bluuebunny/crossref_metadata_2025_split huggingface dataset:
Traceability in Battery Production: Cell-Specific Marker-Free Identification of Electrode Segments
Authors: Riexinger Günther , Regina David J. , Haar Christoph , Schmid-Schirling Tobias , Landwehr Inga , Seib Michael , Lips Jonas , Fehrenbach Simon , Stübing Julian , Carl Daniel , Sauer Alexander 
Published: June 2023

AbstractDigitalization in battery production, as well as the increase and stabilization of product quality of lithium-ion battery cells, require the elimination of information gaps between processes to enable the traceability of components and process steps to the finished product. In lithium-ion battery cell manufacturing, using a traceability system is considered a promising approach to reduce scrap rates and enable more efficient production. Today, traceability is possible from the assembled cell onwards. However, with a view to the new EU battery regulation, complete traceability down to the material needs to be ensured. One of the challenges in this context is to ensure both, traceability in continuous electrode production and cell-specific identification within the production chain. This paper presents an approach for identifying individual electrode segments without identification markers, using the individual microstructure of the electrode surface. The Fraunhofer Institute for Manufacturing Engineering and Automation IPA and the Fraunhofer Institute for Physical Measurement Techniques IPM are further developing and testing an identification technique known as Track & Trace Fingerprint. This technique is dedicated to serialization within battery production as part of the joint project DigiBattPro 4.0 and to be implemented at the Center for Digitalized Battery Cell Manufacturing (ZDB) of Fraunhofer IPA.
Multi-agent Interaction Structure for Enabling Subsidiary Planning and Control in Modular Production Systems
Authors: Komesker Simon , Bartels Jonathan , Wagner Achim , Ruskowski Martin 
Published: June 2023

AbstractModular production systems enable resilient production processes through decoupled production processes. On the way to implementing flexible and adaptable production systems, information support plays a decisive role. Only the use of intelligent and structured information processing across previous system boundaries and areas enables the coordination of requirements and capacities in dynamic production environments. The rigid communication structures in information systems of current production systems therefore need to be replaced by dynamic interaction, both horizontally between entities and vertically between different hierarchical levels. Multi-agent systems (MAS) are one way to meet the requirements for centralized and decentralized decision making in complex (cyber physical production) systems (CPPS). To prepare the instantiation of a MAS, it is necessary to structure and describe the information flows of a production system.In this paper, the results of a simulation experiment for the implementation of collaborative, subsidiary decision making based on a model-based system structure are presented. Productivity potentials of more than 10% can be shown by using collaborative manufacturing strategies.
Requirements for a Process to Remanufacture EV Battery Packs Down to Cell Level and Necessary Design Modifications
Authors: Graner Melina , Heieck Frieder , Fill Alexander , Birke Peter , Hammami Woidy , Litty Katharina 
Published: June 2023

AbstractIn case of electric vehicles (EV) powered by lithium ion traction batteries (LIB), remanufacturing processes nbecome increasingly important due to their rising market share and valuable raw materials. LIB can account for up to 40% of the total EV cost. Often, only a small portion of the cells are significantly degraded when the usable battery capacity falls below 80%, which is currently considered the standard end-of-life criterion. However, in order to enable efficient remanufacturing, novel battery design principles are required. This paper discusses the requirements, opportunities and challenges of future remanufacturing processes of LIBs down to the cell level using a battery system of a commercial vehicle as an example. It gives an overview of the current state-of-the-art manufacturing processes of battery systems and shows the developed overall remanufacturing process including condition assessment, disassembly and reassembly. Subsequently, requirements on future designs are discussed. The state-of-the-art of EV batteries is evaluated based on these requirements to determine where incompatible connections such as welded contacts or adhesive joints conflict with remanufacturing design principles.
Improvement of the Scheduling of Automotive Testing Processes Based on Production Scheduling Methods
Authors: Stütz Leon , König Timo , Bader Roman , Kley Markus 
Published: June 2023

AbstractIncreasing challenges in the automotive industry are caused by shorter development times for products, greater diversity of variants and increasing cost pressure. Testing plays an elementary role within the product development process (PDP). There are already many publications that deal with the early phases of the PDP, but relatively few that address testing. Inefficient scheduling leads to suboptimal use of development and testing resources.Automotive testing is characterized by high momentum and process complexity. The complexity of testing is determined, among other things, by the number of test rigs in a test field, the number and diversity of test objects, the type of testing and the preparatory setups. In addition, complex testing processes at the component and system level require a large number of human and material resources, whose time availability and sequence must be coordinated with the testing process. The sequence planning is subject to a high inherent dynamic because unexpected changes and disturbances of the process can occur during the testing. These changes require a rescheduling of the testing process. If done manually, the rescheduling results in high costs.Based on known production planning methods, a solution approach is derived for improved utilization of test field resources for the automotive sector. The planning is optimized with a multitude of product - and process-related dependencies and restrictions using mixed-integer linear programming, a standardized method from operations research. The test field is simulated via a discrete event simulation. The proposed method considers the availability of essential resources.
A non-PMU-based WAN protection scheme for swing detection and stability enhancement in power systems
Authors: El-Metwally Ehab A. , Attia Mahmoud A. , El-Shimy Mohamed 
Published: June 2023

AbstractThis paper presents a scheme for power swing detection and stability assessment for multi-machine power systems in addition to a brief survey on that topic. The scheme adopts smart grid perception. It is an asynchronous scheme that uses the rate of change of the angle of the generator bus voltage instead of synchronously estimating the absolute rotor angle value for stability assessment. Therefore, it does not require phasor measurement units (PMUs). Instead, it uses commercially available intelligent electronic devices (IEDs). It introduces a new application of the multicast Generic Object Oriented Substation Event (GOOSE) protocol or its routable version (R-GOOSE) to reflect the disturbance in the power grid on the transmission pattern of data packets over the grid data network. In this scheme, the angle change information is transmitted in the form of GOOSE packets simultaneously from the IEDs that readily measure the angles of the affected generators. On the other hand, the detecting device identifies the stability status of each affected generator. The proposed scheme is validated by simulating disturbances on the IEEE 39-bus system. The results show that the scheme can discriminate between stable and unstable swings. In addition, the loss of synchronism of the unstable generator is predicted ahead of its occurrence by enough time to take an appropriate action.
Can coenzyme Q10 alleviate the toxic effect of fenofibrate on skeletal muscle?
Authors: El-Bassouny Dalia R. , Mansour Alyaa A. , Ellakkany Amany S. , Ayuob Nasra N. , AbdElfattah Amany A. 
Published: June 2023

AbstractFenofibrate (FEN) is an antilipidemic drug that increases the activity of the lipoprotein lipase enzyme, thus enhancing lipolysis; however, it may cause myopathy and rhabdomyolysis in humans. Coenzyme Q10 (CoQ10) is an endogenously synthesized compound that is found in most living cells and plays an important role in cellular metabolism. It acts as the electron carrier in the mitochondrial respiratory chain. This study aimed to elucidate FEN-induced skeletal muscle changes in rats and to evaluate CoQ10 efficacy in preventing or alleviating these changes. Forty adult male rats were divided equally into four groups: the negative control group that received saline, the positive control group that received CoQ10, the FEN-treated group that received FEN, and the FEN + CoQ10 group that received both FEN followed by CoQ10 daily for 4 weeks. Animals were sacrificed and blood samples were collected to assess creatine kinase (CK). Soleus muscle samples were taken and processed for light and electron microscopic studies. This study showed that FEN increased CK levels and induced inflammatory cellular infiltration and disorganization of muscular architecture with lost striations. FEN increased the percentage of degenerated collagen fibers and immune expression of caspase-3. Ultrastructurally, FEN caused degeneration of myofibrils with distorted cell organelles. Treatment with CoQ10 could markedly ameliorate these FEN-induced structural changes and mostly regain the normal architecture of muscle fibers due to its antifibrotic and antiapoptotic effects. In conclusion, treatment with CoQ10 improved muscular structure by suppressing oxidative stress, attenuating inflammation, and inhibiting apoptosis.
A high-quality feature selection method based on frequent and correlated items for text classification
Authors: Mamdouh Farghaly Heba , Abd El-Hafeez Tarek 
Published: June 2023

AbstractThe feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. The quality of the selected features plays a critical role in building effective models, and poor-quality data can make this process more difficult. This work explores the use of association analysis in data mining to select meaningful features, addressing the issue of duplicated information in the selected features. A novel feature selection technique for text classification is proposed, based on frequent and correlated items. This method considers both relevance and feature interactions, using association as a metric to evaluate the relationship between the target and features. The technique was tested using the SMS spam collecting dataset from the UCI machine learning repository and compared with well-known feature selection methods. The results showed that the proposed technique effectively reduced redundant information while achieving high accuracy (95.155%) using only 6% of the features.
AWARE A novel web application to rapidly assess cardiovascular risk in type 2 diabetes mellitus
Authors: Berra Cesare , Manfrini Roberto , Mirani Marco , Bucciarelli Loredana , Zakaria Ahmed S. , Piccini Sara , Ghelardi Renata , Lunati Maria Elena , Rodovalho Sylka , Bifari Francesco , Fiorina Paolo , Folli Franco 
Published: June 2023

Abstract
Aim
To describe the development of the AWARE App, a novel web application for the rapid assessment of cardiovascular risk in Type 2 Diabetes Mellitus (T2DM) patients. We also tested the feasibility of using this App in clinical practice.

Methods
Based on 2019 European Society of Cardiology/European Association for the Study of Diabetes criteria for cardiovascular risk stratification in T2DM, the AWARE App classifies patients into very high (VHCVR), high (HCVR) and moderate (MCVR) cardiovascular risk categories. In this retrospective clinical study, we employed the App to assess the cardiovascular risk of T2DM patients, while also collecting data about current glycaemic control and pharmacological treatment.

Results
2243 T2DM consecutive patients were evaluated. 72.2% of the patients were VHCVR, 8.9% were HCVR, 0.8% were MCVR while 18.2% did not fit into any of the risk categories and were classified as “moderate-to-high” (MHCVR). Compared with the other groups, patients with VHCVD were more frequently ≥ 65 years old (68.9%), with a longer disease duration (≥ 10 years [56.8%]), a history of cardiovascular disease (41.4%), organ damage (35.5%) and a higher numbers of cardiovascular risk factors. Patients with MHCVD generally had disease duration < 10 years (96%), younger age (50–60 years [55%]), no history of cardiovascular disease, no organ damage, and 1–2 cardiovascular risk factors (89%). Novel drugs such as Glucagon Like Peptyde 1 Receptor Agonists or Sodium-Glucose Linked Transporter 2 inhibitors were prescribed only to 26.3% of the patients with VHCVR and to 24.7% of those with HCVR. Glycaemic control was unsatisfactory in this patients population (HbA1c 7.5 ± 3.4% [58.7 ± 13.4 mmol/mol]).

Conclusions
The AWARE App proved to be a practical tool for cardiovascular risk stratification of T2DM patients in real-world clinical practice.
Quantification of titanium and zirconium elements in oral mucosa around healthy dental implants: a case–control pilot study
Authors: Cionca Norbert , Meyer Julien , Michalet Sophie , Varesio Emmanuel , Hashim Dena 
Published: June 2023

Abstract
Objectives
Metallic particles are detected in different sites of the oral cavity, mainly in patients with peri-implantitis lesions. The aim of this pilot study was to analyze the levels of titanium and zirconium elements in the oral mucosa around healthy implants and to investigate the impact of titanium exogenous contamination on the measurements.

Materials and Methods
Forty-one participants were included in this three-phase study. Two groups of subjects were defined according to presence of titanium or zirconia implants (n: 20) or without any implants nor metallic restorations (n:21). Thirteen patients (n: 5 with zirconia implant; n: 3 with titanium implants; n: 5 control group) took part to the first part designed to optimize and validate the method of detecting titanium (Ti) and zirconium (Zr) elements in the oral mucosa and gingival tissues by the Inductively Coupled Plasma Mass Spectrometry (ICPMS). The second phase compared the levels of Ti and Zr concentrations in patients with implants (n: 12) and without implants (n: 6) who were controlled for their intake of titanium dioxide (TiO2). The last step included ten control subjects without any metallic devices to measure the concentration of Ti and Zr before and after having candies containing TiO2.

Results
In the first phase, concentrations of Ti and Zr were below the limit of detection (LOD) in most cases, 0.18 μg/L and 0.07 μg/L respectively. In the titanium group, two out of three subjects displayed concentrations above the LOD, 0.21 μg/L and 0.66 μg/L. Zr element was only found in patients with zirconia implants. After controlling the intake of TiO2, all concentrations of Ti and Zr were below the limit of quantification (LOQ). Moreover, in patients with no implants, the Ti concentration in gingiva cells was superior for 75% of the samples after having a TiO2 diet.

Conclusions
Zirconium was only found in patients with zirconia implants, whereas titanium was detected in all groups even in subjects with no titanium implants. Zirconium and titanium elements were not detected in patients who were controlled for their intake of food and their use of toothpaste irrespective of the presence of implants or not. For 70% of the patients, the titanium detection was directly influenced by the intake of TiO2 contained candies.

Clinical relevance
When analyzing titanium particles, it is necessary to pay attention to the risk of contamination bias brought by external products. When this parameter was controlled, no titanium particles were detected around clinically healthy implants.
Scholarly recommendation systems: a literature survey
Authors: Zhang Zitong , Patra Braja Gopal , Yaseen Ashraf , Zhu Jie , Sabharwal Rachit , Roberts Kirk , Cao Tru , Wu Hulin 
Published: June 2023

AbstractA scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.
RETRACTED ARTICLE: ITGA3 acts as a purity-independent biomarker of both immunotherapy and chemotherapy resistance in pancreatic cancer: bioinformatics and experimental analysis
Authors: Zheng Xiaohao , Du Yongxing , Liu Mingyang , Wang Chengfeng 
Published: June 2023

AbstractContribution of integrin superfamily genes to treatment resistance remains uncertain. Genome patterns of thirty integrin superfamily genes were analyzed of using bulk and single-cell RNA sequencing, mutation, copy number, methylation, clinical information, immune cell infiltration, and drug sensitivity data. To select the integrins that are most strongly associated with treatment resistance in pancreatic cancer, a purity-independent RNA regulation network including integrins were constructed using machine learning. The integrin superfamily genes exhibit extensive dysregulated expression, genome alterations, epigenetic modifications, immune cell infiltration, and drug sensitivity, as evidenced by multi-omics data. However, their heterogeneity varies among different cancers. After constructing a three-gene (TMEM80, EIF4EBP1, and ITGA3) purity-independent Cox regression model using machine learning, ITGA3 was identified as a critical integrin subunit gene in pancreatic cancer. ITGA3 is involved in the molecular transformation from the classical to the basal subtype in pancreatic cancer. Elevated ITGA3 expression correlated with a malignant phenotype characterized by higher PD-L1 expression and reduced CD8+ T cell infiltration, resulting in unfavorable outcomes in patients receiving either chemotherapy or immunotherapy. Our findings suggest that ITGA3 is an important integrin in pancreatic cancer, contributing to chemotherapy resistance and immune checkpoint blockade therapy resistance.
Graphical abstract
Multi-GNSS PPP solutions with different handling of system-specific receiver clock parameters and inter-system biases
Authors: Mikoś Marcin , Kazmierski Kamil , Hadas Tomasz , Sośnica Krzysztof 
Published: June 2023

AbstractIn the multi-GNSS solutions integrating GPS, GLONASS, Galileo, and BeiDou, the receiver clock may be treated twofold; the clock parameter may be estimated for each GNSS separately or the common clock can be estimated, e.g., for GPS, with inter-system biases (ISBs) for other systems. The latter strategy reduces the number of estimated independent clock parameters per epoch almost by a factor of four because the clock parameters are estimated epoch-wise, whereas ISBs are estimated as constant values for the entire day or month. Due to the discontinuities in reference satellite clocks, the estimated ISBs and receiver clock parameters have also to be reinitialized at day boundaries. This raises questions about whether only the common clock has to be reset or all ISB values and what is the impact of the reinitialization of clock parameters with covariance values when estimating system-specific clock parameters. We analyze the effects of different types of stochastic modeling applied to the parameters of clocks and ISBs. In this study, we test five different strategies to clock handling in multi-GNSS kinematic Precise Point Positioning derived continuously for one month. We found that two solutions can be considered equivalent: (1) estimating system-specific clocks and (2) estimating the common clock with ISB and resetting at day boundaries the common clock parameter and ISBs. Oppositely, resetting only the common clock parameter or assuming that the ISB keep their stabilities over long periods is insufficient to obtain superior results of station coordinates and reliable time transfer results.
Geometric deep learning and equivariant neural networks
Authors: Gerken Jan E. , Aronsson Jimmy , Carlsson Oscar , Linander Hampus , Ohlsson Fredrik , Petersson Christoffer , Persson Daniel 
Published: June 2023

AbstractWe survey the mathematical foundations of geometric deep learning, focusing on group equivariant and gauge equivariant neural networks. We develop gauge equivariant convolutional neural networks on arbitrary manifolds $$\mathcal {M}$$
M
using principal bundles with structure group K and equivariant maps between sections of associated vector bundles. We also discuss group equivariant neural networks for homogeneous spaces $$\mathcal {M}=G/K$$

M
=
G
/
K

, which are instead equivariant with respect to the global symmetry G on $$\mathcal {M}$$
M
. Group equivariant layers can be interpreted as intertwiners between induced representations of G, and we show their relation to gauge equivariant convolutional layers. We analyze several applications of this formalism, including semantic segmentation and object detection networks. We also discuss the case of spherical networks in great detail, corresponding to the case $$\mathcal {M}=S^2=\textrm{SO}(3)/\textrm{SO}(2)$$

M
=

S
2

=
SO

(
3
)

/
SO

(
2
)

. Here we emphasize the use of Fourier analysis involving Wigner matrices, spherical harmonics and Clebsch–Gordan coefficients for $$G=\textrm{SO}(3)$$

G
=
SO
(
3
)

, illustrating the power of representation theory for deep learning.