bluuebunny/crossref_metadata_2025 huggingface dataset:
Goal‐oriented adaptivity based on a model hierarchy of mean‐field and full‐field homogenization methods in elasto‐plasticity
Authors: Tchomgue Simeu Arnold , Mahnken Rolf
Published: March 2023

Materials such as composites are heterogeneous at the micro scale, where several constituents with different material properties can be distinguished like elastic inclusions and an elasto‐plastic matrix. One has to deal with these heterogeneities on the micro scale and then perform a scale transition to obtain the overall behavior on the macro scale, which is often referred to as homogenization. The present contribution deals with an efficient combination of numerically low cost mean‐field and numerically high cost full‐field homogenization methods in elasto‐plasticity. To this end, model adaptivity is a promising methodology as it has been well‐established for linear elastic composite models [1]. Quite similarly to the adaptive FEM, it starts with an affordable homogenization method, and then through a loop wise error control, a local switch to accurate homogenization methods is performed to enhance the accuracy (referred to as model refinement). Like hierarchical FE structures for adaptive FEM, hierarchical model structures are established for model adaptivity. Additionally, the model adaptivity is coupled to the well established adaptive finite element method (FEM), such that both macro model and macro discretization errors are controlled. The proposed adaptive procedure is driven by a goal‐oriented a posteriori error estimator based on duality techniques. Finally, two numerical examples illustrate the effectiveness of the proposed adaptive approach.
Analysis of strut‐based lattice cores in sandwich panels using homogenization and dehomogenization methods
Authors: Georges Hussam , Mittelstedt Christian , Becker Wilfried
Published: March 2023

The design freedom provided by additive manufacturing offers new opportunities to fabricate novel structures with a high lightweight potential, such as strut‐based lattice structures. These lattice structures consist of periodically repeated unit cells and can be used in several applications due to their outstanding mechanical performance. One of the possible applications are cores of sandwich panels since the strut‐based lattices offer comparable mechanical properties to conventional honeycomb structures. Moreover, multifunctional use of the sandwich core is enabled by allowing the heat and fluid transfer through the sandwich due to the open‐celled lattice structure. However, strut‐based lattices are rarely utilized as cores in sandwich panels in engineering practice. One of the main reasons for that is the unknown mechanical behavior of lattice cores. In particular, when the sandwich is subjected to concentrated loads, localized stresses and deformations occur in the sandwich core, leading to core damage. In this work, we present a novel analytical model to determine stresses and deformations in the struts of lattice cores of sandwich panels using homogenization and dehomogenization methods. The local core compression caused by localized transverse forces can also be determined by the derived model.
A thermodynamic framework for the phase‐field approach considering carbide precipitation during phase transformations
Authors: Westermann Hendrik , Mahnken Rolf
Published: March 2023

Microstructure evolution during recrystallization annealing affects the macroscopic material properties for various steels. The phenomena include phase transformations and carbide precipitation. The requirements for most engineering components are both, high strength and sufficient residual ductility to withstand the mechanical burden and to offer preferable manufacturing conditions. In the present work a thermodynamic framework is proposed to combine two phase‐field approaches for the investigation of bimodal grain sizes. The developed prototype model studies phase transformations, diffusion of the carbon concentration and precipitation of carbides. Recystallization annealing enables the transformation of the purely martensitic microstructure into ferrite accompanied by carbide precipitation. The grain boundary movement is described by grain orientation and microstructure crystallinity. An adopted phase‐field approach describes the microstructure evolution driven by temperature and stored energy. The model is extended to capture carbide precipitation based on the diffusion kinetics of carbon atoms. Fractions of ferrite and carbide phases are depicted by the order parameters of a multiphase‐field model tied to the thermodynamic framework, thus yielding a novel constitutive framework capable of describing the complex material behavior of metals during thermo‐mechanical processing. Subsequently, a suitable algorithm for the presented phase‐field plasticity model in two dimensions is proposed. The evolution of phase fractions and carbide precipitation is illustrated in numerical examples by conclusive finite element simulations.
3D FEM Simulation of Titanium Alloy (Ti6Al4V) Machining with Harmonic Endmill Tools
Authors: Kalu-Uka Abraham , Ozoegwu Chigbogu , Eberhard Peter
Published: March 2023

Usually, end milling operations have been carried out using conventional uniform helix tools with fixed helix angles. Thus, many studies have been conducted to study the effects of these tools on the thermomechanical properties of a milling process. Recently, there have been works that point to the benefits of using harmonic endmills. Harmonic endmills consist of cutting edge profiles that have continuously harmonically varying helix angles. The variation is described using a harmonic function of axial position (elevation) of points on the cutting edge. In this work, a 3D finite element simulation using ABAQUS, is carried out for the complex milling process of Titanium alloy Ti6Al4V. The envelope of the harmonic tool is first generated using a set of MATLAB codes and stored in a Standard Triangle Language (.stl) format. The machine tool is introduced into an FEM program which has been designed to provide for dynamic effects, thermo‐mechanical coupling, material damage law and the criterion for contact associated with the milling process. A Johnson‐Cook material constitutive equation which combines the effects of strain hardening, strain softening, and temperature softening is used. To account for the chip separation criterion, the Johnson Cook damage evolution equation is used. The milling process simulation for Ti6Al4V is then carried out. In the end, the stress distribution and the cutting forces are obtained.
Application and damping mechanism of particle dampers
Authors: Prasad Braj Bhushan , Duvigneau Fabian , Woschke Elmar , Juhre Daniel
Published: March 2023

A particle damper is a passive damping technique, which is based on the high damping properties of granular materials. The energy dissipation rate of a particle damper depends on several factors, like the type of granular materials, filling ratios, particle size, and shape, etc. Out of all these factors, the type of granular materials used to design a particle damper plays a major role. Therefore, this contribution aims to investigate the influence of eleven different granular materials on vibration attenuation. Furthermore, the application of particle dampers for reducing the low‐frequency vibration amplitude of a wind turbine generator has been demonstrated. For this, two different approaches, namely the damping plate concept and the existing cavity concept have been introduced. It has been found that both concepts are capable of reducing the vibration amplitude tremendously. The material investigation has shown that rubber granulate can reduce the vibration amplitude of the test specimen significantly higher in comparison to the other materials by increasing the additional mass to the entire system marginally. The material test is independent of any particular application. Hence, the results can be used to reduce the vibration amplitude of any mechanical structure.
A new matrix equation expression for the solution of non‐autonomous linear systems of ODEs
Authors: Pozza Stefano , Van Buggenhout Niel
Published: March 2023

The solution of systems of non‐autonomous linear ordinary differential equations is crucial in a variety of applications, such us nuclear magnetic resonance spectroscopy. A new method with spectral accuracy has been recently introduced in the scalar case. The method is based on a product that generalizes the convolution. In this work, we show that it is possible to extend the method to solve systems of non‐autonomous linear ordinary differential equations (ODEs). In this new approach, the ODE solution can be expressed through a linear system that can be equivalently rewritten as a matrix equation. Numerical examples illustrate the method's efficacy and the low‐rank property of the matrix equation solution.
A study of energies in pressure dependent plasticity – mathematical model and mechanical analogies
Authors: Jezdan Ghina , Behr Florian , Dolzmann Georg , Hackl Klaus
Published: March 2023

Experiments conducted in granular media like clay, silt, or sand show complex structures which cannot be explained as simple shear bands. Materials in this class are usually heterogeneous mixtures of fluids, usually air and water, and particles that have little to no cementation. The shear strength is provided by friction and interlocking of the particles. This generally leads to pressure dependent plastic behavior.

Consequently, models in soil mechanics require pressure‐dependent yield surfaces resulting, in contrast to models in crystal plasticity, in non‐associated flow rules, which have not been studied as extensively as associated ones. In this study, we introduce a model capable of explaining the occurrence of the complex structures mentioned above starting from a time‐incremental variational formulation.

We consider a basic model giving a reduced description for plasticity in soil mechanics with as few parameters as possible while retaining the essential features of the full model. The goal is to capture the complexity seen in experiments by considering the stability properties of the emerging microstructures. As expected, these complex structures lead to challenging questions concerning numerical simulations, which can be resolved employing a relaxed model in the implementation.
Which neural networks can be computed by an algorithm? – Generalised hardness of approximation meets Deep Learning
Authors: Thesing Laura , Hansen Anders C.
Published: March 2023

Classical hardness of approximation (HA) is the phenomenon that, assuming P ≠ NP, one can easily compute an ϵ‐approximation to the solution of a discrete computational problem for ϵ > ϵ0 > 0, but for ϵ < ϵ0 – where ϵ0 is the approximation threshold – it becomes intractable. Recently, a similar yet more general phenomenon has been documented in AI: Generalised hardness of approximation (GHA). This phenomenon includes the following occurrence: For any approximation threshold ϵ1 > 0, there are AI problems for which provably there exist stable neural networks (NNs) that solve the problem, but no algorithm can compute any NN that approximates the AI problem to ϵ1‐accuracy. Moreover, this issue is independent of the P vs NP question and thus is a rather different mathematical phenomenon than HA. GHA implies that the universal approximation theorem for NNs only provides a partial understanding of the power of NNs in AI. Thus, a classification theory describing which NNs can be computed by algorithms to particular accuracies is needed to fill this gap. We initiate such a theory by showing the correspondence between the functions that can be computed to ϵ‐accuracy by an algorithm and those functions that can be approximated by NNs which can be computed to ϵ̂‐accuracy by an algorithm. In particular, the approximation thresholds ϵ and ϵ̂ cannot differ by more than a factor of 12. This means that computing function approximations through NNs will be optimal – in the sense of best approximation accuracy achievable by an algorithm – up to a small constant, compared to any other computational technique.
First Order Limiting Optimality Conditions in the Coefficient Control of an Obstacle Problem
Authors: Simon Nicolai , Wollner Winnifried
Published: March 2023

We consider an optimal control problem governed by an obstacle problem. The novelty and focus of this work will be the introduction of a control variable into the coefficients of this optimal control problem.

In particular, we discuss the effects of the multiplicative coupling between control and, derivatives of, the state. This prevents the use of standard weak convergence arguments in limit arguments. As it is known, H‐convergence needs to be utilized, e.g., to prove existence of a solution to the problem. To obtain optimality conditions, we present a regularization approach to circumvent the non‐differentiability of the solution operator of the obstacle problems. We then discuss the limiting optimality conditions obtained by the proposed regularization.
Steep resonance of parametrically excited active MEMS cantilevers for dynamic mode in Atomic Force Microscopy
Authors: Ehrmann Jonathan , Reichert Robert , Gutschmidt Stefanie , Sattel Thomas
Published: March 2023

Ongoing developments in nanotechnology demand higher spatial resolution and thus, higher amplitude sensitivity in Atomic Force Microscopy (AFM). In this work, active cantilevers with integrated sensor and actuator systems are parametrically excited using a novel, analog feedback circuit. With that it is possible to adapt the strength and sign of a cubic nonlinearity which provides a bound to the amplitudes in resonance operation . The system response shows steeper resonance curves and therefore higher amplitude sensitivities compared to forced excited cantilevers. Theoretical findings are validated experimentally.
A Python toolbox for the numerical solution of the Maxey‐Riley equation
Authors: Urizarna-Carasa Julio , Ruprecht Daniel , von Kameke Alexandra , Padberg-Gehle Kathrin
Published: March 2023

The Maxey‐Riley equation (MRE) models the motion of a finite‐sized, spherical particle in a fluid. It is a second‐order integro‐differential equation with a kernel with a singularity at initial time. Because solving the integral term is numerically challenging, it is often neglected despite its often non‐negligible impact. Recently, Prasath et al. showed that the MRE can be rewritten as a time‐dependent heat equation on a semi‐infinite domain with a nonlinear, Robin‐type boundary condition. This approach avoids the need to deal with the integral term. They also describe a numerical approach for solving the transformed MRE based on Fokas method. We provide a Python toolbox implementing their approach, verify it against some of their numerical examples and demonstrate its flexibility by computing the trajectory of a particle in a velocity field given by experimental data.
Towards neural network‐based numerical friction models
Authors: Vater Kerstin , Stender Merten , Hoffmann Norbert
Published: March 2023

Friction contacts can be found in almost all mechanical systems and are often of great technical importance. However, they are usually difficult to describe, and their behavior and influence on the whole system are hard to predict accurately. Modern product design and system operation strongly benefit from numerical simulation approaches today, but reliable friction models still represent a major challenge in this context.

To tackle this problem, we employ neural network regression to capture the characteristics of frictional contacts and make them accessible for numerical methods in a minimal intrusive fashion. In particular, we test our approach using a Finite Element model of a 2D cantilever beam subject to stick‐slip vibrations induced by a moving conveyor belt at its free end. As a reference solution, we perform a transient analysis based on a simple analytical friction model, where the kinetic friction force only depends on the normal load and the relative sliding velocity. We take the same friction model, add some artificial noise to mimic uncertainties coming with experimental measurements, and pick a limited set of data points to train a regression neural network. The machine learning friction model is then deployed in the Finite Element code to predict the kinetic friction force acting on the beam tip during the slip phases.

The deflection curves obtained by the transient numerical analysis using the new neural network friction model agree well with the reference solution based on the underlying analytical model. The results indicate that data‐driven approaches may also be capable of capturing more complex frictional contacts, including effects of temperature, humidity, and load history. The trained neural network friction models can then be employed in numerical simulations in a minimally intrusive manner. This approach opens up new possibilities to predict individual mechanical system behavior as accurately as possible.
Shape Optimization Algorithms for Fluid Dynamics Applications
Authors: Pinzon Escobar Jose Alfonso , Siebenborn Martin
Published: March 2023

In this work we present a comparison between shape optimization algorithms in different vector spaces. The main goal is to optimize the surface of an object with respect to a physical quantity. The main focus is on applications that require large element deformations as part of the optimization process, as for instance the removal and creation of geometric singularities such as edges and corners. The algorithms take into account the prevention of element degeneracy and overlapping, for instance by enforcing inequality constraints. For this purpose, an approach in the Hilbert space is compared to another in Banach spaces. The former is based on a nonlinear extension equation, whereas the p‐Laplace operator is used in the latter. Computational results are presented in the context of fluid dynamics applications, where the contour of an object is optimized with respect to the energy dissipation.
On the energy dissipation in confined concrete subjected to shear cyclic loading
Authors: Aguilar Mario , Baktheer Abedulgader , Chudoba Rostislav
Published: March 2023

The establishment of a simple engineering rule for predicting the fatigue failure of concrete has been pursued over the past decades. An energetic approach to the matter seems to be an attractive option that many researchers have embraced. In the present work, the authors attempt to contribute to the establishment of such a rule. In particular, the energy dissipation of confined concrete subjected to shear cyclic loading is studied and quantified. For this purpose, a microplane fatigue model recently introduced by the authors, referred to as MS1, is used. It aims to capture the fundamental inelastic mechanisms driving the tri‐axial stress redistribution within a material zone during the fatigue damage process in concrete. To this end, the fatigue damage evolution is linked to a measure of cumulative inelastic shear strain at the microplane level, reflecting the accumulation of fatigue damage due to internal shear/sliding between aggregates at subcritical pulsating load levels. To isolate the dissipative mechanism mentioned above, test configurations with dominant shear stress seem to be more appropriate. In the present work, a punch‐through shear test (PTST) FE model is used to induce shear‐dominated stresses and strains along the ligament of a specimen. Numerical studies are first presented to evaluate the behavior and energy dissipation at the elemental interface level. The interface is introduced in the MS1 microplane material model, which is capable of reproducing the concrete behavior under monotonic, cyclic, and fatigue loading with consistent set of material parameters. Quantification of the energy dissipation for each introduced dissipative mechanism is performed at each microplane and integrated via a well‐established homogenization scheme to evaluate the macroscopic energy dissipation. Later, an analysis of the energy dissipation of the PTST process zone is performed for cyclic loading under two different subcritical cyclic load amplitudes.
Behavior of additively manufactured lattice structures unter compressive loading
Authors: Bieler Sören , Weinberg Kerstin
Published: March 2023

Lattice structures are attractive in additive manufacturing technology as they are small, lightweight, and especially scaleable. Closely spaced diagonal elements absorb energy through elastic and plastic deformation within a lattice, but the performance depends on many factors like design, material properties, printing process, post‐treatment, etc. Here, we determine the material properties of two types of stereolithography printed specimens and investigate the truss‐radius dependent performance of bcc, fcc, and octet lattice structures. A simulation using the experimental parameters shows that the maximum arising stresses are located at the positions where material failure was observed.
Data Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networks
Authors: Westmeier Tobias , Kreuter Daniel , Bäuerle Simon , Hetzler Hartmut
Published: March 2023

In this work, we propose a novel approach to the data‐driven prediction of vibration responses of nonlinear systems. The main idea is based on Autoregressive Neural Networks (ARNN) to model the nonlinear transfer behaviour between an external excitation and the system response. We propose an autoregressive network architecture with embedded symmetry using bias‐free tanh activation and guarantee Input‐to‐State‐Stability (ISS) by enforcing a special penalty term to the weights. The resulting training procedure is analysed for the example of a DUFFING oscillator with white noise excitation. In a BAYESian optimisation, it is found that beyond enforcing input‐to‐state‐stability, the stabilising penalty term also decreases sensitivity with respect to other training parameters compared to other classical techniques. Furthermore, we show that the stabilised ARNN is able to give excellent approximations of the nonlinear response of the DUFFING oscillator for a wide range of excitation intensities. In contrast, linear models, such as autoregressive models with exogenous input (ARX) in time domain or linear transfer functions in frequency domain, will only find some linear approximation. In particular, by construction, they will not be able to capture nonlinear effects for arbitrary amplitudes and excitation levels.
Machine learning based prediction of the corrosion behaviour of biodegradable magnesium alloys with PEO coatings
Authors: Zhang Dawei , Ma Songyun , Markert Bernd
Published: March 2023

The present work focuses on the application of deep learning methods for predicting the in vitro pitting corrosion behaviour of coated magnesium alloys with different microstructures. The datasets are generated by phase field simulation of pitting corrosion. Performance analysis shows that physics‐informed convolutional neural network (CNN) is superior to regular CNN in predicting corrosion curve. A good agreement between deep learning predictions and the ground truths indicates excellent predictive performance of the current encoder‐decoder architecture regardless of the variation of microstructures. Furthermore, the computational time is reduced by 4 to 6 orders of magnitude compared to the phase field simulation. The computational results demonstrate that machine learning based approach is promising alternative to traditional modelling methods for predicting pitting corrosion of coated magnesium alloys.
Optimizing artificial neural networks for mechanical problems by physics‐based Rao‐Blackwellization: Example of a hyperelastic microsphere model
Authors: Geuken Gian-Luca , Mosler Jörn , Kurzeja Patrick
Published: March 2023

The Rao‐Blackwell scheme provides an algorithm on how to implement sufficient information into statistical models and is adopted here to deterministic material modeling. Even crude initial predictions are improved significantly by Rao‐Blackwellization, which is proven by an error inequality. This is first illustrated by an analytical example of hyperelasticity utilizing knowledge on principal stretches. Rao‐Blackwellization improves a 1‐d uniaxial strain‐energy relation into a 3‐d relation that resembles the classical micro‐sphere approach. The presented scheme is moreover ideal for data‐based approaches, because it supplements existing predictions with additional physical information. A second example hence illustrates the application of Rao‐Blackwellization to an artificial neural network to improve its prediction on load paths, which were absent in the original training process.
In vitro degradation behaviour of biodegradable magnesium alloys
Authors: Nachtsheim Julia , Burja Jaka , Ma Songyun , Markert Bernd
Published: March 2023

Biodegradable magnesium alloys are promising materials for orthopaedic implants. With their mechanical properties similar to native bone, magnesium alloys can overcome some significant disadvantages of conventional metallic implants. Nonetheless, the material degrades too fast for clinical approval. Therefore, the corrosion rate of the implant needs to be controlled and decelerated to guarantee sufficient mechanical support during the entire bone healing process. In this work, we analyse the corrosion behaviour of the magnesium alloy WE43 in a physiological environment and assess the influence of a plasma electrolytic coating to the corrosion process. The experimental results show that the coating significantly decreases the mass loss rate as well as the degradation of the material strength of the WE43 alloy.
A novel electrochemical sensor based on sodium alginate‐decorated single‐walled carbon nanotubes for the direct electrocatalysis of heavy metals ions
Authors: Chrouda Amani
Published: March 2023

In recent years, the presence of heavy metal ions in food chains and water poses serious environmental and human health problems. Therefore, there has been a pressing need for fast simple, sensitive, and accurate methods for their detection. In this study a novel electrochemical sensor based on alginate sodium biopolymer decorating single‐walled carbon nanotubes was elaborated for the detection of trace level of heavy metal ions. Different brown algae were used to extract sodium alginate, with Cystoseira being the most common. Due to its high extraction yield and high mannuronate/guluronate (M/G) ratio, Compress a brown algae was chosen to disperse with single‐walled carbon nanotubes. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), scanning electron microscopy were monitored to investigate each surface modification. Differential pulse anodic stripping voltammetry (DPASV) was used to follow the electrochemical heavy metal ions determination. Moreover, the performance of the optimized heavy metal ions sensor in terms of sensitivity, linear range, limit of detection, and reproducibility was investigated. The detection limits were found to be 0.1, 31, and 1 nM for Pb2+, Cd2+, and Cu2+, respectively. The results reported in this work showed good stability, high sensitivity, and appealing inter‐electrode repeatability. Finally, lead detection in tap water was carried out. The metrological performance of the present sensor makes it a good alternative for heavy metal ions detection.
Like parent, like offspring: Intergenerational association of parental scarcity mindset with offspring's cooperative behaviors
Authors: Zheng Lei , Peng Baozhong , Ye Changchun , Zhang Xiuqin , Qi Wei , Miao Miao
Published: March 2023

A sense of resource availability creates a scarcity mindset, leading people to behave in a more competitive way instead of in a collaborative way. This study aims to examine the relationship between scarcity mindset and cooperative behaviors among parents and offspring, and to explore whether parents' scarcity mindset is related to their offspring's scarcity mindset and cooperative behaviors. We collected 239 parent–offspring pairs from several universities in northwest China. They completed the scarcity mindset scale and a one‐shot public good game (PGG). A path model analysis was conducted to examine the relationship between scarcity mindset and cooperative behaviors for offspring and their parents, respectively. The relationship between parental scarcity mindset and offspring's cooperative behaviors was also examined. The results showed that there was a high correlation of scarcity mindset between parents and their offspring. Scarcity mindset was negatively related to cooperative behaviors among both parents and offspring. In addition, both parents' cooperative behaviors and offspring's scarcity mindset mediated the relationship between the parental scarcity mindset and offspring's cooperative behaviors. Our results supported the idea that scarcity mindset triggers competitive rather than cooperative orientation. The present findings reveal an intergenerational relationship between scarcity mindset and cooperative behaviors by highlighting the impacts of parental scarcity mindset on that of their offspring's and its additional effects on offspring's cooperative behaviors.
Does the Covid‐19 lockdown affect pain and anxiety in high school seniors and high school graduates preparing for the university exam?
Authors: Bayrak Gökhan , Özdemir Hatice , Aslan Ummuhan Baş , Yağcı Nesrin
Published: March 2023

This study aimed to investigate whether the Covid‐19 lockdown affect pain, exam anxiety and general anxiety in students preparing for university exam. A total of 364 students were divided into groups as high school seniors (HS, n = 241) and high school graduates (HG, n = 123). Visual Analog Scale (VAS) for neck and low‐back pain, Beck Anxiety Inventory for general anxiety, Exam Anxiety Scale (EAS) for exam anxiety were used. Both groups showed high level of exam anxiety and generalized anxiety (p < .05). After Covid‐19, neck and low‐back pain rates, and VAS pain scores of both groups increased (p < .05). Neck pain before Covid‐19 and neck and low‐back pain after Covid‐19 were higher in female students than males (p < .05). The Covid‐19 lockdown negatively affected the neck and low‐back pain level, exam anxiety and general anxiety of both HS and high school graduate students. We suggest that community healthcare services should regard physiotherapy and rehabilitation interventions to treat students with neck and low‐back pain via exercise strategies. Psychological and emotional support also could be provided to reduce students' general anxiety and exam anxiety. Future studies should establish the consider menstrual cycle status in female students, and the computer‐using time of students.
Supportive interventions for family caregivers of patients with advanced cancer: A systematic review
Authors: Becqué Yvonne N. , van der Wel Maaike , Aktan‐Arslan Muzeyyen , Driel Anne Geert van , Rietjens Judith A. C. , van der Heide Agnes , Witkamp Erica
Published: March 2023

Objective

Family caregivers are often intensively involved in palliative and end‐of‐life cancer care. A variety of interventions to support family caregivers have been developed, differing in target population, modality, and components. We aimed to systematically examine characteristics and the effectiveness of interventions to support family caregivers of patients with advanced cancer.

Methods

A systematic review was conducted using Embase, Medline Ovid, Web of Science, Cochrane, Google Scholar, and Cinahl. This review included quantitative studies published from January 2004 until January 2020 reporting on interventions to support family caregivers of patients with advanced cancer in all care settings.

Results

Out of 7957 titles, 32 studies were included. Twenty‐two studies were randomized controlled trials. Interventions were delivered to four target populations: individual family caregivers (n = 15), family caregiver‐patient dyads (n = 11), families (n = 2) and peer groups (n = 4). Most interventions (n = 26) were delivered face‐to‐face or by phone, two were delivered online. Most interventions included multiple components and were primarily aimed at supporting family caregivers' self‐care. Twenty‐nine interventions were shown to have beneficial effects on family caregiver outcomes, mostly in the psycho‐emotional (n = 24), daily functioning (n = 13) and social dimension (n = 6). Individual interventions were mainly effective in the psycho‐emotional dimension, dyad and family interventions in the psycho‐emotional and social domain, and group interventions mainly had an effect on daily functioning.

Conclusions

Interventions to support family caregivers in advanced cancer care vary widely. Most intervention studies reported beneficial effects for the wellbeing of family caregivers. There is evidence that the target group is associated with beneficial effects on different outcome dimensions.
AlphaFold predicts novel human proteins with knots
Authors: Perlinska Agata P. , Niemyska Wanda H. , Gren Bartosz A. , Bukowicki Marek , Nowakowski Szymon , Rubach Pawel , Sulkowska Joanna I.
Published: March 2023

The fact that proteins can have their chain formed in a knot is known for almost 30 years. However, as they are not common, only a fraction of such proteins is available in the Protein Data Bank. It was not possible to assess their importance and versatility up until now because we did not have access to the whole proteome of an organism, let alone a human one. The arrival of efficient machine learning methods for protein structure prediction, such as AlphaFold and RoseTTaFold, changed that. We analyzed all proteins from the human proteome (over 20,000) determined with AlphaFold in search for knots and found them in less than 2% of the structures. Using a variety of methods, including homolog search, clustering, quality assessment, and visual inspection, we determined the nature of each of the knotted structures and classified it as either knotted, potentially knotted, or an artifact, and deposited all of them in a database available at: https://knotprot.cent.uw.edu.pl/alphafold. Overall, we found 51 credible knotted proteins (0.2% of human proteome). The set of potentially knotted structures includes a new complex type of a knot not reported in proteins yet. That knot type, denoted 63 in mathematical notation, would necessitate a more complex folding path than any knotted protein characterized to date.
Diagnostic performance of microUltrasound at MRI‐guided confirmatory biopsy in patients under active surveillance for low‐risk prostate cancer
Authors: Maffei Davide , Fasulo Vittorio , Avolio Pier Paolo , Saitta Cesare , Paciotti Marco , De Carne Fabio , Colombo Piergiuseppe , Pasini Luisa , De Zorzi Silvia Zandegiacomo , Saita Alberto , Hurle Rodolfo , Lazzeri Massimo , Guazzoni Giorgio Ferruccio , Casale Paolo , Buffi Nicolò Maria , Lughezzani Giovanni
Published: March 2023

Background

Active surveillance (AS) represents a standard of care of low‐risk prostate cancer (PCa). However, the identification and monitoring of AS candidates remains challenging. Microultrasound (microUS) is a novel high‐resolution imaging modality for transrectal ultrasonography (TRUS). We explored the impact of microUS TRUS and targeted biopsies in mpMRI‐guided confirmatory biopsies.

Methods

Between October 2017 and September 2021, we prospectively enrolled 100 patients scheduled for MRI‐guided confirmatory biopsy at 1 year from diagnosis of ISUP 1 PCa. TRUS was performed using the ExactVu microUS system; PRI‐MUS protocol was applied to identify suspicious lesions (i.e., PRIMUS score ≥ 3). All patients received targeted biopsies of any identified microUS and mpMRI lesions and complementary systematic biopsies. The proportion of patients upgraded to clinically significant PCa (defined as ISUP ≥ 2 cancer; csPCa) at confirmatory biopsies was determined, and the diagnostic performance of microUS and mpMRI were compared.

Results

Ninety‐two patients had a suspicious MRI lesion classified PI‐RADS 3, 4, and 5 in respectively 28, 16, and 18 patients. MicroUS identified 82 patients with suspicious lesions, classified as PRI‐MUS 3, 4, and 5 in respectively 20, 50, and 12 patients, while 18 individuals had no lesions. Thirty‐four patients were upgraded to ISUP ≥ 2 cancer and excluded from AS. MicroUS and mpMRI showed a sensitivity of 94.1% and 100%, and an NPV of 88.9% and 100%, respectively, in detecting ISUP ≥ 2 patients. A microUS‐mandated protocol would have avoided confirmatory biopsies in 18 patients with no PRI‐MUS ≥ 3 lesions at the cost of missing four upgraded patients.

Conclusions

MicroUS and mpMRI represent valuable imaging modalities showing high sensitivity and NPV in detecting csPCa, thus allowing their use for event‐triggered confirmatory biopsies in AS patients. MicroUS offers an alternative imaging modality to mpMRI for the identification and real‐time targeting of suspicious lesions in AS patients.
Exploration of the binding of antifungal drugs to human P450 2C9 based on docking and molecular dynamics simulation
Authors: Cai Juan , Shi Rongwei
Published: March 2023

Azole compounds, commonly used antifungal agents for the treatment of systemic fungal infections, have been confirmed to show inhibition to some P450 enzymes. However, the potential inhibition or interaction mechanism is elusive. Here, the detailed interaction between first‐generation antifungal agents (miconazole, and its derivatives fluconazole and voriconazole) and CYP2C9 are studied using docking and molecular dynamics (MD) simulation methods. Docking results show that azoles are mainly located in the cavity formed by helices A, F, I, and heme group. MD results indicate flu‐1 and vor‐2 undergo large conformational changes mediated by inter/intramolecular hydrogen bonds (H‐bonds), while other azole conformations exhibit low flexibility in the active site. Interestingly, one water (W7255) joins the interaction between flu‐1 and N204, implying the significance of water in regulating the binding of fluconazole. MM‐GBSA analysis manifests that miconazole possesses highest binding affinity compared to fluconazole and voriconazole, which agrees well with previous reports. Moreover, the per‐residue decomposition suggests that the favorable interactions are dominated by ΔGvdW and ΔGnonpl between azoles and residues (V113/F114, I205/L208, A297/E300/T301, F476/A477) and ΔGvdW should be further ascribed to CH⋯Cl, CH⋯N, CH⋯O and CH⋯π interactions.
Adaptive global prescribed performance tracking control of uncertain robotic systems with unknown control directions
Authors: Li Kun , Zhao Kai , Jia Xiaojuan , Gao Ruizhen
Published: March 2023

In this article, we develop an adaptive global prescribed performance tracking control scheme for nonlinear multiple‐input multiple‐output (MIMO) robotic systems in the presence of unknown control directions and nonparametric uncertainties, in which the proposed solution mainly includes the following steps. Firstly, by constructing a global prescribed performance function, together with a series of transformations, the corresponding control guarantees the position tracking error evolves strictly within the prespecified region for all time, which is independent of initial conditions; Secondly, in order to cope with the problem of multiple unknown control directions, we make use of a symmetric matrix lemma to convert the underlying problem from matrix form to scalar form, so that the Nussbaum‐gain lemma for SISO systems becomes effective for MIMO systems with multiple inputs; In addition, we employ the integrable function to handle the nonparametric uncertainties, enabling the control scheme with the capability to drive the tracking error toward zero asymptotically. The effectiveness of the proposed control is validated via a two‐link rigid robotic manipulator.
Event‐triggered finite‐time control for a constrained robotic manipulator with flexible joints
Authors: Feng Yanghe , Kong Linghuan , Zhang Zhongyi , Liu Ruijun , Cheng Guangquan , Yu Xinbo
Published: March 2023

In the article, an event‐triggered control policy is developed for a robotic manipulator with flexible joint (RMFJ) in the presence of finite‐time convergence under the requirement of output constraints. Due to the vibration existing in the flexible joint, compared with the rigid manipulator, it is exceedingly difficult for RMFJ to design an adaptive method for realizing highly accurate control. In order to reduce and restrain the vibration of the flexible joint, a finite‐time convergence policy is designed by introducing the fractional order term. Moreover, the angular displacements of the link are constrained in the predefined region by using the Barrier function, which further enhances the operation safety. Neural networks learning is used to approximate unknown model parameters. Event‐trigger design is used to reduce the communication burden.
State observer‐based predictive proportional‐integral tracking control for fuzzy input time‐delay systems
Authors: Sakthivel R. , Shobana N. , Priyanka S. , Kwon O.M.
Published: March 2023

This study emphasis on the problem of state tracking and disturbance attenuation for Takagi‐Sugeno fuzzy based model with input time‐delay via improved extended state observer based predictive proportional‐integral tracking control. To be precise, a predictive proportional‐integral tracking controller is formulated by implementing the Smith‐Predictor strategy to restrain the influence of input time‐delay. Then, with the aid of improved extended state observer, the lumped disturbance is estimated and it is combined with the predictive proportional‐integral tracking control for disturbance attenuation. Further, closed‐loop control‐system is considered to ensure the satisfactory state tracking results for the considered system regardless of time‐delay in the control input and lumped disturbances. Meanwhile, based on the Lyapunov theory approach, the stability criteria for the proposed system are formulated in terms of linear matrix inequalities following which the gain matrices are derived. Finally, in order to evident the supremacy of the proffered control system, two numerical simulation results are provided.
Rapid Visible Detection of African Swine Fever Virus Using Hybridization Chain Reaction‐Sensitized Magnetic Nanoclusters and Affinity Chromatography
Authors: Lee Hyo , Lee Sojeong , Park Chaewon , Yeom Minjoo , Lim Jong‐Woo , Vu Thi Thu Hang , Kim Eunjung , Song Daesub , Haam Seungjoo
Published: March 2023

African swine fever virus (ASFV) is a severe and persistent threat to the global swine industry. As there are no vaccines against ASFV, there is an immense need to develop easy‐to‐use, cost‐effective, and rapid point‐of‐care (POC) diagnostic platforms to detect and prevent ASFV outbreaks. Here, a novel POC diagnostic system based on affinity column chromatography for the optical detection of ASFV is presented. This system employs an on‐particle hairpin chain reaction to sensitize magnetic nanoclusters with long DNA strands in a target‐selective manner, which is subsequently fed into a column chromatography device to produce quantitatively readable and colorimetric signals. The detection approach does not require expensive analytical apparatus or immobile instrumentation. The system can detect five genes constituting the ASFV whole genome with a detection limit of ≈19.8 pm in swine serum within 30 min at laboratory room temperature. With an additional pre‐amplification step using polymerase chain reaction (PCR), the assay is successfully applied to detect the presence of ASFV in 30 suspected swine samples with 100% sensitivity and specificity, similar to quantitative PCR. Thus, this simple, inexpensive, portable, robust, and customizable platform for the early detection of ASFV can facilitate the timely surveillance and implementation of control measures.
Combining Highly Dispersed Amorphous MoS3 with Pt Nanodendrites as Robust Electrocatalysts for Hydrogen Evolution Reaction
Authors: Guo Ke , Zheng Jinyu , Bao Jianchun , Li Yafei , Xu Dongdong
Published: March 2023

Surface modification of electrocatalysts to obtain new or improved electrocatalytic performance is currently the main strategy for designing advanced nanocatalysts. In this work, highly dispersed amorphous molybdenum trisulfide‐anchored Platinum nanodendrites (denoted as Pt‐a‐MoS3 NDs) are developed as efficient hydrogen evolution electrocatalysts. The formation mechanism of spontaneous in situ polymerization MoS42− into a‐MoS3 on Pt surface is discussed in detail. It is verified that the highly dispersed a‐MoS3 enhances the electrocatalytic activity of Pt catalysts under both acidic and alkaline conditions. The potentials at the current density of 10 mA cm−2 (η10) in 0.5 m sulfuric acid (H2SO4) and 1 m potassium hydroxide (KOH) electrolyte are −11.5 and −16.3 mV, respectively, which is significantly lower than that of commercial Pt/C (−20.2 mV and −30.7 mV). This study demonstrates that such high activity benefits from the interface between highly dispersed a‐MoS3 and Pt sites, which act as the preferred adsorption sites for the efficient conversion of hydrion (H+) to hydrogen (H2). Additionally, the anchoring of highly dispersed clusters to Pt substrate greatly enhances the corresponding electrocatalytic stability.
TorC1 and nitrogen catabolite repression control of integrated GABA shunt and retrograde pathway gene expression
Authors: Tate Jennifer J. , Rai Rajendra , Cooper Terrance G.
Published: March 2023

Despite our detailed understanding of how the lower GABA shunt and retrograde genes are regulated, there is a paucity of validated information concerning control of GAD1, the glutamate decarboxylase gene which catalyzes the first reaction of the GABA shunt. Further, integration of glutamate degradation via the GABA shunt has not been investigated. Here, we show that while GAD1 shares a response to rapamycin‐inhibition of the TorC1 kinase, it does so independently of the Gln3 and Gat1 NCR‐sensitive transcriptional activators that mediate transcription of the lower GABA shunt genes. We also show that GABA shunt gene expression increases dramatically in response to nickel ions. The α‐ketoglutarate needed for the GABA shunt to cycle, thereby producing reduced pyridine nucleotides, derives from the retrograde pathway as shown by a similar high increase in the retrograde reporter, CIT2 when nickel is present in the medium. These observations demonstrate high integration of the GABA shunt, retrograde, peroxisomal glyoxylate cycle, and β‐oxidation pathways.
On $$ \mathcal{N} $$ = 4 supersymmetry enhancements in three dimensions
Authors: Assel Benjamin , Tachikawa Yuji , Tomasiello Alessandro
Published: March 2023

Abstract

We introduce a class of 3d theories consisting of strongly-coupled $$ \mathcal{N} $$
N
= 4 systems coupled to $$ \mathcal{N} $$
N
= 3 Chern-Simons gauge multiplets, which exhibit $$ \mathcal{N} $$
N
= 4 enhancements when a peculiar condition on the Chern-Simons levels is met. An example is the SU(N)3 Chern-Simons theory coupled to the 3d TN theory, which enhances to $$ \mathcal{N} $$
N
= 4 when 1/k1 + 1/k2 + 1/k3 = 0. We also show that some but not all of these $$ \mathcal{N} $$
N
= 4 enhancements can be understood by considering M5-branes on a special class of Seifert manifolds. Our construction provides a large class of $$ \mathcal{N} $$
N
= 4 theories which have not been studied previously.