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Asymmetries in the three-dimensional beat of Chlamydomonas reinhardtii flagella revealed by holographic microscopy
Get PDFWe present the first three-dimensional, time-resolved imaging of the Chlamydomonas reinhardtii flagellar waveform. This freshwater alga is a model system for eukaryotic flagella that allow cells to move and pump fluid. During the power stroke, the flagella show rotational symmetry about the cell's centre line, but during the recovery stroke they display mirror symmetry about the same axis. Furthermore, and in contrast to the usual assumptions about beat planarity, we show a subtle rotational motion of the flagella at the initiation of the power stroke, which is mechanically rectified into a quasi-planar mode. We apply resistive force theory to infer the swimming speed and rotational speed of the cells, when a force-free configuration is approximated using a cell on a micropipette, showing good agreement with experimental results on freely swimming cells
Data-driven parametric modelling of split-Hopkinson pressure bar tests on cohesive soils
Get PDFSoil-filled wire and geotextile gabions stand as vital bulwarks in military bases, harnessing soil's innate capacity to absorb shock and safeguard both personnel and critical assets from blast and fragmentation effects. Yet, the dynamic response of cohesive soils under extreme loads remains largely unexplored, leaving engineers grappling with a significant void in knowledge as they strive to fortify structures against emerging threats. This paper considers the high-strain-rate behaviour of kaolin clay using the split Hopkinson pressure bar in both confined and unconfined configurations, with a range of moisture contents representing dry, partially-saturated and saturated conditions. Analysis of the results indicates distinct phase behaviours in transmitted and radial stress based on strain rate, moisture content and confinement. Leveraging cutting-edge machine learning models such as the Proper Orthogonal Decomposition (POD) and sparse Proper Generalised Decomposition (sPGD), data-driven parametric models were developed based on the experimental data. These models enable the prediction of cohesive soil behaviour at specified strain rate and moisture content, enabling engineers to rapidly predict soil behaviour in response to new threats and ground conditions
Prediction of future railway ballast tamping requirements
Get PDFThis paper presents a numerical framework for predicting future railway ballast tamping requirements under varying operational and environmental conditions. The approach integrates an empirical ballast settlement model, derived from large-scale laboratory tests, into a dynamic track-ground interaction framework using the 2.5D Finite Element Method. It is capable of capturing differential settlement and track geometry deterioration considering different types of ballast fouling material, varying levels of ballast moisture content, train speed and traffic volume. Track geometry profiles are updated iteratively after each axle passage, enabling condition-based assessment of future tamping needs. First the model is calibrated using historical track geometry data from an operational railway line. Following calibration, an analysis is performed to study the effect of different moisture levels and types of ballast fouling material (sand, coal and abrasion-induced fines) on track geometry degradation and ultimately future tamping requirements. Next, to understand the effect of different possible track renewal strategies on future tamping requirements, the effects of increasing the rail section and adding under sleeper pads are compared. Results indicate that moisture content, speed, and traffic volume are dominant factors governing tamping frequency. While the type of fouling material and the proposed renewal strategies have a comparatively moderate effect, their influence remains non-negligible. These findings highlight the importance of maintaining adequate track drainage and implementing measures to control the type and extent of ballast fouling
Conclusions
No full textThis book advances the case study methodology by advocating for a pluralistic and contextsensitive approach that integrates historical critique, ethnographic narratives, participatory action research and speculative design. By employing interdisciplinary perspectives and diverse methods — including narrative inquiry, relational ontology and media analysis — it captures the multifaceted nature of place as simultaneously material and imagined, public and intimate, vulnerable and resilient. These methodological innovations enable a more holistic understanding of place as a dynamic and contested construct shaped by cultural narratives, political power and social practices
Sa1770: CHARACTERIZATION OF IBD MICROBIOMES IN MODELS OF MICROBIOME DISRUPTION AND INFLAMMATION TO EVALUATE CANDIDATE BIOMARKERS FOR PATIENT SELECTION
No full textIncluding linguistics in A-level language classes is easy and effective according to teachers
Get PDFgridmappr: An R package for creating small multiple gridmap layouts
Get PDFWe present gridmappr, an R package that automates the process of generating gridmaps – small multiple data graphics of regular size, laid out with an approximate geographic arrangement. Given a set of real geographic point locations, gridmappr allocates points to a regularly sized grid of stated row–column dimensions. This allocation is constrained such that the distance between points in real and grid space is minimised and with a parameter that affects how compactly points are allocated to the regular grid. For geographies with features such as large bodies of water, fixed spacers can be introduced – reserved cells that cannot be allocated points. Layout examples are presented using different parameterisations, and code for generating a family of information-rich glyphmap and origin-destination maps is demonstrated using standard ggplot2
BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks
Get PDFLarge language model (LLM) based agents
have shown great potential in following human instructions and automatically completing various tasks; to do this, the agent needs to
decompose it into easily executed steps by planning. Existing LLM-based approaches to planning mostly proceed by inferring what steps
should be inserted into the plan next by starting from the agent’s initial state. However,
this forward reasoning paradigm does not work
well for complex tasks. We study this issue
in Minecraft, a virtual environment that simulates complex tasks based on real-world scenarios. The failure of forward reasoning is often
caused by the large perception gap between the
agent’s initial state and task goal. To alleviate
this, we leverage backward reasoning and make
the planning start from the terminal (or goal)
state, by first considering which actions could
directly achieve the task goal in one step, before
proceeding to consider how the preconditions
of those actions can in turn be achieved. Our
BAckward Reasoning based agent (BAR) is
equipped with a recursive goal decomposition
module, a state consistency maintaining module and a stage memory module. Experimental results demonstrate the superiority of BAR
over existing methods and the effectiveness of
proposed modules. The code and dataset are
available in https://github.com/SCUNLP/BAR
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