Real-time Projection Research Articles

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.

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 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.

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.

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

Immunotherapy involves natural and synthetic substances to stimulate the body’s immune response. This treatment approach is practical not only for addressing immune deficiencies but also for combating malignancies. This paper describes a non-parametric approximated adaptive control process for managing cancer dynamics under immunotherapy treatment, utilizing a combination of a differential neural network (DNN) observer and nonlinear control techniques such as sliding mode and local optimal strategies. By employing the state estimation and control methods, close tracking between the estimated states provided by the neural network and the cancer model dynamics is possible. Internal model reconstruction and an observer provided by a variable structure model are essential for controlling unknown plants. Furthermore, the control design has successfully reduced tumor cells despite uncertainties and external perturbations affecting cancer dynamics. This robustness enhances the reliability of the proposed design. A virtual real-time scheme was developed to demonstrate this controller’s feasibility in real clinical scenarios. In this scheme, a simulated patient generates variables of immunotherapy dynamics as electrical signals, which are then analyzed by a real-time project.

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

Statistical Model for Compressive Strength of Metakaolin Concrete

In the present research work, investigations were carried out to improve the performance of concrete in terms of strength by incorporating metakaolin (MK) as mineral admixture in concrete. Parametric study was carried out by considering w/c ratio, various percentage of MK and age of concrete as parameters to understand the effect of each parameter. The study was conducted for different w/c ratios of 0.32, 0.35, 0.4 and 0.5. The MK proportion was varied from 0 to 15% with an increment of 5% and age of concrete from 3 to 90 days were considered and experiments performed accordingly. The effects of above said parameters on the various properties of concrete such as workability, compressive strength and pH of concrete were investigated, and the results of metakaolin concrete were compared with the conventional concrete. From the results, it was observed that MK concrete showed greater strength for higher water cement ratios (0.4 & 0.5). A Multiple non-linear regression analysis was used to develop a statistical model to predict the strength and found to have good correlation between the observed and predicted values. It was concluded that the concrete developed in this study have significant potential for use on real time projects.

Effectiveness of simulation models and digital alternatives in training ophthalmoscopy: A systematic review

Purpose Traditional direct ophthalmoscopy (TDO) is the oldest method of fundus examination; however, it has fallen out of use due to its technical difficulty and limitations to clinical utility, amidst the advent of potentially better options. A spectrum of new technologies may help in addressing the shortcomings of TDO: simulation mannequins with non-tracked TDO, simulation models with tracked TDO, and smartphone ophthalmoscopy (SFO). Methodology A systematic search of PubMed, Embase, and Cochrane databases for all studies evaluating usage of simulation mannequins/models and SFO in ophthalmology education was performed, from inception till April 2023 with no language restriction. We ensured that we included all possible relevant articles by performing backward reference searching of included articles and published review articles. Results We reviewed studies on non-tracked TDO (n = 5), tracked TDO (n = 3) and SFO (n = 12). Non-tracked TDO and SFO were superior in training competency relative to control (TDO on real eyes). Intriguingly, tracked TDO was non superior to controls. SFO appears to enhance the learning effectiveness of ophthalmoscopy, due to real-time projection of the retina view, permitting instantaneous and targeted feedback. Learners reported improved ergonomics, including a wider field of view and more comfortable viewing distance. Retention of images and recordings permitted the audit of learning and paves the way for storage of such images in patients’ electronic medical record and rapid dissemination for specialist referral. Conclusions Smartphone ophthalmoscopy (SFO) permits integration of both the practice and learning of ophthalmoscopy, and the auditing of both. These advantages over traditional methods (with simulation or otherwise) may lead to a paradigm shift in undergraduate ophthalmology education. However, the nascency of SFO necessitates preservation of traditional techniques to tide through this period of transition.

Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.

In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential. To introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target decomposition. We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor. The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8±0.7mm in the superior-inferior (SI) direction and 0.9±0.8mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12h to train. During testing, it took approximately 35ms to generate one sDTI, and 13ms to perform the tumor tracking using template matching. Our method offers the potential solution for nearly real-time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted.