Liability from the use of medical artificial intelligence: a comparative study of English and Taiwanese tort laws

Background: Modern artificial intelligence applications are appearing in healthcare and medical practices. Artificial intelligence is used both in medical research and on patients via medical devices. The aim of this paper is to examine and compare English and Taiwanese tort laws in relation to medical artificial intelligence. Methods: The methodologies employed are legal doctrinal analysis and comparative law analysis. Results: The investigation finds that English tort law treats wrong diagnostic or wrong advice as negligent misstatement, and mishaps due to devices as a physical tort under the negligence rule. Negligent misstatement may occur in diagnosis or advisory systems, while a negligent act may occur in products used in the treatment of the patient. Product liability under English common law applies the same rule as negligence. In Taiwan, the general principles of tort law in Taiwan’s Civil Code for misstatement and negligent action apply, whereas the Consumer Protection Act provides for additional rules on product liability of traders. Conclusions: Safety regulations may be a suitable alternative to tort liability as a means to ensure the safety of medical artificial intelligence systems.

Development of Tailored Early Warning Agromet Advisories for Farmers in Zambia, Indonesia, and South Africa

Farmers do not often use climate and weather information on a regular basis, as the specific influence of weather parameters on farm-level decision making is not well-known. Agromet advisories are needed for local farming systems. Effective agrometeorological advisory systems, need tailored agricultural weather forecasts, and two-way communication. Transdisciplinary teams together with farmers can co-develop early warning Agromet advisory systems to address farmers' needs. Three examples of Agromet advisories are discussed- CAPES in Zambia, Science Field Shops in Indonesia, and the AgriCloud mobile App in South Africa. Community Agrometeorological Participatory Extension Service (CAPES) began in Monze, Zambia to communicate seasonal climate forecasts to farmers through researchers and extension interactions. Participatory groups collected spatial and temporal data about local farming systems to highlight opportunities. Communication methods used were local radio, farmers' days, trials, with farm visits. CAPES resulted in lifelong learning about climate and co-development of tailored Agromet advisories to improve climate resilience. In Science Field Shops (SFS) groups of Indonesian farmers meet experts regularly to exchange information about climate and farming activities. Farmers measure rainfall and observe their agroecological systems each day. At monthly meetings, the seasonal forecasts are discussed using dialogue-discussion methods. Agrometeorological learning is trans-disciplinary through interaction between anthropologists, agrometeorologist, and extension personnel. SFS includes eight climate services that empower farmers to address challenges and sustain their productivity. AgriCloud is an online weather-based agricultural advisory system enriching weather forecasts with agricultural information and local knowledge. Real-time overviews and warnings are tailored to farmer's needs. AgriCloud provides farmers, extension staff, and advisors daily updated weather-related farm-specific advice in 11 South African official languages. AgriCloud is available as an android mobile App, or API to use via a platform. These examples illustrate the use of weather forecasting together with tailored forecasts and communication systems to deliver Agromet advisories, showing different aspects of the incorporation of local knowledge in co-developing advisories for the farmers. In the future, various combinations can be used around the world when co-developing with the farmers.

Semantization of Agricultural Advisory Services for Validation of Outcomes of Non-Formal and Informal Learning. II

Introduction. A characteristic feature of the modern agricultural sector is the use of advisory that provides the implementation of modern technologies into the production process. We analyze the specifics of existing advisory systems, their goals, main activities, and problems. This analysis causes expediency of documentation and validation of informal and non-formal outcomes of learning typical for agriculture and their processing by knowledge-oriented services based on modern Semantic Web technologies and resources. Purpose. This work is intended for the integration of labour and education markets and is aimed at semantization of agricultural advisory services for the expansion of advisory system functionality with the help of validation of outcomes of non-formal and informal learning of potential employees. Processing of semantics is based on the use of knowledge about agriculture subjects from internal and external ontologies by advisory intelligent applications. Such processing requires the creation of a relevant formal model that describes all main objects and subjects of agro-advisory activities, development of formalization methods for model components and defining of matching criteria based on internal and external ontologies. Software realization of proposed solution by AdvisOnt system is aimed at demonstration of its efficiency for practical agro-advisory tasks and advantages of semantic approach. Methods. In this work, we use methods of mathematical modeling, elements of ontological analysis and logical inference. Results. We propose an advisory system AdvisOnt that analyses the outcomes of non-formal and informal learning and ensures their validation for more efficient matching of information about potential employees, employers and agricultural educational resources. AdvisOnt is based on ontological representation of this knowledge formalized by competencies, vacancies, training courses, user profiles, etc. The system is aimed to generate recommendations for employment or further learning of necessary competencies by matching these objects. External knowledge bases are used for semantic formalization of vacancies and resumes for their more pertinent matching with the help of agricultural domain knowledge and competence classifications. AdvisOnt users receive recommendations on employment and about training courses that provide advisable competencies. Conclusion. Sustainable development of agro-industrial production needs rapid dissemination of agricultural knowledge and information, mobility and continuous training of agricultural professionals provided by advisory systems. We suggest how using ontological knowledge for advisory services allows to expand the possibilities of counseling. In the future, we plan to consider the ways of integration of AdvisOnt system that validates outcomes of informal and non-formal learning with other counseling and recommendation systems in the field of education and employment taking into account the specifics of the agricultural sector through external domain and organizational ontologies. The openness of the proposed solution is based on Semantic Web technologies and service-oriented programming.

Higher automation - methods to increase energy efficiency in railway operation

Automation is already present in many areas of the railway sector (e.g. computer-aided dispatching or electronic interlockings). In order to achieve climate goals and offer an attractive transport service, it is essential to advance automation and higher grades of automation (GoA). The four levels of automation range from supporting systems (GoA1) to automotive trains (GoA4). This paper summarises a study which outlines the impacts, requirements and potentials of higher GoA within different segments: passenger transport, freight and mixed traffic on mainlines and branch lines. The findings show that energy-efficiency and capacity can already be increased with the first two GoA for both, passenger and mixed traffic. Enhancements have an influence on costs, not to mention the customer satisfaction. The potential in freight transport, e.g. in shunting, can be exploited with intelligent freight trains (GoA4). This leads to improved safety and reduced costs. Within this study a tool to calculate energy consumption is established. It enables the depiction of various scenarios for different trains and driving behaviours. The simulation tool is validated by real measured data. The outcome of the calculation tool underpins the benefits of driver advisory systems (DAS) and automatic train operation (ATO). It can be stated that higher automation, especially on a dispositive level is essential if energy and capacity improvement are to be achieved, regardless of the type of network (electrified or non-electrified). However, operational optimisation has its limits. For non-electrified lines, alternative drives offer the opportunity to further mitigate environmental impacts.

Safety effects of work zone advisory systems under the intelligent connected vehicle environment: a microsimulation approach

Purpose This study aims to investigate the safety effects of work zone advisory systems. The traditional system includes a dynamic message sign (DMS), whereas the advanced system includes an in-vehicle work zone warning application under the connected vehicle (CV) environment. Design/methodology/approach A comparative analysis was conducted based on the microsimulation experiments. Findings The results indicate that the CV-based warning system outperforms the DMS. From this study, the optimal distances of placing a DMS varies according to different traffic conditions. Nevertheless, negative influence of excessive distance DMS placed from the work zone would be more obvious when there is heavier traffic volume. Thus, it is recommended that the optimal distance DMS placed from the work zone should be shortened if there is a traffic congestion. It was also revealed that higher market penetration rate of CVs will lead to safer network under good traffic conditions. Research limitations/implications Because this study used only microsimulation, the results do not reflect the real-world drivers’ reactions to DMS and CV warning messages. A series of driving simulator experiments need to be conducted to capture the real driving behaviors so as to investigate the unresolved-related issues. Human machine interface needs be used to simulate the process of in-vehicle warning information delivery. The validation of the simulation model was not conducted because of the data limitation. Practical implications It suggests for the optimal DMS placement for improving the overall efficiency and safety under the CV environment. Originality/value A traffic network evaluation method considering both efficiency and safety is proposed by applying traffic simulation.