Determinants of Trademarking: Evidence from Arizona and New Mexico Startups

Trademarks are considered an important indicator of entrepreneurial innovation, especially among nontechnology-based service firms and startups. Therefore, it is essential to understand the motivations and drivers behind trademark applications to get a grasp of firm innovation behavior. This study focuses on the trademark decisions of startup firms. The paper assembles a unique dataset of startup firms linking firm trademark application and registration information with firm characteristics. The goal is to empirically examine the determinants of startup trademark decisions. The key results show that firm size is important, and startups of 51-200 employees have the highest propensity of seeking trademarks. Startup location, firm age, and firm type also matter. Within our study area, for example, startups in the Phoenix metro area are significantly more likely to file trademark applications than those in the Albuquerque metro area. Technology-related startups find trademarks less attractive compared to other startups

Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement

With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 English-German and WMT17 Chinese-English translation datasets. Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.Comment: AAAI 201

Preparation and Capacity-Fading Investigation of Polymer-Derived Silicon Carbonitride Anode for Lithium-Ion Battery

Polymer-derived silicon carbonitride (SiCN) materials have been synthesized via pyrolyzing from five poly(silylcarbondiimide)s with different contents of carbon (labeled as 1-5#). The morphological and structural measurements show that the SiCN materials are mixtures of nanocrystals of SiC, Si3N4, and graphite. The SiCN materials have been used as anodes for lithium-ion batteries. Among the five polymer-derived SiCN materials, 5#SiCN, derived from dichloromethylvinylsilane and di-n-octyldichlorosilane, has the best cycle stability and a high-rate performance at the low cutoff voltage of 0.01-1.0 V. In lithium-ion half-cells, the specific delithiation capacity of 5#SiCN anode still remains at 826.7 mA h g-1 after 100 charge/discharge cycles; it can even deliver the capacity above 550 mA h g-1 at high current densities of 1.6 and 2 A g-1. In lithium-ion full cells, 5#SiCN anode works well with LiNi0.6Co0.2Mn0.2O2 commercial cathode. The outstanding electrochemical performance of 5#SiCN anode is attributed to two factors: (1) the formation of a stable and compact solid electrolyte interface layer on the anode surface anode, which protects the electrode from cracking during the charge/discharge cycle; and (2) a large amount of carbon component and the less Si3N4 phase in the 5#SiCN structure, which provides an electrochemical reactive and conductive environment in the SiCN structure, benefit the lithiation/delithiation process. In addition, we explore the reason for the capacity fading of these SiCN anodes

Preparation and Capacity-Fading Investigation of Polymer-Derived Silicon Carbonitride Anode for Lithium-Ion Battery

Polymer-derived silicon carbonitride (SiCN) materials have been synthesized via pyrolyzing from five poly(silylcarbondiimide)s with different contents of carbon (labeled as 1-5#). The morphological and structural measurements show that the SiCN materials are mixtures of nanocrystals of SiC, Si3N4, and graphite. The SiCN materials have been used as anodes for lithium-ion batteries. Among the five polymer-derived SiCN materials, 5#SiCN, derived from dichloromethylvinylsilane and di-n-octyldichlorosilane, has the best cycle stability and a high-rate performance at the low cutoff voltage of 0.01-1.0 V. In lithium-ion half-cells, the specific delithiation capacity of 5#SiCN anode still remains at 826.7 mA h g-1 after 100 charge/discharge cycles; it can even deliver the capacity above 550 mA h g-1 at high current densities of 1.6 and 2 A g-1. In lithium-ion full cells, 5#SiCN anode works well with LiNi0.6Co0.2Mn0.2O2 commercial cathode. The outstanding electrochemical performance of 5#SiCN anode is attributed to two factors: (1) the formation of a stable and compact solid electrolyte interface layer on the anode surface anode, which protects the electrode from cracking during the charge/discharge cycle; and (2) a large amount of carbon component and the less Si3N4 phase in the 5#SiCN structure, which provides an electrochemical reactive and conductive environment in the SiCN structure, benefit the lithiation/delithiation process. In addition, we explore the reason for the capacity fading of these SiCN anodes

High-Temperature Shock Enabled Nanomanufacturing for Energy-Related Applications

Functional nanomaterials are playing a crucial role in the emerging field of energy-related devices. Recently, as a novel synthesis method, high-temperature shock (HTS), which is rapid, low cost, eco-friendly, universal, scalable, and controllable, has provided a promising option for the rational design and synthesis of various high-quality nanomaterials. In this report, the HTS technique, including the equipment setup and operating principle, is systematically introduced, and recent progress in the synthesis of nanomaterials for energy storage and conversion applications using this HTS method is summarized. The growth mechanisms of nanoparticles and carbonaceous nanomaterials are thoroughly discussed, followed by the summary of the characteristic advantages of the HTS strategy. A series of nanomaterials prepared by the HTS method, including carbon-based films, metal nanoparticles and compound nanoparticles, show high performance in the diverse applications of storage energy batteries, highly active catalysts, and smart energy devices. Finally, the future perspectives and directions of HTS in nanomanufacturing for broader applications are presented