bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Transformer Fault Diagnosis Based on Improved Squirrel Search Algorithm and Support Vector Machine
Authors: Wu Yuhan , Sun Xianbo , Zhang Yi , Zhong Xianjing , Cheng Lei 
Published: February 2022

Abstract
An improved Squirrel Search Algorithm (ISSA) is proposed to overcome the shortcoming of Support Vector Machine (SVM) parameters is difficult to select. Firstly, an adaptive probability formula is used to balance the exploration and foraging ability of SSA, so as to improve the optimization performance of SSA. In order to test the optimization performance of ISSA, two test functions are used. And results show that ISSA has better optimization performance than SSA. Finally, based on ISSA-SVM and SVM, and 3-fold cross-Validation is used, the fault diagnosis of DGA data is carried out. The diagnostic results show that ISSA-SVM has the highest diagnosis performance.
Health Condition Assessment of Braking System Based on Multi-agent Federated Learning
Authors: Gao Dianzhu , Peng Jun , Rong Jieqi , Yang Yingze , Zhang Xiaoyong , Zhao Qin , Liu Weirong , Huang Zhiwu 
Published: February 2022

Abstract
The brake system has the characteristics of multi-component, multi working conditions and complex degradation process, which brings great challenges to its health condition assessment. As it is difficult for a single brake agent to make a comprehensive and accurate health condition assessment, a health condition assessment model based on multi-agent federated learning is proposed in this paper. The different agents train their brake health data under different working conditions and states, which ensures the accuracy of health condition assessment and the safety of data of each agent. Aiming at the problem that it is difficult to determine the credibility of agent data in the process of federated learning, a credibility of agent data scheme based on evidence theory is proposed, which not only reduces the cost of calculation and communication, but also further improves the accuracy of health condition assessment. Simulation results show that the scheme not only has higher prediction accuracy, but also can ensure the security of agent data compared with the conventional centralized training scheme.
Intelligent Scheduling System for Production Line Automatic Matching Based on DSSM-XGBoost
Authors: Yang Shuaihu , Feng Min , Guan Danping 
Published: February 2022

Abstract
Intelligent production scheduling is an important part of intelligent production, and its production time and intensity are reasonably arranged, which can fully improve production efficiency. This article provides an intelligent model for engine manufacturing and scheduling. After the model decomposes the order into processes, the DSSM algorithm solves the semantic similarity of the text in the production content of the process, and generates a semantic vector to convert unstructured data into structured data. Based on XGBoost algorithm, the required production line is marked to establish the association between the production content and the production line, and realize automatic matching. After classification, the average working hours and other information are estimated according to the established logic. Finally, schedule production based on the principle of maximum utilization of the production line. Through testing and evaluation, the scheduling results of this smart scheduling model can be completed in just a few seconds, which saves time and costs in the scheduling process.
Short-term Impact Load Forecasting Model Based on Multi-objective Antlion Optimization Algorithm
Authors: Chen Jing , Tan Aiguo , Zhong Jianwei , Han Mingchong 
Published: February 2022

Abstract
In this study, a novel multi-objective antlion optimization (MOALO) algorithm-based impact load prediction model is proposed to address forecasting problem in the area with a lot of impact load. To reduce the impact of glitch in impact load data, ensemble empirical mode decomposition(EEMD) is applied to decompose the primitive load data into a set of sub-layers. Then, a novel MOALO-based extreme learning machine (ELM) forecasting model is put forward to make short-term prediction by using the decomposed sub-series. Finally, superimpose the prediction results. According to case study, the proposed EEMD-MOALO-ELM model has the best prediction accuracy and stability.
The TaintDroid Based Honeypot Monitoring System for Embedded Device
Authors: Cheng Kai , Wu Zhan , Li Dongkun , Li Xin , Ren Mu 
Published: February 2022

Abstract
Honeypot is a proactive defense technology introduced by defenders. Through the honeypots, defenders can deceive attackers to illegally take advantage of the honeypots and capture and analyze the attack behaviors to understand the attack tools and methods. To build honeypots, defenders first imitate vulnerable systems to entice the attacker to attack, then deploy monitoring systems that is responsible for monitoring and recording the attacker’s behavior. It is of concern that monitoring system is the key to determine the performance of honeypots, because obtaining attackers’ behavior is the main purpose of deploying honeypots, and monitoring system’s performance determines whether attackers’ behavior can be accurately and comprehensively recorded. In this paper, we introduce a novel TaintDroid based honeypot monitoring system for embedded device. This system uses TaintDroid to mark the attackers who hack into the honeypot, monitors the behavior of the marked attackers and then records. Moreover, we tested the feasibility of this system by building a monitoring system based on TaintDroid.
Detection of runaway electrons at the COMPASS tokamak using a Timepix3-based semiconductor detector
Authors: Kulkov S. , Marcisovsky M. , Svihra P. , Tunkl M. , van Beuzekom M. , Caloud J. , Cerovsky J. , Ficker O. , Macusova E. , Mlynar J. , Weinzettl V. , Svoboda V. 
Published: February 2022

Abstract
Runaway electrons are considered dangerous for the integrity
of tokamak vacuum vessels. To secure the success of the future
tokamak-based machines, reliable diagnostics and mitigation
strategies are necessary. The COMPASS tokamak supported the research
of runaway electron physics via regular experimental
campaigns. During the last two experimental campaigns dedicated to
runaway electrons, a semiconductor detector with a Timepix3 readout
chip, Si sensor, and the SPIDR readout system was tested. Time
evolution signals, energy measurements, and sensor snapshots
collected with the Timepix3-based detector are presented.
Determination of ionizing threshold of trimethylamine ions with a high resolution RGA mass spectrometer
Authors: Trindade A.M.F. , Escada J. , Rajado Silva M. , Borges F.I.G.M. , Santos F.P. 
Published: February 2022

Abstract
In this work, a mass spectrometer residual gas analyzer
(RGA) was used to determine the ionization thresholds (ionization
energy and appearance energy) in trimethylamine (TMA,
(CH3)3N), as well as the relative abundance of the ions
produced by electron impact in the 7.5–70 eV electron energy
range. The ten most intense peaks in the RGA mass spectra,
corresponding to the most abundant ions formed by electron impact at
70 eV in TMA, were analyzed. The ionization energy for TMA, as
assessed by this experiment, is 7.9 ± 0.2 eV and it is the
threshold of formation of the C3H9N+ ion. This ion
species is the most abundant one below 18 eV electron impact
energy, being surpassed by the C3H8N+ ion above this
energy.

In order to assess the method used, methane (CH4), whose
ionization properties are well established, was previously
studied. The agreement between the results obtained for CH4 and
those found in the literature provided the validation of the
experimental method used in this work.
Research on proton beam spot imaging based on pixelated gamma detector
Authors: Sun Y.K. , Jing H.T. , Tian B.B. , Gao X.L. , Yang X.Y. 
Published: February 2022

Abstract
The secondary particles from the spallation target of the
China Spallation Neutron Source are mainly gammas and neutrons,
which are related to the incident proton. The reconstruction of the
proton beam spot could be implemented based on the distribution of
the positions of secondary gammas or neutrons. The methods of
pinhole imaging and Compton imaging are developed by measuring the
position distribution of gammas based on the pixelated detector. The
secondary gammas could be detected by the pixelated gamma detector
directly. The neutron can be identified by detecting the
characteristic (478 keV) γ-rays from the
10B(n, α) reactions. In order to detect secondary
neutrons, a layer of 10B converter is added before the
pixelated gamma detector. The pixelated gamma detector is sensitive
to the characteristic (478 keV) γ-rays and then the neutron
imaging could be achieved based on measuring the position
distribution of the characteristic (478 keV) γ-rays.
A detector-emulation method for realistic readout-electronics tests. A case study of VMM3a ASIC for sTGC detector.
Authors: Moleri L. , Lupu N. , Vdovin A. , Kajomovitz E. 
Published: February 2022

Abstract
We present a detector-emulation method for developing,
optimizing, and testing front-end electronics for particle detectors
under realistic experimental conditions. The method is capable of
reproducing bipolar signals shape and amplitude, rates, pileup, and
radiation backgrounds. By controlling the different components of
the experimental environment, the method allows for the assessment
of their individual and combined effects, which is difficult to
achieve in test beam or experiment environment.

The technique emulates the output current of AC coupled detector
terminals for an experimental environment of interest, by combining
a sequence of recorded or simulated signal waveforms. Voltage
waveforms produced with off the shelf arbitrary waveform generators
are converted to current by a simple circuit, which also accounts
for the characteristic capacitance of the detector, and injected
into the front-end electronics. We demonstrate the technique by
applying it in the optimization and characterization of the
front-end electronics of the sTGC detector for the
ATLAS experiment.
Solar park management and design to boost bumble bee populations
Authors: Blaydes H , Gardner E , Whyatt J D , Potts S G , Armstrong A 
Published: February 2022

Abstract
Solar photovoltaics is projected to become the dominant renewable, with much capacity being installed as ground-mounted solar parks. Land use change for solar can affect ecosystems across spatial scales and solar parks offer a unique opportunity for ecological enhancement. One compelling potential benefit beginning to be deployed by the solar industry is management for insect pollinators. Specifically, solar parks can provide refuge for pollinators through the provision of suitable habitat, potentially contributing to halting and reversing widespread declines recorded in some pollinator groups. There is scope to both manage and design solar parks for pollinators, but understanding is limited. Using a geographic information system and a process-based pollinator model, we explore how solar park management, size, shape and landscape context might impact ground-nesting bumble bee density, nest density and nest productivity inside existing solar parks and surrounding landscapes in the UK. We show that bumble bee density and nest density is driven by solar park management, with twice as many bumble bees foraging and nesting inside solar parks managed as wildflower meadows, compared to those with only wildflower margins. In comparison, solar park size, shape and landscape context have a smaller impact on bumble bee response inside solar parks. However, large, elongated resource-rich solar parks were most effective at increasing bumble bee density in surrounding landscapes, with implications for local crop pollination. Specifically, there were double the number of foraging bumble bees surrounding large solar parks managed as meadows compared to smaller parks managed as turf grass. If designed and managed optimally, solar parks therefore have the potential to boost local bumble bee density and potentially pollination services to adjacent crops. Our results demonstrate how incorporating biodiversity into solar park management and design decisions could benefit groups such as pollinators and contribute to the wider environmental sustainability of solar parks.
In vitro and in vivo characterization of a novel tricalcium silicate-based ink for bone regeneration using laser-assisted bioprinting
Authors: Touya Nicolas , Devun Mathilde , Handschin Charles , Casenave Sophia , Ahmed Omar Naïma , Gaubert Alexandra , Dusserre Nathalie , De Oliveira Hugo , Kérourédan Olivia , Devillard Raphaël 
Published: February 2022

Abstract
Grafts aside, current strategies employed to overcome bone loss still fail to reproduce native tissue physiology. Among the emerging bioprinting strategies, laser-assisted bioprinting (LAB) offers very high resolution, allowing designing micrometric patterns in a contactless manner, providing a reproducible tool to test ink formulation. To this date, no LAB associated ink succeeded to provide a reproducible ad integrum bone regeneration on a murine calvaria critical size defect model. Using the Conformité Européenne (CE) approved BioRoot RCS® as a mineral addition to a collagen-enriched ink compatible with LAB, the present study describes the process of the development of a solidifying tricalcium silicate-based ink as a new bone repair promoting substrates in a LAB model. This ink formulation was mechanically characterized by rheology to adjust it for LAB. Printed aside stromal cells from apical papilla (SCAPs), this ink demonstrated a great cytocompatibility, with significant in vitro positive impact upon cell motility, and an early osteogenic differentiation response in the absence of another stimulus. Results indicated that the in vivo application of this new ink formulation to regenerate critical size bone defect tends to promote the formation of bone volume fraction without affecting the vascularization of the neo-formed tissue. The use of LAB techniques with this ink failed to demonstrate a complete bone repair, whether SCAPs were printed or not of at its direct proximity. The relevance of the properties of this specific ink formulation would therefore rely on the quantity applied in situ as a defect filler rather than its cell modulation properties observed in vitro. For the first time, a tricalcium silicate-based printed ink, based on rheological analysis, was characterized in vitro and in vivo, giving valuable information to reach complete bone regeneration through formulation updates. This LAB-based process could be generalized to normalize the characterization of candidate ink for bone regeneration.
All-in-one biofabrication and loading of recombinant vaults in human cells
Authors: Martín Fernando , Carreño Aida , Mendoza Rosa , Caruana Pablo , Rodriguez Francisco , Bravo Marlon , Benito Antoni , Ferrer-Miralles Neus , Céspedes M Virtudes , Corchero José Luis 
Published: February 2022

Abstract
One of the most promising approaches in the drug delivery field is the use of naturally occurring self-assembling protein nanoparticles, such as virus-like particles, bacterial microcompartments or vault ribonucleoprotein particles as drug delivery systems (DDSs). Among them, eukaryotic vaults show a promising future due to their structural features, in vitro stability and non-immunogenicity. Recombinant vaults are routinely produced in insect cells and purified through several ultracentrifugations, both tedious and time-consuming processes. As an alternative, this work proposes a new approach and protocols for the production of recombinant vaults in human cells by transient gene expression of a His-tagged version of the major vault protein (MVP-H6), the development of new affinity-based purification processes for such recombinant vaults, and the all-in-one biofabrication and encapsulation of a cargo recombinant protein within such vaults by their co-expression in human cells. Protocols proposed here allow the easy and straightforward biofabrication and purification of engineered vaults loaded with virtually any INT-tagged cargo protein, in very short times, paving the way to faster and easier engineering and production of better and more efficient DDS.
Silver nanoparticles synthesized via green chemistry with the aid of Delphinium denudatum Wall. root extract modulated gentamicin nephrotoxicity activity with respect to oxidative potential
Authors: Siddique N A , Al-Samman A M M A 
Published: February 2022

Abstract
Inpresent study, we explored the nephroprotective potential of Delphinium denudatum derived silver nanoparticles (DdrAgNPs). Characterisation of DdrAgNPs were carried out using UV–vis spectroscopy, X-ray diffraction (XRD), scanning electron microscope analysis (SEM), high resolution transmission electron microscopy (HR-TEM), energy dispersive x-rays (EDX) and Fourier transform infrared spectroscopy (FTIR). The biosynthesised DdrAgNPs displayed maximum absorbance at 415 nm, were polyspread in nature, sphere-shaped, and had a particle size ranging from 10 to 63 nm and the average diameter was ∼28.2 nm. Because nephroprotection could not be obtained solely through one mechanism, a considerable relationship of DdrAgNPs has been established between antioxidant potential and polyhydroxy herbal contents, suggesting that these blends may be complementary drug candidates to patients on gentamicin (GM) therapy. Biological performance of DdrAgNPs was assessed by estimating the level of renal biomarkers (blood urea, uric acid, creatinine) via antioxidants potential (GSH, CATALASE, SOD and TBARS) in experimental rats against gentamicin and corroborated with histopathological examination of renal tissue. In all, the proposed results indicate that DdrAgNPs are a potent nephroprotective agent, which can be utilised as a therapeutic adjunct to prevent gentamicin-nephrotoxicity. It is first time nephroprotection of silver nanoparticles has been reported using Delphinium denudatum as a plant extract.
Large deflection deformation detection method of steel strip based on ultra-weak fiber bragg grating
Authors: Luo Zhihui , Wang Yemeng , Cheng Wensheng , He Huiling 
Published: February 2022

Abstract
Large deflection deformation of a steel strip is an important part of structural deformation detection. Deformation detection has been achieved using ultra-weak fiber Bragg gratings (FBGs), and the deformation reconstruction inversion algorithm of the tangent recursive algorithm has been analyzed theoretically. The strain distribution of the steel strip (2.5 m × 0.050 m × 0.002 m) with large deflection under different loads was obtained by pasting eight ultra-weak FBG sensors with spacing of 0.3 m on the surface of the strip. The steel strip deformation was inversed by tangent recursive algorithm, and deformation error under loading condition was analyzed by finite element simulation. The inversion value is compared with the theoretical value and the measured value, which proves the effectiveness and accuracy of the ultra weak FBG method proposed in this paper. Results show that the large deflection deformation of a steel strip under different loads can be reconstructed using the tangent recursive algorithm, and the relative errors of the coordinates of the sampling points are less than 6.21%. This paper provides a reference for the detection of structural deformation.
Stepping up to the thermogradient plate: a data framework for predicting seed germination under climate change
Authors: Collette Justin C , Sommerville Karen D , Lyons Mitchell B , Offord Catherine A , Errington Graeme , Newby Zoe-Joy , von Richter Lotte , Emery Nathan J 
Published: February 2022

Abstract

Background and Aims
Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research.

Methods
Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates &gt;40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe.

Key Results
In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures.

Conclusions
Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plant species globally.
The Microbiota–Gut–Brain Axis in Alzheimer’s Disease: A Review of Taxonomic Alterations and Potential Avenues for Interventions
Authors: Murray Emily R , Kemp Mylon , Nguyen Tanya T 
Published: February 2022

Abstract

Objective
The gut microbiome is a complex community of microorganisms that inhabit the gastrointestinal tract. The microbiota–gut–brain axis encompasses a bidirectional communication system that allows the gut to influence the brain via neural, endocrine, immune, and metabolic signaling. Differences in the gut microbiome have been associated with psychiatric and neurological disorders, including Alzheimer’s Disease (ad). Understanding these ad-associated alterations may offer novel insight into the pathology and treatment of ad.

Method
We conducted a narrative review of clinical studies investigating the gut microbiome in ad, organizing the results by phyla to understand the biological contributions of the gut microbial community to ad pathology and clinical features. We also reviewed randomized clinical trials of interventions targeting the microbiome to ameliorate ad symptoms and biomarkers.

Results
Alpha diversity is reduced in patients with ad. Within Firmicutes, taxa that produce beneficial metabolites are reduced in ad, including Clostridiaceae, Lachnospiraceae, Ruminococcus, and Eubacterium. Within Bacteroidetes, findings were mixed, with studies showing either reduced or increased abundance of Bacteroides in mild cognitive impairment or ad patients. Proteobacteria that produce toxins tend to be increased in ad patients, including Escherichia/Shigella. A Mediterranean-ketogenic dietary intervention significantly increased beneficial short-chain fatty acids and taxa that were inversely correlated with changes in ad pathological markers. Probiotic supplementation with Lactobacillus spp. and Bifidobacterium spp. improved cognitive function and reduced inflammatory and metabolic markers in patients with ad.

Conclusions
The gut microbiome may provide insight into ad pathology and be a novel target for intervention. Potential therapeutics include probiotics and dietary intervention.