Construction and Application of a Biological Sample Library Information Management System Based on a Data Governance System in China.

With1 the further development of information technology, data has become one of the core resources of enterprises. In the current era of big data, data governance has gradually become an important means for enterprises to make intelligent decisions, helping enterprises to occupy a favorable position in a highly competitive market environment. This paper briefly describes the current situation of enterprise big data governance, and analyzes the problems of current data governance from the perspectives of enterprise management and data itself. To deal with these problems, this paper proposes several suggestions and strategies from the perspectives of organization and management system construction, construction of a data standard system, improve the level of data quality management, data technology surpport, etc. Fully combining the advanced theories and methods of domestic data governance, this paper designs a data governance framework from the perspective of data application and innovation. The framework includes supervision and control modules, data governance staff organization modules, application and service modules, and data processing and integration modules. And the framework is applied to the data governance of electric power enterprises, which has important reference significance and value for enterprises to carry out data governance.

The management of construction engineering is the core of engineering quality. The quality of construction engineering is directly related to people’s life and safety issues. Quality inspection and quality supervision are very important in the construction of construction engineering. With the introduction of the information management system, the mutually independent status of each project has changed, and the information between the modules can be realized in real time. Starting from the aspect of project management, the introduction is the information management system in project document information management. Studies have shown that with the advent of the information age, all industries will use information technology to achieve informationization and dataization. Information management system technology, construction, and management, is an important foundation for achieving and ensuring the integration of engineering project management and information technology. It can be seen that the information management system technology has great application value in project management.KeywordsInformation management systemConstruction engineeringReal-time interactionInformatizationData

Blockchain provides a secure decentralized information management system that can solve many common problems facing the construction industry. The loose structure of the construction industry, the way that public and private projects are tendered, and the supply chain system it uses for material and service delivery provide unique challenges and problems. New information technology management systems such as BIM and RFID are used to address some of these issues, though not completely. Blockchain technology can be used to further improve the information management systems in construction, provide more automation and mitigate many possible legal conflicts by default. Implementation of blockchain technology in the construction industry can also result in the use of smart contracts with fewer administrative struggles, improve the flow of the project, material, and service delivery, and increase the security and currentness of BIM or project documents. This study aims to explore the applications of blockchain technology in improving the construction industry's information management systems. It is concluded that not only the blockchain technology has potential in addressing some of the common problems in the construction industry but also it is adaptable to the construction industry structure and the way it is practiced. Thus, blockchain technology is a viable option for adaptation in the construction industry.

Blockchain provides a secure decentralized information management system that can solve many common problems facing the construction industry. The loose structure of the construction industry, the way that public and private projects are tendered, and the supply chain system it uses for material and service delivery provide unique challenges and problems. New information technology management systems such as BIM and RFID are used to address some of these issues, though not completely. Blockchain technology can be used to further improve the information management systems in construction, provide more automation and mitigate many possible legal conflicts by default. Implementation of blockchain technology in the construction industry can also result in the use of smart contracts with fewer administrative struggles, improve the flow of the project, material, and service delivery, and increase the security and currentness of BIM or project documents. This study aims to explore the applications of blockchain technology in improving the construction industry's information management systems. It is concluded that not only the blockchain technology has potential in addressing some of the common problems in the construction industry but also it is adaptable to the construction industry structure and the way it is practiced. Thus, blockchain technology is a viable option for adaptation in the construction industry.

In the rapidly evolving digital landscape, the integration of data governance and information management systems has become critical for organizations aiming to enhance decision-making processes across various industries. This model proposes a comprehensive framework that seamlessly integrates data governance principles with advanced information management systems to ensure data accuracy, consistency, security, and accessibility. The model addresses the increasing complexity of data environments by establishing clear governance policies, standardized data practices, and robust data stewardship roles. It emphasizes the importance of aligning data governance with organizational objectives, ensuring that data assets are leveraged effectively to drive strategic decisions. The proposed model also incorporates cutting-edge technologies, such as artificial intelligence (AI) and machine learning (ML), to automate data management tasks, enhance data quality, and provide predictive analytics capabilities. By integrating these technologies, organizations can optimize data processing, reduce operational costs, and improve the timeliness and relevance of decision-making. The model further highlights the significance of a centralized data management platform that facilitates real-time data sharing and collaboration across departments, fostering a data-driven culture within organizations. Moreover, the model underscores the importance of compliance with regulatory requirements and industry standards, ensuring that data governance practices adhere to legal and ethical guidelines. By implementing this integrated approach, organizations can mitigate data risks, enhance data transparency, and build trust with stakeholders. The model is adaptable to various industries, including healthcare, finance, manufacturing, and telecommunications, offering a scalable solution to the challenges of managing complex data ecosystems. In conclusion, this model provides a strategic blueprint for integrating data governance and information management systems, enabling organizations to make informed, data-driven decisions. It supports the transformation of data into valuable insights, driving innovation, operational efficiency, and competitive advantage in the digital era.

Background: Due to the rapid development of translational medicine research, the construction and application clinical biobanks have increasing attention. The information system is the core of the biobank, which plays an important role on sharing of clinical information and specimens. So the development of clinical biobank information system should based on supporting the overall process of operation, management and service.

In view of the insufficiency that the construction of the college student data management system cannot be processed based on the evidence theory, a kind of method for the construction of the college student data management system that is dependent on the association rule algorithm is put forward in this paper, which aims to solve the problems in the construction of the college student data management system and so on. Based on the association rule algorithm, the pre-processing of the college student data management system is established. In addition, dependent on the characteristics of the association rule algorithm, the related centroid is calculated, and the centroid is taken as the representative information of the cluster. The basic probability assignment is carried out in accordance with the generalized triangular fuzzy membership function, and the evidence is formed after the assignment, thus completing the construction of the college student data management system. The simulation results show that the method put forward in this paper is effective and can be used to carry out reasonable evaluation on the college student data management system.

Objective To establish the information management system of performance in the operating room, and provide scientific convenient and effective methods for performance appraisal for nurses . Methods According to anesthesia information management system , performance appraisal software , and the seniority, position, level of nurses etc, we established the information management system of performance appraisal in the operating room .We compared the total annual leave days before and after the application of information management system , and used the self-designed questionnaire to investigate the satisfaction . Results The information management of performance strengthened the consciousness of nurses following the arrangement , decreased the time of leaving from work from 989 days to 312 days.The career satisfaction of nurses was up to 100%.And the satisfaction of patients increased from 76.69%to 98.23%.Besides, the average time of performance appraisal decreased from （5.84 ±0.27） d to （1.04 ±0.64） d.Conclusions Construction and application of information management system for performance appraisal of nursing staff in the operating room mobilize nurses ’ work and guarantee the quality of clinical care , which also improve the satisfaction among anesthetists , surgeons and patients .The information management system helps nurses with time management and improves the efficiency of performance appraisal . Key words: Operating room; Personnel management,hospital; Information technology

With the development of society and the progress of science and technology, people’s demand for electric energy is becoming stronger and stronger. In order to adapt to the increasing demand for electric energy, smart grids are gradually popularized. It not only changed the development mode of our country’s power grid, but also brought new opportunities and challenges for our country’s power companies. Among them, the electricity bill information management system based on network micro-service technology ensures the accuracy and stability of users’ electricity consumption data. The purpose of this paper is to study the construction of electricity bill information management system based on network microservice technology. This article takes the electricity bill information management system as the research object, combined with the network micro-service technology, analyzes the functional characteristics of the system, and details the functional modules of the system’s user management, meter reading management, electricity bill collection management, and electricity monitoring management. This article briefly introduces the system test environment, and conducts main functional module tests and stress tests on the system. The test results show that the shortest response time of each function of the system is 2.016 s, and the longest is 2.341 s. And the success rate of system function operation is above 98%. It can be seen that this system has good performance and meets the performance requirements of the system.KeywordsElectricity bill managementElectricity consumption information collectionManagement analysisNetwork microservices

The information management of engineering investigation quality is actually the process of producing, collecting, inputting, sharing and applying engineering investigation parameter data and management information. Using the theory of total quality management and the information technology such as blockchain, big data and cloud computing, this paper explores the possibility of constructing the information management system of engineering investigation quality from “data standard of engineering investigation quality information”, “information management platform of engineering investigation project” and “geological information database of Engineering Investigation”, and takes Chongqing as an example to carry out the engineering investigation quality. The construction and analysis of information management system verify the effectiveness of the information management system of engineering investigation quality.

PurposeThe purpose of this paper is to describe the technical features, underlying concepts and implementation details of a novel Building Information Modeling (BIM)-integrated, graph-based platform developed to support BIM for facilities management through a usability driven visual representation of the construction operations building information exchange (COBie) spreadsheet data.Design/methodology/approachThis paper is based on the iterative steps of design thinking and agile software development methodology. The conceptual development of the VisualCOBie platform is based on Gestalt’s principles of visual perception to facilitate usability and comprehension of the COBie data.FindingsThe paper demonstrates that Gestalt’s principles of visual perception provide a suitable conceptual as well as implementable basis for improving the usability and comprehension of COBie spreadsheets. The implemented BIM-integrated, graph-based VisualCOBie platform supports visual navigation and dynamic search, reducing the cognitive load of large spreadsheets that are common in facilities management software.Research limitations/implicationsThe usability, visual search and dependencies-based search of VisualCOBie can potentially transform how we implement and use facilities and information management systems in construction, where large spreadsheets are frequently used in conjunction with BIM and other tools. VisualCOBie also provides usability-based step towards BIM for facilities management.Originality/valueThe VisualCOBie approach provides a novel user interface and information management platform. This paper may also foster a potential paradigm shift in our approach to the representation and use of information exchange standards such as COBie, which are required to facilitate the research and practice on BIM for facilities management.

China’s road and bridge engineering construction plays an important position in the national economy. Roads and bridges play an important role and have a close relationship with national life. Judging from the current project construction management work, China’s engineering construction departments generally have problems such as low technology level, imperfect information management system and lack of professional talents. The popularization and development of information technology provides an important technical approach for the scientific management of engineering construction. This paper first expounds the concept and characteristics of the way and bridge engineering construction management information system, and analyzes the current situation and existing deficiencies of the way and bridge engineering project management information construction. On this basis, the paper proposes to construct an engineering construction information management system that conforms to the characteristics of road and bridge engineering. The research and development of way and bridge management information management system will greatly accelerate the renewal of way and bridge management concepts, so it is of great significance.

Data governance in a lakehouse architecture is crucial for managing the lifecycle, quality, and security of data across hybrid and scalable environments. The lakehouse model combines the benefits of data lakes and data warehouses, enabling organizations to handle large volumes of structured and unstructured data while ensuring analytical accuracy. However, the seamless integration of diverse data sources introduces challenges related to governance, including compliance, privacy, and data lineage. This paper explores the principles and practices essential for implementing effective data governance in a lakehouse environment. Key components such as metadata management, data cataloging, access control, and policy enforcement are discussed in detail. The role of automation and machine learning in enhancing governance capabilities, such as detecting anomalies and ensuring compliance with regulatory standards, is also examined. Additionally, the lakehouse architecture's unified nature demands a governance framework that ensures data consistency while fostering accessibility for both data scientists and business analysts. Strategies for integrating data governance within the lakehouse model, including role-based access, audit trails, and encryption, are presented to mitigate risks and enhance trust in data usage. By addressing these challenges and opportunities, this paper demonstrates how a robust data governance framework can empower organizations to balance innovation and control. Ultimately, data governance in the lakehouse architecture serves as a foundation for reliable, secure, and actionable insights, driving informed decision-making in complex and dynamic environments.

This paper proposes a rigorous, enterprise-ready framework for leveraging Artificial Intelligence (AI) to strengthen strategic business decision-making in volatile, uncertain, complex, and ambiguous environments. The framework integrates five reinforcing layers: (1) problem framing and value hypotheses; (2) data readiness and governance; (3) model portfolio design; (4) human–AI teaming and controls; and (5) impact measurement and continuous learning. Strategic questions are decomposed into decision statements, testable hypotheses, and measurable value drivers, linking choices to outcomes with logic models and decision trees. Data readiness establishes standards for quality, lineage, privacy, and security, supported by metadata catalogs, access controls, and stewardship roles. Governance aligns with regulatory obligations and ethical principles to ensure trustworthy, auditable data use across the enterprise. The model portfolio blends predictive, prescriptive, causal, and generative techniques. Forecasting and uplift models quantify demand, risk, and customer propensity; optimization and simulation allocate resources under constraints; causal inference and experimentation estimate treatment effects for policy and pricing; generative AI accelerates knowledge discovery, scenario authoring, and decision briefings. An MLOps backbone orchestrates feature stores, automated pipelines, testing, deployment, and monitoring to sustain reliability, fairness, and performance. Human–AI teaming pairs algorithmic recommendations with expert judgment via decision playbooks, role clarity, and calibrated trust, enabling interrogation, sensitivity analysis, and override when required. Controls integrate model risk management with fairness, accountability, transparency, and ethics requirements, including pre-deployment testing, bias and drift monitoring, audit-ready documentation, and incident response procedures. Impact measurement connects decisions to financial and non-financial outcomes through causal evaluation, OKRs, and benefit tracking. Implementation proceeds through a pragmatic roadmap: prioritize high-leverage use cases (demand shaping, dynamic pricing, supply risk sensing, workforce planning, churn prevention), establish a federated center of excellence, and standardize reusable accelerators such as feature stores, prompt libraries, governance checklists, and value-tracking templates. The contribution is a cohesive blueprint uniting decision science, data engineering, and organizational change to de-risk AI at scale. By aligning architecture, processes, and incentives, the framework enables repeatable value creation, resilient choices under uncertainty, and measurable strategic impact. Results generalize across sectors and varying data maturities. Keywords: Strategic Decision-Making, Artificial Intelligence, Decision Intelligence, MLOPS, Data Governance, Causal Inference, Generative AI, Human–AI Teaming, Model Risk Management, Prescriptive Analytics.

Artificial intelligence and big data technologies have had a profound impact on the intelligent development of the transportation industry, bringing about significant changes in areas such as traffic data collection, traffic planning, and data integration. The smartification of highways cannot be achieved without a unified, interconnected, and shared data platform and planning. With the construction of information systems and data accumulation in the transportation industry, the need for data collaboration and resource sharing is becoming increasingly urgent. However, there are common problems in the transportation industry, such as a lack of unified data standards, and insufficient cross-department collaboration. How to integrate massive data resources and serve the transportation industry is a key focus of research in the field. This paper analyzes the current situation of traffic data collection and governance, researches data governance methods in two fields: highways and railway transportation, conducts data integration and creates databases, and develops a comprehensive traffic data governance platform. It has certain reference significance for traffic data modeling and the construction of traffic data governance systems.