MRC Laboratory of Molecular Biology
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Risk-informed asset management to tackle scouring on bridges across transport networks
No full textScour is one of the main causes of bridge failures resulting in significant macro-economic impacts, often beyond the direct costs of infrastructure damage. Given the pressure to increase the resilience of transport networks, ageing bridge infrastructure, constrained budgets, variable knowledge of asset conditions and limited data, mixed ownership and operation of bridges, and concerns about the risks of climate change, there is a need to implement cost-effective monitoring and maintenance strategies. To this end, this study aims to set the scene for a risk-informed approach for tackling bridge scour, while considering the socio-economic impacts of disruptions due to bridge failures or closures. This study reviews the current practices in predicting, monitoring and managing bridge scour. It discusses the development of a risk-informed approach to aid the whole-life appraisal of bridges while considering the direct and indirect costs associated with bridge failure or closures. The approach provides a rational means to enable asset managers to evaluate the factors that affect bridge failure risk, select and prioritise appropriate mitigation measures, thereby improving the allocation of scarce monitoring and maintenance resources
Multiple decisions about one object involve parallel sensory acquisition but time-multiplexed evidence incorporation
No full textThe brain is capable of processing several streams of information that bear on different aspects of the same problem. Here we address the problem of making two decisions about one object, by studying difficult perceptual decisions about the color and motion of a dynamic random dot display. We find that the accuracy of one decision is unaffected by the difficulty of the other decision. However, the response times reveal that the two decisions do not form simultaneously. We show that both stimulus dimensions are acquired in parallel for the initial ∼0.1 s but are then incorporated serially in time-multiplexed bouts. Thus there is a bottleneck that precludes updating more than one decision at a time, and a buffer that stores samples of evidence while access to the decision is blocked. We suggest that this bottleneck is responsible for the long timescales of many cognitive operations framed as decisions
Human activity recognition through recurrent neural networks for human–robot interaction in agriculture
No full textThe present study deals with human awareness, which is a very important aspect of hu-man–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0– 96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research
Effects of Bacterial Density on Growth Rate and Characteristics of Microbial-Induced CaCO 3 Precipitates: Particle-Scale Experimental Study
No full textMicrobial-induced carbonate precipitation (MICP) has been explored for more than a decade as a promising soil improvement technique. However, it is still challenging to predict and control the growth rate and characteristics of CaCO3 precipitates, which directly affect the engineering performance of MICP-Treated soils. In this study, we employ a microfluidics-based pore-scale model to observe the effect of bacterial density on the growth rate and characteristics of CaCO3 precipitates during MICP processes occurring at the sand particle scale. Results show that the precipitation rate of CaCO3 increases with bacterial density in the range between 0.6×108 and 5.2×108 cells/mL. Bacterial density also affects both the size and number of CaCO3 crystals. A low bacterial density of 0.6×108 cells/mL produced 1.1×106 crystals/mL with an average crystal volume of 8,000 μm3, whereas a high bacterial density of 5.2×108 cells/mL resulted in more crystals (2.0×107crystals/mL), but with a smaller average crystal volume of 450 μm3. The produced CaCO3 crystals were stable when the bacterial density was 0.6×108 cells/mL. When the bacterial density was 4-10 times higher, the crystals were first unstable and then transformed into more stable CaCO3 crystals. This suggests that bacterial density should be an important consideration in the design of MICP protocols
Single-Atom Rhodium on Defective g-C3N4: A Promising Bifunctional Oxygen Electrocatalyst
No full textIt is highly desirable to design bifunctional electrocatalysts to realize highly efficient oxygen evolution/reduction reaction (OER/ORR). Herein, density functional theory (DFT) calculations were conducted to validate the feasibility of a single transition metal (TM) embedded in defective g-C3N4 for bifunctional oxygen electrocatalysis. It was clarified that the TM atom supported on defective g-C3N4 with N vacancy (TM/VN-CN) was stable and possible to be synthesized. Remarkably, Rh/VN-CN exhibited low overpotentials of 0.32 and 0.43 V for OER and ORR, respectively, and was considered as the promising bifunctional catalyst. The volcano plots and contour maps were established based on the scaling relation of adsorption energies of *OH, *O, and *OOH. The OER/ORR activity origin was revealed by descriptors of the d-band center and the number of d-orbital electrons multiplied electronegativity of TM. Furthermore, the machine learning (ML) algorithm was utilized to analyze the intrinsic correlation between catalytic activity and a series of structural and atomic features. Our combined DFT and ML work not only opts for the promising bifunctional oxygen electrocatalysts but also provides guidance for the design of single-atom catalysts and the discovery of more efficient catalysts
Distributed fiber optic monitoring of a CFA pile with a central reinforcement bar bundle
No full textIn this paper, we present an application of distributed fiber optic sensor (DFOS) technology to measure the strain of a continuous flight auger (CFA) test pile with a central reinforcement bar bundle, during a static load test carried out in London. Being distributed in nature, DFOS gives much more information about the pile performance as compared to traditional point sensors, such as identifying cross-sectional irregularities or other anomalies. The strain profiles recorded along the depth of the piles from the DFOS were used to calculate pile deformation (contraction), shaft friction, and tip resistance under various loads. Based on this pile load test, a finite element (FE) analysis was performed using a one-dimensional nonlinear load-transfer model. Calibrated by the shaft friction and tip resistance derived from the monitored data, the FE model was able to simulate the pile and soil performance during the load testing with good accuracy. The effect of the reinforcement cage and central reinforcement bar bundle were investigated, and it was found that the addition of a reinforcement cage would reduce the pile settlement by up to 20%
How does the position of firms in the supply chain affect their performance? An empirical study
No full textThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research
An assessment of phase field fracture: crack initiation and growth
No full textThe phase field paradigm, in combination with a suitable variational structure, has opened a path for using Griffith's energy balance to predict the fracture of solids. These so-called phase field fracture methods have gained significant popularity over the past decade, and are now part of commercial finite element packages and engineering fitness-for-service assessments. Crack paths can be predicted, in arbitrary geometries and dimensions, based on a global energy minimisation - without the need for ad hoc criteria. In this work, we review the fundamentals of phase field fracture methods and examine their capabilities in delivering predictions in agreement with the classical fracture mechanics theory pioneered by Griffith. The two most widely used phase field fracture models are implemented in the context of the finite element method, and several paradigmatic boundary value problems are addressed to gain insight into their predictive abilities across all cracking stages; both the initiation of growth and stable crack propagation are investigated. In addition, we examine the effectiveness of phase field models with an internal material length scale in capturing size effects and the transition flaw size concept. Our results show that phase field fracture methods satisfactorily approximate classical fracture mechanics predictions and can also reconcile stress and toughness criteria for fracture. The accuracy of the approximation is however dependent on modelling and constitutive choices; we provide a rationale for these differences and identify suitable approaches for delivering phase field fracture predictions that are in good agreement with well-established fracture mechanics paradigms
Research on the Application of Superconducting Magnetic Energy Storage in the Wind Power Generation System for Smoothing Wind Power Fluctuations
No full textAs the output power of wind farm is fluctuating, it is one of the important ways to improve the schedule ability of wind power generation to predict the output power of wind farm. The operation mode of tracking planned output takes the planned value issued by the grid dispatching as the control basis of wind power generation. This operation mode is easy to control, which not only meets the needs of power grid dispatching, but also improves the reliability and controllability of new energy generation. This paper introduces the traditional PI control strategy of SMES and combines the advantages of SMES and the characteristics of application scenarios, and introduces sliding mode variable structure control into SMES. Through simulation and comparison of two different control modes, it is verified that SMES adopting sliding mode variable structure control have the advantages of fast response speed and strong robustness, which can make the wind farm output stable power according to the planned value
Intelligent Products through SOHOMA Prism
No full textIn the framework of the SOHOMA 2020 special session “SOHOMA 10th-year anniversary”, this paper aims to make a review of the evolution of one important concept studied in the SOHOMA community, namely the Intelligent Product concept. This paper is not review of Intelligent Products - there are several of these already - but rather examines the history of the development of this concept through the 1st to 9th editions of SOHOMA, while also proposing future developments on this concept