16,590 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Security and Privacy Problems in Voice Assistant Applications: A Survey

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    Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

    One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

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    OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

    Food biodiversity: Quantifying the unquantifiable in human diets

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    Dietary diversity is an established public health principle, and its measurement is essential for studies of diet quality and food security. However, conventional between food group scores fail to capture the nutritional variability and ecosystem services delivered by dietary richness and dissimilarity within food groups, or the relative distribution (i.e., evenness or moderation) of e.g., species or varieties across whole diets. Summarizing food biodiversity in an all-encompassing index is problematic. Therefore, various diversity indices have been proposed in ecology, yet these require methodological adaption for integration in dietary assessments. In this narrative review, we summarize the key conceptual issues underlying the measurement of food biodiversity at an edible species level, assess the ecological diversity indices previously applied to food consumption and food supply data, discuss their relative suitability, and potential amendments for use in (quantitative) dietary intake studies. Ecological diversity indices are often used without justification through the lens of nutrition. To illustrate: (i) dietary species richness fails to account for the distribution of foods across the diet or their functional traits; (ii) evenness indices, such as the Gini-Simpson index, require widely accepted relative abundance units (e.g., kcal, g, cups) and evidence-based moderation weighting factors; and (iii) functional dissimilarity indices are constructed based on an arbitrary selection of distance measures, cutoff criteria, and number of phylogenetic, nutritional, and morphological traits. Disregard for these limitations can lead to counterintuitive results and ambiguous or incorrect conclusions about the food biodiversity within diets or food systems. To ensure comparability and robustness of future research, we advocate food biodiversity indices that: (i) satisfy key axioms; (ii) can be extended to account for disparity between edible species; and (iii) are used in combination, rather than in isolation

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries

    Information-Theoretic GAN Compression with Variational Energy-based Model

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    We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual information in continuous domains is intractable, our approach alternatively optimizes the student network by maximizing the variational lower bound of the mutual information. To achieve a tight lower bound, we introduce an energy-based model relying on a deep neural network to represent a flexible variational distribution that deals with high-dimensional images and consider spatial dependencies between pixels, effectively. Since the proposed method is a generic optimization algorithm, it can be conveniently incorporated into arbitrary generative adversarial networks and even dense prediction networks, e.g., image enhancement models. We demonstrate that the proposed algorithm achieves outstanding performance in model compression of generative adversarial networks consistently when combined with several existing models.Comment: Accepted at Neurips202

    Norsk rÄ kumelk, en kilde til zoonotiske patogener?

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    The worldwide emerging trend of eating “natural” foods, that has not been processed, also applies for beverages. According to Norwegian legislation, all milk must be pasteurized before commercial sale but drinking milk that has not been heat-treated, is gaining increasing popularity. Scientist are warning against this trend and highlights the risk of contracting disease from milkborne microorganisms. To examine potential risks associated with drinking unpasteurized milk in Norway, milk- and environmental samples were collected from dairy farms located in south-east of Norway. The samples were analyzed for the presence of specific zoonotic pathogens; Listeria monocytogenes, Campylobacter spp., and Shiga toxin-producing Escherichia coli (STEC). Cattle are known to be healthy carriers of these pathogens, and Campylobacter spp. and STEC have a low infectious dose, meaning that infection can be established by ingesting a low number of bacterial cells. L. monocytogenes causes one of the most severe foodborne zoonotic diseases, listeriosis, that has a high fatality rate. All three pathogens have caused milk borne disease outbreaks all over the world, also in Norway. During this work, we observed that the prevalence of the three examined bacteria were high in the environment at the examined farms. In addition, 7% of the milk filters were contaminated by STEC, 13% by L. monocytogenes and 4% by Campylobacter spp. Four of the STEC isolates detected were eaepositive, which is associated with the capability to cause severe human disease. One of the eae-positive STEC isolates were collected from a milk filter, which strongly indicate that Norwegian raw milk may contain potential pathogenic STEC. To further assess the possibilities of getting ill by STEC after consuming raw milk, we examined the growth of the four eae-positive STEC isolates in raw milk at different temperatures. All four isolates seemed to have ability to multiply in raw milk at 8°C, and one isolate had significant growth after 72 hours. Incubation at 6°C seemed to reduce the number of bacteria during the first 24 hours before cell death stopped. These findings highlight the importance of stable refrigerator temperatures, preferable < 4°C, for storage of raw milk. The L. monocytogenes isolates collected during this study show genetic similarities to isolates collected from urban and rural environmental locations, but different clones were predominant in agricultural environments compared to clinical and food environments. However, the results indicate that the same clone can persist in a farm over time, and that milk can be contaminated by L. monocytogenes clones present in farm environment. Despite testing small volumes (25 mL) of milk, we were able to isolate both STEC and Campylobacter spp. directly from raw milk. A proportion of 3% of the bulk tank milk and teat milk samples were contaminated by Campylobacter spp. and one STEC was isolated from bulk tank milk. L monocytogenes was not detected in bulk tank milk, nor in teat milk samples. The agricultural evolvement during the past decades have led to larger production units and new food safety challenges. Dairy cattle production in Norway is in a current transition from tie-stall housing with conventional pipeline milking systems, to modern loose housing systems with robotic milking. The occurrence of the three pathogens in this project were higher in samples collected from farms with loose housing compared to those with tiestall housing. Pasteurization of cow’s milk is a risk reducing procedure to protect consumers from microbial pathogens and in most EU countries, commercial distribution of unpasteurized milk is legally restricted. Together, the results presented in this thesis show that the animal housing may influence the level of pathogenic bacteria in the raw milk and that ingestion of Norwegian raw cow’s milk may expose consumers to pathogenic bacteria which can cause severe disease, especially in children, elderly and in persons with underlying diseases. The results also highlight the importance of storing raw milk at low temperatures between milking and consumption.Å spise mat som er mindre prosessert og mer «naturlig» er en pĂ„gĂ„ende trend i Norge og i andre deler av verden. Interessen for Ă„ drikke melk som ikke er varmebehandlet, sĂ„kalt rĂ„ melk, er ogsĂ„ Ăžkende. I Norge er det pĂ„budt Ă„ pasteurisere melk fĂžr kommersielt salg for Ă„ beskytte forbrukeren mot sykdomsfremkallende mikroorganismer. Fagfolk advarer mot Ă„ drikke rĂ„ melk, og pĂ„peker risikoen for Ă„ bli syk av patogene bakterier som kan finnes i melken. I denne avhandlingen undersĂžker vi den potensielle risikoen det medfĂžrer Ă„ drikke upasteurisert melk fra Norge. I tillegg til Ă„ samle inn tankmelk- og speneprĂžver fra melkegĂ„rder i sĂžrĂžst Norge, samlet vi ogsĂ„ miljĂžprĂžver fra de samme gĂ„rdene for Ă„ kartlegge forekomst og for Ă„ identifisere potensielle mattrygghetsrisikoer i melkeproduksjonen. Alle prĂžvene ble analysert for de zoonotiske sykdomsfremkallende bakteriene Listeria monocytogenes, Campylobacter spp., og Shiga toksin-produserende Escherichia coli (STEC). Kyr kan vĂŠre friske smittebĂŠrere av disse bakteriene, som dermed kan etablere et reservoar pĂ„ gĂ„rdene. Bakteriene kan overfĂžres fra gĂ„rdsmiljĂžet til melkekjeden og dermed utfordre mattryggheten. Disse bakteriene har forĂ„rsaket melkebĂ„rne sykdomsutbrudd over hele verden, ogsĂ„ i Norge. Campylobacter spp. og STEC har lav infeksiĂžs dose, som vil si at man kan bli syk selv om man bare inntar et lavt antall bakterieceller. L. monocytogenes kan gi sykdommen listeriose, en av de mest alvorlige matbĂ„rne zoonotiske sykdommene vi har i den vestlige verden. Resultater fra denne oppgaven viser en hĂžy forekomst av de tre patogenene i gĂ„rdsmiljĂžet. I tillegg var 7% av melkefiltrene vi testet positive for STEC, 13% positive for L. monocytogenes og 4% positive for Campylobacter spp.. Fire av STEC isolatene bar genet for Intimin, eae, som er ansett som en viktig virulensfaktor som Ăžker sjansen for alvorlig sykdom. Ett av de eae-positive isolatene ble funnet i et melkefilter, noe som indikerer at norsk rĂ„ melk kan inneholde patogene STEC. For Ă„ videre vurdere risikoen for Ă„ bli syk av STEC fra rĂ„ melk undersĂžkte vi hvordan de fire eae-positive isolatene vokste i rĂ„ melk lagret ved forskjellige temperaturer. For alle isolatene Ăžkte antall bakterier etter lagring ved 8°C, og for et isolat var veksten signifikant. Etter lagring ved 6°C ble antallet bakterier redusert de fĂžrste 24 timene, deretter stoppet reduksjonen i antall bakterier. Disse resultatene viser hvor viktig det er Ă„ ha stabil lav lagringstemperatur for rĂ„ melk, helst < 4°C. L. monocytogenes isolatene som ble samlet inn fra melkegĂ„rdene viste genetiske likheter med isolater samlet inn fra urbane og rurale miljĂžer rundt omkring i Norge. Derimot var kloner som dominerte i landbruksmiljĂžet forskjellige fra kliniske isolater og isolater fra matproduksjonslokaler. Videre sĂ„ man at en klone kan persistere pĂ„ en gĂ„rd over tid og at melk kan kontamineres av L. monocytogenes kloner som er til stede i gĂ„rdsmiljĂžet. Til tross for smĂ„ testvolum av tankmelken (25 mL) fant vi bĂ„de STEC og Campylobacter spp. i melkeprĂžvene. 3% av tankmelkprĂžvene og speneprĂžvene var positive for Campylobacter spp. og ett STEC isolat ble funnet i tankmelk. L. monocytogenes ble ikke funnet direkte i melkeprĂžvene. Landbruket i Norge er i stadig utvikling der besetningene blir stĂžrre, men fĂŠrre. Melkebesetningene er midt i en overgang der tradisjonell oppstalling med melking pĂ„ bĂ„s byttes ut med lĂžsdriftssystemer og melkeroboter. Forekomsten av de tre patogenene funnet i denne studien var hĂžyere i besetningene med lĂžsdrift sammenliknet med besetningene som hadde melkekyrne oppstallet pĂ„ bĂ„s. Pasteurisering er et viktig forebyggende tiltak for Ă„ beskytte konsumenter fra mikrobielle patogener, og i de fleste EU-land er kommersielt salg av rĂ„ melk juridisk begrenset. Denne studien viser at oppstallingstype kan pĂ„virke nivĂ„ene av patogene bakterier i gĂ„rdsmiljĂžet og i rĂ„ melk. Inntak av rĂ„ melk kan eksponere forbruker for patogene bakterier som kan gi alvorlig sykdom, spesielt hos barn, eldre og personer med underliggende sykdommer. Resultatene underbygger viktigheten av Ă„ pasteurisere melk for Ă„ sikre mattryggheten, og at det er avgjĂžrende Ă„ lagre rĂ„ melk ved kontinuerlig lave temperaturer for Ă„ forebygge vekst av zoonotiske patogener

    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

    A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

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    The accurate modeling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socio-economic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms—Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Production—and combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability p of landslide occurrence decreases nearly exponentially with the distance x to the next road, fault, or river. Specifically, the results indicated that p≈exp(−λx) where the length scale λ is about 0.0797 km−1 for road, 0.108 km−1 for fault, and 0.734 km−1 0.734 km−1 for river. Furthermore, according to the results, p follows, approximately, a lognormal function of elevation, while the equation p=p0−K(ξ−ξ0)2 fits well the dependence of landslide modeling on the slope-angle Ξ, with p0≈0.64,Ξ0≈25.6∘and|K|≈6.6×10−4. However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions. Obtained results provide insights for quantitative modeling of landslide risk, as well as for priority planning in landslide risk management
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