Research on the simulation framework in Building Information Modeling

In recent ten years, Building Information Modeling (BIM) has been proposed and applied in the industry of architecture. For the high efficiency and visualization, BIM and correlative technologies are welcomed by architects, engineers, builders and owners, thus the technologies on modeling for design has been widely researched. However, little attention is given to simulation while simulation is an important part of design for building, maybe because it is seen as somewhat less related to the technologies on modeling for design. The paper proposed a simulation framework with four stages of design: concept stage, scheme design stage, preliminary design stage and construction drawing design stage. The principles of simulation are different in each stage, and were given in the paper. In concept stage, simulation focuses on the blocks of building, and the unit of simulation grid is comparable with the unit of field. And the objects of simulation in the stage are wind, light and sound. In scheme design stage, the unit of simulation grid is similar to the one in concept stage. The objects of simulation in the stage are heat, wind, light, energy consumption and sound. In preliminary design stage, simulations turn to pay attention to the systems of the building, such as the energy system, power system, transmission and distribution system, air-conditioning system and ventilation system. The unit of simulation grid is comparable with the unit of indoor space of building. In construction drawing design stage, the details of system is the main objects of simulation, the unit of simulation grid is usually 100-500mm. The data conversion is also discussed, because data conversion is important for the simulation in BIM system. Previous simulation paid much more time on modeling because the buildings were often designed with 2D floor plan while simulation models often were 3D model. The simulation in BIM process makes the modeling easier and quicker. However, the data of different formats have difficulties on modeling conversion. Several main formats of 3D-model are discussed in the paper. The models of gbXML, Sketch Up, 3ds, CATIA, Rhino and Revit can be transferred to simulation software and modified to meet the need of simulation. The transfer principles of each format during different stage are discussed in the paper. The gbXML and Revit have advantages as less workload on modification, less information lost in the conversion and quick response to the change of model. An example of whole process of simulation in BIM design is proposed in the paper. The simulations for the BIM process are applied in the design of Zhuhai Grand Theater. The simulation in Zhuhai Grand Theater also can be divided into four stages. The example mainly focused on the indoor and outdoor wind environment and energy consumption, and the data conversion is applied in the simulation. The efficiency of such simulation process was compared with the traditional process and the result showed that new simulation process in BIM has advantages as time saving, simple and easily controllable

How to Achieve End-to-end Key Distribution for QKD Networks in the Presence of Untrusted Nodes

Quantum key distribution (QKD) networks are expected to enable information-theoretical secure (ITS) communication over a large-scale network. Most researches on relay-based QKD network assume that all relays are completely trustworthy, but the assumption is unrealistic in a complex network. The current study only analyzes the case of passive attacks by untrusted relays (e.g. eavesdropping). However, active attacks by untrusted relays (e.g. spoofing or interfering with the cooperation between honest nodes) are more serious threats and should not be ignored. Taking both passive and active attacks into account, we propose the ITSBFT-QKD networks to defend against untrusted nodes and achieve end-to-end key distribution. In end-to-end key distribution, multiple participating nodes are required to establish trust relationships and cooperate with each other. To prevent attackers from breaking trust relationship and gaining an unreasonable advantage, we incorporate a byzantine consensus scheme to establish and transmit trust relationships in a global QKD network perspective. Moreover, since the security of traditional consensus schemes is lower than the security requirement of QKD networks, we devise a byzantine fault tolerance (BFT) signature scheme to ensure the information-theoretic security of consensus. It provides a new way to construct signature schemes with point-to-point QKD keys in the presence of untrusted relays or nodes. The security of our scheme is analyzed thoroughly from multiple aspects. Our scheme can accommodate up to $MIN\left( C-1,\lfloor \frac{N-1}{3} \rfloor \right)$ untrusted nodes, where $C$ is the node connectivity of the network and $N$ is the number of nodes in the network. Our scheme provides the highest level of security in currently relay-based QKD networks and will significantly promote the application of QKD networks.Comment: 13 pages,7 figure

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance's contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario.Comment: Accepted by COLING 2022. The 29th International Conference on Computational Linguistics, Gyeongju, Republic of Kore

T-cell lymphoblastic lymphoma presenting with pleural effusion: A case report

AbstractAdult lymphoblastic lymphoma (LBL) is an aggressive form of non-Hodgkin lymphoma occurring in predominantly adolescent and young adult men, accounting for 1% to 2% of all non-Hodgkin's lymphomas. In contrast to B-LBL, T-cell LBL is much more common, accounting for up to 90% of disease in adults. Mediastinal mass, pleural and/or pericardial effusions are the major characteristics of T-LBL. We report an 18-year-old male with a pleural effusion, mediastinal mass, a light pericardial effusion, and a normal hemogram. The cytology of the pleural effusion initially suggested malignancy, but definitive diagnosis was unclear. After a medical thoracoscopy, the partial pleura was picked and immunophenotypic study revealed the following: CD3+, TdT+, CD99+, CD20−. The patient was finally diagnosed with T-LBL and died only 6 months after that. The case highlight the point that medical thoracoscopy is a safe and accurate diagnostic procedure for pleural diseases, and partial pleura biopsy with immunophenotyping was essential for achieving the correct diagnosis of LBL

Analysis of the Factors Associated with Negative Conversion of Severe Acute Respiratory Syndrome Coronavirus 2 RNA of Coronavirus Disease 2019

AIM: To understand the factors associated with negative conversion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, targeted surveillance and control measures can be taken to provide scientific basis for the treatment of the disease and to improve the prognosis of the disease. METHODS: Using the method of retrospective cohort study, we collected the data of Coronavirus Disease 2019 (COVID-19) patients in Tongji Hospital of Wuhan, China from 10 January to 25 March, 2020. Among the data of 282 cases, 271 patients, according to whether the negative conversion happened, were divided into negative conversion group and control group. We made the quantitative variables into classification; Chi-square test single-factor and Cox regression were used in univariate analysis and extracted 30 meaningful variables, then through the collinearity diagnosis, excluded the existence of collinear variables. Finally, 22 variables were included in Cox regression analysis. RESULTS: The gender distribution was statistically significant between two groups (p < 0.05). While in the negative conversion group, the patients of non-severe group occupied a large proportion (p < 0.001). The median time for the negative conversion group was 17 days, and at the end of the observation period, the virus duration in control group was 24 days (p < 0.05). A total of 55 variables were included in univariate analysis, among which 30 variables were statistically different between the two groups. After screening variables through collinearity diagnosis, 22 variables were included in the Cox regression analysis. Last, lactate dehydrogenase (LDH), age, fibrinogen (FIB), and disease severity were associated with negative conversion of SARS-CoV-2 RNA. CONCLUSION: Our results suggest that in the treatment of COVID-19, focus on the age of more than 65 years old, severe, high level of LDH, FIB patients, and take some targeted treatment, such as controlling of inflammation, reducing organ damage, so as to provide good conditions for virus clearance in the body

Machine learning facilitated the modeling of plastics hydrothermal pretreatment towards constructing an on-ship marine litter-to-methanol plant

An onboard facility shows promise in efficiently converting floating plastics into valuable products, such as methanol, negating the need for regional transport and land-based treatment. Gasification presents an effective means of processing plastics, requiring their transformation into gasification-compatible feedstock, such as hydrochar. This study explores hydrochar composition modeling, utilizing advanced algorithms and rigorous analyses to unravel the intricacies of elemental composition ratios, identify influential factors, and optimize hydrochar production processes. The investigation begins with decision tree modeling, which successfully captures relationships but encounters overfitting challenges. Nevertheless, the decision tree vote analysis, particularly for the H/C ratio, yielding an impressive R2 of 0.9376. Moreover, the research delves into the economic feasibility of the marine plastics-to-methanol process. Varying payback periods, driven by fluctuating methanol prices observed over a decade (ranging from 3.3 to 7 yr for hydrochar production plants), are revealed. Onboard factories emerge as resilient solutions, capitalizing on marine natural gas resources while striving for near-net-zero emissions. This comprehensive study advances our understanding of hydrochar composition and offers insights into the economic potential of environmentally sustainable marine plastics-to-methanol processes

The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization

Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21–32 years), the adult (age 41–54 years), and the old (age 60–86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing