7,773 research outputs found

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Measurement of the Environmental Impact of Materials

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    Throughout their life cycles—from production, usage, through to disposal—materials and products interact with the environment (water, soil, and air). At the same time, they are exposed to environmental influences and, through their emissions, have an impact on the environment, people, and health. Accelerated experimental testing processes can be used to predict the long-term environmental consequences of innovative products before these actually enter the environment. We are living in a material world. Building materials, geosynthetics, wooden toys, soil, nanomaterials, composites, wastes and more are research subjects examined by the authors of this book. The interactions of materials with the environment are manifold. Therefore, it is important to assess the environmental impact of these interactions. Some answers to how this task can be achieved are given in this Special Issue

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Die akute Appendizitis im Kindes- und Jugendalter: neue diagnostische Verfahren fĂŒr die prĂ€therapeutische Differenzierung histopathologischer EntitĂ€ten zur UnterstĂŒtzung konservativer Therapiestrategien

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    Hintergrund der hier zusammengefassten Studien war die aktuelle Datenlage, die dafĂŒr spricht, dass es sich bei der klinisch unkomplizierten, histopathologisch phlegmonösen und der klinisch komplizierten, histopathologisch gangrĂ€nösen Appendizitis um unabhĂ€ngige EntitĂ€ten handelt. Diese können unterschiedlichen Therapieoptionen (konservativ vs. operativ) zugefĂŒhrt werden. Vor diesem Hintergrund war es ein Ziel der Arbeiten zu untersuchen, wie die Formen der akuten Appendizitis im Kindes- und Jugendalter bereits prĂ€therapeutisch unterschieden werden können. Sowohl in der Labordiagnostik (P1 und P2) als auch im Ultraschall (P3) lassen sich Unterschiede zwischen Patient*innen mit unkomplizierter, phlegmonöser und komplizierter (gangrĂ€nöser und perforierender) Appendizitis aufzeigen. Hierdurch allein kann allerdings aufgrund unzureichender TrennschĂ€rfe noch keine ausreichende Entscheidungssicherheit erreicht werden. Mit Verfahren der kĂŒnstlichen Intelligenz auf Untersucher-unabhĂ€ngige diagnostische Parameter (P4) konnte die Vorhersagegenauigkeit der akuten Appendizitis weiter gesteigert werden. Interessante Ergebnisse bezĂŒglich der unterschiedlichen Pathomechanismen der beiden inflammatorischen EntitĂ€ten ergaben sich durch eine differenzielle Genexpressionsanalyse (P5). In einer Proof-of-Concept-Studie wurden zuvor beschriebene Methoden der kĂŒnstlichen Intelligenz auf die Genexpressionsdaten angewandt (P6). Hierdurch konnte im Modell eine grundsĂ€tzliche Differenzierbarkeit der EntitĂ€ten durch die Anwendung der neuen Methode aufgezeigt werden. Ein mittelfristiges Ziel ist es, eine Biomarkersignatur zu definieren, die ihre Aussagekraft durch einen Computeralgorithmus hat. Hierdurch soll eine schnelle Therapieentscheidung ermöglicht werden. Im Idealfall sollte diese Biomarkersignatur sicher, objektiv und einfach zu bestimmen sein sowie eine höhere diagnostische Sicherheit als die bisherige Diagnostik mittels Anamnese, Untersuchung, Laboranalyse und Ultraschall bieten. Langfristiges Ziel von Folgestudien ist die Identifizierung einer Biomarkersignatur mit der bestmöglichen Vorhersagekraft. Hinsichtlich der routinemĂ€ĂŸigen klinischen Diagnostik ist die Anwendung von Point-of-Care Devices auf PCR-Basis denkbar. Hier könnte eine limitierte Anzahl von Primern fĂŒr eine Biomarkersignatur mit hoher Vorhersagekraft zum Einsatz kommen. Der dadurch ermittelte Biomarker wĂŒrde seine Aussagekraft durch einen einfach anzuwendenden Computeralgorithmus erhalten. Die Kombination aus Genexpressionsanalyse mit Methoden der kĂŒnstlichen Intelligenz kann somit die Grundlage fĂŒr ein neues diagnostisches Instrument zur sicheren Unterscheidung unterschiedlicher AppendizitisentitĂ€ten darstellen

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence

    Preferentialism and the conditionality of trade agreements. An application of the gravity model

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    Modern economic growth is driven by international trade, and the preferential trade agreement constitutes the primary fit-for-purpose mechanism of choice for establishing, facilitating, and governing its flows. However, too little attention has been afforded to the differences in content and conditionality associated with different trade agreements. This has led to an under-considered mischaracterisation of the design-flow relationship. Similarly, while the relationship between trade facilitation and trade is clear, the way trade facilitation affects other areas of economic activity, with respect to preferential trade agreements, has received considerably less attention. Particularly, in light of an increasingly globalised and interdependent trading system, the interplay between trade facilitation and foreign direct investment is of particular importance. Accordingly, this thesis explores the bilateral trade and investment effects of specific conditionality sets, as established within Preferential Trade Agreements (PTAs). Chapter one utilises recent content condition-indexes for depth, flexibility, and constraints on flexibility, established by DĂŒr et al. (2014) and Baccini et al. (2015), within a gravity framework to estimate the average treatment effect of trade agreement characteristics across bilateral trade relationships in the Association of Southeast Asian Nations (ASEAN) from 1948-2015. This chapter finds that the composition of a given ASEAN trade agreement’s characteristic set has significantly determined the concomitant bilateral trade flows. Conditions determining the classification of a trade agreements depth are positively associated with an increase to bilateral trade; hereby representing the furthered removal of trade barriers and frictions as facilitated by deeper trade agreements. Flexibility conditions, and constraint on flexibility conditions, are also identified as significant determiners for a given trade agreement’s treatment effect of subsequent bilateral trade flows. Given the political nature of their inclusion (i.e., the appropriate address to short term domestic discontent) this influence is negative as regards trade flows. These results highlight the longer implementation and time frame requirements for trade impediments to be removed in a market with higher domestic uncertainty. Chapter two explores the incorporation of non-trade issue (NTI) conditions in PTAs. Such conditions are increasing both at the intensive and extensive margins. There is a concern from developing nations that this growth of NTI inclusions serves as a way for high-income (HI) nations to dictate the trade agenda, such that developing nations are subject to ‘principled protectionism’. There is evidence that NTI provisions are partly driven by protectionist motives but the effect on trade flows remains largely undiscussed. Utilising the Gravity Model for trade, I test Lechner’s (2016) comprehensive NTI dataset for 202 bilateral country pairs across a 32-year timeframe and find that, on average, NTIs are associated with an increase to bilateral trade. Primarily this boost can be associated with the market access that a PTA utilising NTIs facilitates. In addition, these results are aligned theoretically with the discussions on market harmonisation, shared values, and the erosion of artificial production advantages. Instead of inhibiting trade through burdensome cost, NTIs are acting to support a more stable production and trading environment, motivated by enhanced market access. Employing a novel classification to capture the power supremacy associated with shaping NTIs, this chapter highlights that the positive impact of NTIs is largely driven by the relationship between HI nations and middle-to-low-income (MTLI) counterparts. Chapter Three employs the gravity model, theoretically augmented for foreign direct investment (FDI), to estimate the effects of trade facilitation conditions utilising indexes established by Neufeld (2014) and the bilateral FDI data curated by UNCTAD (2014). The resultant dataset covers 104 countries, covering a period of 12 years (2001–2012), containing 23,640 observations. The results highlight the bilateral-FDI enhancing effects of trade facilitation conditions in the ASEAN context, aligning itself with the theoretical branch of FDI-PTA literature that has outlined how the ratification of a trade agreement results in increased and positive economic prospect between partners (Medvedev, 2012) resulting from the interrelation between trade and investment as set within an improving regulatory environment. The results align with the expectation that an enhanced trade facilitation landscape (one in which such formalities, procedures, information, and expectations around trade facilitation are conditioned for) is expected to incentivise and attract FDI

    Cultivating Agrobiodiversity in the U.S.: Barriers and Bridges at Multiple Scales

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    The diversity of crops grown in the United States (U.S.) is declining, causing agricultural landscapes to become more and more simplified. This trend is concerning for the loss of important plant, insect, and animal species, as well as the pollution and degradation of our environment. Through three separate but related studies, this dissertation addresses the need to increase the diversity of these agricultural landscapes in the U.S., particularly through diversifying the type and number of crops grown. The first study uses multiple, openly accessible datasets related to agricultural land use and policies to document and visualize change over recent decades. Through this, I show that U.S. agriculture has gradually become more specialized in the crops grown, crop production is heavily concentrated in certain areas, and crop diversity is continuing to decline. Meanwhile, federal agricultural policy, while having become more influential over how U.S. agriculture operates, incentivizes this specialization. The second study uses nonlinear statistical modeling to identify and compare social, political, and ecological factors that best predict crop diversity across nine regions in the U.S. Factors of climate, prior land use, and farm inputs best predict diversity across regions, but regions show key differences in how factors are important, indicating that patterns at the regional scale constrain and enable further diversification. Finally, the third study relied on interviews with farmers and key informants in southern Idaho’s Magic Valley – a cluster of eight counties that is known to be agriculturally diverse. Interviews gauge what farmers are currently doing to manage crop diversity (the present) and how they imagine alternative landscapes (the imaginary). We found that farmers in the Magic Valley manage current diversity mainly through cover cropping and diverse crop rotations, but daily struggles and political barriers make experimenting with and imagining alternative landscapes difficult and unlikely to occur. Together, these three studies provide an integrated view of how and why U.S. agriculture landscapes simplify or diversify, as well as the barriers and bridges such pathways of diversification
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