Project Workflow Research Articles

IntelliDoor: Smart Access Control System with Automated Door Operation

Abstract: The Intelligent Access Control System with Automatic Door Operation is a project aimed at providing secure and efficient access control using artificial intelligence (AI) and automated door mechanisms. This system utilizes facial recognition technology for user identification, Firebase for data storage and management, and Arduino for controlling the automatic door operation. The objective is to develop a robust and intelligent access control system that automatically identifies authorized individuals through facial recognition and opens the door for them. The system also maintains a record of entry for each individual for attendance tracking or security purposes. The project features include facial recognition, automatic door operation, Firebase integration, an alert system, and real-time monitoring. The project workflow involves initialization, facial recognition, recognition results, database updates, and automatic door operation. The project report covers the introduction, literature review, methodology, implementation, results, and conclusion. The Intelligent Access Control System with Automatic Door Operation provides an efficient and secure solution for access control, enhancing security while offering convenience. With further refinement and integration, it has the potential to become a standard solution for access control in modern buildings and facilities.

Accessible History: Putting a Century of "The Chronicles of Oklahoma" Online

Since 1921, the Oklahoma Historical Society has published The Chronicles of Oklahoma, a nonfiction journal focused on topics related to the state’s history. In 2020, the Oklahoma Historical Society partnered with the University of North Texas (UNT) Libraries to make the extensive back catalog of quarterly-published issues of the Chronicles publicly available online through the Gateway to Oklahoma History (The Gateway) digital library. The scope of the project eventually expanded to include preserving individual articles in the Gateway to promote their discovery and impact for researchers. This presentation describes the project management practices, workflows, and metadata standards that were used to support the project. It also explores the challenges and highlights the successes of this multi-year preservation project.

On the Experience of Teaching Monumental Painting Using the Method of Visual Learning

The author of the article investigates the use of a visual teaching approach for monumental painting, as applied to fifth-year students at the Sergey Andriaka Academy of Watercolor and Fine Arts. The project involved students and their teacher collaboratively creating a painting plafond with a total area of 78 m2. The article chronicles the project's workflow, encompassing the creation of sketches, preparation of cardboard, execution of multilayer painting on canvases, and the subsequent installation of the plafond. Emphasis is placed on techniques for creating polychrome images using colorants with a restrained tonal and color palette, as well as on the multilayer oil painting process, which includes a grisaille tempera undercoat and a final glaze.

Strategic approaches for successful digital transformation in project management across industries

Digital transformation has become imperative for organizations across industries seeking to enhance operational efficiency, innovate customer experiences, and maintain competitive advantage in a rapidly evolving digital landscape. This paper explores strategic approaches that enable successful digital transformation in project management, addressing key challenges and leveraging opportunities for growth and sustainability. In the current business environment, digital transformation in project management encompasses the adoption of advanced technologies, integration of digital tools, and reengineering of processes to optimize project delivery and outcomes. Organizations are increasingly leveraging cloud computing, artificial intelligence, big data analytics, and Internet of Things (IoT) to streamline project workflows, enhance decision-making capabilities, and facilitate real-time collaboration among stakeholders. Successful digital transformation initiatives in project management are underpinned by strategic planning and leadership commitment. Alignment of digital transformation goals with organizational objectives ensures clarity of purpose and enhances stakeholder engagement throughout the transformation journey. Moreover, fostering a culture of innovation and continuous learning enables teams to adapt to technological advancements and embrace change effectively. Challenges in achieving successful digital transformation include legacy system integration, data security concerns, skill gaps among team members, and resistance to cultural change. Addressing these challenges requires proactive risk management, investment in training and development programs, and collaboration with technology partners to navigate complexities and ensure seamless implementation. Case studies illustrate diverse approaches to digital transformation in project management, highlighting best practices and lessons learned across various industries. Organizations that successfully navigate digital transformation not only achieve operational efficiencies and cost savings but also create value through enhanced customer experiences and improved competitive positioning. Looking forward, the strategic adoption of emerging technologies and agile methodologies will continue to shape the future of digital transformation in project management. Organizations must remain adaptable and responsive to market dynamics, leveraging digital tools to drive innovation, accelerate time-to-market, and sustain long-term growth in an increasingly digital-first world. In conclusion, strategic approaches to digital transformation in project management empower organizations to harness the full potential of digital technologies, driving efficiencies, fostering innovation, and achieving sustainable success across industries.

A hybrid modelling approach for information processing workflow in inter-cultural heritage projects

PurposeBuilding Information Modelling in building processes brings significant efficiency gains. However, its use in restoration projects is still experimental. On the other hand, more than traditional methods are needed for communication, collaboration, and shared understanding. Hence, the main research question is how to enhance these aspects in multinational projects with information transparency challenges and the need for shared understanding among stakeholders. This research aims to develop and propose a hybrid modelling approach that integrates traditional and BIM-based information process workflows through testing and evaluating to what extent BIM can be used in restoration projects without excessive efforts.Design/methodology/approachConsidering this aim, the case study, the Mahmud Pasha Hammam as the heritage building, a 15th-century structure located in Serbia, was studied with the action research strategy to enable action-based learning by doing. Alongside the 2D documentation as the traditional method for the existing structure, restoration design proposals were also produced for the new additions to the heritage structure with BIM.FindingsA new BIM use scenario was experimented with and proposed, proving BIM implementation's potential impact on heritage restoration projects. A hybrid model was developed that meets the requirements of existing regulations and specifications, where restoration proposals were visualized quickly, quantity take-off was produced, and technical drawings were generated instantly.Originality/valueThis hybrid modelling workflow integrates HBIM with traditional methods in restoration projects to improve communication, efficiency, and collaboration in a real-time professional project.

A CFD simulation platform for surface finishing processes in advanced manufacturing

Products created by additive manufacturing often have surface imperfections that require post-processing operations to remove extraneous material in order to meet design specifications. The usage of computational fluid dynamics (CFD) simulations to predict material removal rates of components, allows practitioners to optimize the setup and usage of post-processing equipment. However, those without in-depth knowledge of CFD or the related specialized software, require an easy-to-use and cost-effective application to manage the computational workflow. The two specific surface finishing applications investigated here, are, abrasive flow machining (AFM) and robotic stream finishing (RSF). In order to satisfy user requirements, a modular, threaded, fault-tolerant and object-oriented project management application, written with the Python programming language and PyQt6 framework, has been developed to conduct surface finishing-related CFD simulations using OpenFOAM®. The advantages of the proposed software are: 1) the modern PyQt6 framework is used to develop a cross-platform and user-friendly application which employs the model-view class architectural paradigm for data management and its display, 2) step-by-step interactive project workflows have been tailored specifically for AFM and RSF simulations, 3) the developed steady-state viscoelastic flow solver for AFM and continuum-based steady-state dense granular flow solver for RSF, offer advantages over those provided by OpenFOAM® and 4) simulation results have been corroborated by experimental data to assess the improved accuracy of material removal prediction of the current software when compared to other commercial applications.

دراسة مقارنة لفعالية منهجية IMaRD وIMGSIE في مجال تكنولوجيا المعلومات

The usefulness of two well-known approaches, IMARD and IMGSIE, is compared in this paper within the context of information technology (IT) domains. Techniques are essential for organizing project workflows, improving productivity, and producing the intended results. IMARD, which comprises phases like Introduction, Method, Analysis, Results, and Discussion, is widely used in scientific academic papers and professional research to guarantee methodical problem-solving. On the other hand, IMGSIE methodology which consists of phases Introduction, Method, Generalization, Specification, Implementation, and Evaluation—has become more popular in technical and IT engineering projects. The objective of this paper is to evaluate the relative merits and demerits of different approaches in IT-related projects, taking into account variables like project and papers success rates, difficulties in implementation, and overall project management effectiveness. Discussion of case studies of IT projects that use both approaches offers practical insights into their efficacy. Key findings indicate that IMGSIE is more appropriate for graduation it projects needing iterative refinement and flexibility, even though IMARD offers a systematic and complete approach. Questionnaire was distributed to supervisors who used the IMARD and PPDIOO methodologies to evaluate its quality and effectiveness. The most important differences between the methodologies have also been identified, as well as where and when each method is used. Key words: IMGSIE methodology, IMaRD methodology, IMGSIE Lifecycle, Research methodology.

The effectiveness of BIM features application strategies for building refurbishment project performance improvement

Abstract This research presents a study that explores the effectiveness of different strategies for implementing Building Information Modelling (BIM) features in enhancing the performance of building refurbishment projects. The study adopts a combined quantitative and qualitative approach to measure the significance of BIM features in relation to building refurbishment project performance improvement. It aims to uncover the correlation between BIM feature application strategies and building refurbishment project performance and provides recommendations on utilizing BIM features to improve project outcomes. The study encompasses a comprehensive review of existing literature on BIM and building refurbishment projects, followed by a mixed-methods approach. This approach involves quantitative analysis of data from refurbishment projects that have implemented BIM, as well as qualitative analysis of expert interviews with industry professionals. The findings of the expert interviews shed light on the factors influencing the effectiveness of BIM in building refurbishment projects, such as the level of BIM adoption, the availability of skilled personnel, and the integration of BIM into project workflows. The research contributes valuable insights to enhance the understanding of BIM utilization in building refurbishment projects, offering guidance to practitioners and decision-makers in leveraging BIM technologies for such endeavourrs.

Implementation of CAD/CAM/CAE Systems for Improved Design and Manufacturing Processes in Industrial Organizations

Abstract The industrial sector rapidly integrates CAD (computer-aided design), CAE (computer-aided engineering) and CAM (computer-aided manufacturing) systems that have changed how firms advance their design and manufacturing processes. Imagine technology spreading like wildfire—this is what our latest study maps out. It details the perks these innovations bring along and weighs them against the hiccups experienced in age-old setups. Highlighted within our findings are impressive improvements concerning efficiency and reliability which boost productivity immensely—if correctly adjusted for optimal use. Specifically, through an extensive literature review it examines application of CAD/CAM/CAE systems within various industrial settings. Better operational results are definitely on the horizon with this strategy. Imagine cutting down on time without sacrificing quality; this is exactly what recent developments contribute to our project workflows—enhanced productivity coupled with pinpoint accuracy. Reviewing case studies across various sectors shows how successfully technology upgrades have been embraced during industrialization. The learning curve may be steep but costs can be recovered quickly if properly managed is the key inference from examining these examples.

INTEGRATİON OF ARTİFİCİAL INTELLİGENCE (AI) İN TASK MANAGEMENT SYSTEMS

The incorporation of artificial intelligence (AI) into task management systems has become a disruptive force in today's fast-paced work environments, changing how teams plan, prioritize, and complete tasks. This trend is best illustrated by platforms such as Trello, Monday.com, and Asana, which use AI technology to streamline project workflows, automate tedious chores, and offer insightful recommendations. These systems enable users to improve decision-making, expedite procedures, and increase productivity using AI-driven capabilities like predictive analytics, automated task assignment, and smart suggestions. Through the utilization of natural language processing (NLP) and data visualization tools, these systems facilitate data-driven insights, preemptive risk management, and intuitive interaction. The incorporation of AI in task management signifies a paradigm change towards more intelligent, cooperative work practices, as companies depend more and more on AI to enhance human talents and boost productivity. This article addresses the consequences of this technological advancement for the nature of work in the future, presents real-world examples of AI-powered features in popular platforms, and examines the many advantages of integrating AI into task management. Keywords: task management, artificial intelligence, task management systems, integration of AI, automated task assignment, Natural Language Processing (NLP).

Challenges in construction readiness for BIM-based building projects

ABSTRACT This study aims to investigate challenges in achieving adequate construction readiness for building information modeling (BIM) based building projects, using Malaysia as a case study. To achieve this aim, 20 semi-structured interviews with BIM professionals were conducted using the purposive sampling method. Then, the data were analyzed using thematic analysis. The study results revealed that the challenges can be categorized into two themes: BIM-related challenges and project-related challenges. BIM-related challenges include knowledge, infrastructure, modeling, adoption, and awareness issues. Project-related challenges include funding, communication, team management, cooperation, timeline, coordination, and change order issues. These findings imply that although BIM offers numerous advantages, its implementation introduces specific challenges that differ from those faced in traditional construction projects. These challenges often revolve around technological adoption, data management, changes in project workflows, and team dynamics. Therefore, investigating the specific challenges in BIM-based construction projects is crucial for achieving project success. This study adds to the existing knowledge by providing valuable insights into the challenges faced during the pre-construction phase of BIM-based building projects. The insights can be used to develop strategies for ensuring BIM-based construction projects are ready for the construction phase.

46 Cross-Disciplinary Education in Biostatistics and Epidemiology in Program Managers for CTSA BERD Cores

OBJECTIVES/GOALS: The overall goals of this project are: (1) to demonstrate the utility of staff with cross-disciplinary skills in biostatistics and epidemiology as program managers for CTSA BERD Cores, and (2) examine streamlined processes in project triaging, consultations, and data extractions for CTSA service requests when individuals are in this role. METHODS/STUDY POPULATION: Biostatistics and epidemiology are partnered disciplines incorporated in a variety of research areas, especially in human health sciences and health care. For interdisciplinary teams, including individuals trained in both specialties results in efficient research collaborations. When these individuals are program managers for research navigation, processes become more effective and expedited for project timelines and workflows across the CTSA. Examples of this integration are described in vignettes from project triaging, statistical consultations, and data extractions from CTSA service requests. Process comparisons of previous and current workflows are presented to show the advantages of utilizing this type of program manager within these areas of BERD. RESULTS/ANTICIPATED RESULTS: This project defines three areas of integration for a program manager with cross-disciplinary training in biostatistics and epidemiology: (1) project triaging to statistical teams, (2) study design and analysis consultations, and (3) data extractions. Each of these areas demonstrates prior processes that once would require multiple steps in CTSA service requests (including time, resources, and personnel) and identified common issues (slow response to requests, data re-extractions, and challenges providing statistical support) that are now avoided with a manager with cross-training in biostatistics and epidemiology methods. The advantages and disadvantages of integrating these individuals are also described. DISCUSSION/SIGNIFICANCE: BERD Cores seeking improvements to research navigation processes can gain efficiency by incorporating program managers with cross-disciplinary training in biostatistics and epidemiology. BERD Cores may also use this as a case study for translational science with innovation to longstanding challenges in CTSA research workflows.

From Theory to Practice: Implementing AI Technologies in Project Management

This research paper explores the integration of Artificial Intelligence (AI) technologies in project management, analysing current trends, challenges, and practical examples. The paper investigates the benefits of AI in streamlining project workflows, enhancing decision-making processes, and mitigating risks. Through case studies and emerging trends, it identifies key recommendations for successful AI implementation, including prioritizing data quality, promoting ethical AI practices, fostering a culture of innovation, embracing collaborative approaches, and monitoring performance. By adhering to these recommendations, organizations can harness the transformative potential of AI to improve project outcomes and achieve strategic objectives in an increasingly digital and competitive landscape.

The Erosion of Competencies in Managing Innovation Projects due to the Impact of Ubiquitous Artificial Intelligence Systems

The rapid advancement of artificial intelligence (AI) systems has had a profound impact on various aspects of business and technology. One area greatly influenced by AI is innovation project management, which plays a critical role in driving organizational growth and success. The purpose of the paper examines the erosion of competencies in innovation project management as a consequence of the increasing reliance on AI systems. The object is innovation project management relying on the expertise, experience, and decision-making capabilities of human managers. However, the introduction of AI systems in project management processes has brought about significant changes. AI-powered tools and algorithms can automate tasks, analyse vast amounts of data, and provide valuable insights and recommendations. While this presents numerous benefits, it also raises concerns about the diminishing role of human competencies in managing innovation projects. AI systems require vast amounts of training data to operate effectively. In the process, they might overlook or undervalue the tacit knowledge and domain expertise possessed by human project managers. The results emphasize the need for project managers to develop new competencies, such as understanding AI capabilities, interpreting AI-generated insights, and effectively integrating AI systems into project workflows. To mitigate the erosion of competencies, organizations should invest in training programs that equip project managers with the necessary skills to work alongside AI systems. Recognizing these challenges and proactively addressing them through training, collaboration, and a balanced approach to human-AI interaction is crucial for organizations seeking to leverage AI while maintaining a high level of project management expertise.

Optimizing IT Project Management: Leveraging Jira Automation for IT implementations

In the dynamic landscape of Information Technology (IT) Project Management, the efficient utilization of resources and the timely execution of tasks are critical for successful project outcomes. The objective of this study is to explore the application of Robotic Process Automation (RPA) in the realm of IT project management, specifically focusing on leveraging Jira automation for software implementations. By automating repetitive tasks and workflows within the Jira platform, IT teams can streamline processes, reduce manual intervention, and enhance productivity. This study is relevant as it investigates implementation strategies associated with integrating RPA into Jira for IT project management. Drawing from industry insights on how methodologies are applied for project development, the research underscores the potential of Jira automation in optimizing the project workflows. Additionally, several scenarios, potential automation candidates, are discussed, and automated workflows are suggested in order to improve team collaboration and accelerate project delivery. Ultimately, this article aims to enhance efficiency and effectiveness in IT project management through the strategic deployment of Jira automation powered by RPA technologies.

Credit Default Analysis and Prediction Based on Machine Learning

The Credit Default Prediction project aims to develop an efficient machine learning model to accurately predict loan default risk on the Lending Club platform. The project is based on historical loan data, including borrower personal information, financial metrics, and credit history records, with the goal of building a robust predictive model. The project's workflow encompasses several critical steps, including data preprocessing, exploratory data analysis, feature engineering, model selection, and performance evaluation. In terms of model selection, the project employs two primary machine learning algorithms, namely Artificial Neural Networks (ANN) and Random Forest. These algorithms are renowned for their outstanding performance in handling extensive borrower data and providing reliable risk predictions. Model training and evaluation are conducted using a substantial amount of historical data to ensure accurate predictions across various scenarios. Furthermore, the project conducts feature importance analysis to identify factors that significantly influence loan default risk. These insights contribute to enhancing Lending Club's risk assessment process and supporting more informed decisions regarding loan approval and pricing strategies. By combining data-driven predictive modeling with in-depth data analysis, this project aims to enhance the efficiency of Lending Club's loan operations and elevate its risk management capabilities, ultimately providing more dependable financial services to investors and borrowers.

Exploring BIM Implementation Challenges in Complex Renovation Projects: A Case Study of UBC’s BRDF Expansion

Renovation of existing buildings pose unique challenges to the projects, especially when facilities must remain operational during construction. Building Information Modeling (BIM) methods offer a potential solution by enhancing project management and coordination. Nevertheless, comprehensive case study research on BIM implementation challenges and benefits in renovation projects is lacking. This research addresses this gap through an ethnographic investigation of BIM implementation in a complex renovation project. The ethnographic methods involved direct observation of project meetings, active engagement in all project communications, and access to project data resources. Additionally, surveys and expert interviews with key decision-makers were conducted. The findings reveal how BIM implementation streamlined project management and improved communication, decision making, and output quality, despite limited prior BIM expertise among the major stakeholders. Challenges included a lack of BIM skills, absence of standardized practices, and unclear data management. Furthermore, valuable lessons were identified, including that the necessity of BIM requirements and proper procurement methods encompassing the entire project workflow, formalizing information exchange, preventing information fragmentation, facilitating model accessibility, and ensuring clarity in model detail and content are crucial for project success. This research sheds light on the potential of BIM in renovation projects and highlights key considerations for successful implementation.

Enhancing Construction Project Workflow Reliability through Observe–Plan–Do–Check–React Cycle: A Bridge Project Case Study

This research aims to determine the appropriate level of effort required for each step of the Observe–Plan–Do–Check–React (OPDCA) cycle to improve the workflow reliability of a construction project. Empirical data on detailed weekly meeting minutes over 18 weeks and the planned and actual starting and finish times of 475 activities were collected from a bridge construction case project. The information theory approach was utilized to measure the information gained from discussions pertaining to the OPDCA cycle during weekly planning meetings. Cooperative game theory and the Shapley notation of fairness were used to compute the contribution of each OPDCA step to workflow reliability. Results showed that “Observe”, “Plan”, “Do”, “Check”, and “reAct”, contributed 18%, 23%, 23%, 24%, and 12% to observed variations in workflow reliability measured by the percent plan complete (PPC). Also, findings revealed that synergy exists between the “Check” step and other steps in the OPDCA cycle. The methodology developed in this paper has potential implications for engineering managers. The method can be generalized to help project managers find the balance between planning and control efforts to improve workflow. It also provides proven techniques for continuous improvement during project execution to facilitate project success. Furthermore, at the organizational level, the developed method can help higher-level managers make informed investment decisions for employees’ training and development to improve performance in future projects.

Development of a hydrate risk assessment tool based on machine learning for CO2 storage in depleted gas reservoirs

Depleted gas reservoirs are attractive sites for Carbon Capture and Storage (CCS) due to their huge storage capacities, proven seal integrity, existing infrastructure and subsurface data availability. However, CO2 injection into depleted formations can potentially lead to hydrate formation near the wellbore due to Joule-Thomson cooling, which might cause injectivity issues. Some challenges encountered when modeling and simulating this process are the computational time caused by Newton’s convergence issues and instability. The objective of this work is to propose a novel approach for hydrate risk assessment during CO2 injection into depleted gas reservoirs using physics-based Machine Learning (ML) approach. First, the selection of input parameters for the ML models is performed based on sensitivity study results using an analytical solution for different operational and petrophysical values. Then the ML models are tuned and tested using datasets from numerical reservoir simulation results based on a wide range of input parameter values. To the best of our knowledge, this is the first time that an ML approach is used for risk assessment of CO2 hydrate in its storage in depleted gas reservoirs. The ML models developed in this study presented an efficient performance to predict hydrate-forming events. The deep neural network model performed best with a 95% recall value and 84% precision value. These results show that the ML model can be further utilized for risk assessment in the screening stage, and the combination of screening by ML, followed by detailed analysis with numerical simulation in high-risk cases can be an efficient probing workflow for future CCS projects.

Building and Deploying a Classification Schema using Open Standards and Technology

This tutorial offers a full introduction to Building and Deploying a Classification schema using Open Standards and Technology. Beyond accessing the command line, no prior knowledge is assumed as all the steps are described in detail. However, the tutorial will take you through some more complex technical steps. In order to profit best from the example workflows of the last chapter, it is good if you have some idea of how to work with OpenRefine and/or to have a TEI Publisher server set up to play around with. With this tutorial, you ‑ the reader ‑ should gain an understanding of: ‑ what different classification schemes can contribute to your project, ‑ what to pay attention to when building a classification scheme, ‑ how to code the classification scheme (in SKOS), ‑ how to publish the classification scheme (on GitHub pages), ‑ what some possible scenarios of using classifications schemes in actual project workflows are.