Promoting consumers’ online brand attention: The study of spatiotemporal analysis in regional apples

With the application and promotion of agricultural products e-commerce, it is of great significance to increase the popularity of brand recognition to promote the market value of agricultural products. Online attention is an important form of regional brand awareness of agricultural products on the Internet. Taking the apple industry as an example, this paper uses 6 years of data from the Baidu search index to analyze the spatiotemporal characteristics of apple brands in China. The study has demonstrated a way to analyze online brand attention and its relationship with regional brands. Further studies are to be made in more detailed attributes associated with the forming of attention to enhance the online brand attention and promote agricultural products

Best Practices in Managing Social Media for Business

The mixed results of social media impacting on businesses have motivated this research to understand the best practices affecting the brand equity of a company utilizing the social media in business. Literature review of relevant theories is conducted. The industry publications of companies doing well in social media are analyzed to distill the best practices implemented in their social media strategies. Multiple case studies are proposed to determine how these best practices explain the desired business outcome in brand equity. The social media strategy concepts, media richness theory and Hagel III and Armstrong’s framework of virtual member development are suggested in guiding our data collection, analysis and interpretation. This research in progress is expected to contribute to the existing knowledge by providing a prescriptive framework of best practices in utilizing Facebook social network, and integrating and extending existing theories to explain the use of social media for developing brand equity

Grey Situation Decision-making Algorithm to Optimize Silicon Wafer Slicing

The slicing of Silicon wafer is a complex manufacturing process in producing the raw materials for electronic chips and requires the efforts to effectively monitor the stability in production line and ensure the quality for the products composed of different shapes and materials. Human decision failure and other analytical errors are the most common source of management problems in such manufacturing stage. This paper presents a case regarding the silicon wafer manufacturing to examine the response to quality errors. The study has adopted the approach of grey situation decision-making algorithm for problem detection that suggests a technique to attain the quality control and reduce potential costs in production

Developing a Big Data-Enabled Transformation Model in Healthcare: A Practice Based View

Healthcare organizations are looking for opportunities to create new business model and value that can be implemented through information technology (IT) enabled transformation. Big data, an overwhelming phenomenon which has been addressed through various new and old data management technologies, hold the key to healthcare transformation. To address this, we developed a big-data-enabled transformation model based on practice-based view showing that the relationships among big data capability, big-data-enabled transformation practice, benefit dimensions, and firm performance. We tested this model by analyzing secondary data regarding big data in the healthcare context. Our results not only conceptually defined four big data capabilities but also found two significant path-to-performance chains. The contributions of this study are twofold. For management research, we establish a big-data-enabled transformation model to explain how big data leads to firm performance. For practitioners, we identify potential patterns that will help understanding big data\u27s potentials and capabilities

An Explanatory Case Study on Passenger Service Systems Adoption: A Taiwanese Air Carrier

This study explains the adoption of Passenger Service Systems (PSS) in airlines through the lens of Inter-organizational Systems adoption and home-region orientation. It examined 20 causal factors influencing PSS adoption and tested an original research model in the technology–organization–environment context, by applying transactional cost theory and neo-institutional theory. Prior to a cross-case study across multiple airlines, the explanatory single-case research analyzed interview data from four informants with a Taiwanese carrier and multiple industry reports; the key findings highlight: (i) interoperable functionality and industry standards as external technological drivers strongly influenced full conversion to the PSS platform; (ii) within the organizational context, commitment to global network, organizational transformation and top management support were identified as salient internal factors in decision-making and resource allocation, while normative pressures from an alliance body exerted a stronger influence over the airline than any other isomorphic forces; and (iii) in the context of environment, full conversion was also facilitated by PSS vendors and powered by the need for mutual worldwide learning to effectively operate foreign markets from home region. This in-depth case study provides insights that PSS in a common-use environment can bring business value to PSS user airlines in accessing a broader global market, offering greater competitive services and expanding distribution channels. To increase causality and generalizability, future work involving an embedded multi-case study with three East Asian carriers will be undertaken. Available at: https://aisel.aisnet.org/pajais/vol11/iss1/2

Oral Administration of MBG to Modulate Immune Responses and Suppress OVA-Sensitized Allergy in a Murine Model

Recently studies performed on mushroom isolated polysaccharides demonstrated that β-(1,3)-glucan may affect the balance of Th1/Th2 cell response. Using ovalbumin (OVA) as a hypersensitivity inducer, we evaluated the ability of mushroom beta-glucan (MBG) in modulating Th1/Th2 cell responses in B6 mice. As compared to the control group, administration of MBG resulted in an increase of phagocytic activities, Th1 cytokine productions, immunoglobulins including IgG2A and IgA, and a significant expression of the splenic surface markers including CD3, CD4, CD8, and F4/80. In contrast, administration of MBG has significantly suppressed IgE and IgG1 levels and Th2 cytokines including IL-4, IL-5, and IL-6. Histopathological observation of MBG-treated followed by OVA-treated mice showed less filtration of eosinophil in pulmonary tissue sections. Our data suggested that administration of MBG treatments alters the natural course of the IgE-mediated hypersensitivities. In this investigation, we realize the mushroom beta glucan alter the Th2 response toward the Th1 in the allergic, resulting in a reduction in IgE productions which played a substantive role in reducing the severity of IgE-mediated hypersensitivity

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.Comment: 14 pages, 7 figures, 13 tables; v2: minor corrections, reference baselines and bibliography updated; v3: corrections based on reviewer feedback, bibliography update

Altered mGluR5-Homer scaffolds and corticostriatal connectivity in a Shank3 complete knockout model of autism

Human neuroimaging studies suggest that aberrant neural connectivity underlies behavioural deficits in autism spectrum disorders (ASDs), but the molecular and neural circuit mechanisms underlying ASDs remain elusive. Here, we describe a complete knockout mouse model of the autism-associated Shank3 gene, with a deletion of exons 4–22 (Δe4–22). Both mGluR5-Homer scaffolds and mGluR5-mediated signalling are selectively altered in striatal neurons. These changes are associated with perturbed function at striatal synapses, abnormal brain morphology, aberrant structural connectivity and ASD-like behaviour. In vivo recording reveals that the cortico-striatal-thalamic circuit is tonically hyperactive in mutants, but becomes hypoactive during social behaviour. Manipulation of mGluR5 activity attenuates excessive grooming and instrumental learning differentially, and rescues impaired striatal synaptic plasticity in Δe4–22−/− mice. These findings show that deficiency of Shank3 can impair mGluR5-Homer scaffolding, resulting in cortico-striatal circuit abnormalities that underlie deficits in learning and ASD-like behaviours. These data suggest causal links between genetic, molecular, and circuit mechanisms underlying the pathophysiology of ASDs

A novel subset of CD4+ TH2 memory/effector cells that produce inflammatory IL-17 cytokine and promote the exacerbation of chronic allergic asthma

Memory CD4+ T cells that produce both Th2 and Th17 cytokines are increased in the blood of patients with atopic asthma and in the lungs of asthmatic mice, where they contribute to inflammation

Broad targeting of resistance to apoptosis in cancer

Apoptosis or programmed cell death is natural way of removing aged cells from the body. Most of the anti-cancer therapies trigger apoptosis induction and related cell death networks to eliminate malignant cells. However, in cancer, de-regulated apoptotic signaling, particularly the activation of an anti-apoptotic systems, allows cancer cells to escape this program leading to uncontrolled proliferation resulting in tumor survival, therapeutic resistance and recurrence of cancer. This resistance is a complicated phenomenon that emanates from the interactions of various molecules and signaling pathways. In this comprehensive review we discuss the various factors contributing to apoptosis resistance in cancers. The key resistance targets that are discussed include (1) Bcl-2 and Mcl-1 proteins; (2) autophagy processes; (3) necrosis and necroptosis; (4) heat shock protein signaling; (5) the proteasome pathway; (6) epigenetic mechanisms; and (7) aberrant nuclear export signaling. The shortcomings of current therapeutic modalities are highlighted and a broad spectrum strategy using approaches including (a) gossypol; (b) epigallocatechin-3-gallate; (c) UMI-77 (d) triptolide and (e) selinexor that can be used to overcome cell death resistance is presented. This review provides a roadmap for the design of successful anti-cancer strategies that overcome resistance to apoptosis for better therapeutic outcome in patients with cancer