24,757 research outputs found

    The Influence of Frontal and Axial Plane Deformities on Contact Mechanics during Squatting: A Finite Element Study

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    Knee Osteoarthritis (KOA) is a degenerative joint disease and a leading cause of disability worldwide. Lower limb malalignment was a risky factor leading to KOA, altering the load distributions. This study aimed to study the influence of knee deformities on knee contact mechanics and knee kinematics during squatting. A full-leg squat FE model was developed based on general open-source models and validated with in vivo studies to investigate the outputs under frontal malalignment (valgus 8° to varus 8°) and axial malalignment (miserable malalignment 30°). As a result, Varus-aligned and miserable aligned models increased medial tibiofemoral force and lateral patellar contact pressures, while the valgus-aligned model increased lateral tibiofemoral force medial patellar contact pressures with no effects on total contact loads. The Model with a higher medial force ratio (medial force/total force) induced a higher internal tibial rotation. In conclusion, we recommended that patients with knee malalignment be taken care of alignments in both frontal and axial planes

    Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

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    According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We tested the seasonal influence of the taxonomic and phenological metrics on spectral variance using linear mixed models. A significant interaction term of percent mature leaves and species diversity was found, with the most parsimonious model explaining 43% of the intra-annual change. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance -species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits

    Understanding interactions between Ramularia collo-cygni and barley leaf physiology to target improvements in host resistance and disease control strategy

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    Ramularia Leaf Spot (RLS) is an increasingly problematic disease of barley. Control options are limited as the causal fungus, Ramularia collo-cygni, has developed resistance to several of the major fungicide groups. Developing new methods for controlling this disease is therefore a priority. R. collo-cygni can grow systemically in barley plants from infected seed, without inducing visible symptoms. In the field, visible symptoms normally only appear after flowering. The relative contribution of the latent and symptomatic stages of the fungal lifecycle to reduction in barley yield is not currently known with any certainty. Two possibilities are that the effect of asymptomatic infection on pre-flowering photosynthetic activity, and the development of grain sink capacity, plays an important role; or that reduction in photosynthetic activity during grain filling, resulting from lesion development and loss of green leaf area, is the predominant factor. This research aimed to increase our understanding of the impact of different phases of the fungal lifecycle on barley photosynthesis and yield formation, to better target host resistance and disease control strategies. Controlled environment and field experiments were used to determine the relative effects of asymptomatic and symptom-expressing phases of R. collo-cygni infection on photosynthesis and yield formation in spring barley. In controlled environment experiments leaf photosynthetic activity was measured in seedlings inoculated with suspensions of R. collo-cygni mycelia. Measurements were made before and after visible symptom development using Infra-Red Gas Analysis (IRGA), chlorophyll fluorescence analysis and chlorophyll fluorescence imaging. No reduction in photosynthetic activity was observed in leaves infected with R. collo-cygni, compared to those of non- infected leaves, during the latent phase of infection. After the appearance of visible symptoms, photosynthetic activity within lesions reduced as the lesions developed. However, this did not lead to reductions in photosynthetic activity when measured across the whole leaf area, suggesting that for there to be a significant effect of disease on whole leaf photosynthetic activity, visible symptoms must develop into mature lesions and coalesce to cover larger areas of the leaf surface. In field experiments plots were treated with a full fungicide regime, left untreated, or inoculated with R. collo-cygni and treated with fungicide to which R. collo-cygni is resistant (the latter as a precaution against lack of natural RLS disease that year and/or other diseases developing on untreated plots). RLS was the only disease of significance that developed in untreated or inoculated plots. Symptoms first appeared after flowering, around Zadoks Growth Stage 72. Fungicide-treated plots remained free of disease. Chlorophyll fluorescence analysis of field plants showed no effect of infection on the maximum quantum efficiency of Photosystem II (Fv/Fm) before visible symptom development, consistent with results from controlled environment experiments. Grain yield of untreated and fungicide-treated plots was predicted from fixed common values of radiation use efficiency (RUE) and utilisation of soluble sugar reserves, and measured values of post-flowering healthy (green) leaf area light interception. Grain yields predicted from the difference in post-flowering light interception between fungicide-treated plants and untreated or inoculated plants displaying symptoms of RLS were comparable with the measured yield response to fungicide. This suggests that yield loss to RLS is primarily associated with a reduction in light capture during grain filling, resulting from lesion development and loss of green leaf area. Results from controlled environment and field experiments suggested that symptom expression was associated with leaf senescence. Further controlled environment experiments tested this relationship by using treatments to vary the onset and rate of leaf senescence. Seedlings that were treated with cytokinin to delay senescence after inoculation with suspensions of R. collo-cygni mycelia developed fewer lesions than control plants. Fungal growth, as measured by quantification of R. collo-cygni DNA in leaves, was also restricted in plants treated with cytokinin. Collectively these results suggest that prevention of visible symptom development, rather than prevention of asymptomatic growth, is the most important target for management of this disease. Control methods targeted at delaying senescence could be a useful avenue for further investigation

    Predicting limit-setting behavior of gamblers using machine learning algorithms: a real-world study of Norwegian gamblers using account data

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    Player protection and harm minimization have become increasingly important in the gambling industry along with the promotion of responsible gambling (RG). Among the most widespread RG tools that gaming operators provide are limit-setting tools that help players limit the amount of time and/or money they spend gambling. Research suggests that limit-setting significantly reduces the amount of money that players spend. If limit-setting is to be encouraged as a way of facilitating responsible gambling, it is important to know what variables are important in getting individuals to set and change limits in the first place. In the present study, 33 variables assessing the player behavior among Norsk Tipping clientele (N = 70,789) from January to March 2017 were computed. The 33 variables which reflect the players’ behavior were then used to predict the likelihood of gamblers changing their monetary limit between April and June 2017. The 70,789 players were randomly split into a training dataset of 56,532 and an evaluation set of 14,157 players (corresponding to an 80/20 split). The results demonstrated that it is possible to predict future limit-setting based on player behavior. The random forest algorithm appeared to predict limit-changing behavior much better than the other algorithms. However, on the independent test data, the random forest algorithm’s accuracy dropped significantly. The best performance on the test data along with a small decrease in accuracy in comparison to the training data was delivered by the gradient boost machine learning algorithm. The most important variables predicting future limit-setting using the gradient boost machine algorithm were players receiving feedback that they had reached 80% of their personal monthly global loss limit, personal monthly loss limit, the amount bet, theoretical loss, and whether the players had increased their limits in the past. With the help of predictive analytics, players with a high likelihood of changing their limits can be proactively approached

    Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

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    5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools

    Diversity, Importance and Decline of Pollinating Insects in Present Era

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    Pollination is a multi-million-year-old co-evolutionary process involving flowering plants and pollinators. It is one of the most important mechanisms in preservation and promotion of biodiversity as well as life on Earth. Pollinator diversity is essential for maintaining overall biological diversity in many habitats including agro-ecosystems. Pollinators are responsible for assisting reproduction in over 80% of the world’s flowering plants. In their absence, humans and wildlife would go hungry. Insects are the most efficient pollinators as they play a crucial part in pollination ecology. Pollinators and their habitats have ecological, economic, cultural and social benefits. Pollination efficiency is highly dependent on certain attributes and characteristics of pollinators such as vision, anatomy, food preferences, olfaction, behaviour and learning ability. With the rapid growth of human population, our demand for food has also risen. Our agricultural systems will need to produce more food in a sustainable manner in the future to cope with this. Pollinators play an important role in these ecosystems and will continue to do so in the future. Because pollinators are so important to agriculture, we need to learn more about which crops require specific pollinators and how to best maintain and promote both wild and controlled species. Their diversity needs protection because there are specific relationships between certain crops and pollinators. Pollinator communities are suffering as a result of man-made habitat disruptions, including severe biodiversity loss. This diversity must be protected by combining conservation measures with sustainable farming practices which could increase crop yields while protecting insect pollinator species

    Unraveling the effect of sex on human genetic architecture

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    Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes, amongst other factors. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides the genome with a distinct hormonal milieu, differential gene expression, and environmental pressures arising from gender societal roles. This thus poses the possibility of observing gene by sex (GxS) interactions between the sexes that may contribute to some of the phenotypic differences observed. In recent years, there has been growing evidence of GxS, with common genetic variation presenting different effects on males and females. These studies have however been limited in regards to the number of traits studied and/or statistical power. Understanding sex differences in genetic architecture is of great importance as this could lead to improved understanding of potential differences in underlying biological pathways and disease etiology between the sexes and in turn help inform personalised treatments and precision medicine. In this thesis we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We further investigated whether sex-agnostic (non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we studied the potential functional role of sex differences in genetic architecture through sex biased expression quantitative trait loci (eQTL) and gene-level analyses. Overall, this study marks a broad examination of the genetics of sex differences. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Furthermore, our results suggest the need to consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms

    How to Be a God

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    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods

    A productive response to legacy system petrification

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    Requirements change. The requirements of a legacy information system change, often in unanticipated ways, and at a more rapid pace than the rate at which the information system itself can be evolved to support them. The capabilities of a legacy system progressively fall further and further behind their evolving requirements, in a degrading process termed petrification. As systems petrify, they deliver diminishing business value, hamper business effectiveness, and drain organisational resources. To address legacy systems, the first challenge is to understand how to shed their resistance to tracking requirements change. The second challenge is to ensure that a newly adaptable system never again petrifies into a change resistant legacy system. This thesis addresses both challenges. The approach outlined herein is underpinned by an agile migration process - termed Productive Migration - that homes in upon the specific causes of petrification within each particular legacy system and provides guidance upon how to address them. That guidance comes in part from a personalised catalogue of petrifying patterns, which capture recurring themes underlying petrification. These steer us to the problems actually present in a given legacy system, and lead us to suitable antidote productive patterns via which we can deal with those problems one by one. To prevent newly adaptable systems from again degrading into legacy systems, we appeal to a follow-on process, termed Productive Evolution, which embraces and keeps pace with change rather than resisting and falling behind it. Productive Evolution teaches us to be vigilant against signs of system petrification and helps us to nip them in the bud. The aim is to nurture systems that remain supportive of the business, that are adaptable in step with ongoing requirements change, and that continue to retain their value as significant business assets
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