Real-time Projection Research Articles

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.

Exploring IoT Integration for Innovative Advancements in Civil Engineering

Civil engineering developments have allocation with the recent trends of integration with IoT technologies and devices. This technology significantly improves automation and remote monitoring for various tasks in the construction process, building maintenance and safety, allowing operators to remain remote free ever than before. IoT is simply the connection of several devices to the internet as per the need. The real implication of IoT is beyond and complex than this. The IoT is meant to signify the establishment of entire networks of devices which are connected to the internet and with each other. Various sensors and monitors recognize the conditions, communicate them to online network or app, and transmit relevant information to your phone automatically. Internet of Things (IoT) is the technology that transforming construction industries, automate processes, and improves construction area and other relevant fields in civil like Global Positioning System (GPS), Water Resources Management and Water Supply, Smart Construction, Water & Waste Water Treatment, Traffic Management and Control, Waste Management, Concrete Technology, Environmental Impact Assessment & Monitoring and Smart Lighting System. This research paper covers implementation of IoT in all these fields of civil engineering and updating the current scenario. IoT is potentially used to increase the productivity, on-site safety, and operational efficiency. Through the placement of low-power sensors, managers can improve worksite visibility at every stage of a project in realtime, from planning to construction, and even task post-construction. The construction industry is bringing real-time information into processes that are long-standing. Internet of Things (IoT) devices and sensors are collecting job site data in a more affordable, efficient and effective way than can ever previously imagined. This latest technology represents exciting hikes forward in the civil engineering world. Foremost principles among these are integrated in this study for Internet of Things with the world of civil engineering.

Environmental Health Risk Analysis of Sulfur Dioxide (SO2) Inhalation Exposure in Ambient Air Among the Tirtonirmolo Community, Bantul.

Introduction: Sulfur dioxide (SO2) is one of the gases that can pollute the ambient air and cause respiratory irritation. This study aims to determine the characterization of health risk and risk management of sulfur dioxide (SO2) exposure to prevent health impact in the Tirtonirmolo community, Bantul. Methods: This study was a quantitative descriptive research with an Environmental Health Risk Analysis (EHRA) approach. The study subjects were the Tirtonirmolo community in Bantul, with a sample of 110 respondents. The sampling method uses purposive sampling. Results and Discussion: Most respondents are female (74%) with ages over 54 years (52%), and the majority work as housewives (44%). The description of EHRA variables consists of an inhalation rate of 0.83 m3/hour, exposure time of 22 hours/day, exposure frequency of 354 days/year, and exposure duration for real-time projections of 35 years and 30 years for lifetime projections. The SO2 measurement results did not exceed the national quality standard, with the highest concentration being on Madukismo Road, with a concentration of 11.72 μg/m3. The dose-response analysis uses data from the US-EPA, which is 0.026 mg/kg/day. The real-time average intake value is 0.0039 mg/kg/day, and the 30-year lifetime average intake value is 0.0033 mg/kg/day. Conclusion: All respondents from this study had an RQ value<1, both in the RQ for real-time and lifetime. Risk management needs to reduce health risk by using masks when doing outdoor activities and installing Flue Gas Desulfurization (FGD) in factories that emit SO2 emissions.

An Efficient Approach for Calibration of Automotive Radar–Camera With Real-Time Projection of Multimodal Data

An Efficient Approach for Calibration of Automotive Radar–Camera With Real-Time Projection of Multimodal Data

Chinese expert consensus on laparoscopic hepatic segmentectomy and subsegmentectomy navigated by augmented- and mixed-reality technology combined with indocyanine green fluorescence imaging

Abstract Augmented- and mixed-reality technologies have pioneered the realization of real-time fusion and interactive projection for laparoscopic surgeries. Indocyanine green fluorescence imaging technology has enabled anatomical, functional, and radical hepatectomy through tumor identification and localization of target hepatic segments, driving a transformative shift in the management of hepatic surgical diseases, moving away from traditional, empirical diagnostic and treatment approaches toward digital, intelligent ones. The Hepatic Surgery Group of the Surgery Branch of the Chinese Medical Association, Digital Medicine Branch of the Chinese Medical Association, Digital Intelligent Surgery Committee of the Chinese Society of Research Hospitals, and Liver Cancer Committee of the Chinese Medical Doctor Association organized the relevant experts in China to formulate this consensus. This consensus provides a comprehensive outline of the principles, advantages, processes, and key considerations associated with the application of augmented reality and mixed-reality technology combined with indocyanine green fluorescence imaging technology for hepatic segmental and subsegmental resection. The purpose is to streamline and standardize the application of these technologies.

Design Guidelines for Low Power Embedded Systems using Low Power Electronics

The design of Low Power Embedded Systems (LPES) and IoT products may include a variety of power management techniques, or they may incorporate sophisticated on-chip capabilities that assist in reduced power usage. The power management and low power consumption of embedded systems are also enabled by complex algorithms, and every Low Power System (LPS) may require a mix of approaches to avoid using extra battery power. There are several strategies we may use when creating an LPES that must be extremely power efficient while simultaneously offering the necessary degree of computational capability. It all depends on the design specifications that must be met. Then, if at all feasible, choose the appropriate low-power components from Low Power Electronics (LPE). After studying and reviewing multiple LPES real-time projects, we compiled a list of a few strategies we may use to approach low-power design and consumption for embedded systems. Following the analysis of numerous LPES real-time projects, we came up with a list of a few approaches to low-power design for embedded systems using LPE. There are additional benefits of LPES design. Less heat is generated through LPES, which is better for the environment. For 1000 LPES devices, one watt of power saved per device equals one KWH i.e. we can save 1 unit of electricity. Design with low power increases the component and system reliability. The embedded system's operating life is enhanced. In many instances, LPES designs may result in a reduction in production costs. The LPE components chosen are more affordable and inexpensive. Hence, the low-wattage power supply, LPES design is easier and less expensive.

Simultaneous Vehicle Localization and Roadside Tree Inventory Using Integrated LiDAR-Inertial-GNSS System

Autonomous driving systems rely on a comprehensive understanding of the surrounding environment, and trees, as important roadside features, have a significant impact on vehicle positioning and safety analysis. Existing methods use mobile LiDAR systems (MLS) to collect environmental information and automatically generate tree inventories based on dense point clouds, providing accurate geometric parameters. However, the use of MLS systems requires expensive survey-grade laser scanners and high-precision GNSS/IMU systems, which limits their large-scale deployment and results in poor real-time performance. Although LiDAR-based simultaneous localization and mapping (SLAM) techniques have been widely applied in the navigation field, to the best of my knowledge, there has been no research conducted on simultaneous real-time localization and roadside tree inventory. This paper proposes an innovative approach that uses LiDAR technology to achieve vehicle positioning and a roadside tree inventory. Firstly, a front-end odometry based on an error-state Kalman filter (ESKF) and a back-end optimization framework based on factor graphs are employed. The updated poses from the back-end are used for establishing point-to-plane residual constraints for the front-end in the local map. Secondly, a two-stage approach is adopted to minimize global mapping errors, refining accumulated mapping errors through GNSS-assisted registration to enhance system robustness. Additionally, a method is proposed for creating a tree inventory that extracts line features from real-time LiDAR point cloud data and projects them onto a global map, providing an initial estimation of possible tree locations for further tree detection. This method uses shared feature extraction results and data pre-processing results from SLAM to reduce the computational load of simultaneous vehicle positioning and roadside tree inventory. Compared to methods that directly search for trees in the global map, this approach benefits from fast perception of the initial tree position, meeting real-time requirements. Finally, our system is extensively evaluated on real datasets covering various road scenarios, including urban and suburban areas. The evaluation metrics are divided into two parts: the positioning accuracy of the vehicle during operation and the detection accuracy of trees. The results demonstrate centimeter-level positioning accuracy and real-time automatic creation of a roadside tree inventory.

Chinese expert consensus on laparoscopic hepatic segmentectomy and subsegmentectomy navigated by augmented and mixed reality technology combined with indocyanine green fluorescence(2023)

Augmented reality and mixed reality technologies have pioneered the realization of real-time fusion and interactive projection for laparoscopic surgery. Indocyanine green molecular imaging technology has realized anatomical, functional and radical hepatectomy through tumor identification and localization of target hepatic segmental extent, which has led to the transformation of hepatic surgical diseases from traditional empirical diagnostic and treatment modes to digital intelligent diagnostic and treatment modes. The Hepatic Surgery Group of the Surgery Branch of the Chinese Medical Association, the Digital Medicine Branch of the Chinese Medical Association, the Digital Intelligent Surgery Committee of the Chinese Society of Research Hospitals and the Liver Cancer Committee of the Chinese Medical Doctor Association have organized the relevant experts in China to formulate this consensus,which detailed advantages, process and key points of augmented reality and mixed reality technology combined with indocyanine green molecular imaging technology in hepatic segmental and subsegmental resection, to streamline and standardize the application of this technology for promotion.