Design, Development, and Validation of an Augmented Reality-Enabled Production Strategy Process

The Production Strategy Process (PSP) is an integral part of production planning and control as it defines how production processes are structured and designed and outlines how production will be executed. PSP involves massive information transfer and communication among project participants. While BIM can improve the flow of information, the paradox of designing 3D models in 2D space remains. This paradox indicates that new visualization technologies are needed to leverage the use of information in the PSP. As Industry 4.0, the fourth industrial revolution, continues to evolve, it is imperative that construction firms seek, find, and adopt new technologies. This research employed Augmented Reality (AR) as a new user interface in the PSP. The current state of practice of PSP was investigated and current challenges are identified. The opportunities to integrate AR were defined, and an AR-enabled future state was proposed. Next, an AR-enabled PSP prototype using the Microsoft HoloLens was implemented and validated on a real-world healthcare project. Usability testing was then conducted using a one-on-one protocol to validate the prototype with 20 participants. Surveys were the deployed to qualitatively assess the impact of integrating AR into PSP. The difference between the traditional PSP and the AR-enabled PSP was tested through a series of hypotheses comparing both processes. The results demonstrate that the AR-enabled PSP offers significant benefits over the Traditional PSP: improved collaboration, reduced miscommunication, increased quality and detection of errors, enhanced decision-making, better documentation, better information access, improved information flow, increased input accuracy, and increased integration of safety considerations. Additionally, the technology, software, and hardware were also evaluated, and, on average, the findings demonstrated the potential of AR in production planning

Perceptions of Safety Climate in Construction Projects between Workers and Managers/Supervisors in the Developing Country of Iran

What are the different perceptions on safety climate (SC) by workers and managers/supervisors engaged in the construction industry of developing countries? Reconciling these two differing views is pivotal for mitigating and avoiding both the injured and fatal accidents in the construction industry, especially in those developing countries where safety conditions are poor and unpredictable, and safety measures are inadequate in most cases. To answer this research question, the collective perceptions of 118 construction workers and 123 managers/supervisors on the SC in construction projects in Iran were gleaned and investigated. In particular, these perceptions were initially collected by two different empirical surveys validated by a sample of university professors and construction managers and then analyzed through the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity under factor analysis, together with a one-sample t-test. Results indicated that “workers’ attitudes and perceptions”, “safety knowledge and training”, “working relationships and roles of colleagues”, and “workers’ risk perceptions” are important categories of SC factors perceived by construction workers, whereas “safety rules and management practices” is the essential category of SC factors discerned by managers/supervisors. The difference in perceptions between workers and managers/supervisors is considered to be beneficial for an overall understanding of SC in general and for developing countries in particular. Moreover, a series of effective suggestions for improving SC in the construction industry of developing countries are provided with reference to each category. The views of SC factors are reinforced as a social process combining the synergies of workers and managers/supervisors, as well as proper safety training to be pushed forward as an essential activity that should be incorporated in human resources development of construction organizations so as to improve the existing level of SC, leading to fewer accidents at the industry level

Evaluating ivosidenib for the treatment of relapsed/refractory AML: design, development, and place in therapy

Samah Nassereddine,1,2 Coen J Lap,2 Imad A Tabbara1,2 1Department of Internal Medicine, The George Washington University School of Medicine, Washington, DC, USA; 2Division of Hematology/Oncology, The George Washington Cancer Center, Washington, DC, USA Abstract: Improvements in the last decade in understanding the molecular mechanisms underlying acute myeloid leukemia (AML) have emphasized that treatment regimens should be personalized with agents that can selectively target genetic abnormalities if present. Neomorphic mutations in isoform 1 of isocitrate dehydrogenase (IDH1) result in the formation of the oncometabolite R-2-hydroxyglutarate, which drives leukemic transformation by affecting processes such as chromatin remodeling, the cellular defense against oxidative stress and cell survival. Preclinical studies with small molecule inhibitors have validated mutant IDH1 as a molecular target, and a recent Phase 1 clinical trial with the first mutant IDH1 inhibitor ivosidenib has prompted approval by the US Food and Drug Association for the treatment of patients with IDH1-mutated AML in the relapsed and refractory setting due to impressive results. This approval has given a group of patients, that otherwise has a very poor prognosis and limited options, new hope, and it is to be expected that more indications for ivosidenib will follow soon. These developments highlight the potential of precision medicine in AML, with more agents currently under evaluation in clinical trials. Although the first reports have also already emerged describing acquired resistance for these mutant IDH inhibitors, combination treatment might overcome this problem, which could drastically change the treatment landscape of AML over the next few years. Keywords: ivosidenib, AML, IDH1, relapsed, refractor

Disseminated intravascular coagulation-like reaction following rituximab infusion

Rituximab generally is a well-tolerated medication used in a variety of haematological and autoimmune conditions. The safety profile of the medication has been reviewed in the literature. Infusion reactions due to cytokine release are the most common side effects. With the increased use of rituximab, there is an increase incidence of cytopenias, most commonly thrombocytopenia and leucopenia. Coagulopathy is quite rare, reported previously in four cases in the literature. We highlighted the clinical course of a 39-year-old patient with precursor B-cell acute lymphoblastic leukaemia who was started on rituximab infusion. The patient developed a cytokine-release syndrome with haemodynamic instability, followed by rapid-onset cytopenias and disseminated intravascular coagulation abnormalities characterised by coagulopathy with fibrinolysis and mucocutaneous bleeding. The report is followed by a review of the literature. It is important to recognise rituximab-induced coagulopathy early as part of the differential diagnosis of thrombocytopenia and disseminated intravascular coagulation following rituximab administration

Augmented Reality in the Construction Industry: Use-Cases, Benefits, Obstacles, and Future Trends

Information is the lifeblood of modern construction. Advances in Information and Communication Technology have been and are continuing to progress at rapid rates. Construction companies that are successfully able to adopt and integrate new technologies will gain a competitive edge. One emerging technology that has great potential to transform the construction industry is Augmented Reality (AR). While AR has been of interest to researchers for some time, no single research effort has yet comprehensively investigated the opportunities, benefits, challenges, and future paths toward implementing AR in modern construction. The main objective of this research is to investigate the potential of AR throughout the lifecycle of a construction project from the perspective of the construction industry. Responses from 93 industry practitioners were collected to investigate use-cases of AR throughout the project lifecycle, highlight potential benefits, and identify obstacles to entry that have slowed the implementation of AR thus far. Cluster analysis was employed to determine AR use-cases with the highest usage potential and identify the highest perceived benefits and obstacles of AR. Finally, the future of AR in construction is forecast through a series of statements that describe potential trends of AR in the construction industry. This study contributes to the existing body of knowledge by exploring the potential of AR as perceived by construction practitioners

Numerical study of melting/solidification by an hybrid lattice method

International audienceIn this paper, we propose a hybrid method coupling a Lattice Boltzmann Method (LBM) and a Finites Volumes Method (FVM), to study melting and solidification problems. The LBM is used to determine the dynamics field while the FVM is applied to discretize the energy equation. This model is validated by comparison to available literature results concerning a square cavity heated without phase change then for the melting of Gallium in an enclosure commonly used as benchmark test case

Acute coronary syndrome prediction in emergency care: A machine learning approach

BACKGROUND AND OBJECTIVE: Clinical concern for acute coronary syndrome (ACS) is one of emergency medicine\u27s most common patient encounters. This study aims to develop an ensemble learning-driven framework as a diagnostic support tool to prevent misdiagnosis. METHODS: We obtained extensive clinical electronic health data on patient encounters with clinical concerns for ACS from a large urban emergency department (ED) between January 2017 and August 2020. We applied an analytical framework equipped with many well-developed algorithms to improve the data quality by addressing missing values, dimensionality reduction, and data imbalance. We trained ensemble learning algorithms to classify patients with ACS or non-ACS etiologies of their symptoms. We used performance evaluation metrics such as accuracy, sensitivity, precision, F1-score, and the area under the receiver operating characteristic (AUROC) to measure the model\u27s performance. RESULTS: The analysis included 31,228 patients, of whom 563 (1.8%) had ACS and 30,665 (98.2%) had alternative diagnoses. Eleven features, including systolic blood pressure, brain natriuretic peptide, chronic heart disease, coronary artery disease, creatinine, glucose, heart attack, heart rate, nephrotic syndrome, red cell distribution width, and troponin level, are reported as significantly contributing risk factors. The proposed framework successfully classifies these cohorts with sensitivity and AUROC as high as 86.3% and 93.3%. Our proposed model\u27s accuracy, precision, specificity, Matthew\u27s correlation coefficient, and F1-score were 85.7%, 86.3%, 93%, 80%, and 86.3%, respectively. CONCLUSION: Our proposed framework can identify early patients with ACS through further refinement and validation