bluuebunny/crossref_metadata_2025 huggingface dataset:
Wave propagation in a honeycomb sandwich panel of variable thickness: model and characterisation of the Black Hole Acoustic effect
Authors: Besse Alex , O’Donoughue Patrick , Aklouche Omar , Ablitzer Frédéric , Pelat Adrien , Gautier François
Published: June 2024

An Acoustic Black Hole (ABH) is a scatterer, embedded in a panel, allowing passive vibration control without adding mass. In practice, it is achieved by means of a local reduction in thickness (axisymmetric pit with a parabolic profile) and the addition of a thin viscoelastic coating in a central region of uniform thickness (plateau). The vibration absorption induced by the ABH, allows the design of stiff, light and non-resonant panels. In this paper, we propose to study this effect for a three-layer sandwich panel (glass fiber skin/honeycomb core/glass fiber skin), which gives rise to both bending and shear effects. The equations of motion of the thick, symmetrical sandwich panel with variable characteristics is obtained within the framework of the zig-zag theory by applying Hamilton’s principle. These equations lead to a sixth-order analytical model, allowing the dispersion curves to be obtained, and then an analytical model of the scatterer inserted in an infinite panel. The analysis of the local modes of the scatterer allows the evaluation of its absorption; the interpretation of the ABH effect is carried out using its scattering matrix. The model allows in particular the analysis of the effect of shear on the ABH effect. Experimental tests, based on vibrational maps obtained by vibrometry, are used to discuss and validate the predictions.
Detection of crack bar deterioration at offshore wind turbine supports using generative adversarial networks and autoencoders
Authors: Prieto-Galarza Ricardo , Tutivén Christian , Vidal Yolanda
Published: June 2024

This work focuses on the structural health monitoring of jacket-type foundations used by offshore wind turbines. A vibration-only response mechanism based on accelerometer data is specifically suggested. This work, based on an anomaly detection model, has developed a methodology to detect crack bar deterioration in the wind turbine jacket consisting of two training phases with only healthy data: training of a generative adversarial network (GAN), and encoder training of an autoencoder based on the GAN model that has already been learned. A generator and a critic may be obtained through the GAN network training process. This model is used to train an encoder that allows the mapping of healthy samples to a latent vector. Following encoder training, the encoder places the data at points in latent space that correspond to the input data’s healthy state. The mapping of the input sample space to the latent space through the encoder and the subsequent mapping of the latent space to the initial space through the generator should closely resemble the input sample in the event of a healthy input. However, when damage-state input samples are used, the model output does not resemble the input. The reconstruction error and a comparison of the residual error of the critic properties are the final two loss functions used for the identification of anomalies. The proposed strategy has been tested through laboratory experiments on a down-scaled model.
Vibration-based system identification of a large steel box girder bridge
Authors: Schneider R , Simon P , Hille F , Herrmann R , Baeßler M
Published: June 2024

The Bundesanstalt für Materialforschung und -prüfung (BAM) collaborates with TNO to develop a software framework for automated calibration of structural models based on monitoring data. The ultimate goal is to include such models in the asset management process of engineering structures. As a basis for developing the framework, a multi-span road bridge consisting of ten simply supported steel box girders was selected as a test case. Our group measured output-only vibration data from one box girder under ambient conditions. From the data, we determined eigenfrequencies and mode shapes. In parallel, we developed a preliminary structural model of the box girder for the purpose of predicting its modal properties. In this contribution, we provide an overview of the measurement campaign, the operational modal analysis, the structural modeling and qualitatively compare the identified with the predicted modes. As an outlook, we discuss the further steps in the calibration process and future applications of the calibrated model.
Dynamic identification of a masonry structure through a MEMS based monitoring system
Authors: Scocciolini D , Romanazzi A , Virgulto G , Parisi E , Barchi F , Acquaviva A , Savoia M , Buratti N
Published: June 2024

The number of applications of structural health monitoring techniques to buildings is steadily increasing, fostered by the diffusion of low-cost accelerometric sensors based on MEMS. In many cases, these sensors may represent an alternative to more expensive transducers, such as piezoelectric accelerometers, even if they typically have lower signal-to-noise ratios. Their higher noise levels may limit the capacity to identify modal parameters of structures when small amplitude vibrations are recorded. The present paper presents the results of an experimental campaign in which two SHM systems were installed on a masonry building built at the CIRI Buildings and construction Laboratory of the The University of Bologna, recording accelerations for more than six months. One system was based on uniaxial piezoelectric accelerometers, and the other on triaxial MEMS. The data recorded by the two systems were used as input for output-only structural identification algorithms. Using the results obtained from the piezoelectric accelerometers as the reference, the capacity to identify the modal features of the building from the MEMS data is analysed.
A Highly Stretchable Strain-based Sensing Sheet for the Integrated Structural Health Monitoring
Authors: Zhang Hui , Beskhyroun Sherif
Published: June 2024

In this study, a flexible strain sensing system that can be applied to full-scale reinforced concrete frame structures is presented. In order to fulfil the criteria for strain detection that are posed by various structural components, the flexible strain gauge is offered in two distinct configurations: one full bridge and one double half bridge. A strain configuration selector is built on the basis of this information. The selector is designed to enable the system to flexibly switch strain modes for measuring axial or bending strain without adjusting the installation location of strain sensors. The first section of this study focuses mostly on elaborating on the methodology behind the development of a flexible strain system. This method was primarily designed with the aim of detecting the abnormalities in the strain field that are brought on by structural damage in order to accomplish the goal of local detection. The creation of a strain configuration selector also enables the conversion between two different strain measures whenever it is necessary without requiring the sensor installation to be moved to a new position, which helps to significantly reduce the amount of cost associated with sensor deployment. The performance of the flexible strain sensing system as well as its sensitivity were evaluated by doing the cyclic load testing on a full-scale RC frame. Both half-bridge and full-bridge strain gauges are installed in the critical components, such as beams and columns. In addition, 14 linear variable displacement transducers (LVDTS) were placed on the RC frame in order to monitor variations in displacement and deformation. The findings of the experiments indicate that the flexible strain sensor exhibits a high degree of sensitivity, and it is therefore suitable for integration into a structural health monitoring (SHM) system for the purpose of tracing the strain caused by localised structural damage. Additionally, it is able to monitor the strain trend on the complete scale of the frame model. In future work, the flexible strain system will be modified and enhanced by using wireless technology for data transmission in order to build a wirelessly integrated structural health monitoring (SHM) system.
A linearised Hybrid FE-SEA analysis method for complex systems with nonlinearities under random excitation
Authors: Allen H G , Langley R S
Published: June 2024

Within complex structures it is not uncommon for localised nonlinearities to be present, this renders conventional linear mid-high frequency analysis techniques such as the Hybrid Finite Element (FE) - Statistical Energy Analysis (SEA) method inapplicable. The development of a linearisation procedure applicable to an ensemble of random systems featuring deterministic nonlinearity is initially considered in this work, which is centred around the technique of Equivalent Linearisation. Constraining the linearisation to remain invariant across the whole ensemble, key to the linearised hybrid formulation developed, produced a relation between the order of both nonlinearity and the required statistical response moment when considered for Gaussian excitation. From the linearisation derived, an extension to hybrid variance theory was necessary to determine the variance of a frequency integrated response of the master system. A numerical study is then used to demonstrate the linearised Hybrid FE-SEA method against a series of Monte Carlo benchmark simulations adopting a Lagrange Rayleigh-Ritz model. The benchmark simulations feature different treatments of the nonlinearity from the linearisation developed to the full nonlinear solution.
Computation of the dynamic response of finite two-dimensional periodic structures
Authors: Duhamel Denis
Published: June 2024

The study of periodic media is mainly focused on one-dimensional periodic structures (periodic along one direction), to determine the dispersion curves or for the calculation of the response to an external excitation. Effective methods such as the Wave Finite Element (WFE) have been obtained for such computations. Two-dimensional periodic media are more complex to analyse but dispersion curves can be obtained rather easily. Obtaining their response to an excitation is much more difficult and the results mainly concern infinite media while for finite media, few results are available. In this communication, the response of finite two-dimensional periodic structures to an excitation is studied by limiting oneself to structures described by a scalar variable (acoustic, thermal, membrane behaviour) and having symmetries. Using the WFE for a rectangular substructure and imposing the wavenumber in one direction, we can calculate the wavenumbers and mode shapes associated with propagation in the perpendicular direction. By building solutions with null forces on parallel boundaries, we can decouple the waves in the two directions parallel to the sides of the rectangle and solve each case by a FFT. Summing the contributions of all these waves gives the global solution with a low computing time even for a large number of substructures. Examples are given for the case of a two-dimensional membrane.
A two-stage model updating using complex modal data
Authors: Henikish E , Bansal S
Published: June 2024

The process of Finite Element (FE) modelling is often associated with uncertainties which necessitates its updating using measured data. Updating dynamical model has a considerable importance in Structural Health Monitoring (SHM), estimating the structural responses, and structural control applications. Among the various presently available approaches for performing model updating, the probabilistic updating approach such as Bayesian model updating has gained a great deal of attention in the last two decades. It can aggregate all the types of uncertainties, e.g., prediction errors, modelling errors, measurement errors, etc., and deal with them as epistemic uncertainties about the system. This paper introduces a two-stage Bayesian model updating procedure of FE dynamical model. The targeted systems encounter a non-classical damping yielding complex modes of vibration. The original FE model is reduced using the method of dynamic condensation, where the reduced FE model is related to the observed Degrees-of-Freedoms (DOFs). Due to the usual availability of a limited number of sensors during real-world applications, modal data used to update the FE model is obtained from multiple setups. Finally, to simulate samples that approximate the posterior Probability Density Function (PDF), a new MWG sampling algorithm is used and compared with Transitional Markov Chain Monte Carlo (TMCMC) algorithm. The proposed approach has been applied to a numerical dynamical system with synthetic modal data acquired from multiple setups. Results revealed that the proposed approach could update the system’s uncertain parameters efficiently with a visible reduction in the uncertainty in both undamaged and damaged system.
SEA approach applied to the estimation of the vibroacoustic response of a statistical system excited through a nonlinear device
Authors: Andrade L , Langley R S
Published: June 2024

Statistical Energy Analysis (SEA) techniques have been largely employed to model vibroacoustic systems as it simplifies the equations of motion of the system optimising the computing time, a useful feature to analyse random ensembles. However, the approach is limited to linear systems. Modelling vibro-acoustic systems with nonlinear characteristics is a challenging problem as there rarely exist analytical solutions for the dynamic response. An example of a system that includes nonlinear features in the transmission path is the suspension system of a vehicle, where vibrations that are result of the interaction between the wheels and the road ultimately arise the sound pressure levels in the car cabin, which are affected by such nonlinearities. A nonlinear interface between the excitation point and a statistical structure has been included in an experimental setup that represents the suspension system with the aim of exploring key features, or otherwise, of the effect that a transmission path with nonlinear stiffness has on the structural response of a randomised statistical system to a random input. A numerical model has been developed to simulate the dynamic response of the structure by adopting the infinite plate assumption to model the statistical structure as an SEA dissipative mechanism, hence the equations of motion are largely simplified as they result in a single-degree-of-freedom second order differential equation. Numerical simulations of the model here developed agree remarkably well with the experimental data, where the averaged dynamic response and the generation of high-order harmonics are accurately estimated. Additionally, the measured loss of coherence at the frequencies where harmonics are present is also predicted by the model.
Smartdynamics a Mobile Phone Application Designed for Bridge Structural Assessments
Authors: Hassani Jihane Tahri , Teixeira Ricardo
Published: June 2024

A mobile phone application, referred to as SMARTdynamics, was developed to take site readings of the dynamic response of a cable-stayed highway bridge and a three-span plate girder railway bridge by making use of built-in accelerometers. The application allows to determine key dynamic properties including natural frequencies, displacements, and damping ratios. SMARTdynamics includes a novel feature for smartphone accelerometers: the ability to control the sensor remotely with automated application feedback, allowing to record the dynamic response at specific times and with precision. The aim of the study was to determine the reliability of smartphone accelerometers, establish the effectiveness of mobile phone usage in the bridges and civil structures industry, and demonstrate that the derivation of more realistic dynamic properties can offer benefits in determining less conservative structural capacities. Acceleration readings were taken for a cable-stayed highway bridge to determine the tension force within the stay cables. The obtained results confirmed the viability of mobile phone ac-celerometers as a serious asset management tool. Site readings were also taken to determine the dynamic amplification factors of plate girder railway bridges and revealed a mean average reduction of 40% in the real dynamic increment factor when compared with values derived from codes of practice. The study confirmed that moving towards a realistic approach to bridge structural assessments with user-friendly and cost-effective tools can have tangible benefits and should be considered as a viable alternative to traditional methods.
A Novel Reduced Column Approach for the Mitigation of Earthquake-Induced Vibrations of Wind Turbines
Authors: Tombari Alessandro , Rostami Rohollah
Published: June 2024

Wind power is a sustainable and renewable energy source which becomes fundamental in order to meet the worldwide rising demand for energy. New onshore and offshore wind farms will be rapidly constructed in challenging environments, especially in earthquake-prone areas. Although a flexible structure, the wind turbine tower is slender and lightly damped which may exhibit a high susceptibility to earthquake-induced vibration. Whilst past studies have been based on traditional passive control devices, such as tuned mass dampers or tuned liquid column dampers, this study aims to propose a novel approach, the Reduced Column Section (RCS), to design an innovative transition piece to mitigate the vibrations on fixed wind turbines. The novel transition piece of hourglass shape will allow the control of the fundamental period of the wind turbine as well as it will limit the maximum stresses within the device by protecting the tower and the monopile.

The proposed approach will be numerically tested on the 15MW benchmark wind turbine proposed by the International Energy Agency. A 3-dimensional finite element model will be generated using the commercial ADINA software and verified through the 1-dimensional baseline model provided for the open-source OPENFAST code. Therefore, the novel transition piece will be implemented and tested under a combination of multi-hazards such as wind and seismic loading. Two proposed devices, with and without auxetic lattice structure, will be investigated. The benefits of the proposed devices will be demonstrated in terms of the mitigation of the stresses on the tower wall and on the soil.
Identification of the dynamic properties of a fluid inerter
Authors: Chillemi M , Furtmüller T , Adam C , Pirrotta A
Published: June 2024

To control vibrations in structures, the mitigation effect of well-known passive control devices such as Tuned Mass Dampers can be used. Since the effectiveness of these devices strongly depends on their mass, the so-called inerter can be exploited to fictitiously increase their mass, allowing a mass amplification effect without any physical addition. The present study aims to identify the dynamic properties of a fluid inerter, consisting of a hydraulic cylinder connected by an external pipe filled with fluid. A test campaign is conducted to reveal the actual behavior of this device, focusing on the effects of its nonlinearities. Specifically, the following nonlinear effects are revealed: energy dissipation due to friction of the piston and pressure drops due to the viscosity of the fluid in the external pipe and at the inlet and outlet. An accurate mechanical model of the device is determined and its efficiency demonstrated by means of comparative numerical studies. The nonlinear layout provides more realistic response predictions than a common linear model and can be implemented in several structural control device configurations.
Hydroelastic analysis of flapping-foil thrusters using a partitioned BEM-FEM
Authors: Anevlavi D , Filippas E , Karperaki A , Belibassakis K
Published: June 2024

Understanding the mechanics of aquatic locomotion has been an active field of research for decades and continues to inspire technological solutions ranging from small-scale propulsion systems for autonomous underwater vehicles (AUVs) to larger-scale energy saving devices (ESDs) for ships. The bio-inspired thrust-producing kinematics are shared among most flapping-foil systems, however joint experimental and numerical research suggests that incorporating additional biomimetic features, such as hydrodynamic shape and elasticity, in new designs can enhance the efficiency. Focusing on the latter, the response of passively deforming wings is implicitly non-linear, since deformations affect the hydrodynamic load excitation and vice-versa. Therefore, fluid-structure interaction simulations are essential for accurate predictions of the wings’ response. In the present work, a cost-effective computational tool is proposed for the hydroelastic analysis of flexible flapping-foil thrusters, which consists of a 3-D unsteady boundary element method (BEM) weakly coupled with a finite element solver (FEM) based on plate elements. The verification of the present method is accomplished by means of comparison against experimental data from the literature. The prediction capabilities and the limitations of the weakly coupled BEM-FEM are discussed. Finally, the proposed numerical tools serve as the building blocks for the fully coupled BEM-FEM scheme that is currently under development.
Structural reliability analysis using imprecise evolutionary power spectral density functions
Authors: Behrendt Marco , Dang Chao , Faes Matthias G.R. , Valdebenito Marcos A. , Beer Michael
Published: June 2024

Buildings and structures in many regions of the world are exposed to environmental factors that can cause damage or failure, making it essential to model these factors accurately in engineering. Stochastic dynamics are crucial for modelling environmental processes, such as earthquake ground motions and wind loads, which can be characterised by a power spectral density (PSD) function that determines the dominant frequencies and corresponding amplitudes of the process. However, when generating a load model described by a PSD function, uncertainties in the processes must be taken into account, which makes a reliable estimation of the PSD function challenging, especially with only limited data available. This study employs the recently developed imprecise PSD function by using a radial basis function network to optimise data-enclosing bounds that produce an interval-valued PSD function. With this approach, a data set in the frequency domain can be bounded for uncertainty quantification. The method described in this work consists of optimising best-case and worst-case PSD functions within the bounds, which are transformed into a separable evolutionary PSD function. The reliability of structures is determined by an upper and lower failure probability, taking into account the present uncertainties. Advanced interval propagation schemes are linked to the imprecise PSD function to determine the failure probabilities efficiently. The method is illustrated by means of three numerical examples.
Probability of failure of nonlinear oscillators with fractional derivative elements subject to imprecise Gaussian loads
Authors: Ni P , Jerez D J , Fragkoulis V C , Mitseas I P , Faes M G R , Valdebenito M A , Beer M
Published: June 2024

In this paper, an approach for bounding the first-passage probability of a class of nonlinear oscillators with fractional derivative elements and subjected to imprecise stationary Gaussian loads is presented. Specifically, the statistical linearization and stochastic averaging methodologies are used in conjunction with an operator norm-based solution framework to estimate the bounds of the failure probability in a fully decoupled manner. The proposed technique can treat a wide range of nonlinear and hysteretic behaviors with relatively low computational cost. A numerical example is considered to demonstrate the applicability of the proposed approach. Specifically, the bounds of the first-passage probability of a bilinear hysteretic oscillator with fractional derivative elements are estimated.
Temperature Prediction Model Based on Low Temperature Release Method of Fire Extinguishing Bottle for Civil Aircraft Flight Test
Authors: Yu JiaZuo , Li Zheng , Feng Chao
Published: June 2024

In the fire extinguishing agent concentration flight test for the power plant system of modern civil aircraft, the low temperature release method of fire extinguishing bottle is usually used to make the test results more representative. Based on the implementation experience of a certain civil aircraft flight test, this paper discusses the detail of the low temperature release method and a critical temperature prediction model in flight test organization process. Also focus on the theoretical analysis, model establishment, model verification and model application of temperature prediction for fire extinguishing bottle. The model has been successfully used to predict the fire extinguishing bottle temperature in the flight test of a certain type aircraft. Improve the implementation success rate to 100%, which has great significance for the fire extinguishing agent concentration flight test. Likewise, it can also provide reference for other aircraft to carry out corresponding flight test.
Key technologies in photovoltaic power generation systems
Authors: Wang Ziming
Published: June 2024

With the increasing concern for environmental issues and the rising demand for renewable energy, photovoltaic (PV) power generation has gradually stepped into the public view. This paper provides a comprehensive literature review of key technologies in photovoltaic power generation systems, aiming to summarize and analyze the current state of research, trends and key issues in PV power generation systems. Inverters play a crucial role in PV power generation systems. The role of the inverter is not only to convert electrical energy, but also to ensure that the system can realize the most efficient energy conversion under different light conditions through advanced technologies such as maximum power point tracking. This paper provides a side-by-side comparison of several common tracking techniques from the maximum power point, including Perturb and Observe, Incremental Conductance and Fuzzy Logic Control. At last, this paper discusses future perspectives for photovoltaic power generation technology, including the development of new photovoltaic materials to improve photovoltaic conversion efficiency and cost, as well as the enhancement of the intelligence and integration of photovoltaic power generation systems. These improvements are aimed at promoting photovoltaic power generation technology in the direction of being more efficient, more reliable, more intelligent, and more sustainable.
All-dielectric metasurface for polarization-multiplexed single-pixel imaging
Authors: Wang Pengsen , Guo Kai , Guo Zhongyi
Published: June 2024

Integration and miniaturization of multi-channel single-pixel imaging systems have become a developing trend. However, it is challenging to meet such development needs solely relying on traditional optical devices. One feasible solution is the utilization of metasurfaces with multiplexing functionality. Here, we propose and validate an all-dielectric, anisotropic metasurface that provides a random mask with polarization multiplexing for single pixel imaging. The design ensures each mask contains 50% target information, allowing adaptive correlated imaging of different targets without needing to redesign the masks. By optimizing the metasurface, we enhance computational efficiency by preventing correlation between different polarization channels and mask patterns. We also adjust the parameters of the compressed sensing algorithm to accommodate various sampling rates, ensuring high-quality image reconstruction. Additionally, the whole system is simulated by the angular spectrum transmission and compressed sensing reconstruction algorithm, providing image reconstruction results for metasurfaces of different sizes, demonstrating the feasibility of the proposed approach. It is noteworthy that the designed metasurface works for single-wavelength operation and could be extended to multispectral imaging by introducing achromatic metasurface technology. The proposed method could miniaturize the optical devices and reduce light loss.
Analysis of speckle-beacon size impact on wavefront sensing and closed-loop control in deep turbulence conditions
Authors: Vorontsov Mikhail A , Lachinova Svetlana L , Polnau Ernst
Published: June 2024

The impact of the laser beacon size on the performance of an ideal phase-conjugation-type adaptive optics (AO) system is analyzed using wave-optics-based numerical simulation. The analysis includes wavefront aberration sensing and closed-loop control for laser beam propagation both in vacuum and in distributed (volume) atmospheric turbulence with the beacon beam scattering off a flat extended target with Lambertian surface roughness. For mitigation of the impact of target-induced speckle effects on the performance of AO systems, speckle-average wavefront sensing and control approaches are introduced and analyzed. The results demonstrate that proposed speckle-average phase conjugation control algorithms enable partial mitigation of turbulence-induced aberrations in presence of strong speckle modulations.
A single-phase grid-tied extendable-level inverter for renewable energy applications
Authors: Aishwarya V
Published: June 2024

The extraction of power from green energy sources (GES) has rapidly popularized owing to increasing energy demands, depletion of fossil fuels and growing concerns about climatic changes. Grid-tied multilevel inverters are critical components in the integration of green energy sources into the power grid and offer numerous benefits over standard two-level inverters. These benefits include superior power quality, diminished harmonic distortion, and the potential to utilize high power levels efficiently. A single-phase Grid-Tied Extendable-Level Inverter (GTELI) for green energy applications is demonstrated in this paper. The GTELI offers superior power quality and distortion-free output relative to existing grid-tied multilevel inverters and hence is an excellent choice for extracting power from sustainable energy sources. To ensure efficient utilization, the extracted power from GTELI is to be integrated into the supply grid. A new current control algorithm for the GTELI is also proposed in this paper. Using this current control technique a grid control technique is established to modulate the power flow to the grid. The proposed GTELI achieved sinusoidal grid voltage and injected current with minimised harmonic distortions of 1.6% and 2.7% respectively, which comply well with the tolerable range of the IEEE 519 standard.
Stuff, Quality, Structure
Authors: Strawson Galen
Published: June 2024

This book argues for identity metaphysics. It argues, in other words, for categorial monism in metaphysics: the view that there is, so far as concrete reality is concerned, only one truly fundamental metaphysical category: stuff. The use of the word ‘stuff’ is an attempt to escape some of the problematic connotations of more traditional terms like ‘substance’. That said, the meaning of ‘stuff’ may be considered to be close to the meaning of ‘substance’ once ‘substance’ has been completely detached from any ideas of substance as something that ‘bears’ or ‘has’ properties or qualities or ‘underlies’ anything. In more detail: the book argues that in fundamental metaphysics [1] the categories of substance and quality, or object and property, which are traditionally understood to be irreducibly distinct, fuse into a single ontological category. So too [2] the notions of power, force, energy, cause, laws of nature, and natural necessity fuse into a single ontological category. Finally [3] there is a fusion of fusions [1] and [2]. For just as there is no fundamental ontological distinction between the quality being of a thing x and the power being of x, so too there is no fundamental ontological distinction between the quality being of x and its being full stop. The final part of the book argues for the view that if two things x and y are structurally identical, where this means identical under all possible true structural descriptions, then they are stuff identical.
Identity Metaphysics: Set Up
Authors: Strawson Galen
Published: June 2024

Chapter 2 introduces ‘identity metaphysics’: the categorial-monist view that there is only one fundamental metaphysical category. It prepares the way for the arguments of Chapters 4, 5, and 6, according to which two supposedly fundamental metaphysical distinctions are in fact superficial: the distinction between substance and quality (object and property) and the distinction between categorical being and dispositional or power being. It argues that, in philosophy, description is at least as important as argument. It introduces a general notion of the dimensionality of a world, arguing that space–time is just one possible kind of dimensionality. It assumes the truth of stuff monism, according to which there is only one kind of fundamental stuff or substance, and argues that causal interaction should be taken to be a sufficient condition of same substancehood. It briefly discusses the view that ‘radical emergence’ is impossible, and that some form of panpsychism is plausible.
A Twelve-Word Metaphysics
Authors: Strawson Galen
Published: June 2024

Chapter 4 offers a twelve-word metaphysics: [M1] Sein ist Sosein: being is quality; [M2] Stoff ist Kraft: matter/stuff is force/energy; [M3] Wesen ist Werden: being is becoming; [M4] Ansichsein ist Fürsichsein: being is mind/consciousness. It then puts [M4] aside in order to gloss [M1]–[M3], by way of further preparation for Chapters 5 and 6. (Chapter 5 focuses on [M1], Chapter 6 on [M2] and [M3].) It returns to the notion of dimensionality, existence-room or Existenzraum, also by way of preparation for Chapters 5 and 6. It proposes the adoption of something close to a (scientifically informed but) common-sense realist view of time. It expresses a preference for thing monism (the view that there is fundamentally only one thing) in addition to the stuff monism endorsed in Chapter 2.
Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students
Authors: Poličar Pavlin G , Špendl Martin , Curk Tomaž , Zupan Blaž
Published: June 2024

Motivation

We learn more effectively through experience and reflection than through passive reception of information. Bioinformatics offers an excellent opportunity for project-based learning. Molecular data are abundant and accessible in open repositories, and important concepts in biology can be rediscovered by reanalyzing the data.

Results

In the manuscript, we report on five hands-on assignments we designed for master’s computer science students to train them in bioinformatics for genomics. These assignments are the cornerstones of our introductory bioinformatics course and are centered around the study of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). They assume no prior knowledge of molecular biology but do require programming skills. Through these assignments, students learn about genomes and genes, discover their composition and function, relate SARS-CoV-2 to other viruses, and learn about the body’s response to infection. Student evaluation of the assignments confirms their usefulness and value, their appropriate mastery-level difficulty, and their interesting and motivating storyline.

Availability and Implementation

The course materials are freely available on GitHub at https://github.com/IB-ULFRI.
Representing mutations for predicting cancer drug response
Authors: Wall Patrick , Ideker Trey
Published: June 2024

Motivation

Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent.

Results

Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding.

Availability and implementation

Code and example datasets are available at https://github.com/pgwall/qms.
Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data
Authors: Dorešić Domagoj , Grein Stephan , Hasenauer Jan
Published: June 2024

Motivation

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared.

Results

We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods.

Availability and implementation

Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.
Sigmoni: classification of nanopore signal with a compressed pangenome index
Authors: Shivakumar Vikram S , Ahmed Omar Y , Kovaka Sam , Zakeri Mohsen , Langmead Ben
Published: June 2024

Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10–100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications.

Availability and implementation

Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.
Oncotree2vec — a method for embedding and clustering of tumor mutation trees
Authors: Baciu-Drăgan Monica-Andreea , Beerenwinkel Niko
Published: June 2024

Motivation

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient’s tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis.

Results

Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.

Availability and implementation

https://github.com/cbg-ethz/oncotree2vec.
A learned score function improves the power of mass spectrometry database search
Authors: Ananth Varun , Sanders Justin , Yilmaz Melih , Wen Bo , Oh Sewoong , Noble William Stafford
Published: June 2024

Motivation

One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools.

Results

To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.
Maximum likelihood phylogeographic inference of cell motility and cell division from spatial lineage tracing data
Authors: Mai Uyen , Hu Gary , Raphael Benjamin J
Published: June 2024

Motivation

Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division.

Results

We introduce a new model—the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework—MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)—to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems.

Availability and Implementation

The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.
Partial RNA design
Authors: Runge Frederic , Franke Jörg , Fertmann Daniel , Backofen Rolf , Hutter Frank
Published: June 2024

Motivation

RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space.

Results

In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness.

Availability and Implementation

libLEARNA is open-source and publicly available at: https://github.com/automl/learna_tools.