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Stochastic seakeeping analysis of nonlinear ship rolling dynamics under non-stationary and irregular sea states
Get PDFThis paper presents an efficient semi-analytical methodology for quantifying the capsizing risk and seakeeping performance of ships undergoing nonlinear rolling motions under realistic, non-white sea-wave excitations. The dynamic response is captured through a comprehensive and physically consistent nonlinear formulation that incorporates both softening and hardening restoring moment characteristics, nonlinear hydrodynamic damping mechanisms, and evolutionary stochastic wave loads representative of complex maritime environments. By leveraging a refined blend of stochastic averaging and statistical linearization techniques, the study yields computationally efficient, time-dependent seakeeping probability estimates, rigorously accounting for the critical behaviors of both bounded and unbounded ship roll motions, including those associated with negative stiffness regions, through an appropriately tailored, non-stationary response amplitude probability density function (PDF). A notable advancement of the proposed framework lies in its robust capability to address stochastic sea-wave excitations with time-varying intensity and frequency content, thereby accurately reflecting the evolving nature of real-world open-sea environments. Numerical analyses across a range of case studies, validated against benchmark Monte Carlo simulations, demonstrate the accuracy and efficiency of the methodology, underscoring its promise as a practical performance-based tool for evaluating vessel stability and seakeeping under dynamic and uncertain maritime operational scenarios
Spherical Acoustic Spatial Entropy:Predicting Acoustic Scene Complexity in Virtual Environments
Get PDFThe objective quantification of Acoustic Scene Complexity (ASC) remains a significant challenge. While existing entropy-based metrics capture spectro-temporal variability, a metric accounting for the spatial distribution of sources has been lacking. We introduce Spherical Acoustic Spatial Entropy (SASE), a novel information-theoretic metric designed for Virtual Acoustic Environments (VAEs). SASE leverages ground-truth spatial data, utilizing an equal-area spherical partition around the listener and weighting source contributions by their perceptual loudness (ITU-R BS.1770-4). We validated SASE through a psychoacoustic experiment (N=21) using a 2×2×2 factorial design that manipulated masker count, spatial distribution, and motion. SASE was evaluated alongside energy and spectral entropy metrics against subjective ratings of complexity, effort, and spatial spread. Results show that SASE mean was the most robust predictor of perceived complexity in condition-level ratings (R2=0.714, p=0.008), outperforming spectral and energy entropy. A random-effects pooling of participant regression coefficients confirmed this relationship at the population level (R2 pseudo=0.740, p<.001). Furthermore, a model combining SASE mean with spectral entropy standard deviation explained 84.4% of the variance in perceived complexity, indicating spatial and spectro-temporal metrics capture complementary scene dynamics. SASE provides an objective measure of spatial complexity, enhancing existing frameworks for predicting ASC in virtual environments
AntibioticDB: An Updated and Improved Open-Access Database for the Antibacterial Research and Development Community
Get PDFAntibioticDB (https://www.antibioticdb.com/), originally established in 2017 and since 2021 led by the Global Antibiotic Research & Development Partnership (GARDP), is a freely available database of antibacterial agents to facilitate research and development of new antibacterial therapeutics. Here, we describe a new release of AntibioticDB that has been significantly expanded and updated with the aid of user feedback and which offers additional functionality through a redesigned web portal. Improvements include reciprocal integration with the IUPHAR/BPS Guide to Pharmacology (https://www.guidetopharmacology.org), capturing of compound structure information in the form of standard chemical identifiers (canonical and isomeric SMILES, InChI, and InChI Key), chemical 2D structure images, and harmonizing terminology to optimize database searching. Ongoing curation efforts have increased the number of individual entries to >3,500, a process driven mostly by a significant expansion of historical natural product antibiotics that were previously under-represented in the database. The database is continuously updated by mining the published literature and capturing newly discovered antibacterial compounds as they are reported, making AntibioticDB the most complete global resource on antibacterial agents
InspectorORF: a tool for visualising Ribo-Seq and additional genomic or transcriptomic data
Get PDFMotivation
The advent of ribosome profiling (an adaptation of RNA sequencing) to determine the translatome, has led to a huge improvement in our understanding of what parts of the transcriptome are translated. Many alternative open reading frames (ORFs) are now regularly being detected such as out-of-frame, overlapping, upstream or downstream reading frames, and alternative reading frames using non-canonical start codons. Various tools have been developed for the detection of such novel ORFs, but they lack the capacity to visually inspect reads—an important aspect of validation and prediction of translation.
Results
The integrated and visualisation of ribosome profiling and RNA sequencing reads enables discrimination between transcriptional and translational signals, facilitating validation of predicted novel open reading frames. Furthermore, the inclusion of complementary evidence such as proteomic and long-read sequencing enables further validation of predicted novel open reading frames.
Availability and implementation
Here, we present, InspectorORF (https://www.github.com/aylz83/inspectorORF), an R package that readily plots ribosome profiling reads, alongside RNA sequencing reads across transcripts and/or ORFs. Additionally, custom information can be plotted including data from additional conditions and samples, proteomic analyses and reads from long-read sequencing
Interventions to support parents, families and caregivers in caring for preterm or low birth weight infants at home:A systematic review and meta-analysis
Get PDFThe aim of this study was to determine what interventions, approaches, or strategies to support mothers/fathers/caregivers and families in caring for preterm (<37 gestational weeks) or low birthweight (<2,500g) infants in the home have been effective in improving outcomes. We conducted a systematic review and meta-analysis. A comprehensive search of relevant electronic databases, including MEDLINE, Embase, CINAHL and Cochrane Central Register of Controlled Trials was completed in September 2024. Studies were included if they utilised interventions which focused on providing support to participants (mother/father/parents, families or caregivers) to care for their infants in the home. Two reviewers independently screened papers in Covidence and extracted data. Random effects meta-analyses were undertaken. Quality of studies and certainty of evidence were assessed using CASP and GRADE, respectively. Critical outcomes based on WHO preterm and low birthweight criteria comprised infant mortality, morbidity, growth and neurodevelopment. Priority outcomes comprised breastfeeding, care seeking, parent-infant interaction, mother-child attachment and parental health and wellbeing. Forty-seven studies were included. There is some evidence that support interventions may improve outcomes related to infant mortality, improvements in infant growth, exclusive breastfeeding, infant cognitive development, immunisation uptake, and reduction in maternal stress and depression. However, the overall certainty of evidence is low or very low in the majority of studies. We conclude that interventions providing support for parents to care for infants in the home may improve outcomes for this population. There is a need for well-considered large scale support interventions, prioritised and developed with women and families
Digital Dong: Heritage assessment, reality capturing and 3D modelling
No full textThis original paper presents the digital documentation and reconstruction of the endangered drum tower building and its contemporary surroundings in Dong villages of peripheral mountains of southwestern China. The research methods adopted a series of digital techniques, including 3D terrestrial LiDAR scanning, aerial and close-range photography and photogrammetry, alongside conventional humanities methods such as ethnographic observation and oral histories with local carpenters and communities. Much as an unknown construction heritage from outside Dong and a heavily used everyday space inside Dong, the drum tower has met severe threats from human and wild fires, natural decay due to material degradation and climate change, as well as urbanisation and modern tourism. Hence a timely and thoroughly digital assessment, analysis, and documentation of the historic structures is a necessity, which will serve as an evidence base for subsequent conservation, maintenance and repair interventions. The paper will present latest case studies highlighting the above workflow from on-site assessment to surveying, reality capturing and 3D digital modelling. This paper is part of the results from the EWAP Large Grant ‘Decoding Dong: Documentation of Dong Minority Villages’ Drum Tower and Wooden Heritage’, funded by Arcadia (EWAP2039LG, 2023-2025)
QoS-aware placement of interdependent services in energy-harvesting-enabled multi-access edge computing
Get PDFThe advent of 5G drives the growth of multi-access edge computing (MEC), a revolutionary paradigm that utilises edge resources to enable low-latency mobile access and support complex service execution. Deploying services across geographically distributed edge nodes challenges providers to optimise performance metrics like end-to-end latency and resource efficiency, impacting user experience, operational cost, and environmental footprint. The energy harvesting (EH) technology provides clean and renewable energy at the edge, promoting the MEC system to minimise the impacts on the environment. However, the integration of EH can introduce energy limits and uncertainty to the powered devices. In the context of service scheduling with data flow dependencies, we propose two offline and heuristic-based service placement algorithms that balance minimising latency and maximising resource efficiency with fast execution. The two algorithms, evaluated in a simulated environment using state-of-the-art workload benchmarks, achieve significant energy consumption improvements while maintaining comparable latency. Based on the designed algorithms, we take a step further by developing an online dynamic resource scheduling and service offloading approach for MEC systems with EH capabilities. Simulation results demonstrate that the proposed strategy effectively utilise the harvested energy while granting a low user-experienced latency and low operational cost
Unseen data detection using routing entropy in mixture-of-experts for autonomous vehicles
No full textUnseen data that differ significantly from the training data can cause machine learning models to behave unpredictably, which is particularly problematic in safety-critical systems like autonomous vehicles. Detecting such data, commonly called out-of-distribution (OOD) data, is essential for ensuring the robustness of these models. Existing methods often rely on the model’s final output, which are limited since the model can be overconfident on unseen data. In this paper, we propose Routing Entropy, a novel OOD detection method that leverages the internal routing behavior of Mixture-of-Experts (MoE) models, a design increasingly adopted in modern neural networks. We hypothesize that MoE models exhibit high confidence routing for in-distribution (ID) inputs, but greater uncertainty for OOD inputs. We quantify this uncertainty by calculating the entropy of the routing scores for a given input. Experimental results on a MoE-based semantic segmentation model used for perception in autonomous driving demonstrate that Routing Entropy is effective on its own and, more importantly, provides a complementary signal to existing output-based methods. Combining Routing Entropy with an existing method significantly improves OOD detection performance. These results suggest that leveraging internal routing behavior of MoE models is a promising direction for robust OOD detection
A Neolithic rock engraving apparently showing a Great Auk being captured
Get PDFWe evaluate whether a Neolithic engraved rock image at the Alta archaeological site in Finnmark, Norway – of a bird being held by a person – represents a Great Auk Pinguinus impennis. There are several thousand engraved animal figures at Alta, created between 5000 and 2000 years ago, in various hunting panoramas. Of these images, 24 represent aquatic birds, including four others that might also be Great Auks. Based on the size of the bird relative to the person holding it, the size and shape of the beak, wings and webbed feet, and comparisons with some other bird images at Alta, we conclude that it is likely that this one does represent a Great Auk
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