Showing papers by "University of Lincoln published in 2020"
University of Paderborn1, University of Zurich2, Lawrence Berkeley National Laboratory3, IBM4, McGill University5, ETH Zurich6, Victoria University, Australia7, Aalto University8, University of Regensburg9, University of Lincoln10, Intel11, Pacific Northwest National Laboratory12, University of Insubria13, Bosch14, Science and Technology Facilities Council15, Paul Scherrer Institute16
TL;DR: CP2K as discussed by the authors is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems, especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations.
Abstract: CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.
938 citations
University of Paderborn1, University of Zurich2, Lawrence Berkeley National Laboratory3, IBM4, McGill University5, ETH Zurich6, Victoria University, Australia7, Aalto University8, University of Regensburg9, University of Lincoln10, Intel11, Pacific Northwest National Laboratory12, University of Insubria13, Bosch14, Science and Technology Facilities Council15, Paul Scherrer Institute16
TL;DR: This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations and puts the emphasis on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.
Abstract: CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2k to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.
632 citations
Royal Museum for Central Africa1, Ghent University2, University of Leeds3, University College London4, Forestry Commission5, University of York6, University of Kisangani7, Wildlife Conservation Society8, University of Plymouth9, World Wide Fund for Nature10, Norwegian University of Life Sciences11, University of Yaoundé I12, Manchester Metropolitan University13, Center for International Forestry Research14, University of British Columbia15, Bioversity International16, University of Toronto17, University of Stirling18, Forestry Research Institute of Ghana19, University of Montpellier20, Centre de coopération internationale en recherche agronomique pour le développement21, Mbarara University of Science and Technology22, Marien Ngouabi University23, University of Buea24, Duke University25, University of Edinburgh26, National Park Service27, Smithsonian Institution28, University of Cambridge29, Gembloux Agro-Bio Tech30, University of Birmingham31, University of Exeter32, Smithsonian Tropical Research Institute33, Chinese Academy of Sciences34, Royal Botanic Garden Edinburgh35, African Wildlife Foundation36, American Museum of Natural History37, University of Bristol38, University of Hong Kong39, Royal Society for the Protection of Birds40, Royal Botanic Gardens41, Environmental Change Institute42, University of the Sunshine Coast43, Fleming College44, Sokoine University of Agriculture45, University of Southampton46, University of Lincoln47, University of Florence48, University of Aberdeen49, Innovate UK50, National University of Singapore51, Washington State University Vancouver52, Yale University53, University of Nottingham54, Florida International University55, Université libre de Bruxelles56, Bangor University57, University of Liberia58
TL;DR: Overall, the uptake of carbon into Earth’s intact tropical forests peaked in the 1990s and independent observations indicating greater recent carbon uptake into the Northern Hemisphere landmass reinforce the conclusion that the intact tropical forest carbon sink has already peaked.
Abstract: Structurally intact tropical forests sequestered about half of the global terrestrial carbon uptake over the 1990s and early 2000s, removing about 15 per cent of anthropogenic carbon dioxide emissions. Climate-driven vegetation models typically predict that this tropical forest ‘carbon sink’ will continue for decades. Here we assess trends in the carbon sink using 244 structurally intact African tropical forests spanning 11 countries, compare them with 321 published plots from Amazonia and investigate the underlying drivers of the trends. The carbon sink in live aboveground biomass in intact African tropical forests has been stable for the three decades to 2015, at 0.66 tonnes of carbon per hectare per year (95 per cent confidence interval 0.53–0.79), in contrast to the long-term decline in Amazonian forests. Therefore the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged. The difference is largely driven by carbon losses from tree mortality, with no detectable multi-decadal trend in Africa and a long-term increase in Amazonia. Both continents show increasing tree growth, consistent with the expected net effect of rising atmospheric carbon dioxide and air temperature. Despite the past stability of the African carbon sink, our most intensively monitored plots suggest a post-2010 increase in carbon losses, delayed compared to Amazonia, indicating asynchronous carbon sink saturation on the two continents. A statistical model including carbon dioxide, temperature, drought and forest dynamics accounts for the observed trends and indicates a long-term future decline in the African sink, whereas the Amazonian sink continues to weaken rapidly. Overall, the uptake of carbon into Earth’s intact tropical forests peaked in the 1990s. Given that the global terrestrial carbon sink is increasing in size, independent observations indicating greater recent carbon uptake into the Northern Hemisphere landmass reinforce our conclusion that the intact tropical forest carbon sink has already peaked. This saturation and ongoing decline of the tropical forest carbon sink has consequences for policies intended to stabilize Earth’s climate.
395 citations
TL;DR: In this paper, the authors examined the impact of the COVID-19 pandemic on psychological health and well-being in the UK during a period of "lockdown" (15th-21st May 2020).
Abstract: The COVID-19 pandemic has profoundly altered the daily lives of many people across the globe, both through the direct interpersonal cost of the disease, and the governmental restrictions imposed to mitigate its spread and impact. The UK has been particularly affected and has one of the highest mortality rates in Europe. In this paper, we examine the impact of COVID-19 on psychological health and well-being in the UK during a period of ‘lockdown’ (15th–21st May 2020) and the specific role of Psychological Flexibility as a potential mitigating process. We observed clinically high levels of distress in our sample (N = 555). However, psychological flexibility was significantly and positively associated with greater wellbeing, and inversely related to anxiety, depression, and COVID-19-related distress. Avoidant coping behaviour was positively associated with all indices of distress and negatively associated with wellbeing, while engagement in approach coping only demonstrated weaker associations with outcomes of interest. No relationship between adherence to government guidelines and psychological flexibility was found. In planned regression models, psychological flexibility demonstrated incremental predictive validity for all distress and wellbeing outcomes (over and above both demographic characteristics and COVID-19-specific coping responses). Furthermore, psychological flexibility and COVID-19 outcomes were only part-mediated by coping responses to COVID-19, supporting the position that psychological flexibility can be understood as an overarching response style that is distinct from established conceptualisations of coping. We conclude that psychological flexibility represents a promising candidate process for understanding and predicting how an individual may be affected by, and cope with, both the acute and longer-term challenges of the pandemic.
249 citations
TL;DR: It is hoped this review will stimulate further advances in the sustainable production of value-added products from lignin to integrate this invaluable "bio-waste" into the chemical/materials supply chain.
Abstract: Despite the enormous research efforts in recent years regarding lignin depolymerisation and functionalisation, few commercial products are available. This review provides a summary and viewpoint of extensive research in the lignin-to-product valorisation chain, with an emphasis on downstream processing of lignin derived feedstock into end products. It starts with an introduction of available platform chemicals and polymeric derivatives generated from lignin via existing depolymerisation and functionalisation technologies. Following that, detailed analyses of various strategies for the downstream processing of lignin derived platform chemicals and materials into fuels, valued-added chemicals and functional polymers are provided. A concise techno-economic analysis of various downstream processes is conducted based on the market demand of the end product, economic potential and technological readiness, enabling the identification of processes that are potentially both economically competitive and commercially feasible, and shedding light on processes which deserve further technological development. We wish this review will stimulate further advances in the sustainable production of value-added products from lignin to integrate this invaluable “bio-waste” into the chemical/materials supply chain.
232 citations
TL;DR: The contribution of business and management scholars to the discussion surrounding the sustainable development goals and their impact for business organizations has grown exponentially in the last years as discussed by the authors, through bibliometric and systematic literature review methods, the scientific knowledge about SDGs and the business sector, analyzing 266 articles published in leading journals between 2012 and 2019.
184 citations
TL;DR: In this paper, a new class of hydrated porous δ-Ni0.nH2O nanoribbons for use as an AZIB cathode is presented, and the host vanadate lattice has favorable Zn2+ diffusion properties, arising from the atomic-level structure of the well-defined lattice channels.
Abstract: Cost-effective and environmentally friendly aqueous zinc-ion batteries (AZIB) exhibit tremendous potential for application in grid-scale energy storage systems but are limited by suitable cathode materials. Hydrated vanadium bronzes have gained significant attention for AZIBs and can be produced with a range of different pre-intercalated ions, allowing their properties to be optimised. However, gaining a detailed understanding of the energy storage mechanisms within these cathode materials remains a great challenge due to their complex crystallographic frameworks, limiting rational design from the perspective of enhanced Zn2+ diffusion over multiple length scales. Herein, we report on a new class of hydrated porous δ-Ni0.25V2O5.nH2O nanoribbons for use as an AZIB cathode. The cathode delivers reversibility showing 402 mAh g-1 at 0.2 A g-1 and a capacity retention of 98 % over 1200 cycles at 5 A g-1. A detailed investigation using experimental and computational approaches reveal that the host ‘δ’ vanadate lattice has favourable Zn2+ diffusion properties, arising from the atomic-level structure of the well-defined lattice channels. Furthermore, the microstructure of the asprepared cathodes is examined using multi-length scale X-ray computed tomography for the first time in AZIBs and the effective diffusion coefficient is obtained by image-based modelling, illustrating favourable porosity and satisfactory tortuosity.
172 citations
Martin J. P. Sullivan1, Martin J. P. Sullivan2, Simon L. Lewis3, Simon L. Lewis2 +247 more•Institutions (104)
TL;DR: This synthesis of plot networks across climatic and biogeographic gradients shows that forest thermal sensitivity is dominated by high daytime temperatures, and biome-wide variation in tropical forest carbon stocks and dynamics shows long-term resilience to increasing high temperatures.
Abstract: The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate.
172 citations
TL;DR: A research framework capable of capturing the imbricated and complex relations among stakeholder pressure, barriers to and motivators of the CE, circular business models, and firms' sustainable performance is tested.
159 citations
TL;DR: In this article, a bibliometric and systematic review classifies SME and IEs research findings into three echelons: (i) subjects; (ii) theories; and (iii) methods.
Abstract: Business is dynamic and rapidly changing. Global markets were previously the playing field of multinational corporations (MNCs), while small and medium enterprises (SMEs) were local; however, the removal of imposed barriers, and recent technological advances in manufacturing, transportation and communications have indorsed SMEs and international entrepreneurs (IE) global access. SMEs and IEs are increasingly fueling economic growth and innovation and these trends are presenting both opportunities and challenges to both MNCs and SMEs in the global arena. This review systematically examines comparative SME and IE research, analyzing (after fine tuning) 762 articles published in leading journals from 1992 to September 2018. Our bibliometric and systematic review classifies SME and IE research findings into three echelons: (i) subjects; (ii) theories; and (iii) methods.
159 citations
TL;DR: This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD), finding wide-reaching and diverse applications among a variety of vertical sectors.
Abstract: This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD). Specifically, the study investigates the operations and value propositions of these distinct but increasingly converging technologies. Due to the dynamic nature of innovation, the potential of this ABCD hybridization, integration, recombination and convergence has yet to be considered. Using a multidisciplinary approach, the findings of the study show wide-reaching and diverse applications among a variety of vertical sectors, presenting exploratory research avenues for future investigation. The study also highlights the practical implications of these new technologies.
University of Tübingen1, Marien Ngouabi University2, Centers for Disease Control and Prevention3, Zambian Ministry of Health4, Lusaka Apex Medical University5, University of Lincoln6, Charité7, National Institute for Medical Research8, University College London9, Sokoine University of Agriculture10, Royal Veterinary College11, Chatham House12, University of Montpellier13, University of Khartoum14
TL;DR: Nathan Kapata, Chikwe Ihekweazu, Francine Ntoumi, Tajudeen Raji, Pascalina Chanda-Kapata, Peter Mwaba, Victor Mukonka, Matthew Bates, John Tembo, Victor Corman, Sayoki Mfinanga, Danny Asogun, Linzy Elton, Liã Bárbara Arruda, Margaret J Thomason, Leonard Mboera, Alexei Yavlinsky
TL;DR: In this article, the authors provide an overview of the state-of-the-art literature on big data-driven sustainable supply chain management, and propose seven gaps in the literature in order to foster future investigations on sustainable supply chains.
University of Zagreb1, University of Wolverhampton2, Leeds Beckett University3, Fordham University4, University of Malta5, Aalborg University6, Chapman University7, Teesside University8, Université du Québec en Outaouais9, University of Hong Kong10, University of Seville11, University College of Northern Denmark12, Beijing Normal University13, University of Sydney14, University College West15, Auckland University of Technology16, University of Auckland17, Queen's University Belfast18, University of Indianapolis19, Umeå University20, Victoria University, Australia21, University of Newcastle22, DePauw University23, Mzumbe University24, Mid Sweden University25, Dublin City University26, RMIT University27, University of Calgary28, London Metropolitan University29, University of South Carolina30, University of Split31, University of Lincoln32, University of Melbourne33, Community College of Philadelphia34, Global University (GU)35, University of Notre Dame Australia36, University of Latvia37, Tata Institute of Social Sciences38, University of Minnesota39, University of South Africa40, International Institute of Minnesota41, University of Waikato42, Northeast Normal University43, Curtin University44, University of Ibadan45, Zhejiang Normal University46, Adekunle Ajasin University47, National University of Ireland, Galway48
TL;DR: A collection of 84 author's testimonies and workspace photographs between 18 March and 5 May 2020 was published by as discussed by the authors, with the purpose of collecting the author's workspace photographs and their testimonies.
Abstract: A collection of 84 author's testimonies and workspace photographs between 18 March and 5 May 2020
TL;DR: Machine learning, an artificial neural network (ANN) and a simple statistical test are used to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals to greatly improve initial screening for patients where PCR based diagnostic tools are limited.
TL;DR: The human-animal bond is a construct that may be linked to mental health vulnerability in animal owners and animal ownership seemed to mitigate some of the detrimental psychological effects of Covid-19 lockdown.
Abstract: Background The Covid-19 pandemic raises questions about the role that relationships and interactions between humans and animals play in the context of widespread social distancing and isolation measures. We aimed to investigate links between mental health and loneliness, companion animal ownership, the human-animal bond, and human-animal interactions; and to explore animal owners’ perceptions related to the role of their animals during lockdown. Methods A cross-sectional online survey of UK residents over 18 years of age was conducted between April and June 2020. The questionnaire included validated and bespoke items measuring demographics; exposures and outcomes related to mental health, wellbeing and loneliness; the human-animal bond and human-animal interactions. Results Of 5,926 participants, 5,323 (89.8%) had at least one companion animal. Most perceived their animals to be a source of considerable support, but concerns were reported related to various practical aspects of providing care during lockdown. Strength of the human-animal bond did not differ significantly between species. Poorer mental health pre-lockdown was associated with a stronger reported human-animal bond (b = -.014, 95% CI [-.023 - -.005], p = .002). Animal ownership compared with non-ownership was associated with smaller decreases in mental health (b = .267, 95% CI [.079 - .455], p = .005) and smaller increases in loneliness (b = -.302, 95% CI [-.461 - -.144], p = .001) since lockdown. Conclusion The human-animal bond is a construct that may be linked to mental health vulnerability in animal owners. Strength of the human-animal bond in terms of emotional closeness or intimacy dimensions appears to be independent of animal species. Animal ownership seemed to mitigate some of the detrimental psychological effects of Covid-19 lockdown. Further targeted investigation of the role of human-animal relationships and interactions for human health, including testing of the social buffering hypothesis and the development of instruments suited for use across animal species, is required.
TL;DR: Zhang et al. as discussed by the authors proposed two transfer learning schemes, appliance transfer learning (ATL) and cross-domain transfer learning(CTL), to recover source appliances from only the recorded mains in a household.
Abstract: Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a ‘complex’ appliance, e.g., washing machine, can be transferred to a ‘simple’ appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM .
TL;DR: In this article, a 20-year dataset collected from the Web of Science database is used to present a comprehensive knowledge map of the intellectual structure of the field of study of sustainability and financial performances in SMEs.
Abstract: Based on a 20-year dataset collected from the Web of Science database, this study aims to present a comprehensive knowledge map of the intellectual structure of the field of study of sustainability and financial performances in SMEs. A bibliometric analysis and systematic literature review method was employed by analyzing articles published between 1999 and 2018, using the VOSViewer software. The analyses provide an overview of articles, authors, the most influential journals, and themes of research. The results reveal the existence of three themes in research: the role of innovation and entrepreneurship their impact on sustainability in SMEs (cluster 1); CSR in the context of SMEs (cluster 2); and, green management and environmental issues for SMEs (cluster 3). In sum, this paper discusses prominent insights from the research analyses and recommends future research directions for the field.
Princeton University1, University of California, Los Angeles2, Massachusetts Institute of Technology3, University of Edinburgh4, Montana State University5, University of California, Santa Cruz6, Virginia Tech7, Cornell University8, University of Washington9, University of Oxford10, RTI International11, University of Lincoln12, University of Colorado Boulder13, Tilburg University14, Harvard University15, Indiana University16, New York University Abu Dhabi17, Brigham Young University18, Stanford University19, University of Zurich20, University of Southern California21, Columbia University22, New York University23, Ohio State University24, University of Michigan25, Kyoto University26, Khalifa University27, California State University28, George Washington University29, MDRC30, Northeastern University31, Syracuse University32, University of Cambridge33, The Turing Institute34, University of California, Berkeley35, University of Pennsylvania36
TL;DR: Practical limits to the predictability of life outcomes in some settings are suggested and the value of mass collaborations in the social sciences is illustrated.
Abstract: How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.
TL;DR: These are extraordinary times as mentioned in this paper, less because we are currently in the midst of a global pandemic; humanity has been here multiple times in the past, sometimes with even more devastating results (the...
Abstract: These are extraordinary times. Less because we are currently in the midst of a global pandemic; humanity has been here multiple times in the past, sometimes with even more devastating results (the ...
TL;DR: In this paper, a field experiment with control, compost only, biochar only, and a mixed compost-biochar application (co-composted and only mixed) at low and high application rates (9 −70 t ha−1) in southern Germany was established, and surface and subsurface (10−30 cm) soil samples were analyzed for pH, microbial biomass carbon (Cmic), water holding capacity (WHC), and cation exchange capacity (CEC).
TL;DR: An approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference.
Abstract: Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments. Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 field images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 field images, showing that the combination of synthetic and original field images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected field images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-off between accuracy and speed. Specifically, the average precisions (APs@IoU0.5) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800 × 1200) on a NVIDIA Titan X GPU environment. The developed model has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference. It is recommendable to use synthetic images and empirical field images together in training stage to improve the performance of models.
TL;DR: Findings from a systematic literature review answer the question: how collaborations help supply chains respond and recover from a disruption and highlight the role of each collaboration mechanism based on each severity level of disruptions.
Abstract: The supply chain collaboration has gained significant attention, especially in the presence of disruptions. This paper presents findings from a systematic literature review to answer the question: ...
TL;DR: In this article, the relationship between extensive forms of urbanization and emerging infectious disease, using empirical examples from the COVID-19 pandemic, is examined using empirical data from the Centers for Disease Control and Prevention.
Abstract: This commentary focuses on the relationship between extensive forms of urbanization and emerging infectious disease, using empirical examples from the COVID-19 pandemic. Specifically, it examines t...
TL;DR: In this article, the theoretical development of some fundamental entropy measures are reviewed and the relations among them are clarified, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault-diagnostic systems.
Abstract: Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault-diagnostic systems. The occurrence of failures in a machine will typically lead to nonlinear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this article, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault-diagnostic systems are summarized. Furthermore, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful toward future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault-diagnostic systems.
TL;DR: In this paper, a protein-based halochromic nanosensor was designed to assess the quality of rainbow trout fillets using electrospun zein nanofibers containing alizarin as the indicator dye.
TL;DR: In this article, the role of pre-intercalated ions via density functional theory simulations is revealed, and it is shown that above a threshold K/Mn ratio of ca. 0.26, the K ions suppress structural transformations by stabilizing the δ phase.
Abstract: The primary issue faced by MnO2 cathode materials for aqueous Zn-ion batteries (AZIBs) is the occurrence of structural transformations during cycling, resulting in unstable capacity output. Pre-intercalating closely bonded ions into the MnO2 structures has been demonstrated as an effective approach to combat this. However, mechanisms of the pre-intercalation remain unclear. Herein, two distinct δ-MnO2 (K0.28MnO2·0.1H2O and K0.21MnO2·0.1H2O) are prepared with varying amounts of pre-intercalated K+ and applied as cathodes for AZIBs. The as-prepared K0.28MnO2·0.1H2O cathodes exhibit relatively high specific capacity (300 mA h g−1 at 100 mA g−1), satisfactory rate performance (35% capacity recovery at 5 A g−1) and competent cyclability (ca. 95% capacity retention after 1000 cycles at 2 A g−1), while inferior cyclability and rate performance are observed in K0.21MnO2·0.1H2O. A stable δ-MnO2 phase is observed upon cycling, with the reversible deposition of Zn4SO4(OH)6·5H2O (ZSH), ion migration between electrodes and synchronous transition of Mn valence states. This work firstly and systematically reveals the role of the pre-intercalated ions via density functional theory simulations and show that above a threshold K/Mn ratio of ca. 0.26, the K ions suppress structural transformations by stabilizing the δ phase. To demonstrate its commercial potential, AZIBs with high-loading active materials are fabricated, which deliver adequate energy and power densities compared with most commercial devices.
TL;DR: It is shown that transfer learning between different crop types is possible and reduces training times for up to 80% and even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.
Abstract: Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds in order to perform selective treatments, increase yield and crop health while reducing the amount of chemicals used. Deep learning approaches have recently achieved both excellent classification performance and real-time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labelling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep-learning-based classifiers for different crop types, with the goal of reducing the retraining time and labelling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds, and compare the performance and retraining efforts required when using data labelled at pixel level with partially labelled data obtained through a less time-consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible, and reduces training times for up to $80\%$. Furthermore, we show that even when the data used for re-training is imperfectly annotated, the classification performance is within $2\%$ of that of networks trained with laboriously annotated pixel-precision data.
TL;DR: The analysis of survey data on 181 software developers shows that the adoption of Stage-Gate principles is negatively associated with speed and cost performance, and the use of sprints for Agile is positively related to new product quality, on-time and on-budget completion.
TL;DR: Combined computational and in situ spectroscopic techniques show P is present as a surface phosphate ion; that electron holes localize on the surface ions and both (P—O1−) and Co3+—OH− are prospective surface active sites for the HER.
Abstract: The hydrogen evolution reaction (HER) is a critical process in the electrolysis of water. Recently, much effort has been dedicated to developing low-cost, highly efficient and stable electrocatalysts. Transition metal phosphides are investigated intensively due to their high electronic conductivity and optimized absorption energy of intermediates in acid electrolytes. However, the low stability of metal phosphide materials in air and during electrocatalytic processes causes a decay of performance and hinders the discovery of specific active sites. The HER in alkaline media is more intricate, which requires further delicate design due to the Volmer steps. In this work, we develop phosphorus modified monoclinic β-CoMoO4 as a low-cost, efficient and stable HER electrocatalyst for the electrolysis of water in alkaline media. The optimized catalyst shows a small overpotential of 94 mV to reach a current density of 10 mA cm-2 for the HER with high stability in KOH electrolyte, and an overpotential of 197 mV to reach a current density of 100 mA cm-2. Combined computational and in-situ spectroscopic techniques show P is present as a surface phosphate ion; that electron holes localise on the surface ions and both (P-O1-) and Co3+-OH- are prospective surface active sites for the HER.