Using the Internet to Create Positive Social Changes: Case Studies in China

In recent years, companies have been increasingly under pressure to deliver programs that can create both business value and social value. Building on the positive social change framework developed by Stephan et al., this paper uses two case studies (Gongyi Baobei and Jutudi) of the Alibaba Group, a leading Internet company in China, to investigate how companies can use the Internet to bring about positive social changes (PSC) to target groups. Our focus is placed on the nature of projects, i.e., surface-level and deep-level PSC projects. Our decision to use different case studies from the same company is based on the assumption that the enabling effects of internal organizational practices should be similar. To be more specific, we want to study the link between PSC projects and the company’s existing businesses, the role of the Internet in raising customers’ awareness and participation in the programs, and the change mechanism designed and implemented to bring positive social changes to customers. Data were collected through interviews and literature review. Our research provides empirical evidence to show a deep-level PSC project (i.e., Jutudi) can be very different from a surface-level PSC project (i.e., Gongyi Baobei) in terms of the reliance on existing business operations and the design of change mechanisms. Our research limitations and direction for future research will also be discussed

Poisson quadrature method of moments for 2D kinetic equations with velocity of constant magnitude

This work is concerned with kinetic equations with velocity of constant magnitude. We propose a quadrature method of moments based on the Poisson kernel, called Poisson-EQMOM. The derived moment closure systems are well defined for all physically relevant moments and the resultant approximations of the distribution function converge as the number of moments goes to infinity. The convergence makes our method stand out from most existing moment methods. Moreover, we devise a delicate moment inversion algorithm. As an application, the Vicsek model is studied for overdamped active particles. Then the Poisson-EQMOM is validated with a series of numerical tests including spatially homogeneous, one-dimensional and two-dimensional problems.Comment: 26 pages, 9 figure

Influence of isoniazid on T lymphocytes, cytokines, and macrophages in rats

T lymphocytes, cytokines, and macrophages play important roles in the clearance of Mycobacterium tuberculosis (Mtb) by the immune system. This study aimed to investigate the effects of isoniazid on the functions of both innate and adaptive immune cells. Healthy rats were randomly divided into experimental and control groups. Each group was randomly divided into three subgroups and named according to the duration of drug feeding, 1, 3, and 3 months followed by drug withdrawal for 1 month. The experimental groups were fed with isoniazid (12 mg/mL) and the control groups with normal saline. The percentage of CD4+ and CD8+ T lymphocytes, level of interleukin (IL)-12 and interferon (IFN)-γ, and function of macrophages were determined at these three time points. Isoniazid significantly increased the percentage of CD4+ T lymphocytes and the CD4+/CD8+ T lymphocyte cell ratio (P < 0.05). It transiently (<1 month) enhanced the functions of rat macrophages significantly (P < 0.05). In summary, isoniazid could increase the percentage of CD4+ T lymphocytes, CD4+/CD8+ T lymphocyte cell ratio, and enhance macrophage function in healthy rats

Integrated Systems Biology Analysis of Transcriptomes Reveals Candidate Genes for Acidity Control in Developing Fruits of Sweet Orange (Citrus sinensis L. Osbeck)

Organic acids, such as citrate and malate, are important contributors for the sensory traits of fleshy fruits. Although their biosynthesis has been illustrated, regulatory mechanisms of acid accumulation remain to be dissected. To provide transcriptional architecture and identify candidate genes for citrate accumulation in fruits, we have selected for transcriptome analysis four varieties of sweet orange (Citrus sinensis L. Osbeck) with varying fruit acidity, Succari (acidless), Bingtang (low acid), and Newhall and Xinhui (normal acid). Fruits of these varieties at 45 days post anthesis (DPA), which corresponds to Stage I (cell division), had similar acidity, but they displayed differential acid accumulation at 142 DPA (Stage II, cell expansion). Transcriptomes of fruits at 45 and 142 DPA were profiled using RNA sequencing and analyzed with three different algorithms (Pearson correlation, gene coexpression network and surrogate variable analysis). Our network analysis shows that the acid-correlated genes belong to three distinct network modules. Several of these candidate fruit acidity genes encode regulatory proteins involved in transport (such as AHA10), degradation (such as APD2) and transcription (such as AIL6) and act as hubs in the citrate accumulation gene networks. Taken together, our integrated systems biology analysis has provided new insights into the fruit citrate accumulation gene network and led to the identification of candidate genes likely associated with the fruit acidity control

Bandgap engineering of zigzag graphene nanoribbons by manipulating edge states via defective boundaries

One of severe limits of graphene nanoribbons (GNRs) in future applications is that zigzag GNRs (ZGNRs) are gapless, so cannot be used in field effect transistors (FETs). In this paper, using tight-binding approach and first principles method, we derived and proved a general edge (boundary) condition for the opening of a significant bandgap in ZGNRs with defective edge structures. The proposed semiconducting GNRs have some interesting properties including the one that they can be embedded and integrated in a large piece of graphene without the need of completely cutting them out. We also demonstrated a new type of high-performance all-ZGNR FET

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate time-series data which they typically produce can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs. Deep learning models, such as Long Short-term memory (LSTM) networks can be used to automate these tasks and to provide clear explanations of diverse anomalies detected in real-time multivariate data-streams. In this paper we present RESAM, a requirements process that integrates knowledge from domain experts, discussion forums, and formal product documentation, to discover and specify requirements and design definitions in the form of time-series attributes that contribute to the construction of effective deep learning anomaly detectors. We present a case-study based on a flight control system for small Uncrewed Aerial Systems and demonstrate that its use guides the construction of effective anomaly detection models whilst also providing underlying support for explainability. RESAM is relevant to domains in which open or closed online forums provide discussion support for log analysis

View-Disentangled Transformer for Brain Lesion Detection

Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to balance between the granularity of 3D context aggregation and the computational complexity. In this paper, we propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view, which approximately achieves the 3D correlation computing in an efficient way. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions. The experimental results show that our proposed view-disentangled transformer performs well for brain lesion detection on a challenging brain MRI dataset.Comment: International Symposium on Biomedical Imaging (ISBI) 2022, code: https://github.com/lhaof/ISBI-VDForme

SAR Target Detection Method Based On Empirical Mode Decomposition

Abstract. Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective