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The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people's trajectories, to help plan efficient and inclusive decisions. However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city's DT by consolidating other DT's data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

The study objective was to conduct a systematic review and meta-analysis to assess the effectiveness of immersive virtual reality (VR)-supported interventions for patients with disorders or impairments. Web Science, MEDLINE, CINAHL Plus, and PsycINFO databases were searched from January 1990 to December 2020. We performed random-effects meta-analyses and calculated the standardized mean difference in the health outcomes before and after applying immersive VR-supported interventions. Forty-eight studies representing 52 experiments were meta-analyzed. Six different disorders or impairments were found to be examined in the experiments: anxiety disorders (n=40), psychotic disorders (n=3), poststroke motor impairment (n=3), mild cognitive impairment (MCI) (n=2), amblyopia (n=2), and unilateral vestibular hypofunction (UVH) (n=2). The use of VR interventions resulted in improvement in the following outcomes for the six disorders or impairments: anxiety-related symptoms (e.g., depression, anxiety), psychiatric symptoms, upper extremity function, cognitive function, visual acuity, and confidence in balance during daily activities and vestibulo-ocular reflex. Immersive VR has been mostly studied in individuals with anxiety disorders and can be effective in improving anxiety-related symptoms. Additionally, immersive VR has shown potential in motor rehabilitation after stroke and treating psychotic disorders, MCI, amblyopia, and UVH, and more research is recommended to further validate the current findings.

The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data. Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air condition, pollution, climate change, etc. However, centralized conventional ML models rely on all clients’ data at a central server, which seriously threatens user privacy. Federated learning (FL) emerges as a promising solution aiming to protect user privacy by enabling model training on a large corpus of decentralized data. The recent studies indicate FL suffers from the heterogeneity issue as it treats all clients’ data equally, that is, FL might sacrifice the performance of the majority of clients to accommodate the performance of the minority of clients with low usability data. In order to overcome this issue, a reinforcement learning (RL)-based intelligent central server with the capability of recognizing heterogeneity is implemented, which can help lead the trend toward better performance for majority of clients. To be specific, an FL central server analyses the benefits of different collaboration by capturing the intricate patterns in heterogeneous clients based on rating feedback and then updates clients’ weights iteratively, until it establishes a coalition of clients with quasioptimal performance. The experimental results on three real data sets under various heterogeneity levels demonstrate the superior performance of the proposed solution.

Mobile health (mHealth) appears to be a promising assistive tool for the long-term health management of the elderly. However, mHealth adoption rates remain relatively low among the elderly. In this study, we surveyed 108 elderly adults to determine their perceptions of mHealth applications (apps) and conducted a path analysis with the aim of studying the factors that affected the acceptance of mHealth in this population. Notably, the intention to use health apps was positively affected by the perceived usefulness (PU) (β=0.32, p Based on these findings, we suggest that high-quality information, a user-friendly interface, intriguing features, low storage consumption, a large display screen, and the involvement of healthcare professionals are needed to promote the acceptance of mHealth interventions among the elderly.

The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high-quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant’s expectations deeply. We propose a semiopportunistic concept-based solution to overcome this issue. Similar to the “shared mobility” concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area’s virtual representation and provide the participant with a payoff-maximized route. Such a method makes our solution adapt to most of MCS scenarios’ conditions instead of using fixed system settings. Then, a reinforcement learning- (RL-) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution.

BackgroundPatients with coexisting type 2 diabetes and hypertension generally exhibit poor adherence to self-management, which adversely affects their disease control. Therefore, identification of the factors related to patient adherence is warranted. In this study, we aimed to examine (i) the socio-demographic correlates of patient adherence to a set of self-management behaviors relevant to type 2 diabetes and hypertension, namely, medication therapy, diet therapy, exercise, tobacco and alcohol avoidance, stress reduction, and self-monitoring/self-care, and (ii) whether health attitudes and self-efficacy in performing self-management mediated the associations between socio-demographic characteristics and adherence.MethodsWe performed a secondary analysis of data collected in a randomized controlled trial. The sample comprised 148 patients with coexisting type 2 diabetes mellitus and hypertension. Data were collected by a questionnaire and analyzed using logistic regression.ResultsFemale patients were found to be less likely to exercise regularly (odds ratio [OR] = 0.49,P = 0.03) and more likely to avoid tobacco and alcohol (OR = 9.87,P P = 0.01) and self-monitoring/self-care (OR = 2.17,P = 0.02). Patients living with family or others (e.g., caregivers) were found to be more likely to exercise regularly (OR = 3.44,P = 0.02) and less likely to avoid tobacco and alcohol (OR = 0.10,P = 0.04) than those living alone. Patients with better perceived health status were found to be more likely to adhere to medication therapy (OR = 2.02,P = 0.03). Patients with longer diabetes duration (OR = 2.33,P = 0.01) were found to be more likely to adhere to self-monitoring/self-care. Self-efficacy was found to mediate the association between older age and better adherence to diet therapy, while no significant mediating effects were found for health attitudes.ConclusionsAdherence to self-management was found to be associated with socio-demographic characteristics (sex, age, living status, perceived health status, and diabetes duration). Self-efficacy was an important mediator in some of these associations, suggesting that patient adherence may be improved by increasing patients’ self-management efficacy, such as by patient empowerment, collaborative care, or enhanced patient–physician interactions.

Background Mobile app-assisted self-care interventions are emerging promising tools to support self-care of patients with chronic diseases such as type 2 diabetes and hypertension. The effectiveness of such interventions requires further exploration for more supporting evidence. Objective A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to examine the effectiveness of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension in improving patient outcomes. Methods We followed the Cochrane Collaboration guidelines and searched MEDLINE, Cochrane Library, EMBASE, and CINAHL Plus for relevant studies published between January 2007 and January 2019. Primary outcomes included changes in hemoglobin A1c (HbA1c) levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Changes in other clinical-, behavioral-, knowledge-, and psychosocial-related outcomes were included as secondary outcomes. Primary outcomes and objective secondary outcomes that were reported in at least two trials were meta-analyzed; otherwise, a narrative synthesis was used for data analysis. Results A total of 27 trials were identified and analyzed. For primary outcomes, the use of mobile app-assisted self-care interventions was associated with significant reductions in HbA1c levels (standardized mean difference [SMD] −0.44, 95% CI −0.59 to −0.29; P Conclusions Mobile app-assisted self-care interventions can be effective tools for managing blood glucose and blood pressure, likely because their use facilitates remote management of health issues and data, provision of personalized self-care recommendations, patient–care provider communication, and decision making. More studies are required to further determine which combinations of intervention features are most effective in improving the control of the diseases. Moreover, evidence regarding the effects of these interventions on the behavioral, knowledge, and psychosocial outcomes of patients is still scarce, which warrants further examination.

The use of machine learning (ML) algorithms to conduct prediction or analysis tasks in a data center networking (DCN) environment is gaining increasing attention today. Recent research in traffic prediction, abnormal traffic monitoring, and routing selection has led to significant progress by making full use of historical data and improved ML models. However, such approaches face challenges when dealing with graphical data. These approaches have limited capabilities to explore the information that is hiding in the network's topological structure. To solve these challenges, we study the problem of representation learning in DCN topologies. To serve as a bridge, we proposed a novel method, “Topology2Vec,”to learn the network topology and represent the nodes using low-dimensional vectors, which is useful in many topology-related tasks. Both network structure and performance are considered in our method to ensure that the representation can adapt to different requirements. To evaluate the effectiveness, we demonstrate this method in a controller placement problem as a typical use case using topological data from real-world data centers. The experiments show that the use of “Topology2Vec”as a premise has produced better results in terms of network latency.

A systematic review was conducted to examine the effects of interventions aimed at reducing the negative consequences of interruptions on task performance. Medline, PsycINFO, PsycARTICLES, and the ABI/INFORM Collection were searched for relevant publications. Thirty-three laboratory-based experiments, containing 49 interventions, were reviewed. Seven types of interventions were identified. Overall, the use of interventions significantly increased primary task accuracy (standardized mean difference (SMD) = 1.03, P = 0.001) and reduced resumption lag (SMD = -0.51, P 0.001), whereas no significant difference was observed for interrupting task accuracy. Subgroup analyses indicated that intervention effects varied by (i) the type of intervention and (ii) the type of primary task (procedural, decision-making, or problem-solving tasks). The narrative synthesis provided additional evidence regarding interruption lag and time spent on a primary task. In sum, this review identified the types of interventions that were particularly effective and provided implications for application and further investigation.

The image classification models based on neural networks recently have outperformed most of the traditional models, and rapidly been developed and implemented by industry because of the capability of qualifying various computer vision tasks. Hence, the exposure of users’ image data to unauthorized powerful models causes more information leak in a shorter time. Through experiments, we find that for each input image, the change of the image’s prediction scores by each pixels’ RGB value change is different. Also, the pattern of the sensitivity on each pixel is highly related to the category and composition of the input image. By utilizing this feature, we present Pivot Pixel Noise Generator by Particle Swarm Optimization to generate noise points on original images to lower the target model’s accuracy of correctly predicting the target image’s label, so to protect the information contained in the target image from the image classification models. The model performs in a semi-black-box manner and balances the number of queries to the target and total number of modified points. We also propose an initialization strategy for the model, PSO Knowledge Transfer, which initializes the model’s parameters with experience learned from previous runs to further reduce the number of query times and noise points. The model is evaluated using the image classification benchmark model ResNet50 and shows an advantage compared to the baseline algorithm.

In recent years, the Internet has become an indispensable part of people’s lives, and it offers increasingly comprehensive information tailored to people’s personal preferences as well as commodity attribute information. Consequently, many researchers have used external information to improve recommendation technology. However, most previous studies consider only adding single relationship types, such as social networking friend-relationships. In the real world, considering multiple types of external relations can more accurately determine the reason why a user selected an item. To address this problem, in this paper, we propose a hybrid method called the semantic preference-based personalized recommendation on heterogeneous information networks (SPR), which combines user feedback scores with heterogeneous information networks. This method can improve recommendation problems by considering multiple types of external relationships. To apply the method, we first introduce a similarity measure between users based on a user’s potential preferences in the meta-path and design the recommended model at the global and individual level. Finally, we perform experiments on two real-world data sets, finding that the SPR method achieves better results compared with the several widely employed and the state-of-the-art recommendation methods.

Governments increasingly recognise the benefits of direct subsidies and tax credits for supporting private sectors to adopt research and development (R&D) activities. However, the different reactions of firms toward these two types of measures have remained with advance financial support, whereas tax credits wait until the R&D expense has been incurred to pay back. Thus, the former is better at attenuating risk, but leaves recipients with less discretion in their research efforts. Firms with more financial resources can more easily overcome such constraints. Tax credits provide more flexibility to using the money. This can be especially beneficial for firms whose financial resources are limited. These ideas were tested using data on research support and research outcomes describing 2,748 Chinese firms in Beijing Zhongguancun Science Park between 2012 and 2015. Analyses of those data delivered support for the preceding arguments.

Poorly designed user interfaces limit the effective use of medical devices, leading to user errors. Hence, before such devices are implemented, it is essential to identify potential usability issues. A common approach to testing usability is a simulation-based test, which allows practitioners to identify usability issues by observing how users interact with the product being tested. Infusion pumps are frequently used in medical settings, and much effort has been made to advance their design and features. To enhance patient safety, smart intravenous (IV) pumps with safety functions (e.g., drug library, alarms) have been developed. However, they do not always have usable interface designs. A simulation-based test was thus conducted in this study to examine the usability of smart IV pumps. Four smart IV pumps (Pumps 1, 2, 3, and 4) were tested. Table 1 presents their key characteristics. Thirty registered nurses with little experience of using such pumps were recruited from five units at three local public hospitals to participate in the test. They were asked to perform a series of tasks that simulated medication administration involving infusion pumps. A senior registered nurse and human factors professionals developed the following tasks for the test: priming an infusion set; loading an infusion set; accessing the drug library and selecting drugs; and programming a basic infusion. The tasks were performed in a simulated ward to reproduce the nurses’ actual practice. All tasks were performed with each pump, and the order in which the pumps were used was counterbalanced. The nurse participants wore video glasses that recorded their interactions with the pumps during the test. The study’s outcome measures included total task completion time, the number of deviations, the number of alarms, and the number of requests for help. Total task completion time was the amount of time taken to complete all of the tasks. A deviation occurred when the operation of a pump diverged from its standard operation, defined according to the instructions in the relevant pump user manual. Alarms were recorded when aural or visual alerts were caused by deviations and incorrect operation. Requests for help occurred when the participants asked the research assistant how to solve problems they had encountered. Repeated measures analysis of variance was performed to compare the total task completion time between the four pumps. Deviations, alarms, and requests for help were compared by the total number of occurrences. Table 1. Main features and operation of the pumps. Pump 1 Pump 2 Pump 3 Pump 4 Infusion set Gravity feeding set. Gravity feeding set. Gravity feeding set. Gravity feeding set. Flow sensor and Internal flow sensor; no External flow sensor; must Internal flow sensor; no Internal flow sensor; no installation need to install manually. be installed manually. need to install manually. need to install manually. How to access Automatic access via a page Automatic access via a page Users need to find and enter Automatic access via a “drug library” offering entry to the drug offering entry to the drug the main menu, then access page offering entry to the feature library. library. the drug library. drug library. How to select drugs in the drug library Press keys on the keypad. Press keys on the keypad. Press keys on the keypad or turn a dial. Press keys on the keypad. How to input numbers Use numeric keys and decimal keys. Use increment/decrement keys to increase or decrease value. Turn a dial to increase/decrease value. Use increment/decrement keys to increase or decrease value. The results of the test showed significant differences in total task completion time between the four pumps (F(3,87) = 14.56, p < 0.0001). The tasks were completed most quickly using Pump 1 (208.2 s), followed by Pump 2 (255.7 s), Pump 4 (304.6 s), and Pump 3 (369.2 s). The participants made the fewest deviations when using Pump 1 (36 deviations), followed by Pump 4 (92 deviations), Pump 2 (139 deviations), and Pump 3 (157 deviations). The participants triggered the fewest alarms when using Pump 1 (14 alarms), followed by Pump 2 (56 alarms), Pump 3 (67 alarms), and Pump 4 (67 alarms). As measured by their requests for help, the participants encountered the fewest problems with Pump 2 (8 requests for help), followed by Pump 1 (11 requests for help), Pump 4 (12 requests for help), and Pump 3 (27 requests for help). The overall findings were as follows. Pump 1 allowed a reasonable workflow, but the participants using this pump found it difficult to prime the infusion set and understand the alarm messages. Pump 2 was relatively easy to use, but the participants tended to forget to attach the external flow sensor. Using Pump 3, many participants found it difficult to access the drug library and program an infusion because the button labels were unclear. This may lead to misunderstandings and errors. In addition, using Pump 3 took the most time, which could have been due to the difficulty of inputting numbers using this pump. The design of Pump 3 requires users to input numbers by turning a dial, which takes longer than using keys. Using Pump 4, the participants were able to rapidly access the drug library and program a basic infusion, but some found it difficult to prime the infusion set. This study demonstrated that a simulation-based test is an effective way of identifying usability issues with medical devices. Developers of medical devices should improve their interface design to avoid ineffective use. Users should try to overcome usability issues by following the relevant user manuals closely. They should also receive sufficient training before using the devices. We observed a number of human errors during the test, and thus recommend that more training be provided in using the safety features of the pumps. Furthermore, practitioners should use different outcome measures to indicate performance when conducting similar kinds of simulation-based test, depending on the characteristics of the clinical settings in which the infusion pumps are used. Acknowledgments This study was conducted with the support of the Department of Industrial and Manufacturing Systems Engineering at the University of Hong Kong and the Theme-based Research Scheme of the Hong Kong Research Grants Council (grant number T32-102/14-N).

This research investigates imitation in cross-border acquisitions (CBAs) by emerging market multinationals (EMNCs) in terms of the equity share sought in such acquisitions. Earlier acquisitions by peer compatriots and by developed-market multinationals (DMNCs) are both models for imitation, but the former are more similar and potentially relevant. Analysis of 608 CBAs by Chinese firms between 1987 and 2008 supports the above argument. Further, it reveals that state-owned Chinese firms are less likely to imitate in general. Both state-owned and privately-owned Chinese acquirers are most likely to imitate privately-owned Chinese acquirers previously invested in the same environment. For state-owned Chinese acquirers, more frequent pursuit of a particular target share by earlier state-owned Chinese acquirers decreases rather than increases the likelihood of a later state-owned Chinese investors buying a similar share.

Prior research has shown mixed findings about the relationship between exporting and a firm's innovation performance. This study applied organizational learning theory with a demand-side perspective to explore whether the mixed findings could be due to confounding a positive effect of knowledge variety with a negative effect arising from market separation. Compared with firms with only domestic or foreign customers, firms with both types of customers have more opportunities to acquire diverse knowledge and form innovative combinations, but serving disparate customers tends to inhibit knowledge transfer, sharing and integration. Data on 8529 Chinese automobile and component manufacturers were analyzed to test these arguments, and firms competing in less separated domestic and overseas markets were found to demonstrate the best innovation performance. Firms with greater absorptive capacity were found better able to overcome the difficulties caused by market separation and enjoy more benefits from market variety.

A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed. A thorough investigation of Collaborative Filtering (CF) techniques preceded the development of this algorithm. The proposed algorithm improved the user-item similarity approach by extracting the item feature and applying various item features' weight to the item to confirm different item features. User preferences for different item features were obtained by employing user evaluations of the items. It is expected that providing better recommendations according to preferences and features would improve the accuracy and efficiency of recommendations and also make it easier to deal with the data sparsity. In addition, it is expected that the potential semantics of the user evaluation model would be revealed. This would explain the recommendation results and increase accuracy. A portion of the MovieLens database was used to conduct a comparative experiment among the proposed algorithms, i.e., the collaborative filtering algorithm based on the item and the collaborative filtering algorithm based on the item feature. The Mean Absolute Error (MAE) was utilized to conduct performance testing. The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.

Getting engaged in competitive international markets motivates exporting firms to enhance their technological competitiveness and invest in research and development (R&D). While in-house R&D investment is important for getting advanced technologies, the technologies needed by exporters located in emerging economies (EEs) typically readily exist overseas. Drawing on organizational learning literature, we argue that the R&D intensity stimulated by exporting may be reduced when 1) EE exporters are better prepared and motivated to absorb foreign knowledge, 2) foreign sources of knowledge are more available, and/or 3) local technology supply is poor. An analysis on 5592 automobile parts and component manufacturers in China during 2005–2007 supports these arguments. To compete in markets abroad, Chinese firms' R&D intensity increases with export intensity when their export intensity is low. As the export intensity exceeds a certain threshold, their R&D intensity starts to decrease as intensive exporters are better prepared and motivated to acquire knowledge from foreign sources. When substitutive foreign knowledge sources such as foreign parents and FDI spillovers in the local industry is available, firms' R&D intensity stimulated by exporting can be further reduced. Better local technology supply can increase the R&D intensity stimulated by exporting.

BackgroundMobile health apps have changed the way people obtain health information and services and advance their understanding and management of their health. Although many health apps are available, little is known about the prevalence of their use for different purposes, whether such use is associated with demographic characteristics, and the impacts of their use on health knowledge and management. ObjectiveThe main objectives of this study were to examine the prevalence, extent, and demographic correlates of health app use and the perceived impacts of health app use on increased health knowledge and improved health condition management. MethodsWe conducted a cross-sectional questionnaire survey of 633 Chinese adults randomly drawn from the general population in Hong Kong. ResultsOf the 633 participants, 612 (96.7%) reported using mobile devices. Of them, 235 (38.4%) reported using multiple types of health apps. The most-used type of health app was about healthy living information (197/612, 32.2%), followed by measuring/recording vital signs (80/612, 13.1%), health and medical reminders (64/612, 10.5%), recovery and rehabilitation information (42/612, 6.9%), diagnosis assistance (28/612, 4.6%), emergency services (16/612, 2.6%), telehealth (11/612, 1.8%), and “other” (19/612, 3.1%). Multivariate logistic regression analysis found that health app users were more likely to be women (odds ratio [OR] 1.68, 95% CI 1.14-2.48, P=.01) of a higher self-rated social class (OR 3.66, 95% CI 1.11-12.11, P=.03). Participants who worked in education/culture/academia (OR 2.31, 95% CI 1.16-4.59, P=.02) or disciplinary forces (OR 5.07, 95% CI 1.25-20.62, P=.02) were more likely to believe that using health apps could increase their health knowledge; participants working in education/culture/academia were also more likely to believe that using health apps could improve the effectiveness of health condition management (OR 2.18, 95% CI 1.10-4.34, P=.03). ConclusionsEffort should be made to promote health app use, especially to demographic groups that are currently less likely to use health apps (eg, males, individuals from lower social classes). From the public health perspective, guidelines could be developed to help individuals identify quality health apps that meet their needs. Moreover, app developers could improve the usability of health apps to promote health app use.

By more and more, privacy preservation problem is widely discussed among users and researchers. For mobile sensing network, an imperfect privacy preservation scheme will directly put participants into a dangerous situation. The better privacy protection applied, the better sensing data quality will be achieved. In this paper, we present a privacy-aware data aggregation scheme for mobile sensing networks. We considered both the smart nodes like smart-phone and dumb nodes like wearable device or GPS device. We take the location information and the sensing content into consideration separately. And this thought will make sure the sensing content will be k-anonymous and the accurate location will be protected well either. We use erasure coding technology to slice the sensing data record according to the k-anonymity rules. For the sake of efficiency and stability, we compare two coding technology in two sensing data types and give the experiment results and explanations in detail. After that, we give a social model to describe the social relation and a security data sharing protocol among the participants. The introduction of the participants’ social relation may give a new way to the reputation and data trustworthy evaluation mechanism.

How do business idea sources, entrepreneurs' background, and institutional environment at the time of founding interplay to influence the performance of entrepreneurial ventures? Combining resource...

Recent studies have found contradictory evidence about the relationship between a firm's exporting intensity and its success in product innovation. Data on 6,197 firms in the Chinese auto industry were analysed to demonstrate that the relationship is actually inverted-U shaped, at least in that specific context. Exporters in emerging economies can be technology and market leaders in their domestic markets, but internationally they tend to be technology and market laggards. When the export intensity is low firms that also export can benefit from knowledge gained in overseas markets, improving their product innovation. When the export intensity is very high, focused exporters tend to focus on the overseas markets where they may not have competitive advantages in product innovation, developing fewer product innovations. Furthermore, the paper found that the inverted-U shaped relationship between export intensity and product innovation is strengthened by more intense competition in the home market.

With the continued refinement of the social division of labor, China’s real estate industry is ranging from the “big and complete” to professional development, therefore the real estate developers establish business with a growing number of partners. What are the transaction costs between them, and what factors affect their transaction costs, this is the problem that is worthy of exploring. This paper uses transaction cost theory in the new institutional economics to study the influencing factors and mechanisms of the transaction costs between real estate developers and its partners, and tries to use system dynamics to analysis these factors.

The real estate industry and financial industry rely on each other. On one hand, the bank credit capital is the real estate industry primary source of capital, at the same time, the real estate credit is an important component of bank capital. Consequently, with meeting the capital needs of real estate credit, commercial banks bear much credit risk of the real estate industry. It is very necessary for commercial banks and property developers achieving a win-win to understand the risk profoundly, and find the appropriate measures to prevent or control measures. Basing on the understanding of the real estate credit risk, the article builds a real estate credit risk causal feedback which relies on the system dynamics theory and method. Next the paper identifies the factors and the outcomes, which affect the real estate credit risk. Finally, some suggestion is made to strengthen the real estate credit risk control and management.

One of the difficult problems encountered when implementing artificial neural networks based on FPGA is the approximation of the activation function. The sigmoid function is the most widely used and is difficult to approximate. This paper is devoted to show a saving hardware resources and accurate way to compute the sigmoid function based on FPGA by non-linear approximation. This is done by subsection analysis involved a new low-leakage FPGA Look-up Tables (LUTs), introducing a non-linear approximation algorithm in detail, analyzing the approximating accuracy and the FPGA hardware resources, which can achieve some kind of balance between the approximating precision and the limited hardware resources of FPGA, shows improvements over the previous known algorithms. The implementation of sigmoid function and the simulation are completed by the development software of QUARTUS II.

The Kepler General Purpose GPU (GPGPU) architecture was developed to directly support GPU virtualization and make GPGPU cloud computing more broadly applicable by providing general purpose computing capability in the form of on-demand virtual resources. This paper describes a baseline GPGPU cloud system built on Kepler GPUs, for the purpose of exploring hardware potential while improving task performance. This paper elaborates a general scheme which defines the whole cloud system into a cloud layer, a server layer, and a GPGPU layer. This paper also illustrates the hardware features, task features, scheduling mechanism, and execution mechanism of each layer. Thus, this paper provides a better understanding of general-purpose computing on a GPGPU cloud.