Sub-10 nm metal-based nanoparticles have garnered immense interest due to their unique properties and versatile applications. In this study, we created sub-10 nm Ag-based particles with a spark discharge generator and explored the parameters impacting their size distribution and charging properties, including carrier gas flow rates, spark discharge voltage, electrode gap distances, and capacitance. Our findings illuminate that there is a comparable influence of different factors on both self-charged and neutral particles. Among the different factors, carrier gas flow rates emerging as a paramount determinant in particle size. While increasing spark discharge voltage and capacitance within the spark circuit increases particle concentrations, the associated changes in particle size prove to be less straightforward. Significant differences between the concentration of positive and negative self-charged particles manifest when the carrier gas flow rate surpasses 5.0 L min⁻¹, with positive particles ranging from 0.8 to 1.2 nm and negative particles spanning 0.8 to 3.0 nm. Self-charged particles close to 1 nm tend to exhibit positive charges, whereas those larger than 2 nm tend to acquire negative charges, which suggests the growth of negative particles is faster than positive ones in the spark chamber. Nevertheless, these disparities between bipolar particles diminish with the increase of residence time, leading to the observation of similar particle size distributions. Positive particles consistently bear a single charge, while some negative particles exceeding 3 nm exhibit multiple charges, primarily under carrier gas flow rates exceeding 7.5 L min⁻¹. This study provides insights into the control of properties of nano-sized metal particles, which are crucial for their practical utilization.

Model partitioning is a promising solution to reduce the high computation load and transmission of high-volume data. Within the scope of Edge AI, the fundamentals of model partitioning involve splitting the model for local computing at the edge and offloading heavy computation tasks to the cloud or server. This approach benefits scenarios with limited computing and battery capacity with low latency requirements, such as connected autonomous vehicles. However, while model partitioning offers advantages in reducing the onboard computation, memory requirements and inference time, it also introduces challenges such as increased energy consumption for partitioned computations and overhead for transferring partitioned data/model. In this work, we explore hybrid model partitioning to optimize computational and communication energy consumption. Our results provide an initial analysis of the tradeoff between energy and accuracy, focusing on the energy-aware model partitioning for future Edge AI applications.

Social networks now connect billions of people around the world, where individuals occupying different positions often represent different social roles and show different characteristics in their behaviors. The structural hole (SH) theory demonstrates that users occupying the bridging positions between different communities have advantages since they control the key information diffusion paths. Users of this type, known as SH spanners, are important when it comes to assimilating social network structures and user behaviors. In this article, we review the use of SHs theory in social network analysis, where SH spanners take advantage of both information and control benefits. We investigate the existing algorithms of SH spanner detection and classify them into information flow-based algorithms and network centrality-based algorithms. For practitioners, we further illustrate the applications of SH theory in various practical scenarios, including enterprise settings, information diffusion in social networks, software development, mobile applications, and machine learning (ML)-based social prediction. Our review provides a comprehensive discussion on the foundation, detection, and practical applications of SHs. The insights can facilitate researchers and service providers to better apply the theory and derive value-added tools with advanced ML techniques. To inspire follow-up research, we identify potential research trends in this area, especially on the dynamics of networks.

We identified three temporal patterns shown in commit activities among Chinese and American companies and found that Chinese businesses are more likely to follow long work hours than American ones. We also conducted a survey on the trends of, reasons for, and results of overtime work. Our study could provide references for developers to choose workplaces and for companies to make regulations.