Improving BIM Authoring Process Reproducibility with Enhanced BIM Logging

This paper presents an enhanced building information modeling (BIM) logger that captures building element geometry and attributes to accurately represent the BIM authoring process. The authors developed the logger and reproducing algorithm using the Revit C# API based on the analysis of information required to define building elements and associated attributes. The enhanced BIM log was evaluated through a case study of Villa Savoye designed by Le Corbusier, and the results show that it can accurately represent the BIM authoring process to a substantial level of reproducibility. The study provides a tool for capturing and reproducing the BIM authoring process. Future research can focus on improving the accuracy of the logging and reproducing algorithm and exploring further applications to automate the BIM authoring process using enhanced BIM logs

A new formal and analytical process to product modeling (PPM) method and its application to the precast concrete industry

The current standard product (data) modeling process relies on the experience and subjectivity of data modelers who use their experience to eliminate redundancies and identify omissions. As a result, product modeling becomes a social activity that involves iterative review processes of committees. This study aims to develop a new, formal method for deriving product models from data collected in process models of companies within an industry sector. The theoretical goals of this study are to provide a scientific foundation to bridge the requirements collection phase and the logical modeling phase of product modeling and to formalize the derivation and normalization of a product model from the processes it supports. To achieve these goals, a new and formal method, Georgia Tech Process to Product Modeling (GTPPM), has been proposed. GTPPM consists of two modules. The first module is called the Requirements Collection and Modeling (RCM) module. It provides semantics and a mechanism to define a process model, information items used by each activity, and information flow between activities. The logic to dynamically check the consistency of information flow within a process also has been developed. The second module is called the Logical Product Modeling (LPM) module. It integrates, decomposes, and normalizes information constructs collected from a process model into a preliminary product model. Nine design patterns are defined to resolve conflicts between information constructs (ICs) and to normalize the resultant model. These two modules have been implemented as a Microsoft Visio ™ add-on. The tool has been registered and is also called GTPPM ™. The method has been tested and evaluated in the precast concrete sector of the construction industry through several GTPPM modeling efforts. By using GTPPM, a complete set of information items required for product modeling for a medium or a large industry can be collected without generalizing each company's unique process into one unified high-level model. However, the use of GTPPM is not limited to product modeling. It can be deployed in several other areas including: workflow management system or MIS (Management Information System) development software specification development business process re-engineering.Ph.D.Committee Chair: Eastman, Charles M.; Committee Co-Chair: Augenbroe, Godfried; Committee Co-Chair: Navathe, Shamkant B.; Committee Member: Hardwick, Martin; Committee Member: Sacks, Rafae

Impact of organizational factors on delays in BIM-based coordination from a decision-making view: a case study

This study analyzed the impact of organizational factors on delays in building information modeling (BIM)- based coordination for mechanical, electrical, and plumbing (MEP) systems from the decision-making perspective. Recently BIM-based coordination has been regarded as a critical phase in project delivery but suffers from delays during the coordination process. This study investigated three complexity factors that often contribute to coordination delays: the number of participants – the total number of participants involved in a decision-making process for resolving a coordination issue; the level of the decision makers – the highest decision-maker involved in a problem-resolution process; and the heterogeneity of participants –the number of trades related to an issue. Using 95 major coordination issues derived from 11,808 clashes in a case study, the correlations between the coordination time and the complexity factors were analyzed. The coordination time linearly increased as each factor increased. The number of participants had the highest correlation with the coordination time, followed by the level of decision makers and the heterogeneity of participants. The findings stress the significance of integration between BIM and lean approaches, such as Obeya (big room) and Shojinka (flexible manpower line), during BIM-based coordination to expedite decision-making processes and eventually to reduce the coordination time

Incident and recurrent herpes zoster for first-line bDMARD and tsDMARD users in seropositive rheumatoid arthritis patients: a nationwide cohort study

Background : There is limited information regarding disease-modifying antirheumatic drug (DMARD)-dependent risks of overall, incident, and recurrent herpes zoster (HZ) during first-line biologic DMARD (bDMARD) or targeted synthetic DMARD (tsDMARD) treatment among patients with seropositive rheumatoid arthritis (RA) in terms of HZ risk. Methods : A total of 11,720 patients with seropositive RA who were prescribed bDMARD or tofacitinib between January 2011 and January 2019 from the Korean Health Insurance Review & Assessment Service database were studied. A multivariate Cox proportional hazards regression model was adopted to evaluate the adjusted hazard ratio (aHR) with 95% confidence interval (CI) for the risk of HZ dependent on the choice of first-line bDMARDs or tsDMARD, including etanercept, infliximab, adalimumab, golimumab, tocilizumab, rituximab, tofacitinib, and abatacept. Results : During the 34,702 person-years of follow-up, 1686 cases (14.4%) of HZ were identified, including 1372 (11.7%) incident and 314 (2.7%) recurrent HZs. Compared with that of the abatacept group, tofacitinib increased the overall risk (aHR, 2.46; 95% CI, 1.61–3.76; P<0.001), incidence (aHR, 1.99; 95% CI, 1.18–3.37; P=0.011), and recurrence (aHR, 3.69; 95% CI, 1.77–7.69; P<0.001) of HZ. Infliximab (aHR, 1.36; 95% CI, 1.06–1.74; P=0.017) and adalimumab (aHR, 1.29; 95% CI, 1.02–1.64; P=0.032) also increased the overall HZ risk. Moreover, a history of HZ was found to be an independent risk factor for HZ (aHR, 1.54; 95% CI, 1.33–1.78; P<0.001). Conclusions : HZ risk is significantly increased in RA patients with a history of HZ after the initiation of bDMARDs or tsDMARD. The risk of incident and recurrent HZ was higher after tofacitinib treatment in patients with RA than that after treatment with bDMARDs. Individualized characteristics and history of HZ should be considered when selecting bDMARDs or tsDMARD for RA patients considering HZ risks.This study was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health Welfare, Republic of Korea (grant number HI14C1277)

General Bootstrapping Approach for RLWE-based Homomorphic Encryption

We propose a new bootstrapping approach that works for all three Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski/Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS) schemes. This approach adopts a blind rotation technique from FHEW-type schemes. For BGV and BFV, our bootstrapping does not have any restrictions on plaintext modulus unlike typical cases of the previous methods. For CKKS, our approach introduces an error comparable to a rescaling error which enables more than 70 bits of precision after bootstrapping while consuming only 1-2 levels. Due to the high precision of the proposed bootstrapping algorithm, it is the first bootstrapping resistant to the security vulnerability of CKKS found by Li and Micciancio (Eurocrypt 2021). In addition, we introduce methods to reduce the size of public keys required for blind rotations generated by a secret key holder

Antidepressant-induced mania in panic disorder: a single-case study of clinical and functional connectivity characteristics

BackgroundMental health issues, including panic disorder (PD), are prevalent and often co-occur with anxiety and bipolar disorders. While panic disorder is characterized by unexpected panic attacks, and its treatment often involves antidepressants, there is a 20–40% risk of inducing mania (antidepressant-induced mania) during treatment, making it crucial to understand mania risk factors. However, research on clinical and neurological characteristics of patients with anxiety disorders who develop mania is limited.MethodsIn this single case study, we conducted a larger prospective study on panic disorder, comparing baseline data between one patient who developed mania (PD-manic) and others who did not (PD-NM group). We enrolled 27 patients with panic disorder and 30 healthy controls (HCs) and examined alterations in amygdala-based brain connectivity using a seed-based whole-brain approach. We also performed exploratory comparisons with healthy controls using ROI-to-ROI analyses and conducted statistical inferences at a threshold of cluster-level family-wise error-corrected p &lt; 0.05, with the cluster-forming threshold at the voxel level of uncorrected p &lt; 0.001.ResultsThe patient with PD-mania showed lower connectivity in brain regions related to the default mode network (left precuneous cortex, maximum z-value within the cluster = −6.99) and frontoparietal network (right middle frontal gyrus, maximum z-value within the cluster = −7.38; two regions in left supramarginal gyrus, maximum z-value within the cluster = −5.02 and −5.86), and higher in brain regions associated with visual processing network (right lingual gyrus, maximum z-value within the cluster = 7.86; right lateral occipital cortex, maximum z-value within the cluster = 8.09; right medial temporal gyrus, maximum z-value within the cluster = 8.16) in the patient with PD-mania compared to the PD-NM group. One significantly identified cluster, the left medial temporal gyrus (maximum z-value within the cluster = 5.82), presented higher resting-state functional connectivity with the right amygdala. Additionally, ROI-to-ROI analysis revealed that significant clusters between PD-manic and PD-NM groups differed from HCs in the PD-manic group but not in the PD-NM group.ConclusionHere, we demonstrate altered amygdala-DMN and amygdala-FPN connectivity in the PD-manic patient, as reported in bipolar disorder (hypo) manic episodes. Our study suggests that amygdala-based resting-state functional connectivity could serve as a potential biomarker for antidepressant-induced mania in panic disorder patients. Our findings provide an advance in understanding the neurological basis of antidepressant-induced mania, but further research with larger cohorts and more cases is necessary for a broader perspective on this issue

Cost-effectiveness of using amyloid positron emission tomography in individuals with mild cognitive impairment

Background Amyloid positron emission tomography (PET) makes it possible to diagnose Alzheimers disease (AD) in its prodromal phase including mild cognitive impairment (MCI). This study evaluated the cost-effectiveness of including amyloid-PET for assessing individuals with MCI. Methods The target population was 60-year-old patients who were diagnosed with MCI. We constructed a Markov model for the natural history of AD with the amyloid positivity (AP). Because amyloid-PET can detect the AP MCI state, AD detection can be made faster by reducing the follow-up interval for a high-risk group. The health outcomes were evaluated in quality-adjusted life years (QALYs) and the final results of cost-effectiveness analysis were presented in the form of the Incremental Cost-Effectiveness Ratio (ICER). To handle parameter uncertainties, one-way sensitivity analyses for various variables were performed. Results Our model showed that amyloid-PET increased QALYs by 0.003 in individuals with MCI. The estimated additional costs for adopting amyloid-PET amounted to a total of 1250 USD per patient when compared with the cost when amyloid-PET is not adopted. The ICER was 3,71,545 USD per QALY. According to the sensitivity analyses, treatment effect of Donepezil and virtual intervention effect in MCI state were the most influential factors. Conclusions In our model, using amyloid-PET at the MCI stage was not cost-effective. Future advances in management of cognitive impairment would enhance QALYs, and consequently improve cost-effectiveness.This project was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HC15C1509 to JW Han, JH Park, TJ Lee, and HG Jeong). Additional funding came from the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea [HI09C1379 (A092077) to KW Kim]

Decision tree-based approach for online management of PEM fuel cells for residential application

This thesis demonstrates a new intelligent technique for the online optimal management of PEM fuel cells units for onsite energy production to supply residential utilizations. Classical optimization techniques are based on offline calculations and cannot provide the necessary computational speed for online performance. In this research, a Decision Tree (DT) algorithm is employed to obtain the optimal, or quasioptimal, settings of the fuel cell online and in a general framework. The main idea is to employ a classification technique, trained on a sufficient subset of data, to produce an estimate of the optimal setting without repeating the optimization process. A database is extracted from a previously-performed Genetic Algorithm (GA)-based optimization has been used to create a suitable decision tree, which was intended for generalizing the optimization results. The approach provides the flexibility of adjusting the settings of the fuel cell online according to the observed variations in the tariffs and load demands. Results at different operating conditions are presented to confirm the high accuracy of the proposed generalization technique. The accuracy of the decision tree has been tested by evaluating the relative error with respect to the optimized values. Then, the possibility of pruning the tree has been investigated in order to simplify its structure without affecting the accuracy of the results. In addition, the accuracy of the DTs to approximate the optimal performance of the fuel cell is compared to that of the Artificial Neural Networks (ANNs) used for the same purpose. The results show that the DTs can somewhat outperform the ANNs with certain pruning levels

Gevab: a prototype genome variation analysis browsing server

Background: The first Korean individual diploid genome sequence data (KOREF) was publicized in December 2008. Results: A Korean genome variation analysis and browsing server (Gevab) was constructed as a database and web server for the exploration and downloading of Korean personal genome(s). Information in the Gevab includes SNPs, short indels, and structural variation (SV) and comparison analysis between the NCBI human reference and the Korean genome(s). The user can find information on assembled consensus sequences, sequenced short reads, genetic variations, and relationships between genotype and phenotypes. Conclusion: This server is openly and publicly available online at http://koreagenome.org/en/ or directly http://gevab.orgclose2

COMUS: Clinician-Oriented locus-specific MUtation detection and deposition System

Background: A disease-causing mutation refers to a heritable genetic change that is associated with a specific phenotype (disease). The detection of a mutation from a patient's sample is critical for the diagnosis, treatment, and prognosis of the disease. There are numerous databases and applications with which to archive mutation data. However, none of them have been implemented with any automated bioinformatics tools for mutation detection and analysis starting from raw data materials from patients. We present a Locus Specific mutation DB (LSDB) construction system that supports both mutation detection and deposition in one package. Results: COMUS (Clinician-Oriented locus specific MUtation detection and deposition System) is a mutation detection and deposition system for developing specific LSDBs. COMUS contains 1) a DNA sequence mutation analysis method for clinicians' mutation data identification and deposition and 2) a curation system for variation detection from clinicians' input data. To embody the COMUS system and to validate its clinical utility, we have chosen the disease hemophilia as a test database. A set of data files from bench experiments and clinical information from hemophilia patients were tested on the LSDB, KoHemGene http://www.kohemgene.org, which has proven to be a clinician-friendly interface for mutation detection and deposition. Conclusion: COMUS is a bioinformatics system for detecting and depositing new mutations from patient DNA with a clinician-friendly interface. LSDBs made using COMUS will promote the clinical utility of LSDBs. COMUS is available at http://www.comus.info. &#169; 2009 Jho et al; licensee BioMed Central Ltdclose