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Songhua Hu, Xudan Yang, Yaqin Huang, Zhihao Xu Department of Respiratory and Critical Care Medicine, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, 322000, People’s Republic of ChinaCorrespondence: Zhihao Xu, Department of Respiratory and Critical Care Medicine, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, No. N1, Shangcheng Avenue, Yiwu, Zhejiang, 322000, People’s Republic of China, Email xuzhihao@zju.edu.cnAbstract: Broncholithiasis coupled with Aspergillus infection is a rare disease of the respiratory system with complex pathogenesis and non-specific clinical manifestations that can be easily confused with other types of infectious diseases of the respiratory system. The lack of pertinent clinical manifestations in patients increases the risk of clinical misdiagnosis, omission, and incorrect treatment plan selection, which can result in permanent lung structural alterations and lung function decompensation and ultimately harm the lung. We report a rare case of asymptomatic broncholithiasis coupled with Aspergillus infection that was treated at our hospital and discuss the pathophysiology, diagnosis, differential diagnosis, and prognostic follow-up course. Furthermore, relevant studies from China and other countries, including this case, were reviewed. We gathered eight reports, summarized their significant diagnoses and treatments for broncholithiasis and broncholithiasis coupled with Aspergillus infection, and discussed their clinical features. Our study may help improve physicians’ awareness of these types of diseases and serve as a resource for future diagnosis and treatment.Keywords: broncholithiasis, Aspergillus infection, obstructive pneumonia, infection

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance plans and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions such as probe vehicle data, while still suffering from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, Mobile Device Location Data (MDLD) have been growing dramatically in terms of the spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume at a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, i.e., Annual Vehicle-Miles of Travel (AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is implemented on the all-street network in the state of Maryland using MDLD for the entire year of 2019. Results indicate that our proposed framework produces reliable vehicle volume estimates and also demonstrate its transferability and the generalization ability.

Traditional travel demand models heavily rely on travel surveys, simplify future demand forecasting, and show low sensitivity in response to spatiotemporal dynamics. This study proposes a deep-learning-driven framework based on mobile device location data (MDLD) for estimating and predicting large-scale travel demand at both individual and aggregated levels. This study first introduces how raw MDLD should be parsed to distill trip rosters and estimate population flow. Based on derived information, this study reexamines relations between average population flow and its determinants such as built environment, socioeconomics, and demographics, via a set of explainable machine learning (EML) models. Different interpretation approaches are employed and compared to understand nonlinear and interactive relations learned by EML models. Next, this study proposes a Multi-graph Multi-head Adaptive Temporal Graph Convolutional Network (Multi-ATGCN), a general deep learning framework that fuses multi-view spatial structures, multi-head temporal patterns, and various external effects, for multi-step citywide population flow forecasting. Multi-ATGCN is designed to comprehensively address challenges such as complex spatial dependency, diverse temporal patterns, and heterogeneous external effects in spatiotemporal population flow forecasting. Last, at an individual level, this study proposes a Hierarchical Activity-based Framework (HABF) for simultaneously predicting the activity, departure time, and location of the origin and destination of the next trip, incorporating both internal (individual characteristics) and external (calendar, point-of-interests (POIs)) information. For each individual, HABF first predicts activities via an Interpretable Hierarchical Transformer (IHTF). IHTF can efficiently handle big data benefiting from its transformer-based design to avoid recursion. Meanwhile, loss functions used in semantic segmentation are introduced into IHTF to address imbalanced distributions of activity types. Then, a local plus global probabilistic generator is designed to generate locations based on predicted activities and historical places, allowing individuals to visit new or historically-sparse places. Analyses are performed on several real-world datasets to demonstrate the model's capability in forecasting large-scale high-resolution human mobility in a timely and credible manner. Altogether, this study provides sound evidence, practically and theoretically, of the feasibility and reliability of realizing data-driven travel demand estimation and prediction at different spatiotemporal resolutions and scales.

Due to immature treatment and rapid transmission of COVID-19, mobility interventions play a crucial role in containing the outbreak. Among various non-pharmacological interventions, community infection control is considered to be a quite promising approach. However, there is a lack of research on improving community-level interventions based on a community's real conditions and characteristics using real-world observations. Our paper aims to investigate the different responses to mobility interventions between communities in the United States with a specific focus on different income levels. We produced six daily mobility metrics for all communities using the mobility location data from over 100 million anonymous devices on a monthly basis. Each metric is tabulated by three performance indicators: "best performance," "effort," and "consistency." We found that being high-income improves social distancing behavior after controlling multiple confounding variables in each of the eighteen scenarios. In addition to the reality that it is more difficult for low-income communities to comply with social distancing, the comparisons between scenarios raise concerns on the employment status, working condition, accessibility to life supplies, and exposure to the virus of low-income communities.

One approach to delay the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. It is yet unclear how effective those policies are on suppressing the mobility trend due to the lack of ground truth and large-scale dataset describing human mobility during the pandemic. This study uses real-world location-based service data collected from anonymized mobile devices to uncover mobility changes during COVID-19 and under the 'Stay-at-home' state orders in the U.S. The study measures human mobility with two important metrics: daily average number of trips per person and daily average person-miles traveled. The data-driven analysis and modeling attribute less than 5% of the reduction in the number of trips and person-miles traveled to the effect of the policy. The models developed in the study exhibit high prediction accuracy and can be applied to inform epidemics modeling with empirically verified mobility trends and to support time-sensitive decision-making processes.

PurposeThe purpose of this paper is to clarify the influence of country risk (CR) and cultural distance (CD) on transnational equity investments. It also tries to find out the international equity investment patterns of enterprises from developing countries like China.Design/methodology/approachFrom the perspectives of internalization theory, transaction cost theory, etc. this paper tries to explain the relationships between country‐level factors and transnational investment. Based on the data collected from overseas listed companies, it also empirically analyses the impacts of CR and CD on Chinese enterprises' ownership pattern in transnational equity investment.FindingsThe empirical results of this paper indicate that both the risk of host country and CD between host country and home country have significant and negative impacts on the level of ownership equity, but insignificant impacts on ownership status.Research limitations/implicationsAs there are still some doubts about the existence of country culture, especially when dealing with a huge country like China, the use of Hofstede's instrument may be one of the limitations of this paper. Also, by focusing on Chinese enterprises, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions when they study enterprises from other developing countries.Practical implicationsThe paper sheds light on international investment activities of Chinese enterprises, and also provides insights for the decision making on equity arrangement in transnational investment.Originality/valueThis paper is one of the first to analyse the international equity investment activities of Chinese enterprises and it provides new evidence on how the country‐level factors influence transnational equity investment decisions.

The seed of the Momordica cochinchinensis (Lour.) Spreng as a traditional Chinese medicine has been utilized in China for more than 1 200 years. It is traditionally used for the treatment of inflammatory swelling, scrofula, tinea, diarrhea as well as suppurative skin infections such as sore, carbuncles, furuncles and boils in both humans and animals. In this study, an extract from Cochinchina momordica seeds (ECMS) is evaluated for its adjuvant effect and safety. The results suggest that when co-administered with ECMS in Balb/c mice, ovalbumin (OVA, 10 µg) may induce significantly higher specific antibody production than OVA used alone (P < 0.05). Analysis of antibody isotypes indicates that the ECMS can promote the production of both IgG1 and IgG2a, but favor the IgG2a. Splenocyte proliferative responses to concanavalin A, lipopolysaccharides or OVA are significantly higher in mice immunized with OVA mixed with ECMS than immunized with OVA alone or mixed with aluminum hydroxide (P < 0.05). No local reactions and negative effects on the body weight gain occurred after the injection of OVA mixed with various amounts of ECMS in mice. Therefore, the ECMS is safe for injection and can be used as a potential vaccine adjuvant biasing the production of IgG2a in mice.

The cluster synchronization (CS) is a very important characteristic for the higher harmonic cou- pling Kuramoto system. A novel method from the symmetry transformation is provided, and it gives CS a profoundly mathematical explanation and clear physical annotation. Detailed numerical studies for the order parameters in various conditions confirm the theoretical predictions from this new view of the symmetry transformation. The work is very beneficial to the further study on CS in various systems., 6 pages,some figures are replaced

The cluster synchronization is a very important characteristic for the higher harmonic coupling Kuramoto system. A novel transformation is provided, and it gives cluster synchronization by the periodic properties of the density function. The periodic properties of the density function also make the cluster sections' boundaries barrier-like, which helps to explain the sensitiveness of cluster synchronization on the initial conditions of the oscillators. Detailed numerical studies confirm the theoretical predictions from this new view of the symmetry transformation. The work is very beneficial to the further study on cluster synchronization in various systems., Comment: 5 pages, 10figures

Drawing on the perspectives of Dunning's eclectic paradigm, this paper examines the impact of ownership, internalization and location factors on foreign entry mode choice of Chinese firms. Based on a sample of 413 China's listed companies with overseas operations, we find the evidence that the enterprises prefer a high entry mode when they possess more international experience or higher innovative ability. Furthermore, our results also indicate that tacit knowledge encourages the firms to select wholly-owned subsidiary as an entry mode, but it does not induce them to favor joint venture over export. A low entry mode is more likely to be chosen in the presence of a higher country risk. High-tech firms tend to select a high entry mode.

Based on a sample of 282 Chinese enterprises with international operations, this study examines the impact of asset specificity on their choice of foreign entry modes. Multivariate logistic regression models are employed to test the hypothesized effect. The results reveal that asset specificity has more influence on entry mode choice of service firms than that of manufacturing firms. A high level of asset specificity tends to induce both types of the firms to use high control entry modes1.

The People's Republic of China's fishery and offshore petroleum development industries have been in urgent need of a classifier of noise signals. In this paper, a local adaptive wavelet neural network is proposed, and an efficient engineering classifier based on the local adaptive wavelet neural network is designed and applied to classifying actual ship noises. The classification experiment results are encouraging, which shows that the classifier above is an efficient engineering classifier for actual ship noises.