Digital innovation is becoming increasingly important in today's economy. Many digital innovations are developed not within organizations, but in innovation-driven entrepreneurial ecosystems, where various entrepreneurship related stakeholders collaborate and cooperate. Despite its significance, studies on digital entrepreneurship ecosystems (DEEs) are limited and the concept is largely undertheorized. This study intends to fill that gap by studying how a DEE organizes. This organizing issue is challenging, because stakeholders of a DEE are self-organizing and are not governed by any formal authority. To answer that question, we adopt forms of organizing as a theoretical lens, which provides structure to examine organizing issues. Through an in-depth case study of Zhongguancun, the Silicon Valley of China, we unveil eight processes around the themes of division of labor and integration of efforts. We further show that the forms of organizing feature a balance of centralized design and de-centralized emergence. This balanced view extends the forms of organizing literature, which takes an either/or perspective. Ecosystem architects and policy makers who intend to build entrepreneurship ecosystems to promote local economies can derive practical implications from our findings.

There are a substantial number of papers in the scientific literature reporting on the chemical composition of the plant. None of these investigations are truly comprehensive nor address the differences in composition that occur through processing variations in fresh leaves and commercially available product forms. This work was to analytically examine a range of these forms and compile the findings. Fresh leaves and a number of commercial aloe juice powders were investigated for their major chemical constituents. Samples included fresh leaves from China and Mexico, plus commercial powders from different manufacturers made from different plant parts and/or manufacturing processes. The test results include moisture, ash, fiber, protein, lipids, minerals, organic acids, free sugars, and polysaccharides. The analytical methods employed comprise inductively coupled plasma-optical emission spectroscopy for minerals, high-performance anion-exchange chromatography equipped with pulsed amperometric detection for free sugars, HPLC for organic acids, and size exclusion chromatography (SEC)-multi-angle laser light scattering (MALS)-differential refractive index (dRI) for polysaccharide analyses. The absolute MW and MW distribution were determined using MALS measurement. The major constituents of fresh leaf are fibers, proteins, organic acids, minerals, monosaccharides, and polysaccharides, which accounted for 85-95% of the total composition determined. In the commercial powdered aloe juice samples, four major components-organic acids, minerals, monosaccharides, and polysaccharides-accounted for 78-84% of the total composition. Apart from the four major components, products manufactured by ethanol precipitation contained high amounts of fiber and protein, while the free sugars were removed. In ethanol-precipitated products, the polysaccharide MW was less affected by manufacturing conditions and the concentration of aloe polysaccharides was higher than in products made in the nonethanol manufacturing processes. The overall chemical profiles were found to be consistent, except for the MW and content of polysaccharides in the commercial aloe samples analyzed, which were largely dependent on the types of manufacturing processes employed. This present study provides a comprehensive investigation of the major chemical composition of leaf and commercially derived products. The use of the SEC combined with MALS and differential RI detectors has proved to be an improved tool for the accurate determination of polysaccharide MW and contents of the various commercially available products in this study.

No abstract

No abstract

Consider a multi-agent system where agents perform a given task with different levels of ability. Agents are initially not aware of how well they perform in comparison with their peers, and are willing to self-assess. This scenario is relevant, e.g., in wireless sensor networks, or in crowdsensing applications, where devices with embedded sensing capabilities collaboratively collect data to characterize the environment: the global performance is very sensitive to the measurement accuracy, and agents providing outliers should restrain to participate. This paper presents a distributed algorithm enabling each agent to self-assess its own ability. The algorithm tracks the outcomes of a local comparison test performed by pairs of agents when they randomly meet, and able to gauge their relative level of ability. The dynamics of the proportions of agents with similar assessments are described using continuous-time state equations. The existence of an equilibrium is shown. Closed-form expressions for the various proportions of agents with similar assessments are provided at equilibrium. In simulations, a community of agents equipped with sensors, and trying to determine the performance of their equipment is considered. Simulation results show a good fitting with theoretical predictions.