By the end of the nineteenth century, railway expansion had led to the formation of a technocratic bureaucracy in Chile and other countries in Latin America. Central to this formation were the engineers who oversaw and regulated both public and private railways. Recently, historians have begun to re-examine engineers’ roles in this period. By employing methods and theoretical framings from the history of technology, this article argues that engineering was an important framework through which state–capital relations evolved, making engineers pivotal actors in the evolution of political economy at the time.

Health technology assessment (HTA) conducted to inform developers of health technologies (development-focused HTA, DF-HTA) has a number of distinct features when compared to HTA conducted to inform usage decisions (use-focused HTA). To conduct effective DF-HTA, it is important that analysts are aware of its distinct features as analyses are often not published. We set out a framework of ten features, drawn from the literature and our own experience: a target audience of developers and investors; an underlying user objective to maximize return on investment; a broad range of decisions to inform; wide decision space; reduced evidence available; earlier timing of analysis; fluid business model; constrained resources for analysis; a positive stance of analysis; and a “consumer”-specific burden of proof. This paper presents a framework of ten features of DF-HTA intended to initiate debate as well as provide an introduction for analysts unfamiliar with the field.

Neural machine translation (NMT) has recently shown promising results on publicly available benchmark datasets and is being rapidly adopted in various production systems. However, it requires high-quality large-scale parallel corpus, and it is not always possible to have sufficiently large corpus as it requires time, money, and professionals. Hence, many existing large-scale parallel corpus are limited to the specific languages and domains. In this paper, we propose an effective approach to improve an NMT system in low-resource scenario without using any additional data. Our approach aims at augmenting the original training data by means of parallel phrases extracted from the original training data itself using a statistical machine translation (SMT) system. Our proposed approach is based on the gated recurrent unit (GRU) and transformer networks. We choose the Hindi–English, Hindi–Bengali datasets for Health, Tourism, and Judicial (only for Hindi–English) domains. We train our NMT models for 10 translation directions, each using only 5–23k parallel sentences. Experiments show the improvements in the range of 1.38–15.36 BiLingual Evaluation Understudy points over the baseline systems. Experiments show that transformer models perform better than GRU models in low-resource scenarios. In addition to that, we also find that our proposed method outperforms SMT—which is known to work better than the neural models in low-resource scenarios—for some translation directions. In order to further show the effectiveness of our proposed model, we also employ our approach to another interesting NMT task, for example, old-to-modern English translation, using a tiny parallel corpus of only 2.7K sentences. For this task, we use publicly available old-modern English text which is approximately 1000 years old. Evaluation for this task shows significant improvement over the baseline NMT.

Introducing irrigation to smallholder farms in Sub-Saharan Africa (SSA) can increase food security, improve nutrition, and reduce poverty. To explore the possibility of using drip irrigation on smallholder farms in SSA, we introduce a feasibility study that views the design space from both a user-centered lens, explaining how drip might be successful in the future, and from an engineering lens. With a first-order model, we compare estimated capital costs of drip and sprinkler systems for various farm profiles and show that drip has the potential to be a viable technology for many farms in SSA.

Sustainable Design Engineering (SDE) is an emerging research field and the development of programmes aiming at educating sustainable design engineers is very limited. One example is the SDE program at the Aalborg University in Copenhagen, which is based on a Problem Based Learning (PBL) model. In this article we aim to address the following three research questions: i) why Sustainable Design Engineering? ii) what is Sustainable Design Engineering? iii) How can Sustainable Design Engineering be implemented? By means of two examples from master thesis projects in the building and food sectors.

Platform design has been firmly established in the automotive industry as a strategy to provide wider product variety while maintaining cost effective production. But this strategy can struggle to keep up with the pace and nature of emerging technologies. This paper reviews the existing approaches to modelling product platforms, and showcases the challenges at OEMs introducing new technological innovations in their platforms. A gap is identified in the methods to assess the ability of existing platforms to integrate new technologies whenever they become available.

Design & Manufacture Knowledge Mapping is a critical activity in medium-to-large organisations supporting many organisational activities. However, techniques for effective mapping of knowledge often employ interviews, consultations and appraisals. Although invaluable in providing expert insight, the application of such methods is inherently intrusive and resource intensive. This paper presents word co-occurrence graphs as a means to automatically generate knowledge maps from technical documents and validates against expert generated knowledge maps.

This paper proposes an analytical framework for estimating the domain where a type of technology can be used in a system. In order to achieve this aim, we have elaborated on the concepts of technology critical, technology sensitive, and the technology comfort zone, to analytically assess the impact of a new technology in the early phases of system design. The result is a general method to indicate the range of requirements that can result in valid designs. This tool can assist in the decision-making processes for technology portfolio selection based on sustainable principles.

Crucial in the design process, Technology Readiness Levels are a common form of technology maturity assessment. Studies suggest that the TRL scale can be subjective and biased. Automating the assessment can reduce human bias. This paper highlights important challenges of automation by presenting data collected on 15 technologies from the nanotechnology sector. Our findings show that, contrary to claims from the literature, patent data exists for low maturity technologies and may be useful for automation. We also found that there exists unexpected trends in data publications at TRL 2, 3 and 4.