STRATEGIES OF INCUMBENT FIRMS IN DE NOVO ECOSYSTEMS: EXPLORING THE ELECTRO MOBILITY SECTOR

Current research has highlighted multiple facets of established ecosystem formations. We intend to add to the body of ecosystem literature by raising the question of how incumbent firms navigate in emerging de novo ecosystems. Using a grounded theory approach, we analyze incumbent firms in the nascent German electro mobility industry. The electro mobility sector is a particularly relevant field since different incumbents with different capabilities need to collaborate closely and build integrated solutions to realize electronic enabled mobility. Furthermore, several actors, previously operating in separated industries such as automotive, energy and infrastructure, converge in the electro mobility ecosystem and cooperatively pursue new strategies for value creation and capture. Our aim is to understand how the incumbents’ capabilities influence the navigation in the de novo electro mobility ecosystem

Determinants Influencing Knowledge Sharing Behavior A Case Study Among Banking Members in Vietnam

Due to the increasing competition of financial sector, banks are striving for their tangible and intangible benefits. Banks can get the sustainable competitive advantage in the market by enhancing their performance through knowledge sharing behavior. The study aims to evaluate different factors level influencing knowledge sharing behavior among banking members in Vietnam. The model and hypotheses are developed based on quantitative research. Data was collected through self-administered close-ended questionnaire from a sample of 432 banking members. For analysis purpose, SPSS 22 were used to confirm the validity concerns and determine the proposed relationship among selected variables. The output reveals that useage of social media are the strongest influencers of students’ satisfaction, followed by knowledge self-efficacy, management support, knowledge management, social trust and Personal use of IT infrastructure, while Reward System, Bank are found to have no impact on knowledge sharing behavior. This study provides a ‘snapshot’ to the management about the provision of current situation and proposes suggestions to improve the sharing culture within minimum resources to get the sustainable competitive advantage in the market. Keywords: Evaluation, knowledge sharing behavior, knowledge management, banking sector, banking members. DOI: 10.7176/JESD/14-8-02 Publication date: April 30th 202

Electrically stable carbon nanotube yarn under tensile strain

We report a highly stable electrical conductance of a compact and well-oriented carbon nanotube yarn under tensile strain. The gauge factor of the yarn was found to be extremely small of approximately 0.15 thanks to the improvements in the dry spinning process, includingmultiweb spinning and heat treatment. The threshold strain εs, below which the yarn retains its electrical conductance stability, has also been determined to be approximately 15 × 103 ppm. Owing to its highly stable resistance under mechanical strain, the yarn has a good potential as a wiring material for niche applications,where lightweight and resistance stability are required

Deep heterogeneous ensemble.

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations such as needing a large number of labeled data for training and having a large number of parameters. Ensemble learning, meanwhile, provides a learning model by combining many different classifiers such that an ensemble of classifiers is better than using single classifier. In this study, we propose a deep ensemble framework called Deep Heterogeneous Ensemble (DHE) for supervised learning tasks. In each layer of our algorithm, the input data is passed through a feature selection method to remove irrelevant features and prevent overfitting. The cross-validation with K learning algorithms is applied to the selected data, in order to obtain the meta-data and the K base classifiers for the next layer. In this way, one layer will output the meta-data as the input data for the next layer, the base classifiers, and the indices of the selected meta-data. A combining algorithm is then applied on the meta-data of the last layer to obtain the final class prediction. Experiments on 30 datasets confirm that the proposed DHE is better than a number of well-known benchmark algorithms

Multichannel Photon Counting Lidar Measurements Using USB-based Digital Storage Oscilloscope

We present a simple method of making multichannel photon counting measurements of weak lidar signal from large ranges, using commonly available USB-based digital storage oscilloscopes. The single photon pulses from compact photomultiplier tubes are amplified and stretched so that the pulses are large and broad enough to be sampled efficiently by the USB oscilloscopes. A software interface written in Labview is then used to count the number of photon pulses in each of the prescribed time bins to form the histogram of LIDAR signal. This method presents a flexible alternative to the modular multichannel scalers and facilitate the development of sensitive lidar systems

Study protocol: Early neurological deterioration in patients with minor stroke, frequency, predictors, and outcomes in Vietnam single-centre study

Early neurological deterioration (END) is progressive neurological deterioration with an increase in NIHSS score of 2 points or more in the first 72 hours from the onset of acute ischemic stroke. END increases the risk of poor clinical outcomes at day 90 of ischemic stroke. We will study the frequency, predictors, and outcomes of patients with END in a case-control study at a comprehensive stroke centre in Vietnam. of the design is a descriptive observational study, longitudinal follow-up of patients with minor stroke hospitalized at the Stroke Center of Bach Mai Hospital from December 1, 2023, to December 1, 2024. Minor stroke patients characterized by NIHSS score ≤ 5 hospitalized within 24 hours of symptom onset will be recruited. The estimated END rate is about 30%, relative accuracy ε = 0.11, 95% reliability, expected 5% of patients lost data or follow-up, and an estimated sample size of 779 patients. This study will help determine the END rate in patients with minor stroke and related factors, thereby building a prognostic model for END. Our study determined the END rate in patients with minor stroke in Vietnam and also proposed risk factors for minor stroke management and treatment

Carbon nanotube four-terminal devices for pressure sensing applications

Carbon nanotubes (CNTs) are of high interest for sensing applications, owing to their superior mechanical strength, high Young’s modulus and low density. In this work, we report on a facile approach for the fabrication of carbon nanotube devices using a four terminal configuration. Oriented carbon nanotube films were pulled out from a CNT forest wafer and then twisted into a yarn. Both the CNT film and yarn were arranged on elastomer membranes/diaphragms which were ar-ranged on a laser cut acrylic frame to form pressure sensors. The sensors were calibrated using a precisely controlled pressure system, showing a large change of the output voltage of approximately 50 mV at a constant supply current of 100µA and under a low applied pressure of 15 mbar. The results indicate the high potential of using CNT films and yarns for pressure sensing applications

Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage have not been well established among patients in Vietnam. 2885 consecutive diagnosed patients with ischemic stroke and intracerebral hemorrhage at ten stroke centres across Vietnam were involved in this prospective study. Posthoc analyses were performed in 2209 subjects (age was 65.4 ± 13.7 years, with 61.4% being male) to explore the clinical characteristics and prognostic factors associated with 90-day mortality following treatment. An explainable machine learning model using extreme gradient boosting and SHapley Additive exPlanations revealed the correlation between original clinical research and advanced machine learning methods in stroke care. In the 90 days following treatment, the mortality rate for ischemic stroke was 8.2%, while for intracerebral hemorrhage, it was higher at 20.5%. Atrial fibrillation was an elevated risk of 90-day mortality in the ischemic stroke patient (OR 3.09; 95% CI 1.90–5.02, p 0.05). The baseline NIHSS score was a significant predictor of 90-day mortality in both patient groups. The machine learning model can predict a 0.91 accuracy prediction of death rate after 90 days. Age and NIHSS score were in the top high risks with other features, such as consciousness, heart rate, and white blood cells. Stroke severity, as measured by the NIHSS, was identified as a predictor of mortality at discharge and the 90-day mark in both patient groups

A Wearable, Bending-Insensitive Respiration Sensor Using Highly Oriented Carbon Nanotube Film

Recently, wearable electronics for health monitoring have been demonstrated with considerable benefits for early-stage disease detection. This article reports a flexible, bending-insensitive, bio-compatible and lightweight respiration sensor. The sensor consists of highly oriented carbon nanotube (HO-CNT) films embedded between electro-spun polyacrylonitrile (PAN) layers. By aligning carbon nanotubes between the PAN layers, the sensor exhibits a high sensitivity towards airflow (340 mV/(m/s)) and excellent flexibility and robustness. In addition, the HO-CNT sensor is insensitive to mechanical bending, making it suitable for wearable applications. We successfully demonstrated the attachment of the sensor to the human philtrum for real-time monitoring of the respiration quality. These results indicate the potential of HO-CNT flow sensor for ubiquitous personal health care applications