bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection and explanation of anomalies in healthcare data
Authors: Samariya Durgesh , Ma Jiangang , Aryal Sunil , Zhao Xiaohui 
Published: April 2023

AbstractThe growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field; however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.
Microanalysis of TiN phases formed in inclusions of electrogas weld metal
Authors: Seo Kangmyung , Kang Yongjoon , Kim Hee Jin , Yoon Joong Geun 
Published: April 2023

Abstract
This study has been conducted to confirm the formation of TiN phase in the electrogas (EG) weld metal and, if it formed, to identify the nucleation potency of TiN for acicular ferrite. To facilitate this, a high-nitrogen EG weld meal deposited using a very high heat input was chosen. Inclusion analyses were mostly carried out in a scanning electron microscope (SEM) and analytical transmission electron microscope (TEM) equipped with energy dispersive analyzer (EDS) and electron energy loss spectroscopy (EELS). It was confirmed that the inclusions were nearly fully covered by TiO layer alloyed with a small amount of nitrogen, this TiO layer being partly coated with TiN phases having two different types of morphology: layer-type or patch-type. The layer-type was identified to be nearly pure TiN, being formed with a thickness of 10 nm on the outer surface of thick TiO layer. The patch-type appeared to be formed on the TiN/TiO layer and was characterized by its fine lamellar mixture of TiN and (Al,Mn)-oxide. As all these phases formed on the inclusion surface, they were suspected to influence the nucleation potency of inclusions but the relative potency of these phases to TiO layer was not clearly understood.
Modeling and simulation of biomass anaerobic digestion for high biogas yield and CO2 mineralization
Authors: Mokraoui Salim , Halilu Ahmed , Hashim Mohd Ali , Hadj-Kali Mohamed Kamel 
Published: April 2023

AbstractBioenergy is one of several renewable energy options derived from biomass that can help satisfy our energy needs. Anaerobic digestion is a viable method for producing bioenergy in the form of biogas from biomass. The anaerobic digestion process is challenged with low biogas recovery, and low-quality effluent or CO2 emission, which contribute to environmental pollution and the carbon footprint in the atmosphere. Computational process modelling and simulation can provide realistic information for dealing with the technological challenges involved with anaerobic digestion. In this study, modeling and simulation of the simplified anaerobic digestion process were done using SuperPro Designer software fed with biomass feedstock containing carbohydrates, proteins, and fats, as well as yeast, at 37 °C mesophilic temperature. The anaerobic digestion process yielded 89.655% of CH4 and 10.345% of CO2 and confirmed that the carbohydrate feedstock produces more CH4 composition in the biogas. Mineralization of CO2 using MgO yielded 0.23% MgCO3, consuming > 99% of the CO2 produced during the anaerobic digestion process. Environmental impact assessment of the effluent discharge yielded 0.142 kg Slds/L volatile solid with 6.01% COD reduction per batch of the anaerobic digestion process in an anaerobic digester with 90% (1.925 kg/batch) feedstock dosage. The data indicate that single-batch effluent cannot be discharged into the environment, hence indicating the possible recycling for multiple anaerobic digestion processing. The results are a significant guide for the realistic scalable production of high-quality biogas for bioenergy application, CO2 mineralization, and environmental remediation.
Exclusion of older adults and immunocompromised individuals in influenza, pneumococcal and COVID-19 vaccine trials before and after the COVID-19 pandemic
Authors: Bukan Katrine , Pearce-Slade Toby , Eiberg Mads , Tinelli Marco , Yahav Dafna , Tuells Jose , Epaulard Olivier , Holler Jon G. , Roed Casper , Søborg Christian , Jensen Jens-Ulrik Stæhr , Harboe Zitta Barrella 
Published: April 2023

Abstract
Background
Older adults and immunocompromised individulas are often excluded from vaccine trials.

Aim
We hypothesised that during the coronavirus disease 2019 (COVID-19) pandemic, the proportion of trials excluding these patients decreased.

Methods
Using the US Food and Drug Administration and and European Medicines Agency search engines, we identified all vaccines approved against pneumococcal disease, influenza (quadrivalent vaccines), and COVID-19 from 2011 to 2021. Study protocols were screened for direct and indirect age exclusion criteria and exclusion of immunocompromised individuals. In addition, we reviewed the studies with no explicit exclusion criteria and investigated the actual inclusion of those individuals.

Results
We identified 2024 trial records; 1702 were excluded (e.g., use of other vaccine or risk group); and 322 studies were eligible for our review. Among the pneumococcal and influenza vaccine trials (n = 193), 81 (42%) had an explicit direct age exclusion, and 150 (78%) had an indirect age-related exclusion. In total, 163 trials (84%) trials were likely to exclude older adults. Among the COVID-19 vaccine trials (n = 129), 33 (26%) had direct age exclusion and 82 (64%) had indirect age exclusion; in total, 85 (66%) trials were likely to exclude older adults. Therefore was a 18% decrease in the proportion of trials with age-related exclusion between 2011 and 2021 (only influenza and pneumococcal vaccine trials) and 2020–2021 (only COVID-19 vaccine trials) (p = 0.014). In a sub-analysis assessing observational and randomised trials, the decrease was 25% and 9%, respectively. Immunocompromised individuals were included in 87 (45%) of the pneumococcal and influenza vaccine trials compared with 54 (42%) of the COVID-19 vaccine trials (p = 0.058).

Conclusions
During the COVID-19 pandemic, we found a decrease in the exclusion of older adults from vaccine trials but no significant change in the inclusion of immunocompromised individulas.
Iséki spaces of semirings
Authors: Goswami A. 
Published: April 2023

AbstractThe aim of this paper is to study Iséki spaces of distinguished classes of ideals of a semiring endowed with a topology. We show that every Iséki space is quasi-compact whenever the semiring is Noetherian. We characterize Iséki spaces for which every non-empty irreducible closed subset has a unique generic point. Furthermore, we provide a sufficient condition for the connectedness of Iséki spaces and show that the strongly connectedness of an Iséki space implies the existence of non-trivial idempotent elements of semirings.
Thyroid dysfunction during gestation and gestational diabetes mellitus: a complex relationship
Authors: Pinto S. , Croce L. , Carlier L. , Cosson E. , Rotondi M. 
Published: April 2023

Abstract
Purpose
Gestational diabetes mellitus (GDM) and thyroid dysfunction during gestation (GTD) are the two most prevalent endocrinopathies during pregnancy. The aim of the present review is to provide an overview of the peculiar aspects of GDM and GTD, to highlight the potential interactions and clinical consequences of these two frequent clinical conditions.

Methods
A literature review regarding GDM and GTD was carried out with particular interest on meta-analyses and human studies dealing with the (i) shared risk factors between GDM and GTD, (ii) the epidemiological link between GTD and GDM, (iii) physiopathologic link between GTD and GDM, (iv) clinical consequences of GDM and GTD, and (v) post-partum implications of GDM and GTD.

Results
The association between GDM and GTD is common and may be explained by the insulin-resistance state due to maternal GTD, to alterations in the placentation process or to the many shared risk factors. Discrepant results of epidemiologic studies can be explained, at least in part, by the changes in diagnostic criteria and screening strategies throughout the years for both conditions. GDM and GTD impact pregnancy outcome and have post-partum long-term consequences, but more studies are needed to prove an additional adverse effect.

Conclusions
Based on the epidemiological and physio-pathological link between GDM and GTD, it could be suggested that a diagnosis of GTD could lead to screen GDM and the other way round.
RetinaMOT: rethinking anchor-free YOLOv5 for online multiple object tracking
Authors: Cao Jie , Zhang Jianxun , Li Bowen , Gao Linfeng , Zhang Jie 
Published: April 2023

AbstractIn recent years, YOLOv5 networks have become a research focus in many fields because they are capable of outperforming state-of-the-art (SOTA) approaches in different computer vision tasks. Nevertheless, there is still room for improvement in YOLOv5 in terms of target tracking. We modified YOLOv5 according to the anchor-free paradigm to be on par with other state-of-the-art tracking paradigms and modified the network backbone to design an efficient module, thus proposing the RetinaYOLO detector, which, after combining state-of-the-art tracking algorithms, achieves state-of-the-art performance: we call it RetinaMOT. To the best of our knowledge, RetinaMOT is the first such approach. The anchor-free paradigm SOTA method for the YOLOv5 architecture and RetinaYOLO outperforms all lightweight YOLO architecture methods on the MS COCO dataset. In this paper, we show the details of the RetinaYOLO backbone, embedding Kalman filtering and the Hungarian algorithm into the network, with one framework used to accomplish two tasks. Our RetinaMOT shows that MOTA metrics reach 74.8, 74.1, and 66.8 on MOT Challenge MOT16, 17, and 20 test datasets, and our method is at the top of the list when compared with state-of-the-art methods.
Progressive multi-level distillation learning for pruning network
Authors: Wang Ruiqing , Wan Shengmin , Zhang Wu , Zhang Chenlu , Li Yu , Xu Shaoxiang , Zhang Lifu , Jin Xiu , Jiang Zhaohui , Rao Yuan 
Published: April 2023

AbstractAlthough the classification method based on the deep neural network has achieved excellent results in classification tasks, it is difficult to apply to real-time scenarios because of high memory footprints and prohibitive inference times. Compared to unstructured pruning, structured pruning techniques can reduce the computation cost of the model runtime more effectively, but inevitably reduces the precision of the model. Traditional methods use fine tuning to restore model damage performance. However, there is still a large gap between the pruned model and the original one. In this paper, we use progressive multi-level distillation learning to compensate for the loss caused by pruning. Pre-pruning and post-pruning networks serve as the teacher and student networks. The proposed approach utilizes the complementary properties of structured pruning and knowledge distillation, which allows the pruned network to learn the intermediate and output representations of the teacher network, thus reducing the influence of the model subject to pruning. Experiments demonstrate that our approach performs better on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets with different pruning rates. For instance, GoogLeNet can achieve near lossless pruning on the CIFAR-10 dataset with 60% pruning. Moreover, this paper also proves that using the proposed distillation learning method during the pruning process achieves more significant performance gains than after completing the pruning.
Real-time permeability evolution of limestone under high temperature and triaxial stresses
Authors: Chen Zhengnan , Feng Zijun , Mi Chen , Zhang Chao 
Published: April 2023

AbstractIn deep underground geological disposal of nuclear waste, geothermal energy development, underground coal gasification, geothermal resources development and other underground high temperature projects, they are all closely related to the permeability and thermal expansion characteristics of rocks in high temperature high-pressure real-time state. In this experiment, the real-time evolution of permeability of limestone under high-temperature and triaxial stresses condition was studied using the HTHP rock mechanics triaxial test system and compared with the permeability of limestone after high temperature cooling treatment. The test results show that: the effect of temperature on limestone permeability facilitation under high temperature cooling condition is significant, and the permeability increases by 1–2 orders of magnitude relative to the initial permeability for every 100 °C increase in the preheat temperature after 300 °C. Under high-temperature and high-pressure in real time condition, the difference values of permeability at different temperature points relative to room temperature always do not more than one order of magnitude. During the heating process at 500 °C, the permeability curves of the triaxial pressures range from 15 to 35 MPa are very different. Under the pressure condition of 15 MPa, the permeability curve increases monotonically and rapidly with the increasing temperature; When the pressure reaches 25 MPa pressure, there is a peak value area in the permeability curve; the permeability decreases monotonically with temperature when the pressure up to 35 MPa. Compared with the only thermal effect, the thermal- mechanical coupling effect has a significant inhibitory effect on limestone permeability development, and the permeability disparity between the two types of heat treatment rocks become more and more significant with increasing temperature and hydrostatic pressure.
Population structure and genome-wide evolutionary signatures reveal putative climate-driven habitat change and local adaptation in the large yellow croaker
Authors: Chen Baohua , Bai Yulin , Wang Jiaying , Ke Qiaozhen , Zhou Zhixiong , Zhou Tao , Pan Ying , Wu Renxie , Wu Xiongfei , Zheng Weiqiang , Xu Peng 
Published: April 2023

AbstractThe large yellow croaker (Larimichthyscrocea) is one of the most economically valuable marine fish in China and is a notable species in ecological studies owing to a serious collapse of wild germplasm in the past few decades. The stock division and species distribution, which have important implications for ecological protection, germplasm recovery, and fishery resource management, have been debated since the 1960s. However, it is still uncertain even how many stocks exist in this species. To address this, we evaluated the fine-scale genetic structure of large yellow croaker populations distributed along the eastern and southern Chinese coastline based on 7.64 million SNP markers. Compared with the widely accepted stock boundaries proposed in the 1960s, our results revealed that a climate-driven habitat change probably occurred between the Naozhou (Nanhai) Stock and the Ming-Yuedong (Mindong) Stock. The boundary between these two stocks might have shifted northwards from the Pearl River Estuary to the northern area of the Taiwan Strait, accompanied by highly asymmetric introgression. In addition, we found divergent landscapes of natural selection between the stocks inhabiting northern and southern areas. The northern population exhibited highly agminated signatures of strong natural selection in genes related to developmental processes, whereas moderate and interspersed selective signatures were detected in many immune-related genes in the southern populations. These findings establish the stock status and genome-wide evolutionary landscapes of large yellow croaker, providing a basis for conservation, fisheries management and further evolutionary biology studies.
Phosphoinositides in plant-pathogen interaction: trends and perspectives
Authors: Zarreen Fauzia , Kumar Kamal , Chakraborty Supriya 
Published: April 2023

AbstractPhosphoinositides are important regulatory membrane lipids, with a role in plant development and cellular function. Emerging evidence indicates that phosphoinositides play crucial roles in plant defence and are also utilized by pathogens for infection. In this review, we highlight the role of phosphoinositides in plant-pathogen interaction and the implication of this remarkable convergence in the battle against plant diseases.
g-C3N4-based photocatalysts for organic pollutant removal: a critical review
Authors: Luo Yidan , Zhu Yaowei , Han Yu , Ye Huiyin , Liu Ruochen , Lan Yuanwang , Xue Mingshan , Xie Xianchuan , Yu Shuohan , Zhang Longshuai , Yin Zuozhu , Gao Bin 
Published: April 2023

AbstractWith rapid global industrialization, environmental pollution has become one of the major problems of human society. Photocatalysis can be applied to solve environmental problem by using inexhaustible solar energy. As a novel two-dimensional material, graphite carbon nitride (g-C3N4) has been widely applied in many photocatalytic reactions. However, in terms of its practical applications, g-C3N4still has limitations, including poor photoexcited charge separation, limited range of visible light absorption, and low surface areas. Hence, current research on g-C3N4mainly focuses on improving its photocatalytic performance. In this review, we summarize various methods to improve g-C3N4’s light absorption and photo-induced charge transfer, including vacancy engineering, morphology control, heteroatom doping, and forming heterostructure. The applications of g-C3N4and its derivatives in photodegradation of organic pollutants are also discussed in detail. Finally, the challenges and future research directions of g-C3N4photocatalysts are summarized to promote their environmental applications.Graphical Abstract
A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning
Authors: Mvula Paul K. , Branco Paula , Jourdan Guy-Vincent , Viktor Herna L. 
Published: April 2023

AbstractIn Machine Learning, the datasets used to build models are one of the main factors limiting what these models can achieve and how good their predictive performance is. Machine Learning applications for cyber-security or computer security are numerous including cyber threat mitigation and security infrastructure enhancement through pattern recognition, real-time attack detection, and in-depth penetration testing. Therefore, for these applications in particular, the datasets used to build the models must be carefully thought to be representative of real-world data. However, because of the scarcity of labelled data and the cost of manually labelling positive examples, there is a growing corpus of literature utilizing Semi-Supervised Learning with cyber-security data repositories. In this work, we provide a comprehensive overview of publicly available data repositories and datasets used for building computer security or cyber-security systems based on Semi-Supervised Learning, where only a few labels are necessary or available for building strong models. We highlight the strengths and limitations of the data repositories and sets and provide an analysis of the performance assessment metrics used to evaluate the built models. Finally, we discuss open challenges and provide future research directions for using cyber-security datasets and evaluating models built upon them.
Network Science in Archaeology
Authors: Brughmans Tom , Peeples Matthew A. 
Published: April 2023

The Cambridge Manual to Archaeological Network Science provides the first comprehensive guide to a field of research that has firmly established itself within archaeological practice in recent years. Network science methods are commonly used to explore big archaeological datasets and are essential for the formal study of past relational phenomena: social networks, transport systems, communication, and exchange. The volume offers a step-by-step description of network science methods and explores its theoretical foundations and applications in archaeological research, which are elaborately illustrated with archaeological examples. It also covers a vast range of network science techniques that can enhance archaeological research, including network data collection and management, exploratory network analysis, sampling issues and sensitivity analysis, spatial networks, and network visualisation. An essential reference handbook for both beginning and experienced archaeological network researchers, the volume includes boxes with definitions, boxed examples, exercises, and online supplementary learning and teaching materials.
Empirical Parameterization of Broadband VLF Attenuation in the Earth‐Ionosphere Waveguide
Authors: Said Ryan , Gołkowski Marek , Harid Vijay 
Published: April 2023

AbstractThe Earth‐ionosphere waveguide (EIW) determines the propagation of Very Low Frequency (VLF; ∼3−30 kHz) waves. Characterizing the waveguide is a longstanding challenge due to its large spatial scale and the complex variability of the lower ionosphere. Here we apply a novel linear basis function regression technique to characterize attenuation in the EIW using broadband measurements of lightning‐generated radio waves. The process begins with defining a basis function set, which ideally encompasses a feature set that can predict the variability seen in VLF attenuation properties. With this basis set defined, a system of linear equations is then constructed using sensor pair observations to eliminate the dependency on source amplitude in each observation. Using this formalism, an empirical attenuation model for broadband signals from lightning is constructed and the dependence on attenuation properties with the boundary conditions is explored. The empirically derived results show attenuation rates over ice that are 12 dB/Mm higher compared to paths over saltwater. Well‐known east/west attenuation rate asymmetry stemming from anisotropic reflection coefficients of the ionosphere is also demonstrated and investigated. For example, under a daytime ionosphere, westward‐propagating waves suffer up to 2.8 dB/Mm greater attenuation compared to eastward‐propagating waves. The trained model is used for propagation corrections in a global lightning locating system (LLS), but this technique can be expanded to further study VLF attenuation rates by employing different sets of basis functions.