A Coding-Free Software Framework for Developing Lightweight Web Data Management Systems

Data management systems are complex and have specific requirements such as data control, legacy data, version control, traceability, contributions from multiple researchers and multiple topics, varying security requirements, physical storage and location, access control and verification of authorization/security clearance, and a file naming and structure systematic approach. The benefits of an appropriate data management system are an increased cost effectiveness, enhanced return-on-investment, reduction or elimination of work duplication, scientific advances, improvement to existing process, maintaining the quality and efficiency of the product/research, and contribution to a richer database benefiting the entire organization and future researchers. A data management system can be created either in-house or through the purchase of a commercial-off-the-shelf system. An inhouse system is typically setup within the organization and can be relatively fast, simple, and inexpensive to setup but can also be severely limited in its capabilities and in its potential for expansion. Commercial-off-the-shelf systems may be more sophisticated and potentially more expensive on the initial startup costs but such systems typically possess varying degrees of automation. Additional benefits are that the development and maintenance costs are shared over a large number of users and technical support is often provided as part of the cost. Selection of an appropriate data management system depends heavily on the particular needs and available resources of an organization interested in a data management system.

With the advent of the digital age, brand marketing faces unprecedented opportunities and challenges. Traditional brand marketing informationization models can no longer meet the rapidly changing market demands and the diversity of consumer behaviors. This paper explores the intelligent transformation path of brand marketing informationization, analyzing the challenges faced by current brand marketing informationization in data management, technology application, and organizational structure, while also pointing out the innovative opportunities brought by intelligent technologies. Through the case analysis of the BrandScan system, this paper proposes data-driven precision marketing strategies, intelligent technology application strategies, cross-channel integration strategies, and talent and organizational management strategies for transformation. It also elaborates on the implementation steps of intelligent transformation, including formulating transformation plans, infrastructure construction, data management and application system construction, technology application and business process optimization, and talent and organizational culture building. The research results show that intelligent transformation can significantly improve the efficiency and effectiveness of brand marketing and enhance brand competitiveness. This paper aims to provide theoretical guidance and practical references for the future development of brand marketing informationization, promoting innovation and development in the digital age.

The continuous reform of the domestic economic system also has certain requirements for the reform and innovation of the tobacco industry. At present, when the tobacco industry is carrying out party building work, it must be in line with the development of the times and be able to form a target management system, so as to ensure that the party building work is more effective, standardized and scientific. Therefore, the party organization of the tobacco industry must have a correct understanding of the construction of the target management system, know its importance, and be able to take effective measures to continuously optimize and improve. At present, the construction of the party building target management system in the tobacco industry is in good condition, and it has been incorporated into the daily work of the industry development, forming a positive development trend. However, there are still some problems in the construction of the party building target management system in individual tobacco industries, which limits the scientific level of the overall party building work and does not attract the attention of relevant departments. In response to these problems, continuous improvement is required, and only in this way can the construction of the party building target management system achieve better results.

Digital oil and gas field is an overly complex integrated information system, and with the continuous expansion of business scale and needs, oil companies will constantly raise more new and higher requirements for digital transformation. In the previous system construction, we adopted multi-phase, multi-vendor, multi-technology and multi-method, resulting in the problem of data silos and fragmentation. The result of the data management problems is that decisions are often made using incomplete information. Even when the desired data is accessible, requirements for gathering and formatting it may limit the amount of analysis performed before a timely decision must be made. Therefore, through the use of advanced computer technologies such as big data, cloud computing and IOT (internet of things), it has become our current goal to build an integrated data integration platform and provide unified data services to improve the company's bottom line. As part of the digital oilfield, offshore drilling operations is one of the potential areas where data processing and advanced analytics technology can be used to increase revenue, lower costs, and reduce risks. Building a data mining and analytics engine that uses multiple drilling data is a difficult challenge. The workflow of data processing and the timeliness of the analysis are major considerations for developing a data service solution. Most of the current analytical engines require more than one tool to have a complete system. Therefore, adopting an integrated system that combines all required tools will significantly help an organization to address the above challenges in a timely manner. This paper serves to provide a technical overview of the offshore drilling data service system currently developed and deployed. The data service system consists of four subsystems. They are the static data management system including structured data (job report) and unstructured data (design documentation and research report), the real-time data management system, the third-party software data management system integrating major industry software databases, and the cloud-based data visual application system providing dynamic analysis results to achieve timely optimization of the operations. Through a unified logical data model, it can realize the quick access to the third-party software data and application support; These subsystems are fully integrated and interact with each other to function as microservices, providing a one-stop solution for real-time drilling optimization and monitoring. This data service system has become a powerful decision support tool for the drilling operations team. The learned lessons and gained experiences from the system services presented here provide valuable guidance for future demands E&P and the industrial revolution.

Enhancing knowledge exchange through web map-based knowledge management system in construction: Lessons learned in Taiwan

The processes at the construction stages are characterized by considerable complexity due to the large amount of information contained in the design and construction documentation, and its interaction. Information management requires engineering data management systems and an object information model. Technology of building information modeling is based on the development and use of a virtual model of a construction object in the form of a three-dimensional information model and related documents. The article analyzes the Polterovich-Tonis evolutionary model as applied to information modeling processes in Russia. The functional approach to the modeling of complex project management systems in construction and visual modeling of construction objects is considered. Links between various design and calculation systems, estimated systems, scheduling and resource management systems are revealed. A formal description of the conceptual model of an integration information system for design options in construction and a strategy of data exchange between the system for automation of accounting and control and the BIM-system of visual presentation was developed.

<p>ABSTRACT<br />Over the last few decades, most of the pharmaceutical companies and research sponsors are facing a lot of challenges in clinical research for their<br />new drug approval. The sponsor research needs a high-quality data report for getting new drug approval from Food and Drug Administration for their<br />medical products. Clinical trial data are important for the drug and medical device development processing pharmaceutical companies to examine<br />and evaluate the efficacy and safety of the new medical product in human volunteers. The results of the clinical trial studies generate the most<br />valuable data and in recent years; there has been massive development in the field of clinical trials. A good clinical data management system reduces<br />the duration of the study and cost of drug development. Further a well-designed case report form (CRF) assists data collection and make facilitates<br />data management and statistical analysis. Nowadays, the electronic data capture (EDC) is very beneficial in data collection. EDC helps to speed up the<br />clinical trial process and reduces the duration, errors and make the work easy in the data management system. This article highlights the importance<br />of data management processes involved in the clinical trial and provides an overview of the clinical trial data management tools. The study concluded<br />that data management tools play a key role in the clinical trial and well-designed CRFs reduces the errors and save the time of the clinical trials and<br />facilitates the drug discovery and development.<br />Keywords: Pharmaceutical, Clinical trial, Clinical data management, Data capture.</p>

BackgroundA research area that has greatly benefited from the development of new and improved analysis technologies is Proteomics and large amounts of data have been generated by proteomic analysis as a consequence. Previously, the storage, management and analysis of these data have been done manually. This is, however, incompatible with the volume of data generated by modern proteomic analysis. Several attempts have been made to automate the tasks of data analysis and management. In this work we propose PRODIS (Proteomics Database Integrated System), a system for proteomic experimental data management. The proposed system enables an efficient management of the proteomic experimentation workflow, simplifies controlling experiments and associated data and establishes links between similar experiments through the experiment tracking function.ResultsPRODIS is fully web based which simplifies data upload and gives the system the flexibility necessary for use in complex projects. Data from Liquid Chromatography, 2D-PAGE and Mass Spectrometry experiments can be stored in the system. Moreover, it is simple to use, researchers can insert experimental data directly as experiments are performed, without the need to configure the system or change their experiment routine. PRODIS has a number of important features, including a password protected system in which each screen for data upload and retrieval is validated; users have different levels of clearance, which allow the execution of tasks according to the user clearance level. The system allows the upload, parsing of files, storage and display of experiment results and images in the main formats used in proteomics laboratories: for chromatographies the chromatograms and lists of peaks resulting from separation are stored; For 2D-PAGE images of gels and the files resulting from the analysis are stored, containing information on positions of spots as well as its values of intensity, volume, etc; For Mass Spectrometry, PRODIS presents a function for completion of the mapping plate that allows the user to correlate the positions in plates to the samples separated by 2D-PAGE. Furthermore PRODIS allows the tracking of experiments from the first stage until the final step of identification, enabling an efficient management of the complete experimental process.ConclusionsThe construction of data management systems for Proteomics data importing and storing is a relevant subject. PRODIS is a system complementary to other proteomics tools that combines a powerful storage engine (the relational database) and a friendly access interface, aiming to assist Proteomics research directly at data handling and storage.

The clinical period (i.e. clinical trial and long term animal testing) development costs of a random sample of new chemical entities (NCEs) were examined for differences in average cost. All of the NCEs studied were first tested in humans between 1970 and 1982, and were classified for the purposes of the study by therapeutic class. The costs of unsuccessful projects were included with those of projects that resulted in US marketing approval. Including income forgone from expending funds before returns are earned ('time costs'), the capitalised (i.e. out-of-pocket plus time) clinical period costs per approved NCE were $US70, $US98, $US103 and $US163 million (1993 dollars) for anti-infective, cardiovascular, neuropharmacological and nonsteroidal anti-inflammatory drugs, respectively. Combining the data for all therapeutic categories, the mean clinical period cost per approved NCE was $US93 million. Omitting costs associated with unsuccessful projects, the mean capitalised clinical period costs for approved NCEs ranged from $US7.1 million (for topical steroids) to $US66.7 million (for cardiovascular agents) [1993 dollars]. The estimates of total clinical period costs per approved NCE depend on average out-of-pocket clinical phase costs, attrition rates across phases (i.e. the rates at which compounds drop out of active testing), the probability of marketing approval, and development and regulatory review times. Phase attrition and approval rates are the most important sources of variability in total clinical period costs between therapeutic categories. Development cost estimates by therapeutic category did not correlate strongly with US sales in the fifth year of marketing. Cardiovascular NCEs had much higher than average sales revenues, but clinical development costs for these drugs were only slightly above average. Conversely, nonsteroidal anti-inflammatory drugs attained average sales revenues, but had much higher than average development costs.

In the past two years, due to the frequent combination of extreme weather and high primary energy prices and other factors, resulting in the continuous tension of power supply and demand in many places, the risk of power rationing has gradually increased. In order to implement the strategic decision deployment of the Party Central Committee and The State Council, in the context of “carbon peak, carbon neutral” strategy to ensure the security of power supply work. This paper studies the construction of a load management system for the goal of power supply assurance. Through the overall analysis and unified management of the province’s load, it realizes the transformation from one-way load management to users’ active participation in grid interaction through the construction of system structure, policies, mechanism, and the creation and deepening of fine management application functions such as load forecasting, monitoring, and analysis, and effectively alleviates the imbalance between supply and demand. Ensure the safe and reliable supply of electricity, ensure people’s livelihood electricity, and provide strong support for achieving the “two carbon” goal.

Exploration and implementation of commonality valuation method in commercial aircraft family design

Objective To explore the methods of nursing quality management in order to provide safety,high quality and satisfactory nursing for patients,Methods To establish one grid management model of nursing quality combined with double three-level.Then,the report of security events,the implemention of nursing consultation,the incidence of adverse events,and patients' satisfaction were observed and compared before and after the management model.Results Mter implementing the grid management model of nursing quality,satisfaction average scores of hospitalized patients,emergency department patients,discharged patients were ( 98.22 ± 1.12 ),( 91.97 ± 1.67 ),(97.16 ± 2.41 ) respectively,and before it was implemented the average scores respectively were ( 91.59 ± 1.67 ),( 87.92 ± 1.98 ),( 87.89 ± 1.81 ),and the differences were statistically significant ( t =3.57,2.69,4.72,respectively ; P ＜ 0.05 ).There was a significant change in the report of security events,implemention of nursing consultation and checking.The incidence of administration errors,falling down and pressure sores were significantly reduced.Conclusions Construction of the grid management system of nursing quality can ensure the implemention of nursing core institution,improve risk awareness of nurses,improve the nursing quality and increase patients' satisfaction. Key words: Nursing management research; Nursing quality; Grid management; System construction

With the continuous development and maturity of technology, the Internet of Things has gradually been applied to various fields of work and life, for example, the use of the Internet of Things and road traffic, the use of the Internet of Things smart city for vehicle identification and GPS positioning, the use of the Internet of Things smart medical smart heart pulse monitoring device, and so on. And, the construction industry bears important responsibilities for the development of national infrastructure and people’s livelihood. This article aims to study the application of BIM and Internet of Things technology in the safety management of construction sites, to drive technological development with industry needs, help expand the application fields of the Internet of Things, and promote the improvement of the national construction safety management system. This article uses BIM and Internet of Things technology to build a construction safety management system, focusing on the protection of people, through technical means, such as personnel activity control, hazard warning, major hazard monitoring, and multiple institutional measures, such as education, training, supervision, and protection, that collaborate to complete the protection and risk avoidance of operators in the process of building construction. The evaluation index of the safety management system is proposed through the analytic hierarchy process, and the dimension of the safety management system to prevent accidents is evaluated by the analytic hierarchy process. Taking the actual project as an example, the application effect of the remote monitoring system is qualitatively analyzed. On the basis of the analysis, from the three levels of policy, enterprise, and practitioners, several suggested measures that will help new technology to be applied to construction safety management are put forward. In the experiment part, the construction of the safety management system is first explained, then the appropriate system evaluation index is selected, and then the comprehensive ranking is conducted according to the relative importance weight of the highest-level elements in the same level, and the MATLAB software is used to test the consistency. The experimental results show that the construction safety management system constructed in this paper has been well perfected. The calculated weights of people and dangerous areas are as high as 0.578, so that people and dangerous areas can be controlled effectively. Fall accidents from high places can be prevented, full-time monitoring of foundation pit projects can also avoid fall and collapse accidents from high places, improve the comprehensiveness and advancement of construction safety management, and improve the level of construction safety management.

This article focuses on the construction and practical exploration of a quality management system for higher vocational education driven by big data. Through innovative thinking and methods, this paper deeply analyzes the application value of big data in quality management of higher vocational education, explores the path to building a scientific and reasonable quality management system, and proposes targeted countermeasures for the problems faced in practice, aiming to improve the quality of higher vocational education.

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.