Formation Mechanism of Knowledge Stickiness in the Collaborative Innovation of Industry-University-Research

In the context of deepening the cooperation of Industry-University-Research (IUR), enterprises must gain competitive advantage by transferring external knowledge to the enterprise for knowledge appreciation. Based on the knowledge stickiness encountered in the process of knowledge transfer in the collaborative innovation of IUR, the formation process and causes of knowledge stickiness are analyzed. In this study, the knowledge flow model based on heat conduction theory was proposed. The dynamic simulation was carried out using MATLAB software. Results show that the process of knowledge transfer between IUR is the process of continuously realizing knowledge increment and knowledge creation, and knowledge stickiness has a direct impact on the efficiency of knowledge transfer. Strengthening the cognition between IUR, increasing the number of activities between IUR, and creating a collaborative innovation atmosphere between IUR will reduce knowledge stickiness and improve the efficiency of knowledge transfer

What Is a Better Marketing Strategy for Live Streaming Broadcasters? A Topic Model of Social Interactions

Live streaming has spawned a new business model called live-streaming commerce (LSC). Interactive LSC features affect viewer purchasing behavior. This study empirically examines two types of social interactions in danmaku: transaction-oriented and relationship-oriented. Viewers in the first category focus on products and transactions and tend to talk non-emotionally. While relationship-oriented viewers might treat broadcasters as friends, using emotional language in their interactions. Our econometric model shows a curvilinear association of relationship-oriented social interaction and viewer purchase behaviors in LSC, but social interactions have varying effects on viewer purchase behaviors.We discuss implications of heterogeneous social-interaction strategies across different broadcasters

Risk Factors and Prevalence of Helicobacter pylori

Aim. The aim of this study was to investigate the prevalence and risk factors of H. pylori infection in areas with high prevalence of gastric cancer in Jiangsu Province, China. Methods. A prospective epidemiologic survey of H. pylori infection was accomplished in a natural population of 5417 individuals in Yangzhong city. Questionnaires and 13C-urea breath test for H. pylori infection were performed. Results. Among 5417 subjects who completed questionnaires and 13C-urea breath test, 3435 (63.41%) were H. pylori positive. The prevalence reached a peak at the age of 30–39 years (90.82%). There was significant difference between sexes and women had a higher infection rate than men. The prevalence of H. pylori infection was also associated with eating kipper food and fried food. No association between H. pylori prevalence and smoking or drinking was found. Compared to healthy individuals, people with dyspeptic diseases (peptic ulcer, gastroenteritis) presented a high prevalence of H. pylori infection. Using multivariate logistic regression analysis, age and history of peptic ulcer and gastroenteritis were the independent predictors for H. pylori infection. Conclusions. Yangzhong city had a high prevalence of H. pylori infection and was related to several risk factors. The underlying mechanisms are needed to be further investigated

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT

IntroductionThree-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.Materials and MethodsA total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.ResultsThe average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8.ConclusionThe proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs

A Resource-Based View on Sustaining Competitive Advantage: A Case Discussion

This paper discusses Apple Computer, Inc.'s (Apple) distinctive competencies in line with its competitive environment to identify its competitive advantage and its sustainability. We find that Apple's distinctive competencies lie with its innovative capabilities, proprietary ecosystems, and marketing. Given the highly competitive nature of Apple's competitive environment, we find it hard to sustain a competitive advantage with constant updates of internal resources. In light of these findings, we recommend Apple adopts a learning (knowledge-based) organization approach and positions itself for speedy innovation in order to consistently "shift the rules of the game”. In conclusion, the sustainability of its competitive advantage and superior performance would eventually rely on Apple's continuous endeavors to shape, mold or influence the future market and technological evolutions. This means it should focus its strategy and management on continuous improvements and innovations, i.e., change

Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo-NER tasks for meta-training by computing sentence similarities. To further improve the model's generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over five target languages. The results show that our approach significantly outperforms existing state-of-the-art methods across the board.Comment: This paper is accepted by AAAI2020. Code is available at https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cros

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure

A Case Study of Extraterritorial Application of the Japanese Antimonopoly Act

This paper discusses extraterritorial applications of the Japanese Antimonopoly Act through a well debated case over CRT TVs in 2016 in Japan involving multiple companies in several countries. The case is theoretically and practically significant because it was the first case in the world in which the ‘demand' side was united as a group to be considered under similar situations. By doing so, the scope of extraterritorial application of antitrust laws in countries involved is expected to be expanded. In other words, Japanese companies with bases and subsidiaries around the world will be able to file lawsuits in Japan regardless of which country is affected, and seek trials based on Japanese antitrust laws. Finally, we argue that, under the Japanese Antimonopoly Act, the effect doctrine is not yet the basis for officially judging related cases

Enhancing Working Memory Based on Mismatch Negativity Neurofeedback in Subjective Cognitive Decline Patients: A Preliminary Study

Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer's disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline