Real-time Projection Research Articles

Clinical Utility of Sentinel Lymph Node Biopsy Using the Medical Imaging Projection System in Breast Cancer Patients, Including Those Following Neoadjuvant Chemotherapy.

The Medical Imaging Projection System (MIPS) is a real-time projection mapping system with a highly sensitive fluorescence sensing system. The aim of this study was to investigate the clinical utility of sentinel lymph node (SLN) biopsy using MIPS and radioisotopes (RI) in patients with breast cancer including those who had undergone neoadjuvant chemotherapy (NAC). This retrospective observational study included patients with breast cancer who underwent SLN biopsy using both MIPS and RI at Hyogo Medical University Hospital from August 2023 to August 2024. In all, 151 patients with breast cancer (157 axillary lesions, considering bilateral cancer as two lesions) were included in this study. MIPS identified significantly more SLNs than RI (median, 2 vs. 1, p<0.001). MIPS alone identified SLNs in 96.2% of lesions, and when combined with RI, identified SLNs in 99.4% of lesions, including in patients who had undergone NAC. Eleven of 157 lesions (7.0%) had positive SLNs, and the combination method identified positive nodes in 10 of these 11 lesions (90.9%). Importantly, all five lesions with metastases after NAC were observed in patients with cN1/ycN0 disease and the luminal subtype. MIPS combined with RI achieves a high identification rate of SLNs in patients with breast cancer, including those who had undergone NAC. Patients with cN1 disease prior to NAC should undergo axillary surgery tailored to their characteristics.

THE VA GERIATRIC SCHOLARS PROGRAM IMPACT ON CLINICAL INNOVATIONS: QI COMPONENT IMPACTS OVER THE PAST DECADE

Abstract Since 2009 one of the key components of the Geriatric Scholars Program (GSP) has been the Quality Improvement Workshop and Practicum, offering the fundamentals of improvement science to scholars and guidance through real-time improvement projects within rural clinical settings. The GSP has offered this component to a wide range of providers, starting in 2009 with prescribing providers and pharmacists, expanding to an interprofessional consortium including the initial professions as well as social work, psychiatry, psychology, and functional therapists and assistants. Impact on the aging Veteran population is demonstrated through completion of improvement projects at the local level with 79 national presentations completed from 2010 - 2024. This session will explore the evolution of the QI Program over the past decade and how the program has adapted to changes in audience type based on clinical training, presentation format for the workshop material, and impact on project topic and completion rates over time. Improvement projects throughout the years and their impact on the national push for High Reliability Organization (HRO) status will also be discussed.

Difftree/Diffitems Rest Api for Guidewire Policy Center Cloud Implementation

Aims: This study provides a high-level design and technical solution for comparing two PolicyPeriods in Guidewire Policy Center application and exposing the differences as a REST API service, The differences provide payloads containing diffItems and that can be sent to downstream system via KAFKA. It also addresses the Guidewire application Out-Of-The-Box limitation which is currently not offering this solution. This new solutioning framework is flexible, reusable, and scalable for various integrations in Guidewire InsuranceSuite products. Study Design: This study outlines the technical solution to address the limitations of Guidewire PolicyCenter’s OOTB Difftree functionality, and focusing on usability, Scalability, architectural design for exposing diffItems via REST APIs and KAFKA payloads. Place and Duration of Study: The analysis was performed between December 2023 and July 2024, based on real time project situations from an array of software development environments, project scenarios and feedback from customers. Methodology: The methodologylaid out in this paper includes systematic approach to addressing the limitations of Guidewire PolicyCenter’s out-of-the-box Difftree functionality and outlines the technical solution designed to expose the Difftree functionality as a REST API Service and provide diffItems JSON payloads through KAFKA messaging for downstream/external vendor systems. Results: The end results provide a sturdy, scalable, and high-performing framework for comparing Policy Periods, utilizing a REST API to provide diffItems and KAFKA to distribute payloads, this enhances the accessibility, usability, and efficiency of the Difftree functionality in Guidewire Policy Center, enabling seamless integration with external systems and addressing essential business needs. Conclusion: The study concludes that the Out-of-The-Box Difftree/DiffItems functionality in Guidewire Policy Center comes with significant limitations, such as being restricted to UI users, only comparing immediate predecessor PolicyPeriods,and being incapable of making the data usable by external systems or exposing the capability as a service. To overcome these challenges, a custom framework was developed that is flexible, scalable, and reusable.The proposed solution successfully exposes the Difftree functionalityas a REST API service to external systems with filtering capabilities and a KAFKA payload delivery mechanism that uses events to provide unfiltered diffItems to downstream systems.

Leveraging Artificial Intelligence for optimized project management and risk mitigation in construction industry

The construction industry is increasingly adopting artificial intelligence (AI) to optimize project management processes and enhance risk mitigation strategies. As construction projects grow in complexity, with tight deadlines, evolving regulations, and high costs, the traditional approaches to managing projects and assessing risks are often inefficient and prone to human error. AI technologies, particularly machine learning and predictive analytics, offer powerful tools to address these challenges by providing data-driven insights, improving decision-making, and automating various project management tasks. By analysing historical data and real-time project information, AI can predict potential risks, such as delays, cost overruns, and safety hazards, enabling proactive interventions. AI-powered tools help streamline project scheduling, resource allocation, and performance tracking, ensuring that projects stay on track and within budget. Additionally, AI can optimize supply chain management, reducing material waste and ensuring the timely availability of resources. Machine learning algorithms can continuously learn from project data, improving their predictive accuracy over time and adapting to changing conditions. This paper explores the role of AI in transforming construction project management and risk mitigation strategies. It examines the specific AI applications in areas such as risk assessment, safety management, cost estimation, and scheduling optimization. Case studies and examples from the construction industry highlight the successful implementation of AI tools in real-world projects, demonstrating tangible improvements in project outcomes. The paper also addresses the barriers to AI adoption in construction, including data quality, integration challenges, and the need for specialized skills. Ultimately, the integration of AI into construction project management holds the potential to create more efficient, cost-effective, and risk-resilient projects.

TASK PULSE

Effective workforce management is essential in today's fast-paced work environment. Task Pulse is a mobile-based solution designed to address common workforce management challenges. It offers real-time project and task tracking, a skill development tracker, and seamless integration with productivity tools. The app's features promote employee growth and align organizational objectives. Planned enhancements aim to further optimize workflows and foster a culture of continuous improvement. Keywords: Employee Management, Workforce Analytics, Task Tracking, Mobile Solutions, Skill Development

Design and Evaluation of Augmented Reality-Enhanced Robotic System for Epidural Interventions.

The epidural injection is a medical intervention to inject therapeutics directly into the vicinity of the spinal cord for pain management. Because of its proximity to the spinal cord, imprecise insertion of the needle may result in irreversible damage to the nerves or spinal cord. This study explores enhancing procedural accuracy by integrating a telerobotic system and augmented reality (AR) assistance. Tele-kinesthesia is achieved using a leader-follower integrated system, and stable force feedback is provided using a novel impedance-matching force rendering approach. In this domain, augmented reality employs a magnetic-tracker-based approach for real-time 3D model projection onto the patient's body, aiming to augment the physician's visual field and improve needle insertion accuracy. Preliminary results indicate that our AR-enhanced robotic system may reduce the cognitive load and improve the accuracy of ENI, highlighting the promise of AR technologies in complex medical procedures. However, further studies with larger sample sizes and more diverse clinical settings must comprehensively validate these findings. This work lays the groundwork for future research into integrating AR into medical robotics, potentially transforming clinical practices by enhancing procedural safety and efficiency.

Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach

The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models' preliminary assessment about Omicron's transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.

Real-time rainfall projection in hilly areas using ARLSTM model: a case of Dharamshala, India

Abstract Rainfall forecasting is essential in many industries, including agriculture, hydrology, urban planning, disaster management, and renewable energy. However, typical forecasting approaches frequently fail to reflect the complex spatiotemporal dynamics inherent in rainfall patterns, particularly in hilly areas. Since, the mountainous terrain influences wind patterns, moisture flow, and temperature inversions which affect precipitation patterns, adding challenge to meteorologists. So, designing a predictive forecasting model is essential for people and governments to make preventive measures in time. In recent years, the introduction of Internet of Things (IoT) technology and advances in machine learning (ML) techniques have provided a promising route for improving rainfall prediction accuracy. Real-time data on environmental elements such as temperature, wind speed, air pressure, and humidity can be collected using IoT sensor networks. This research paper investigates the efficacy of the Auto-Regressive Integrated Moving Average - Long Short-Term Memory (ARIMA-LSTM) model for real-time rainfall prediction in hilly towns like Dharamshala. Utilizing real-time data collected from sensors and using edge to preprocess it before sending it to the cloud the study explores the integration of traditional time series analysis techniques with deep learning architectures to enhance predictive accuracy. The propounded model is paralleled against alternative forecasting approaches and shows a significant reduction in RMSE, RRMSE, and MSE. Furthermore, it had a 0% Absolute Percentage Bias (APB), a high Nash-Sutcliffe Efficiency (NSE) of 0.99, and a low Theil's coefficient (U2) of 0.01. These results demonstrate that the ARLSTM model outperforms other forecasting models in terms of rainfall prediction.

ENHANCING PRODUCT LAUNCH SUCCESS AMONG INDONESIAN SME IMMIGRANTS IN PENANG: A COMMUNITY-BASED APPROACH

Accounting is vital for novice entrepreneurs, given the many roles they must undertake during business growth. Competence in accounting allows entrepreneurs to evaluate company performance, identify which aspects are thriving and worth investing in, and pinpoint areas that require improvement or are unprofitable. This community engagement program aims to educate small and medium-sized entrepreneurs, particularly Indonesian immigrants affiliated with the Indonesian Community Organization (PERMAI) in Pulau Pinang, Malaysia, on specific aspects of decision- making in Management Accounting. Understanding these aspects is essential for the sustainability of businesses and serves as a crucial strategic tool, enabling small business entrepreneurs to make real-time projections and predictions. The program contributes to developing decision-making skills in creating innovative products through knowledge of CVP analysis. We use case studies to discuss the implementation of CVP Analysis. Implementing the "Cost Volume Profit Analysis: Building Innovative Products" program is expected to yield tangible outcomes, including the emergence of new innovative products, job creation, and local business growth. Furthermore, by fostering collaboration and knowledge-sharing, it aims to build an ecosystem supportive of entrepreneurship and innovation in Penang, ultimately contributing to the region's sustainable development. In conclusion, this program positively impacts the development of SME entrepreneurs' financial decision-making skills.

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Жиһаз индустриясы үшін мамандар даярлаудағы инновациялық цифрлық шешімдер

In the context of the rapid development of technologies and changing market requirements, this approach is becoming increasingly relevant, as it contributes to the formation of creative and innovative specialists. The purpose of the study is to analyze the use of digital design and design thinking in the educational process, as well as to assess their impact on the formation of professional competencies of students. The paper examines modern practices and methodologies, including the use of 3D modeling software, virtual reality and other digital tools that allow students to interact with real-time projects. The study used interactive multimedia tools, digital methods of graphic design, and virtual experiences adapted in the learning process. The results of the analysis show that the integration of digital technologies into training not only increases the level of student participation, but also contributes to the development of critical thinking, teamwork skills and the ability to solve practical problems in furniture design and production. These conclusions indicate the need to adapt educational programs to modern requirements and challenges, and also open up new opportunities for the use of innovative teaching methods in the furniture industry, which, in turn, can contribute to increasing the competitiveness of specialists in the labor market.

Predictive Analytics in Cardiothoracic Care: Enhancing Outcomes with the Healthcare Enabled by Artificial Intelligence in Real Time (HEART) Project

Predictive Analytics in Cardiothoracic Care: Enhancing Outcomes with the Healthcare Enabled by Artificial Intelligence in Real Time (HEART) Project

Impact of Critical Success Factors on Productivity Gain during Automation Testing

Software testing, a linchpin of quality assurance in software development, encompasses both manual and automation approaches. While manual testing ensures meticulous scrutiny, automation accelerates efficiency, reducing time and costs in dynamic landscape of software development. This research investigates the impact of critical success factors on productivity gain in software testing, with a focus on automation, within CMMI Level 5 companies. Drawing from a dataset comprising thirty real-time projects spanning banking, retail, and industrial applications, study explores dynamics of software development under agile and waterfall models. Interviews with developers and testers reveal insights into key aspects influencing productivity, including skills, experiences, and training. Emphasizing the need for strategic automation in projects subject to changes and maintenance, the research analyzes over thirty projects, scrutinizing variables such as manually designed test cases, execution time, and subsequent automation of test cases. Strategic selection of test cases for automation emerges as a cost-efficient practice. Results highlight a correlation between manual and automated test cases, indicating productivity gains. Research introduces a productivity gain metric, showcasing a break-even point where automation significantly reduces testing time. Overall, the findings offer a comprehensive understanding of software testing within CMMI Level 5, guiding organizations toward efficient automation practices and improved productivity.

A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England.

We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.

Rapid review methods series: Guidance on the use of supportive software

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RRs) use modified systematic review methods to accelerate the review process...

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.

BlastAsia – its quest for innovation in enterprise software application development

ABSTRACT The case sheds light on the rapid evolution in the no-code application development space. It presents opportunities for entrepreneurs looking to support customers with their digital transformation journeys. The protagonist of the case, Arup Maity, is the founder of a traditional software development company, BlastAsia. He is contemplating launching a no-code application development platform, given evolving market preferences and customer expectations. The case outlines the emergence of no-code application development platforms as a potent alternative to traditional software development techniques. It highlights exactly how business uncertainty coupled with rapidly evolving technology requirements demands shorter software development lifecycles, which traditional methods might not be able to cater to. It also explores evolving customer and employee expectations to engage in applications which are intuitive, work across devices seamlessly, and deliver real-time project status and data. The case also discusses budgetary considerations, which companies looking to deploy enterprise software have to grapple with. The landscape is further complicated by cutthroat competition in the industry, start-ups offering enhanced value propositions at competitive prices, and black-swan events like the COVID-19 pandemic, which pose existential challenges for organizations.

Real Time Image Animation

The Real-Time Image Animation project is designed to revolutionize the way static images are transformed into dynamic animations by leveraging state-of-the-art advancements in computer vision, deep learning, and neural networks. At its core, the project utilizes generative adversarial networks (GANs) and convolutional neural networks (CNNs) to meticulously analyze static images and synthesize realistic motion patterns, facial expressions, and other dynamic elements in real-time. The system begins with an image processing pipeline that enhances the quality of input images and extracts crucial features necessary for generating animations..

Efficient project management of historical building groups: a BIM-based approach with critical chain method and 5D simulation

This article introduces a project management method for historical building groups using BIM technology. Historical building group projects encompass the entire lifecycle, from design, repair and reconstruction to construction and maintenance. To efficiently manage these complex endeavors, the Critical Chain Method is employed to control project progress, enabling effective coordination between various project aspects and reducing project duration. By leveraging the BIM 5D platform, the article incorporates schedules, site layouts and bidding quotations to simulate the construction processes involved in historical building projects. This comprehensive approach facilitates real-time project management, supported by video documentation and virtual walkthroughs. The utilization of BIM technology in this context allows for the successful realization of project management objectives for historical building groups. The article’s methodology aims to address the specific challenges associated with historical building group projects, enhancing their overall efficiency, cost-effectiveness and coordination among various stakeholders.

UNIFIDE EMPLOYEE DATA TEAM TRCKER

Abstract : A Team Track: “Unified Employee Data” provides a thorough overview of important metrics, tasks, and insights pertinent to each individual’s function inside a business, acting as a centralized center for them. With the use of this digital technology, staff members can easily access critical information, improving productivity, efficiency, and transparency all around. Fundamentally, an employee dashboard compiles information from multiple sources—such as project management software, HR systems, and performance metrics—and presents it in an intuitive manner. Employees can monitor their progress, evaluate critical updates, and remain up to date on company-wide efforts thanks to this consolidation. With its many features and functionalities, the employment dashboard acts as a focal point for effective job matching and recruitment. The dashboard uses data analytics and state-of-the-art technology to give users real-time insights into market trends, demand signals, and talent assessments. This allows users to make well-informed decisions regarding their hiring strategies and career routes. The dashboard gives staff members the ability to efficiently manage their obligations by giving them access to training materials and project timetable tracking.Additionally, the employee dashboard encourages teamwork and communication. Through the provision of real-time project progress updates Yes, the following are some important details you should provide in an abstract regarding an employee dashboard: Overview: Give a succinct explanation of the purpose of an employee dashboard in contemporary work environments. Mention how it provides a consolidated platform where staff members may access a range of tools, services, and data pertaining to their jobs and companies. Features: Emphasize the key elements that are commonly present in an employee dashboard, such as customized employee profiles, task management tools, collaboration features, access to corporate announcements, HR papers, and payroll data. Benefits: Talk about how having an employee dashboard can lead to better decision-making through real-time data analytics, increased productivity through streamlined resource access, improved communication and transparency within the company, and enhanced employee engagement. Index Terms – Chatgpt, AI tool, Java script