Currently, various algorithms have been developed to retrieve regional and global Land surface temperature (LST) from satellite thermal infrared (TIR) observations, among which, the split window (SW) algorithm is the most widely used one. However, the LST retrieval accuracy would be affected by the channel centers and channel widths owing to the vast atmospheric conditions and land surface types around the world. The theoretical channel configuration leading to the best performance of the SW algorithm is still not well investigated currently. In this study, the LST retrieval accuracies of the SW algorithm using different channel configurations were studied iteratively through the whole TIR atmospheric window. Consequently, the two channels centered at 10.3 μm and 11.5 μm with the widths of 0.3 μm and 0.4 μm were found to be the optimal channel configuration for applying the SW algorithm. Based on the global atmospheric profiles provided in the ERA5 and SeeBor V5.0 database and the emissivity spectra provided in the ECOSTRESS library, the performance of the SW algorithm using the determined channel configuration was delicately evaluated. Results show that the LST retrieval Root Mean Square Error (RMSE) of the determined channel configuration was 1.09 K, better than that of the MODIS (1.28 K), Landsat-9 (1.24 K), and Sentinel-3A (1.24 K) instruments regarding the global atmospheric profiles provided in the ERA5 database. Similar results were obtained corresponding to SeeBor V5.0 atmospheric profiles with the LST retrieval RMSE of 1.28K (determined channel configuration), 1.49 K (MODIS), 1.96 K (Landsat-9), and 1.94 K (Sentinel-3A).

Deep Reinforcement Learning (DRL) combines the perceptual capabilities of deep learning with the decision-making capabilities of Reinforcement Learning RL, which can achieve enhanced decision-making. However, the environmental state data contains the privacy of the users. There exists consequently a potential risk of environmental state information being leaked during RL training. Some data desensitization and anonymization technologies are currently being used to protect data privacy. There may still be a risk of privacy disclosure with these desensitization techniques. Meanwhile, policymakers need the environmental state to make decisions, which will cause the disclosure of raw environmental data. To address the privacy issues in DRL, we propose a differential privacy-based online DRL algorithm. The algorithm will add Gaussian noise to the gradients of the deep network according to the privacy budget. More important, we prove tighter bounds for the privacy budget. Furthermore, we train an autocoder to protect the raw environmental state data. In this work, we prove the privacy budget formulation for differential privacy-based online deep RL. Experiments show that the proposed algorithm can improve privacy protection while still having relatively excellent decisionmaking performance.

Summary To improve time efficiency for structured environment exploration of multi‐robot systems, a novel coordinated strategy based on predicate logic reasoning is proposed in this paper. The strategy assigns the room with maximal utility to the robot with the optimal exploring property, and realizes cooperative exploration by considering the room capacity. A set of predicate logic reasoning formulas is established to implement the coordination. The coordination takes into account robot performance and environment property and is conducted in a distributed manner. In addition, to save computing and storage resources, a triple set named as task information was proposed instead of traditional unfinished job list for environment information sharing among robots. The results are validated by computer simulations for two simulated office like environments.

A virtual community is a type of online structure that enables Internet users to communicate and collaborate. Users' knowledge contributions are critical to the viability and sustainability of virtual communities. This article studies virtual community members' knowledge sharing from the perspective of citizenship behavior defined as members' spontaneous contribution to the community without expectation of return or reciprocation. The social-relational antecedents of citizenship behavior are explored through an examination of how members' general attitude and desire for relationship building and maintaining, including attachment motivation, social support orientation, and disposition to trust influence their trusting beliefs and citizenship knowledge-sharing behavior. Hypotheses are developed and tested with survey data from Chinese and American users of virtual communities. In general, the results of data analyses support our research model. This article contributes empirically to virtual community research and has practical implications for virtual community development.

Computer-mediated communication (CMC) technologies like instant messenger have been increasingly used as a tool for interpersonal relationship development and maintenance. To better understand such use of CMC, we investigate copresence—defined as a communicator's perception of being with other communication partners through CMC—and its role in affecting the intention to continue using the medium. We explore three social-relational antecedents of copresence: prior use of the medium, perceived critical mass, and user commitment to maintain the relationship with others. Hypotheses are tested with survey data on the use of instant messenger. In general, the results support the research hypotheses. We suggest that the concept of copresence can be a novel and helpful theoretical perspective for understanding the use of CMC for social-relational purposes.

Despite the importance of international trade firms in China’s economic development, there is only limited empirical evidence about how these firms assimilate Internet-based e-business in global supply chain operations. Using the Technology-Organization-Environment framework, this study investigates technological, organizational, and environmental factors which determine e-business assimilation in these firms. Based on survey data collected from 307 international trade firms in the Beijing area, we found that environmental uncertainty was negatively associated with e-business assimilation, while a firm’s internal IT capability, relative advantage of e-business, learning orientation, and inter-organizational dependence were positive determinants of e-business assimilation. The effect of a firm’s ownership type was also significant. Environmental uncertainty was the most important inhibitor, and IT capability and inter-organizational dependence were the most salient enablers of e-business assimilation.

This study examines the impact of affiliation motivation on the intention to use a groupware system. Building on attachment theory, one personality attribute, affiliation motivation, was incorporated into the Technology Acceptance Model to explore groupware use. A study using a group of MBA students that were just beginning to use groupware was conducted to determine if affiliation motivation influences perceived usefulness, perceived ease of use, and intention to use. The results indicated that affiliation motivation was significantly associated with behavioral intention and perceived ease of use when use was voluntary. Implications for instructors who are using or planning to use groupware in their classes are discussed.