bluuebunny/crossref_metadata_2025_split huggingface dataset:
Engineering and validation of a dual luciferase reporter system for quantitative and systematic assessment of regulatory sequences in Chinese hamster ovary cells
Authors: Toktay Yagmur , Dayanc Bengisu , Senturk Serif 
Published: April 2022

AbstractOngoing research efforts to identify potent regulatory sequences that deliver robust and sustained transgene expression are critical for Chinese hamster ovary (CHO) cell line development technologies to meet the growing demand for recombinant proteins. Here we report the engineering and validation of a highly customizable single vector toolkit that comprises an all-in-one dual luciferase reporter system for quantitative and systematic interrogation of transcriptional regulatory sequences in transient and stable transfectants of CHO cells. To model the execution of the reporter system, we implemented a battery of known constitutive promoters including human CMV-mIE, SV40, HSV-TK, mouse PGK, human EF1α, EF1α short (EFS), human UBC, synthetic CAG, and Chinese hamster EF1α (CHEF1α). Of the nine promoters, CMV-mIE yielded the highest transcriptional activity in transient transfection settings, while CHEF1α was the strongest among a select subset of promoters in stable transfectants of CHO-DG44 pools. Remodeling the vector toolkit to build a dual fluorescent reporter system featured an alternative to bioluminescence based reporters. We infer that the findings of this study may serve as a basis to establish new vectors with weak or strong constitutive promoters. Furthermore, the modular all-in-one architecture of the reporter system proved to be a viable tool for discovering novel regulatory sequences that ensure high levels of transient and stable transgene expression in CHO and perhaps other mammalian cell lines.
The application and limitations of exposure multiplication factors in sublethal effect modelling
Authors: Sherborne Neil , Jager Tjalling , Goussen Benoit , Trijau Marie , Ashauer Roman 
Published: April 2022

AbstractThanks to growing interest and research in the field, toxicokinetic–toxicodynamic (TKTD) models are close to realising their potential in environmental risk assessment (ERA) of chemicals such as plant protection products. A fundamental application is to find a multiplicative scale factor which—when applied to an exposure profile—results in some specified effect relative to a control. The approach is similar to applying assessment factors to experimental results, common in regulatory frameworks. It also relies on the same core assumption: that increasing the scaling always produces more extreme effects. Unlike experimental approaches, TKTD models offer an opportunity to interrogate this assumption in a mathematically rigorous manner. For four well-known TKTD models we seek to prove that the approach guarantees a unique scale factor for any percentage effect. Somewhat surprisingly, certain model configurations may have multiple scale factors which result in the same percentage effect. These cases require a more cautious regulatory approach and generate open biological and mathematical questions. We provide examples of the violations and suggest how to deal with them. Mathematical proofs provide the strongest possible backing for TKTD modelling approaches in ERA, since the applicability of the models can be determined exactly.
An in vivo model for overloading-induced soft tissue injury
Authors: Chatzistergos Panagiotis E. , Chockalingam Nachiappan 
Published: April 2022

AbstractThis proof-of-concept study demonstrates that repetitive loading to the pain threshold can safely recreate overloading-induced soft tissue damage and that localised tissue stiffening can be a potential marker for injury. This concept was demonstrated here for the soft tissue of the sole of the foot where it was found that repeated loading to the pain threshold led to long-lasting statistically significant stiffening in the overloaded areas. Loading at lower magnitudes did not have the same effect. This method can shed new light on the aetiology of overloading injury in the foot to improve the management of conditions such as diabetic foot ulceration and heel pain syndrome. Moreover, the link between overloading and tissue stiffening, which was demonstrated here for the first time for the plantar soft tissue, opens the way for an assessment of overloading thresholds that is not based on the subjective measurement of pain thresholds.
Prognostics of unsupported railway sleepers and their severity diagnostics using machine learning
Authors: Sresakoolchai Jessada , Kaewunruen Sakdirat 
Published: April 2022

AbstractRailway sleepers are safety–critical components of a railway structure. They support ballasted track superstructure and are a critical factor in track geometry and track components’ deterioration. Unsupported sleepers are a common issue incurred after tracks have been utilized. When unsupported sleepers are present, they cause differential settlements of track superstructures, additional dynamic loading, and excessive train-track vibrations which affect passenger comfort, safety, and maintenance cost. This study is the world's first to develop new machine learning models to prognose and better diagnose defect severities of unsupported sleepers aligned with practical track inspection guidelines. Data used to develop machine learning models are based on a verified finite element model with actual field measurements, enabling unbiased full data ranges that govern all defect conditions. Different conditions of unsupported sleepers can be explored by varying locations of unsupported sleepers and the number of unsupported sleepers. Also, other operational parameters can be addressed such as speeds of rolling stock, the roughness of rail surface, and vertical stiffness of wheel-rail contact. In total, 2016 data sets have been obtained. Axle box accelerations are adopted as key indicators for machine learning models. Machine learning techniques used in the study are the convolutional neural network, recurrent neural network, ResNet, and fully convolutional neural network. Data fusion and assimilation have been conducted since the data points are dependent on the train speeds. Our new results reveal a breakthrough essential for real-world applications that the convolutional neural network model provides the best accuracy in both unsupported sleeper prognostics and severity identification. The accuracies of detection and severity identification are 99.34% and 97.02% respectively.
The influence of EEG oscillations, heart rate variability changes, and personality on self-pain and empathy for pain under placebo analgesia
Authors: De Pascalis Vilfredo , Vecchio Arianna 
Published: April 2022

AbstractWe induced placebo analgesia (PA), a phenomenon explicitly attenuating the self-pain feeling, to assess whether this resulted in reduced empathy pain when witnessing a confederate undergoing such pain experience. We recorded EEG and electrocardiogram during a painful Control and PA treatment in healthy adults who rated their experienced pain and empathy for pain. We derived HRV changes and, using wavelet analysis of non-phase-locked event-related EEG oscillations, EEG spectral power differences for self-pain and other-pain conditions. First-hand PA reduced self-pain and self-unpleasantness, whereas we observed only a slight decrease in other unpleasantness. We derived linear combinations of HRV and EEG band power changes significantly associated with self-pain and empathy for pain changes using PCAs. Lower Behavioral Inhibition System scores predicted self-pain reduction through the mediating effect of a relative HR-slowing and a decreased midline ϑ-band (4–8 Hz) power factor moderated by lower Fight-Flight-Freeze System trait scores. In the other-pain condition, we detected a direct positive influence of Total Empathic Ability on the other-pain decline with a mediating role of the midline β2-band (22–30 Hz) power reduction. These findings suggest that PA modulation of first-hand versus other pain relies on functionally different physiological processes involving different personality traits.
Nature of the 1/f noise in graphene—direct evidence for the mobility fluctuation mechanism
Authors: Rehman Adil , Delgado Notario Juan Antonio , Salvador Sanchez Juan , Meziani Yahya Moubarak , Cywiński Grzegorz , Knap Wojciech , Balandin Alexander A. , Levinshtein Michael , Rumyantsev Sergey 
Published: April 2022

Measurements under conditions of geometrical magnetoresistance prove that 1/f noise in graphene is dominated by mobility fluctuations.
Effective modelling of sinkhole detection algorithm for edge‐based Internet of Things (IoT) sensing devices
Authors: Bilal Ahmad , Hasany Syed Muhammad Noman , Pitafi Abdul Hameed 
Published: April 2022

AbstractThe sinkhole attacks for Internet of Things (IoT) situations can overcome the network and interrupt communication. Sinkhole attacker nodes can advertise the best possible shortest path towards the destination, and once the normal node starts transferring the packets by the given path, the attacker node starts troublemaking the flow of the network. In this environment, the destination is unable to receive proper information or may not receive complete information. Sinkhole attacked network keeps the problems like the enhanced end‐to‐end delay, less throughput, and reduced packet delivery. Furthermore, it can affect other network constraints. So, it has become important to design an effective model to prevent the IoT environment from sinkhole attacks. In this article, an intrusion detection model is proposed to protect the IoT environment from sinkhole attacks. A model proposed by using rich resourced edge nodes to detect various kinds of sinkhole attacker nodes by exchanging messages. A well‐known NS2 simulator is used for the practical implementation of the model. The proposed model attains more than 95% detection rate with a near about 1.4% false‐positive rate, which seems better than previously given schemes. Finally, the proposed scheme is the appropriate match for a sensitive platform like a monitoring and security system.
Parameter estimation of multiple frequency‐hopping signals based on Bayesian compressive sensing and atomic norm soft thresholding with missing observations
Authors: Wang Hongbin , Zhang Bangning , Wang Heng , Wu Binbin , Guo Daoxing 
Published: April 2022

AbstractIn this study, the problem of estimating multiple frequency‐hopping (FH) signal parameters in situations in which random observations are missing is addressed. A uniform linear array (ULA) is utilized to receive FH signals, and the missing observations are considered to be equivalent to randomly missing elements in the space‐time matrix obtained from the ULA. Bayesian compressed sensing (BCS) estimates the hopping time of received FH signals by the spatial information from the space‐time matrix and restores the missing observations. The estimated hopping time is implemented as the boundary to divide the signals such that each segment contains a superposition of time‐invariant multiple components. Then, the instantaneous frequency (IF) of multiple components can be estimated precisely by atomic norm soft thresholding (AST) within each segment. After estimating the hopping time and IF, the direction of arrival (DOA) of multiple signals is directly calculated using these two parameters. The simulation results show that the proposed method is superior to existing approaches. Provided that sufficient array elements are available, multi‐FH signal parameters can be estimated with satisfactory accuracy even when a large portion of observations is missing.
Closed‐form expressions for spatial correlation parameters for performance analysis of fluid antenna systems
Authors: Wong K. K. , Tong K. F. , Chen Y. , Zhang Y. 
Published: April 2022

AbstractThe emerging fluid antenna technology enables a high‐density position‐switchable antenna in a small space to obtain enormous performance gains for wireless communications. To understand the theoretical performance of fluid antenna systems, it is important to account for the strong spatial correlation over the different positions (referred to as ‘ports'). Previous works used a classical, generalised correlation model to characterise the channel correlation among the ports but were limited by the lack of degree of freedom of the model to imitate the correlation structures in an actual antenna. In this letter, it is proposed to use a common correlation parameter and to choose it by setting the correlation coefficient of any two ports to be the same as the average correlation coefficient of an actual fluid antenna taking up a linear space. A closed‐form expression for the spatial correlation parameter is first derived assuming that the number of ports is large, and it is illustrated that the correlation parameter depends only on the size of the fluid antenna but not the port density. Simpler expressions are then obtained for small and large sizes of fluid antenna. The resulting model is finally used to study the performance of fluid antenna systems. Simulation results based on the proposed model are provided to confirm the promising performance of fluid antenna in single and multiuser environments.
Adaptive aggregation with self‐attention network for gastrointestinal image classification
Authors: Li Sheng , Cao Jing , Yao Jiafeng , Zhu Jinhui , He Xiongxiong , Jiang Qianru 
Published: April 2022

AbstractAutomatic classification of diseases in endoscopic images is essential to the improvement of diagnostic performance and the reduction of colorectal cancer mortality. However, due to the ambiguous boundary between background and foreground, abnormal classification in endoscopic images is still challenging. To tackle such a situation, an adaptive aggregation with self‐attention network (AASAN), including a global branch, a local branch, and a fusion branch, is proposed imitating the diagnosis process of endoscopists. On this basis, the self‐attention with relative position encoding (SA‐RPE) module is designed to capture long‐range dependencies and gather lesion neighborhood information. Furthermore, an adaptive aggregation feature (AAF) module is proposed and embedded into the fusion branch for final image label prediction, which is helpful to capture more discriminant features. Extensive experiments show that the classification accuracy of the authors' method on Kvasir public dataset reaches 96.37% in a fivefold cross‐validation, higher than the state‐of‐the‐art deep learning algorithms.
Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation
Authors: Biazar Dariush , Khaloozadeh Hamid , Siahi Mehdi 
Published: April 2022

AbstractThe dynamics of the wind turbine behaviour and identifying the factors that change turbine performance are very complex and challenging. Quantifying the impact of these factors on improving the wind turbine performance is invaluable. Many attempts have been made to describe the behaviour of the wind turbine variables. Sensitivity analysis and Monte Carlo simulation are methods to identify important parameters affecting model
behavior. Here, with these methods, the authors investigate the effect of error in sensors on output power and measured variables of the wind turbine in partial load region and full load region. Then, using the importance factor, the effect of error in the different sensors on the output power of the wind turbine is ranked in the two operating regions, so that, in practice, the sensors that have the greatest impact on the output power are designed as redundant.
Optimisation methods for dispatch and control of energy storage with renewable integration
Authors: Guo Zhongjie , Wei Wei , Shahidehpour Mohammad , Wang Zhaojian , Mei Shengwei 
Published: April 2022

AbstractRenewable energy integration is an effective measure to resolve environmental problems and implement sustainable development, yet the volatility of wind and solar generation has a profound impact on power system operation, calling for sufficient backup capacity. Energy storage can shift demand over time and mitigate real‐time power mismatch and thus help integrate renewable energy resources into power grids. However, the unit capacity price of energy storage is still relatively high, and the capacity of energy storage is usually limited. Given the prominent uncertainty and finite capacity of energy storage, it is crucially important to take full advantage of energy storage units by strategic dispatch and control. From the mathematical point of view, energy storage dispatch and control give rise to a sequential decision‐making process involving uncertain parameters and inter‐temporal constraints. Multitudinous optimisation methods have been developed for such a problem while they differ in two aspects: the modelling of uncertainty and the mechanism to handle uncertainty. This review aims to draw a methodological picture of historical developments and state‐of‐the‐art advances in this research field. These methods are subsumed into three major categories: multistage optimisation, online optimisation, and multi‐timescale optimisation. In addition to introducing the advancements and applications, the authors also explain the motivations and theoretical foundations of these methodologies. The authors hope interested readers can attain a holistic outlook of these sophisticated approaches and choose the most appropriate one for their specific applications.
Polarization of gravitational waves from helical MHD turbulent sources
Authors: Roper Pol Alberto , Mandal Sayan , Brandenburg Axel , Kahniashvili Tina 
Published: April 2022

Abstract
We use direct numerical simulations of decaying primordial
hydromagnetic turbulence with helicity to compute the resulting
gravitational wave (GW) production and its degree of circular
polarization. The turbulence is sourced by magnetic fields that are
either initially present or driven by an electromotive force applied
for a short duration, given as a fraction of one Hubble time. In
both types of simulations, we find a clear dependence of the
polarization of the resulting GWs on the fractional helicity of the
turbulent source. We find a low frequency tail below the spectral
peak shallower than the f 3 scaling expected at super-horizon
scales, in agreement with similar recent numerical simulations.
This type of spectrum facilitates its observational detection with
the planned Laser Interferometer Space Antenna (LISA). We show that
driven magnetic fields produce GWs more efficiently than magnetic
fields that are initially present, leading to larger spectral
amplitudes, and to modifications of the spectral shape. In
particular, we observe a sharp drop of GW energy above the spectral
peak that is in agreement with the previously obtained results. The
helicity does not have a huge impact on the maximum spectral
amplitude in any of the two types of turbulence considered.
However, the GW spectrum at wave numbers away from the peak becomes
smaller for larger values of the magnetic fractional helicity. Such
variations of the spectrum are most noticeable when magnetic fields
are driven. The degree of circular polarization approaches zero at
frequencies below the peak, and reaches its maximum at the peak. At
higher frequencies, it stays finite if the magnetic field is
initially present, and it approaches zero if it is driven. We
predict that the spectral peak of the GW signal can be detected by
LISA if the turbulent energy density is at least ∼3% of the
radiation energy density, and the characteristic scale is a
hundredth of the horizon at the electroweak scale. We show that the
resulting GW polarization is unlikely to be detectable by the
anisotropies induced by our proper motion in the dipole response
function of LISA. Such signals can, however, be detectable by
cross-correlating data from the LISA-Taiji network for turbulent
energy densities of ∼5%, and fractional helicity of 0.5 to
1. Second-generation space-base GW detectors, such as BBO and
DECIGO, would allow for the detection of a larger range of the GW
spectrum and smaller amplitudes of the magnetic field.
Compressive creep behavior of Ti-6Al-4V-xSi-ySc alloys under high-pressure at room temperature
Authors: Deng Tongsheng , Cao Wei , Yang Kemai , Shen Jianxin 
Published: April 2022

Abstract
One of the characteristics of Titanium alloys is that they are prone to creep due to the service conditions under high-pressure, such as deep-sea equipment. The compressive properties and deformation mechanism of Ti-6Al-4V-xSi-ySc alloys at room temperature are investigated in this study. The alloy samples are subjected to uniaxial compressive and creep tests. The obtained compression creep properties at room temperature follow the power-law function, and creep is mainly found in the first and second stage. Si and Sc are observed to improve the creep properties where the latter shows significant effect. This can be attributed to the strengthening of the solid solution of Si and Sc. Sc and Si strengthen the α and β phases respectively. The stability of these phases contributes to increasing the creep resistance of Titanium alloys. The creep resistance mechanisms of the alloys involve dislocation movement and twins.
Metallic and complex hydride-based electrochemical storage of energy
Authors: Cuevas Fermin , Amdisen Mads B , Baricco Marcello , Buckley Craig E , Cho Young Whan , de Jongh Petra , de Kort Laura M , Grinderslev Jakob B , Gulino Valerio , Hauback Bjørn C , Heere Michael , Humphries Terry , Jensen Torben R , Kim Sangryun , Kisu Kazuaki , Lee Young-Su , Li Hai-Wen , Mohtadi Rana , Møller Kasper T , Ngene Peter , Noréus Dag , Orimo Shin-ichi , Paskevicius Mark , Polanski Marek , Sartori Sabrina , Skov Lasse N , Sørby Magnus H , Wood Brandon C , Yartys Volodymyr A , Zhu Min , Latroche Michel 
Published: April 2022

Abstract
The development of efficient storage systems is one of the keys to the success of the energy transition. There are many ways to store energy, but among them, electrochemical storage is particularly valuable because it can store electrons produced by renewable energies with a very good efficiency. However, the solutions currently available on the market remain unsuitable in terms of storage capacity, recharging kinetics, durability, and cost. Technological breakthroughs are therefore expected to meet the growing need for energy storage. Within the framework of the Hydrogen Technology Collaboration Program—H2TCP Task-40, IEA’s expert researchers have developed innovative materials based on hydrides (metallic or complex) offering new solutions in the field of solid electrolytes and anodes for alkaline and ionic batteries. This review presents the state of the art of research in this field, from the most fundamental aspects to the applications in battery prototypes.