104 research outputs found

    Optimization with artificial intelligence in additive manufacturing: a systematic review

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    In situations requiring high levels of customization and limited production volumes, additive manufacturing (AM) is a frequently utilized technique with several benefits. To properly configure all the parameters required to produce final goods of the utmost quality, AM calls for qualified designers and experienced operators. This research demonstrates how, in this scenario, artificial intelligence (AI) could significantly enable designers and operators to enhance additive manufacturing. Thus, 48 papers have been selected from the comprehensive collection of research using a systematic literature review to assess the possibilities that AI may bring to AM. This review aims to better understand the current state of AI methodologies that can be applied to optimize AM technologies and the potential future developments and applications of AI algorithms in AM. Through a detailed discussion, it emerges that AI might increase the efficiency of the procedures associated with AM, from simulation optimization to in-process monitoring

    Adolescent property offending and socialisation

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    A stable aspect of adolescent property offending has been coincidence of the peak age of such offences with the last year of compulsory schooling. This fact is taken as the focus of three epidemiological analyses. These were: 1. A survey of child welfare records of adolescent property offences for Hamilton, N.Z., in 1971. 2. The collection of teacher ratings of behaviour for a delinquent sample and a random sample, in the classroom situation. 3. Investigation of an unexpected finding, the disappearance of the peak age in Scotland in 1961. Study 1. confirmed earlier work suggesting that the property offences of the younger adolescent are more likely to be first offences and of a casual, low expertise nature. It added to such findings the fact that this is not so much an age effect as a difference between those attending school and those who have left school. Study 2. replicated the well established finding of close association between difficult behaviour at school and delinquent acts. It added to such studies the fact that while property offending increases rapidly in early adolescence, difficult behaviour remains fairly stable. Study 3. provided an unusual example of variation in delinquency rates, and showed that children born immediately after World War 2 in Scotland were significantly less delinquent than expectation. The results of these studies are extensively discussed in relation to a general concept of primary deviance, the interaction between socialisation and situational or life style variables, and the nature of socialisation processes

    As dead as a dodo? : public understanding and support vis à vis biodiversity and biodiversity loss

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    The Convention on Biological Diversity and its derivative literature call for increases in public understanding and support as a condition for successful strategies to conserve biodiversity. Yet practically no relevant data exist. This research attempts to redress this situation by exploring UK public understanding and support vis-à-vis biodiversity. It employs a structured in-depth interview as the main data-gathering instrument, applying it to 126 individuals selected according to their relationships to nature and wildlife, their positions in relation to local and regional government decision-making, and their representation of different occupationally-based social classes. The findings, if representative of the wider population, suggest that the public’s understanding of biodiversity is poor, its levels of participation in efforts to conserve it are low, that attitudes towards biodiversity per se are largely non-existent, but that there is a considerable amount of interest in wildlife and nature. In looking at ways in which biodiversity education might be developed, consideration is given to the influences and debates that are likely to have greatest influence, and to the potential sources for this education. The principle obstacle to an effective biodiversity education is identified as the science/public divide, but the characteristics of biodiversity as a subject are recognised as enabling it to form a bridge between the two. Stables’ (1998) three-tier conceptualisation of literacy is adopted as part of the framework for assessing the different sources of biodiversity education, and some, notably wildlife gardening and wildlife NGO activities, are found to provide significant opportunities in this respect. Given the nature of the subject and the research findings, it is argued that a good level of literacy should be coupled with good communication skills and the ability to address the issues beyond the science base to include the social, cultural, political, moral and aesthetic aspects. It is concluded that those best qualified to provide ‘critical biodiversity literacy’ should perhaps be sought in the discipline conservation biology rather than that of environmental education. The ramifications of the research for implementation of the Convention on Biological Diversity are considered. Recommendations for further research and biodiversity education are also made

    Examining customer's intention to rely on online reviews

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    The use of online reviews among online shoppers has increased significantly in recent years and has reduced uncertainty and risks associated with online shopping. The objective of this research is to identify the segment of online shoppers relying on online reviews. Consumers were classified based on their shopping motivation, trait and online behavior. A quantitative survey involving 375 Indian online shoppers were performed to identify and understand their reliance of online reviews. The findings show that consumer with high price consciousness, value consciousness, brand consciousness and self-esteem rely on online reviews for their online purchases. On the other side consumer who are quality conscious and having online shopping anxiety don’t rely on online review. This research adds to the growing literature on consumer information theory and validates the link between consumer shopping motivation and their informational needs

    Understanding and Engineering of Sub-gap States in Photodetection

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    Emerging applications for light sensing, including wearable electronics, internet of things and autonomous driving, are pushing conventional semiconductors technologies to their limits when it comes to ease of fabrication, power consumption and device design. Organic semiconductors are considered next-generation absorber materials for photodetection in the visible and near infrared part of the electromagnetic spectrum, which hold some promise of addressing the aforementioned problems of conventional materials. So far, only a handful of companies are putting organic semiconductors to the test for commercial photodetectors, however, research on organic photodetectors is thriving – in particular on photodetectors with a diode architecture called photodiodes. The goal is to make flexible, light-weight devices with improved performance metrics and high stability to realize viable alternatives to conventional photodiodes. The performance limits of organic photodiodes are often associated with the presence of electronic states with energies below the bandgap edge – the so-called sub-gap states. A powerful tool to study the properties of sub-gap states is to measure the external quantum efficiency (EQE), however, the subsequent analysis is complicated by the presence of static disorder and optical interference. In the first part of this work, it is shown how the true absorption coefficient can be extracted from a series of interference affected sub-gap EQE spectra of organic photodiodes with different thicknesses. In consequence, the effect of chemical structure modification on the absorption coefficient in the spectral range of charge transfer absorption is demonstrated. By adjusting the molecular energy levels through target chemical substitutions, a redshift and an increase of the oscillator strength are achieved. The increased spectral coverage in the near infrared is then exploited in micro-cavity photodiodes. The second part of this work deals with the sub-gap absorption coefficient of donor and acceptor materials and how it is affected by the molecular energy level offset. For materials with low energetic offset, it is shown that the sub-gap absorption coefficient follows the Urbach rule in the spectral range of excitonic absorption, dictating the broadening of the sub-gap absorption coefficient at energies right below the bandgap. Lastly, the origin of the high dark current in organic photodiodes is identified as non-radiative recombination via mid-gap trap states. An upper limit to the specific detectivity is calculated that is expected viable in organic photodiodes. The findings of this thesis contribute to the understanding of the sub-gap states by studying their absorption features and distinguishing them from the ubiquitous optical interference effects. The spectroscopic observation of mid-gap trap states is linked to the dark current generation dictating the upper performance limits of organic photodiodes

    Real-time text classification of user-generated content on social media: Systematic review

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    The aim of this systematic review is to determine the current state of the art in the real-time classification of user-generated content from social media. Focus is on the identification of the main characteristics of data used for training and testing, the types of text processing and normalization that are required, the machine learning methods used most commonly, and how these methods compare to one another in terms of classification performance. Relevant studies were selected from subscription-based digital libraries, free-to-access bibliographies, and self-curated repositories and then screened for relevance with key information extracted and structured against the following facets: natural language processing (NLP) methods, data characteristics, classification methods, and evaluation results. A total of 25 studies published between 2014 and 2018 covering 15 types of classification algorithms were included in this review. Support vector machines (SVMs), Bayesian classifiers, and decision trees were the most commonly employed algorithms with recent emergence of neural network approaches. Domain-specific, application programming interface (API)-driven collection is the most prevalent origin of datasets. The reuse of previously published datasets as a means of benchmarking algorithms against other studies is also prevalent. In conclusion, there are consistent approaches taken when normalizing social media data for text mining and traditional text mining techniques are suited to the task of real-time analysis of social media

    The Emerging Threat of Ai-driven Cyber Attacks: A Review

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    Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.publishedVersio

    Formation of corrosive compounds from biomass/waste combustion.

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    Biomass/waste is a renewable energy source which can be fired in combustion power plants. These fuels can be used to replace coal combustion, with its associated environmental impacts, however challenges associated with biomass include accelerated fireside corrosion of heat exchangers (HX) due to producing different corrosive compounds; i.e. fireside corrosion is fuel dependent. As such compromises may be needed. For example, virgin wood fuels have lower fireside corrosion risks, although alternative biomass/waste fuels are cheaper. This thesis compares the fireside corrosion influences of several biomass/waste fuel categories to further understand their attack mechanisms on conventional HX steels, T91 and 374HFG. The corrosive species generated during the fuels’ combustion have been investigated using thermodynamic modelling by MTData. Corrosion damage has been evaluated using high temperature corrosion furnace tests, conducted with isothermal conditions. In contrast to isothermal test conditions, further testing developed an alteration to the furnace setup to address heat gradient impacts on corrosion damage. In both fireside corrosion methodologies, the well-established deposit re-coat technique has been employed to simulate the deposition of representative salts. To evaluate corrosion trends influenced by the biomass/waste fuels with different exposure parameters, temperature, gas and deposition flux conditions have also been varied. Corrosion data evaluation is both quantitative (dimensional and weight changes of metals samples) and qualitative (scanning electron microscopy and energy dispersive X-ray spectroscopy). Thermodynamic modelling shows higher partitioning of alkali metal species into the flue gas occurs for Wood Waste Fuels (WWF) than for Agricultural Plants & Residues (APR) and Herbaceous Grass Biomass (HGB) fuels. However, APR and HGB fuels release higher absolute amounts of these species in combustion. The high chloride percentages in APR and HGB deposits (69-89 mol.%) do not always correlate with higher corrosion rates as determined in the laboratory tests. Indeed, WWF deposits, with chlorides as low as 30 mol.%, have proven to be more destructive under certain operating conditions. Despite this, the increased deposition flux on plant HXs of corrosive species from HGB and APR firing on plant HXs is responsible for their shorter operational lives as compared to WWF. This has implications for the type of biomass/waste fuel combusted for power generation.PhD in Energy and Powe
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