Open Sesame: The Myth of Alibaba\u27s Extreme Corporate Governance and Control

In September 2014, Alibaba Group Holding Limited (Alibaba) successfully launched a $25 billion initial public offering (IPO), the largest IPO ever, on New York Stock Exchange. Alibaba’s IPO success witnessed a wave among Chinese Internet companies to raise capital in U.S capital markets. A significant number of these companies have employed a novel, but poorly understood corporate ownership and control mechanism—the variable interest entity (VIE) structure and/or the disproportional control structure. The VIE structure was created in response to the Chinese restriction on foreign investments; however, it carries the risk of being declared illegal under Chinese law. The disproportional control structure, usually in the form of dual-class shares, helps founders or controlling shareholders maintain control post-IPO with less equity contribution. Around 30 percent of U.S.-listed Chinese companies adopted a dual-class share structure or similar mechanism to enhance insider control. This percentage is much higher than that of U.S. public companies, which is only about 6 percent. This Article uses Alibaba as a case study to analyze the legal challenges posed by the VIE and disproportional control structures. Specifically, it dissects the structure of the VIE and sheds important light on inherent legal and governance risks associated with the VIE structure, along with potential policy solutions to protect investors and reduce information asymmetry. Similar to most U.S. high-tech companies that adopt dual-class share structures to maintain control by founders, Alibaba grants a partnership, consisting of its founders and executives, an exclusive right to nominate a majority of its directors. Furthermore, Alibaba implements various anti-takeover measures to strengthen insider control, many of which are considered detrimental to the interests of minority shareholders. Such excessive insider control presents a puzzle as to the success of the world’s largest IPO and casts doubt on the long-debated issue of whether corporate governance truly matters. In this Article, we argue that the idiosyncratic value brought by a charismatic founder-executive—in this case, Alibaba’s Jack Ma—together with voluntary commitments made by Ma himself in Alibaba’s prospectus, help mitigate the potential abuse inherent in disproportional insider control structures. However, the success of such a structure hinges on the reputation and commitments of specific founders and may not function to the benefit of all investors in the long run

Open Sesame: The Myth of Alibaba\u27s Extreme Corporate Governance and Control

In September 2014, Alibaba Group Holding Limited (Alibaba) successfully launched a $25 billion initial public offering (IPO), the largest IPO ever, on New York Stock Exchange. Alibaba’s IPO success witnessed a wave among Chinese Internet companies to raise capital in U.S capital markets. A significant number of these companies have employed a novel, but poorly understood corporate ownership and control mechanism—the variable interest entity (VIE) structure and/or the disproportional control structure. The VIE structure was created in response to the Chinese restriction on foreign investments; however, it carries the risk of being declared illegal under Chinese law. The disproportional control structure, usually in the form of dual-class shares, helps founders or controlling shareholders maintain control post-IPO with less equity contribution. Around 30 percent of U.S.-listed Chinese companies adopted a dual-class share structure or similar mechanism to enhance insider control. This percentage is much higher than that of U.S. public companies, which is only about 6 percent. This Article uses Alibaba as a case study to analyze the legal challenges posed by the VIE and disproportional control structures. Specifically, it dissects the structure of the VIE and sheds important light on inherent legal and governance risks associated with the VIE structure, along with potential policy solutions to protect investors and reduce information asymmetry. Similar to most U.S. high-tech companies that adopt dual-class share structures to maintain control by founders, Alibaba grants a partnership, consisting of its founders and executives, an exclusive right to nominate a majority of its directors. Furthermore, Alibaba implements various anti-takeover measures to strengthen insider control, many of which are considered detrimental to the interests of minority shareholders. Such excessive insider control presents a puzzle as to the success of the world’s largest IPO and casts doubt on the long-debated issue of whether corporate governance truly matters. In this Article, we argue that the idiosyncratic value brought by a charismatic founder-executive—in this case, Alibaba’s Jack Ma—together with voluntary commitments made by Ma himself in Alibaba’s prospectus, help mitigate the potential abuse inherent in disproportional insider control structures. However, the success of such a structure hinges on the reputation and commitments of specific founders and may not function to the benefit of all investors in the long run

Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement

Experimental studies of gypsum plasterboards and composite panels under fire conditions

Gypsum plasterboards are commonly used to protect the light gauge steel-framed walls in buildings from fires. Single or multiple plasterboards can be used for this purpose, whereas recent research has proposed a composite panel with a layer of external insulation between two plasterboards. However, a good understanding of the thermal behaviour of these plasterboard panels under fire conditions is not known. Therefore, 15 small-scale fire tests were conducted on plasterboard panels made of 13 and 16 mm plasterboards and four different types of insulations with varying thickness and density subject to standard fire conditions in AS 1530.4. Fire performance of single and multiple layers of gypsum plasterboards was assessed including the effects of interfaces between adjacent plasterboards. Effects of using external insulations such as glass fibre, rockwool and cellulose fibre were also determined. The thermal performance of composite panels developed from different insulating materials of varying densities and thicknesses was examined and compared. This paper presents the details of the fire tests conducted in this study and their valuable time–temperature data for the tested plasterboard panels. These data can be used for the purpose of developing and validating accurate thermal numerical models of these panels

Assessing and Representing Livability through the Analysis of Residential Preference

Livability reflects the quality of the personenvironment relationship, namely how well the built environment or the available services in a city fulfill the residents needs and expectations. We argue that livability assessment can aid the implementation of certain New Urban Agenda (NUA) goals by providing a flexible way to assess urban environments and their quality. However, a reliable and transferable assessment framework requires the key elements of livability to be defined in such a way that measurable factors adequately represent the personenvironment relationship. As an innovative approach, we determined key livability elements accordingly and asked over 400 residents worldwide to evaluate their urban environments using these parameters. Thereby, we could calibrate the livability assessment workflow by including personal aspects and identifying the most relevant livability factors through an ordinal regression analysis. Next, we performed relational-statistical learning in order to define the individual and combined contribution of these statistically significant factors to the overall livability of a place. We found that urban form and mobility-related factors tend to have the highest influence on residential satisfaction. Finally, we tested the robustness of the assessment by using geospatial analysis to model the livability for the city of Vienna, Austria. We concluded that the workflow allows for a reliable livability assessment and for further utilization in urban planning, improving urban quality by going beyond simple city rankings.(VLID)445081

Recombinant Factor C (rFC) Assay and Gas Chromatography/Mass Spectrometry (GC/MS) Analysis of Endotoxin Variability in Four Agricultural Dusts

Endotoxin exposure is a significant concern in agricultural environments due to relatively high exposure levels. The goals of this study were to determine patterns of 3-hydroxy fatty acid (3-OHFA) distribution in dusts from four types of agricultural environments (dairy, cattle feedlot, grain elevator, and corn farm) and to evaluate correlations between the results of gas chromatography/mass spectrometry (GC/MS) analysis (total endotoxin) and biological recombinant factor C (rFC) assay (free bioactive endotoxin). An existing GC/MS-MS method (for house dust) was modified to reduce sample handling and optimized for small amount ( dairy (0.53) > corn farm (0.33) > grain elevator (0.11). In livestock environments, both odd- and even-numbered carbon chain length 3-OHFAs correlated with rFC assay response. The GC/EI-MS method should be especially useful for identification of specific 3-OHFAs for endotoxins from various agricultural environments and may provide useful information for evaluating the relationship between bacterial exposure and respiratory disease among agricultural workers

Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement

Candidate innate immunity genes and cross shift pulmonary function changes among Western US dairy workers

Objectives: Organic dust inhalation has been associated with adverse respiratory responses among dairy workers. Worker susceptibility may be driven by dust constituents, e.g. Gramnegative bacteria (endotoxin), Gram-positive bacteria (muramic acid); intrinsic factors, e.g. genetic traits, immune system; and extrinsic factors, e.g. smoking and work-related behaviors. The goal of this study was to characterize genetic markers related to lung disease and the endotoxin pathway by testing associations with cross-shift changes in forced expiratory volume in 1 second (FEV1) and candidate innate immunity genes. Methods: This study quantified breathing-zone personal work-shift exposures and pulmonary function among dairy workers during a variety of tasks. Inhalable dust was collected with Button samplers and analyzed for endotoxin (rFCassay). Pulmonary function tests (PFT) before and after the work shift included: forced vital capacity (FVC), FEV1 and the FEV1/FVC ratio. The maximum of three valid maneuvers was used in analyses. Venous blood samples were collected using Qiagen PAXgene tubes. Following DNA isolation (Puregene), candidate gene single nucleotide polymorphisms (SNPs) were analyzed using a custom genotyping array (Illumina GoldenGate assay on VeraCode technology). The assay was designed to include tagging SNPs for candidate genes in Hispanic populations. Genotyping data were cleaned and exported for analysis using the Illumina BeadStudio. Additive genetic modeling approaches were used to describe the distribution of SNPs and their relation to FEV1 cross-shift changes. Based on the frequency of haplotypes, genes were categorized as major (dominant) homozygous (AA), minor homozygous (bb), or heterozygous (Ab). Results: Eighty-eight participants (91% Hispanic, 88% male) had PFT and genetic results. Geometric mean levels of endotoxin were 469 EU/m3. On average, FEV1 was significantly reduced across the work shift among all dairy workers (-1.6%, 95% CI: -2.5, -0.7). No clear patterns were observed in FEV1 reductions by exposure tertiles. Differences in cross-shift reductions of FEV1 were observed across SNPs of the TLR4 (rs11536878, rs10759930 and rs1927911), TLR2 (rs3804099) and LY96 (rs7838114 and rs16938761) genes. Grouping heterozygous and minor homozygous SNPs (i.e., recessive model) may result in observable trends for CD14 SNPs. Conclusions: This is the first study characterizing candidate gene SNPs associated with the endotoxin pathway among Hispanic dairy workers. Associations with FEV1 changes were observed with SNPs for TLR4, TLR2 and LY96. The direction of the effect was not always consistent with previous literature on other populations, including northern Europeans and children. Next steps include multifactor analysis of relationships among genetic SNPs, exposure and cross-shift FEV1

Candidate TLR and NLR innate immunity genes and cross shift pulmonary function changes among Western US dairy workers

Objectives: Organic dust inhalation has been associated with obstructive and restrictive respiratory disease among dairy workers. Workers are exposed to a wide variety of known agents of respiratory disease including microorganisms and their cellular components which can stimulate innate immune responses through pattern recognition receptors (PRRs) such as Toll‐like (TLR) and NOD‐like (NLR) receptors. The goal of this study was to characterize cross‐shift changes in lung function for various single nucleotide polymorphisms (SNPs) in candidate innate immunity genes in the TLR and NLR pathways. Methods: Breathing‐zone personal work‐shift inhalable dust samples were collected with Button samplers and analyzed for endotoxin (rFCassay), 3‐Hydroxy Fatty Acids, Muramic Acid and Ergosterol (GC‐MSMS). Pulmonary function tests (PFT) before and after the work shift included: forced vital capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), Forced Expiratory Flow (FEF) and the FEV1/FVC ratio. Venous blood samples were collected using Qiagen PAXgene tubes. Following DNA isolation (Puregene), candidate gene SNPs were analyzed using a custom genotyping array (Illumina GoldenGate assay on VeraCode technology) to include tagging SNPs for candidate genes in Hispanic populations. Genotyping data were managed using the Illumina BeadStudio. Additive genetic modeling approaches were used to describe the distribution of SNPs and their relation to PFT cross‐shift changes. Results: Eighty‐eight participants (91% Hispanic, 88% male) had PFT and genetic results. Geometric mean levels of endotoxin were 469 EU/m3. On average, FEV1 was significantly reduced across the work shift among all dairy workers (‐1.6%, 95% CI: ‐2.5, ‐0.7). Cross‐shift PFT changes were observed across SNPs of the TLR4, TLR2, LY96, NOD1, NOD2, interferon gamma (IFNG), and tumor necrosis factor alpha (TNFα) genes. No observable trends were identified for cluster of differentiation 14 (CD14) or TLR 9 genes. Conclusions: This is the first study among Hispanic dairy workers which characterizes candidate gene SNPs associated with the innate immune gene pathways. Crossshift PFT changes across SNPs were not always consistent with previous literature on other populations, including northern Europeans and children; however, evidence suggests that certain genetic pathways may modify the respiratory effects of primarily Hispanic workers exposed to high levels of dust and endotoxin