupling state. ③ From the analysis of driving factors, from 2011 to 2016, four factors, namely carbon emission coefficient, energy efficiency, industrial structure, and economic level, all had a restraining effect on the decoupling of carbon emissions in Zhengzhou Economic Development Zone, and from 2016 to 2020, they all turned into promotion except for the economic level. This study showed that among the factors for the decoupling of carbon emissions in the Zhengzhou Economic Development Zone, the economic level played a major inhibitory role, and energy efficiency played a major role in promoting it. The results of this study can provide a reference for the industry-city integrated industrial park represented by Zhengzhou Economic Development Zone to formulate corresponding carbon emission reduction policies and achieve the carbon peaking and carbon neutrality goals.