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Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users’ learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users’ learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD).

In this study, a topological matching algorithm is introduced for semantic vertex detection to automate dimension generation in a building information modeling (BIM) environment based on the Industry Foundation Classes (IFC) standard. Conventional IFC-based quantity take-off (QTO) methods provide only standardized attributes, such as height, length, width, and area; therefore, user-defined custom dimensions—such as net opening sizes or parameter lengths—must be calculated manually. This study proposes a method for fully automating the dimensions required by users by automatically tagging and visualizing semantic vertices for geometrically identical IFC objects. These semantic vertices correspond to representative topological feature points (e.g., left–bottom–origin, left–top–front, left–bottom–back, and right–bottom–front). Based on these defined semantic vertices, the method automatically establishes vertex correspondence among objects to generate dimensions. The proposed workflow comprises four main stages: (1) geometry normalization of IFC objects, (2) semantic vertex definition, (3) automatic detection of semantic vertices, and (4) dimension generation and visualization. The experimental results demonstrate that the proposed approach successfully enables the computation of dimensions for geometrically identical objects, thereby significantly improving the efficiency of QTO processes.

Cyclic induction is a powerful reasoning technique that consists in blocking the proof development of certain subgoals already encountered during the proof process. In the setting of first-order logic with inductive definitions and equality ([FOL.sub.ID]), cyclic proofs can be built automatically by the Cyclist prover, but their implementations are error-prone and the human validation may be tedious. On the other hand, cyclic induction is not yet integrated into certifying proof environments that support first-order logic and inductive definitions, such as Isabelle and Coq. We propose a solution to check, using Coq, the cyclic proofs produced by E-Cyclist, an extension of Cyclist that implements a more efficient soundness validation method, by using the general Noetherian induction principle integrated into Coq. Our work is based on a methodology for certifying first-order formula-based Noetherian induction proofs, such as those based on implicit induction. The advantages of our approach are threefold: -1) The certification of cyclic [FOL.sub.ID] proofs is mechanical. Coq can validate every single step from the E-Cyclist proofs, as well as the induction arguments; also, it helps to identify errors in a very precise way.–II) There is a great potential for automation. The methodology has already been used to automatically convert to Coq scripts implicit induction proofs.–III) Cyclic induction can be directly performed in Coq. Coq functions are provided to manage the induction part. Keywords Automated reasoning * Cyclic induction * First-order logic with inductive definitions * Proof certification * Coq * E-Cyclist

Although Adam Smith (1776) and Alfred Marshall (1920) emphasized the gains from specialization that arise from the division of labor, their focus was on the manufacturing sector. Both saw farming as being on too small a scale and bereft of economics of scale, with a market that was too small and local, with too sharp a seasonality, and too quick a succession of tasks to support either the development of a division of labor over the tasks of a cropping season or of mechanization. Smith and Marshall's vision of farming-and its implications for division of labor and mechanization-was manifest again in the 1950s to the present in Asia. Ruttan (2001) puts forward nearly the same ideas and terms as Smith and Marshall, but for contemporary small rice farms in Asia. He emphasizes that the use of machines for the series of short tasks performed on tiny farms would imply costly investment in specialized machines that small farmers would be loath to make. And even if these farmers mechanized, Ruttan posited that it would not induce a segmented and specialized farm labor market as again, the critical mass of demand for each segment would not be present. Otsuka (2012) goes further along these lines to note that only on larger farms would the mechanization investment, at least for large machines, pay off to farmers. Thus the path to efficient mechanization must have as a first step a sharp increase in Asian farm size from the current 1-3 ha average to considerably more. In contrast to this bleak prognosis for the Asian small farm sector to develop a division of labor and to mechanize, here we show that China with farm sizes averaging below one ha, a high degree of land fragmentation, and a decline in labor supply in the countryside since the mid-2000s (Cai and Wang 2008; Zhang, Yang, and Wang 2011) has seen steadily increasing farm output and yields over the past two decades. We argue that the contradiction can, in part, be explained by increasing mechanization. Especially since 2004, there has been rapid farm mechanization in the areas of ownership and rental, plus rapid development of farm mechanization services that combine the provision of specialized labor and the services of large harvesting machines. We focus on the latter services, in particular, their manifestation in the emergence of a cluster of farmer companies that sell harvesting services (as harvesting is the most `heavy' of the tasks) across the provinces of China and throughout the year. By taking advantage of a national services market that includes labor and machinery, these farmer companies have overcome the constraints logically identified by the economists cited above. Reprinted by permission of the American Agricultural Economics Association

The future of work has become a prominent topic for research and policy debate. However, the debate has focused entirely on paid work, even though people in industrialized countries on average spend comparable amounts of time on unpaid work. The objectives of this study are therefore (1) to expand the future of work debate to unpaid domestic work and (2) to critique the main methodology used in previous studies. To these ends, we conducted a forecasting exercise in which 65 AI experts from the UK and Japan estimated how automatable are 17 housework and care work tasks. Unlike previous studies, we applied a sociological approach that considers how experts' diverse backgrounds might shape their estimates. On average our experts predicted that 39 percent of the time spent on a domestic task will be automatable within ten years. Japanese male experts were notably pessimistic about the potentials of domestic automation, a result we interpret through gender disparities in the Japanese household. Our contributions are providing the first quantitative estimates concerning the future of unpaid work and demonstrating how such predictions are socially contingent, with implications to forecasting methodology.