Background The increased environmental uncertainty caused by the global pandemic creates challenges for academic incubators. The lack of vital services has decreased the chances of entrepreneurial success for student ventures. This study aims to provide insights into the impact of environmental uncertainties on students’ relationships with academic incubators, thereby affecting user satisfaction and entrepreneurial success. This study further investigates the moderating role of entrepreneurial orientation the internal locus of control on the relationships between environmental uncertainty, relationship with academic incubator, and entrepreneurial successMethods The PLS-SEM method was used to analyze the relationships between all the constructs.Results The findings show that environmental uncertainty can create an urgent need for student entrepreneurs to approach the academic incubator for support and maintain good relationships.Conclusion The ad-hoc relationship formed during the environmental uncertainty does not necessarily help student entrepreneurs increase their chance of success in launching their business.

Over the past few years, a large number of mobile devices are being used in real life with the advancement of wireless technology. The explosive increase in the number of wireless mobile devices makes it impossible to avoid the co-channel interference (CCI) or adjacent channel interference (ACI) phenomenon. Therefore, this interference phenomenon has been recognized as an important problem as it causes network performance degradation. In this paper, we conducted a CCI experiment in an outdoor environment where a Wi-Fi signal is not detected and in the ns-3 simulator, and compared the CCI phenomenon with the experiment results and simulation results. As we evaluated network performance between real network and simulation, we analyzed the performance impact at each network stack how packet errors propagate to upper layers in wireless ad-hoc network and propose a theoretical maximum throughput considering error rate in the data link layer and TCP layer.

Researchers in bioinformatics and medical science fields have been developing various innovative information systems and software tools using emerging technologies. Metabolite profiling is one of the fields in which bioinformatics researchers are intensely developing various innovative methods and software tools. Metabolites are the intermediate and end products of metabolism, which is the set of life-sustaining chemical reactions in living organisms. Accurate and complete identification of metabolites can advance many medical science and bioinformatics fields by providing a direct way of observing metabolic activities. The analysis of tandem mass spectrometry (MS/MS) data has been a long-term computational challenge in bioinformatics. This study examines various existing and emerging methods and software tools for analyzing MS/MS data for metabolite identification and discusses challenges and future perspectives in this important bioinformatics research area.

This study investigates the underlying motivational factors of knowledge exchange intentions (intention to obtain and to provide knowledge) within virtual community contexts. Perceived virtual network structures, namely virtual network connectivity (CN) and virtual network closeness (CL), are suggested as the important antecedents of knowledge sharing intentions in the context of virtual knowledge exchange communities. Anonymity (AN), one of the unique characteristics of virtual communities, but controversial due to its multi-faceted effects, is considered in a structural model as a factor having an impact on a virtual network structure. Data collected from participants of virtual communities through online surveys are analyzed using Partial Least Squares (PLS) structural equation modeling (SEM) to empirically test the proposed hypotheses. The results reveal that both CN and CL have a significant impact on both of the knowledge exchange intentions although CL shows an opposite direction of the impact. The results also show that AN has a significant impact on CL as expected but not on CN. Implications of this study may shed some light on better understanding community participants’ intentions to obtain and provide knowledge, along with the impact of anonymity on the perceived network structure.

The static k-Nearest Neighbor (k-NN) method for localization has limitations in accuracy due to the fixed k value in the algorithm. To address this problem, and achieve better accuracy, we propose a new dynamic k-Nearest Neighbor (Dk-NN) method in which the optimal k value changes based on the topologies and distances of its nearest neighbors. The proposed method has been validated using the WLAN-fingerprint data sets collected at COEX, one of the largest convention centers in Seoul, Korea. The proposed method significantly reduced both the mean error distances and the standard deviations of location estimations, leading to a significant improvement in accuracy by ~ 23% compared to the cluster filtered k-NN (CFK) method, and ~ 17% compared to the k-NN (k = 1) method.