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Alternative treatment for asthma – case study of success of Traditional Chinese Medicine treatment of children from urban areas with different levels of environmental pollution

The present study examined efficacy of traditional Chinese medicine (TCM) treatment in Dutch children with asthma in areas with differing air pollution. The study results indicate that TCM treatment of children living in more polluted urban area is less successful then that of children living in cleaner air area. http://dx.doi.org/10.5402/2012/547534 https://www.linkedin.com/in/helenkopnina/

Aviation safety concerns for the future

Big Image Data and Artificial Intelligence

The field of data science and artificial intelligence (AI) is growing at an unprecedented rate. Manual tasks that for thousands of years could only be performed by humans are increasingly being taken over by intelligent machines. But, more importantly, tasks that could never be performed manually by humans, such as analysing big data, can now be automated while generating valuable knowledge for humankind

Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.

SERA: A study into the different elements of data collection in cars

Data collection is crucial in modern automotive engineering. Yet, the focus on technological development often overlooks legal, ethical, and privacy aspects. This feasibility study aims to bridge this knowledge gap for SMEs and research entities by examining the technical, legal, and ethical aspects impacting vehicle data collection. It provides a guide for organisations within the European context with a global perspective. The crucial importance of each aspect is highlighted within the modern context of connected vehicles, increasing cyber-attacks and the legal demands of handling and processing data. This report provides guidelines for hardware selection, software development, data handling and storage, cybersecurity, and privacy and legal compliance. It discusses the considerations for choosing data collection hardware, outlines the software methodologies for data acquisition and cloud synchronization, and provides an overview of cybersecurity in the context of automotive applications. The report also covers the handling and storage of both low-throughput and high-throughput data, with a focus on data types, retrieval, and storage options. It concludes with a discussion on legal aspects, particularly data ownership, protection under GDPR, and liability implications. The importance of these topics in the modern context of IoT connectivity, edge computing and the application of various AI technologies cannot be understated. The broad applications of such technologies encourages the use of data standards and interoperability for modern connected and autonomous vehicles. This document serves as a guide for SMEs and research entities involved in automotive data collection, providing information so they may better navigate and understand the complexities of modern automotive data collection, ensuring they consider all aspects. It highlights the importance of interdisciplinary expertise for modern automotive data collection and bridges the gaps in knowledge that organisations may have within a number of important topics for data collection. By bridging knowledge gaps, the report empowers organisations to make informed decisions about automotive data collection, ensuring accuracy, efficiency, and legal compliance.

Exploring the Evolution of Autonomous Vehicle Acceptance through Hands-On Demonstrations

This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have not yet had the opportunity to experience autonomous driving technology. It primarily centers on the adaptation of insurance products to align with the imminent implementation of this technology. The second study is directed at individuals who have had the opportunity to test an autonomous driving platform first-hand. Specifically, it examines users’ experiences after conducting test drives on public roads using an autonomous research platform jointly developed by MAPFRE, Universidad Carlos III de Madrid, and Universidad Politécnica de Madrid. The study conducted demonstrates that the user acceptance of autonomous driving technology significantly increases after firsthand experience with a real autonomous car. This finding underscores the importance of bringing autonomous driving technology closer to end-users in order to improve societal perception. Furthermore, the results provide valuable insights for industry stakeholders seeking to navigate the market as autonomous driving technology slowly becomes an integral part of commercial vehicles. The findings reveal that a substantial majority (96% of the surveyed individuals) believe that autonomous vehicles will still require insurance. Additionally, 90% of respondents express the opinion that policies for autonomous vehicles should be as affordable or even cheaper than those for traditional vehicles. This suggests that people may not be fully aware of the significant costs associated with the systems enabling autonomous driving when considering their insurance needs, which puts the spotlight back on the importance of bringing this technology closer to the general public.