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1. SPARC4 control system

Author

Bernardes, Denis, Junior, Orlando Verducci, Rodrigues, Francisco, Rodrigues, Claudia Vilega, Fraga, Luciano, Martioli, Eder, Gneiding, Clemens D., Alves, André Luiz de Moura, Romão, Juliano, Andrade, Laerte, de Almeida, Leandro, Mattiuci, Ana Carolina, Ribeiro, Flavio Felipe, Schlindwein, Wagner, Santos, Jesulino Bispo dos, Jablonski, Francisco Jose, Campagnolo, Julio Cesar Neves, and Laporte, Rene

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

SPARC4 is a new astronomical instrument developed entirely by Brazilian institutions, currently installed on the 1.6-m Perkin-Elmer telescope of the Pico dos Dias Observatory. It allows the user to perform photometric or polarimetric observations simultaneously in the four SDSS bands (g, r, i, and z). In this paper, we describe the control system developed for SPARC4. This system is composed of S4ACS, S4ICS, and S4GUI softwares and associated hardware. S4ACS is responsible for controlling the four EMCCD scientific cameras (one for each instrument band). S4ICS controls the sensors and motors responsible for the moving parts of SPARC4. Finally, S4GUI is the interface used to perform observations, which includes the choice of instrument configuration and image acquisition parameters. S4GUI communicates with the instrument subsystems and with some observatory facilities, needed during the observations. Bench tests were performed for the determination of the overheads added by SPARC4 control system in the acquisition of photometric and polarimetric series of images. In the photometric mode, SPARC4 allows the acquisition of a series of 1400 full-frame images, with a deadtime of 4.5 ms between images. Besides, several image series can be concatenated with a deadtime of 450 ms plus the readout time of the last image. For the polarimetric mode, measurements can be obtained with a deadtime of 1.41 s plus the image readout time between subsequent waveplate positions. For both photometric and polarimetric modes, the user can choose among operating modes with image readout times between 5.9 ms and 1.24 s, which ultimately defines the instrument temporal performance., Comment: 20 pages, 14 figures, peer-reviewed paper

Published
2024

2. General Geospatial Inference with a Population Dynamics Foundation Model

Author

Agarwal, Mohit, Sun, Mimi, Kamath, Chaitanya, Muslim, Arbaaz, Sarker, Prithul, Paul, Joydeep, Yee, Hector, Sieniek, Marcin, Jablonski, Kim, Mayer, Yael, Fork, David, de Guia, Sheila, McPike, Jamie, Boulanger, Adam, Shekel, Tomer, Schottlander, David, Xiao, Yao, Manukonda, Manjit Chakravarthy, Liu, Yun, Bulut, Neslihan, Abu-el-haija, Sami, Eigenwillig, Arno, Kothari, Parth, Perozzi, Bryan, Bharel, Monica, Nguyen, Von, Barrington, Luke, Efron, Niv, Matias, Yossi, Corrado, Greg, Eswaran, Krish, Prabhakara, Shruthi, Shetty, Shravya, and Prasad, Gautam

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even, related tasks. To address this, we introduce a Population Dynamics Foundation Model (PDFM) that aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on all 27 geospatial interpolation tasks, and on 25 out of the 27 extrapolation and super-resolution tasks. We combined the PDFM with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers., Comment: 28 pages, 16 figures, preprint; v3: updated affiliations

Published
2024

3. When is a CPD weighted shift similar to a subnormal operator?

Author

Jabłoński, Zenon Jan, Jung, Il Bong, and Stochel, Jan

Subjects
Mathematics - Functional Analysis, Primary 47B20, 47B37, Secondary 43A35
Abstract

We prove that a CPD unilateral weighted shift $W_{\lambda}$ of type III is a quasi-affine transform of the operator $M_z$ of multiplication by the independent variable on the $L^2(\rho)$-closure of analytic complex polynomials on the complex plane, where $\rho$ is a measure precisely determined by $W_{\lambda}$. By using this model, we provide necessary and sufficient conditions for similarity of $W_{\lambda}$ to $M_z$. Necessary conditions for a CPD operator to be similar to a subnormal one are given. A variety of concrete classes of non-subnormal CPD unilateral weighted shifts similar to subnormal operators are established., Comment: 29 pages

Published
2024

4. Y and mitochondrial chromosomes in the heterogeneous stock rat population

Author

Okamoto, Faith, Chitre, Apurva S, Missfeldt Sanches, Thiago, Chen, Denghui, Munro, Daniel, Aron, Allegra T, Beeson, Angela, Bimschleger, Hannah V, Eid, Maya, Garcia Martinez, Angel G, Han, Wenyan, Holl, Katie, Jackson, Tyler, Johnson, Benjamin B, King, Christopher P, Kuhn, Brittany N, Lamparelli, Alexander C, Netzley, Alesa H, Nguyen, Khai-Minh H, Peng, Beverly F, Tripi, Jordan A, Wang, Tengfei, Ziegler, Kendra S, Adams, Douglas J, Baud, Amelie, Carrette, Lieselot LG, Chen, Hao, de Guglielmo, Giordano, Dorrestein, Pieter, George, Olivier, Ishiwari, Keita, Jablonski, Monica M, Jhou, Thomas C, Kallupi, Marsida, Knight, Rob, Meyer, Paul J, Solberg Woods, Leah C, Polesskaya, Oksana, and Palmer, Abraham A

Subjects
Biological Sciences, Genetics, Substance Misuse, Human Genome, 2.1 Biological and endogenous factors, Animals, Rats, Y Chromosome, Mitochondria, Male, Genome-Wide Association Study, Female, Genotype, heterogeneous stock, rat, mitochondria, haplotype, low-coverage, PheWAS, RNA-seq, Biochemistry and cell biology, Statistics
Abstract

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial (MT) Chromosomes. We genotyped the Y and MT Chromosomes in heterogeneous stock (HS) rats (Rattus norvegicus), an outbred population created from 8 inbred strains. We identified 8 distinct Y and 4 distinct MT Chromosomes among the 8 founders. However, only 2 types of each nonrecombinant chromosome were observed in our modern HS rat population (generations 81-97). Despite the relatively large sample size, there were virtually no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and MT Chromosomes were strongly associated with the expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern HS rats there are no Y and MT Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and MT Chromosomes that do not appear in modern HS rats, nor do they address effects that may exist in other rat populations, or in other species.

Published
2024

5. Hairmony: Fairness-aware hairstyle classification

Author

Meishvili, Givi, Clemoes, James, Hewitt, Charlie, Hosenie, Zafiirah, Xiao, Xian, de La Gorce, Martin, Takacs, Tibor, Baltrusaitis, Tadas, Criminisi, Antonio, McRae, Chyna, Jablonski, Nina, and Wilczkowiak, Marta

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a method for prediction of a person's hairstyle from a single image. Despite growing use cases in user digitization and enrollment for virtual experiences, available methods are limited, particularly in the range of hairstyles they can capture. Human hair is extremely diverse and lacks any universally accepted description or categorization, making this a challenging task. Most current methods rely on parametric models of hair at a strand level. These approaches, while very promising, are not yet able to represent short, frizzy, coily hair and gathered hairstyles. We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system. Previous classification approaches have been restricted by poorly labeled data that lacks diversity, imposing constraints on the usefulness of any resulting enrollment system. We use only synthetic data to train our models. This allows for explicit control of diversity of hairstyle attributes, hair colors, facial appearance, poses, environments and other parameters. It also produces noise-free ground-truth labels. We introduce a novel hairstyle taxonomy developed in collaboration with a diverse group of domain experts which we use to balance our training data, supervise our model, and directly measure fairness. We annotate our synthetic training data and a real evaluation dataset using this taxonomy and release both to enable comparison of future hairstyle prediction approaches. We employ an architecture based on a pre-trained feature extraction network in order to improve generalization of our method to real data and predict taxonomy attributes as an auxiliary task to improve accuracy. Results show our method to be significantly more robust for challenging hairstyles than recent parametric approaches.

Published
2024
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6. Assisted Data Annotation for Business Process Information Extraction from Textual Documents

Author

Neuberger, Julian, van der Aa, Han, Ackermann, Lars, Buschek, Daniel, Herrmann, Jannic, and Jablonski, Stefan

Subjects
Computer Science - Computation and Language
Abstract

Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can utilize business process management and its benefits. Yet, research towards this is severely restrained by an apparent lack of large and high-quality datasets. This lack of data can be attributed to, among other things, an absence of proper tool assistance for dataset creation, resulting in high workloads and inferior data quality. We explore two assistance features to support dataset creation, a recommendation system for identifying process information in the text and visualization of the current state of already identified process information as a graphical business process model. A controlled user study with 31 participants shows that assisting dataset creators with recommendations lowers all aspects of workload, up to $-51.0\%$, and significantly improves annotation quality, up to $+38.9\%$. We make all data and code available to encourage further research on additional novel assistance strategies.

Published
2024

7. The young exoplanetary system TOI-4562: Confirming the presence of a third body in the system

Author

Fermiano, V., Saito, R. K., Ivanov, V. D., Caceres, C., Almeida, L. A., Aires, J., Beamin, J. C., Minniti, D., Ferreira, T., Andrade, L., Borges, B. W., de Almeida, L., Jablonski, F., and Schlindwein, W.

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

Young planetary systems represent an opportunity to investigate the early stages of (exo)planetary formation because the gravitational interactions have not yet significantly changed the initial configuration of the system. TOI-4562 b is a highly eccentric temperate Jupiter analogue orbiting a young F7V-type star of $<700$ Myr in age with an orbital period of $P_{orb} \sim 225$ days and an eccentricity of $e=0.76$, and is one of the largest known exoplanets to have formed in situ. We observed a new transit of TOI-4562 b using the 0.6-m Zeiss telescope at the Pico dos Dias Observatory (OPD/LNA) in Minas Gerais, Brazil, and combine our data with Transiting Exoplanet Survey Satellite (TESS) and archive data, with the aim being to improve the ephemerides of this interesting system. The $O-C$ diagram for the new ephemeris is consistent with the presence of a giant planet in an outer orbit around TOI-4562. TOI-4562 c is a planet with a mass of $M=5.77 M_{Jup}$, an orbital period of $P_{orb}= 3990$ days, and a semi-major axis of $a = 5.219$ AU. We report the discovery of TOI-4562 c, the exoplanet with the longest orbital period discovered to date via the transit timing variation (TTV) method. The TOI-4562 system is in the process of violent evolution with intense dynamical changes - judging by its young age and high eccentricity - and is therefore a prime target for studies of formation and evolution of planetary systems., Comment: 5 pages and 3 figures (+ appendix). Accepted for publication in Astronomy & Astrophysics, Letters to the Editor

Published
2024

8. Attention Please: What Transformer Models Really Learn for Process Prediction

Author

Käppel, Martin, Ackermann, Lars, Jablonski, Stefan, and Härtl, Simon

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, 68T07, 68T01, 68U35, H.4.2, I.2.1, I.2.6
Abstract

Predictive process monitoring aims to support the execution of a process during runtime with various predictions about the further evolution of a process instance. In the last years a plethora of deep learning architectures have been established as state-of-the-art for different prediction targets, among others the transformer architecture. The transformer architecture is equipped with a powerful attention mechanism, assigning attention scores to each input part that allows to prioritize most relevant information leading to more accurate and contextual output. However, deep learning models largely represent a black box, i.e., their reasoning or decision-making process cannot be understood in detail. This paper examines whether the attention scores of a transformer based next-activity prediction model can serve as an explanation for its decision-making. We find that attention scores in next-activity prediction models can serve as explainers and exploit this fact in two proposed graph-based explanation approaches. The gained insights could inspire future work on the improvement of predictive business process models as well as enabling a neural network based mining of process models from event logs.

Published
2024

9. A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models

Author

Neuberger, Julian, Ackermann, Lars, van der Aa, Han, and Jablonski, Stefan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Over the past decade, extensive research efforts have been dedicated to the extraction of information from textual process descriptions. Despite the remarkable progress witnessed in natural language processing (NLP), information extraction within the Business Process Management domain remains predominantly reliant on rule-based systems and machine learning methodologies. Data scarcity has so far prevented the successful application of deep learning techniques. However, the rapid progress in generative large language models (LLMs) makes it possible to solve many NLP tasks with very high quality without the need for extensive data. Therefore, we systematically investigate the potential of LLMs for extracting information from textual process descriptions, targeting the detection of process elements such as activities and actors, and relations between them. Using a heuristic algorithm, we demonstrate the suitability of the extracted information for process model generation. Based on a novel prompting strategy, we show that LLMs are able to outperform state-of-the-art machine learning approaches with absolute performance improvements of up to 8\% $F_1$ score across three different datasets. We evaluate our prompting strategy on eight different LLMs, showing it is universally applicable, while also analyzing the impact of certain prompt parts on extraction quality. The number of example texts, the specificity of definitions, and the rigour of format instructions are identified as key for improving the accuracy of extracted information. Our code, prompts, and data are publicly available.

Published
2024

10. Modeling configuration-performance relation in a mobile network: a data-driven approach

Author

Panek, Michał, Jabłoński, Ireneusz, and Woźniak, Michał

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Mobile network performance modeling typically assumes either a fixed cell's configuration or only considers a limited number of parameters. This prohibits the exploration of multidimensional, diverse configuration space for, e.g., optimization purposes. This paper presents a method for performance predictions based on a network cell's configuration and network conditions, which utilizes neural network architecture. We evaluate the idea by extensive experiments, with data from more than 50,000 5G cells. The assessment included a comparison of the proposed method against models developed for fixed configuration. Results show that combined configuration-performance modeling outperforms single-configuration models and allows for performance prediction of unknown configurations, i.e., it is not used for model training. A substantially lower mean absolute error was achieved (0.25 vs. 0.45 for fixed-configuration MLP-based models).

Published
2024

11. POLygraph: Polish Fake News Dataset

Author

Dzienisiewicz, Daniel, Graliński, Filip, Jabłoński, Piotr, Kubis, Marek, Skórzewski, Paweł, and Wierzchoń, Piotr

Subjects
Computer Science - Computation and Language
Abstract

This paper presents the POLygraph dataset, a unique resource for fake news detection in Polish. The dataset, created by an interdisciplinary team, is composed of two parts: the "fake-or-not" dataset with 11,360 pairs of news articles (identified by their URLs) and corresponding labels, and the "fake-they-say" dataset with 5,082 news articles (identified by their URLs) and tweets commenting on them. Unlike existing datasets, POLygraph encompasses a variety of approaches from source literature, providing a comprehensive resource for fake news detection. The data was collected through manual annotation by expert and non-expert annotators. The project also developed a software tool that uses advanced machine learning techniques to analyze the data and determine content authenticity. The tool and dataset are expected to benefit various entities, from public sector institutions to publishers and fact-checking organizations. Further dataset exploration will foster fake news detection and potentially stimulate the implementation of similar models in other languages. The paper focuses on the creation and composition of the dataset, so it does not include a detailed evaluation of the software tool for content authenticity analysis, which is planned at a later stage of the project., Comment: 14 pages, 1 figure, accepted to the 14th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA'24)

Published
2024

12. Recent Advances in Data-Driven Business Process Management

Author

Ackermann, Lars, Käppel, Martin, Marcus, Laura, Moder, Linda, Dunzer, Sebastian, Hornsteiner, Markus, Liessmann, Annina, Zisgen, Yorck, Empl, Philip, Herm, Lukas-Valentin, Neis, Nicolas, Neuberger, Julian, Poss, Leo, Schaschek, Myriam, Weinzierl, Sven, Wördehoff, Niklas, Jablonski, Stefan, Koschmider, Agnes, Kratsch, Wolfgang, Matzner, Martin, Rinderle-Ma, Stefanie, Röglinger, Maximilian, Schönig, Stefan, and Winkelmann, Axel

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence, 68U35 68T07 68T07, 68U35, 68T01, H.4.1, I.2.1, I.2.6, I.2.7, H.2.8, K.6.1
Abstract

The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM., Comment: position paper, 34 pages, 10 figures

Published
2024

13. Bishop-like theorems for non-subnormal operators

Author

Jabłoński, Zenon Jan, Jung, Il Bong, and Stochel, Jan

Subjects
Mathematics - Functional Analysis, Primary 47A20, 47B20 Secondary 47B28
Abstract

The celebrated Bishop theorem states that an operator is subnormal if and only if it is the strong limit of a net (or a sequence) of normal operators. By the Agler-Stankus theorem, $2$-isometries behave similarly to subnormal operator in the sense that the role of subnormal operators is played by $2$-isometries, while the role of normal operators is played by Brownian unitaries. In this paper we give Bishop-like theorems for $2$-isometries. Two methods are involved, the first of which goes back to Bishop's original idea and the second refers to Conway and Hadwin's result of general nature. We also investigate the strong and $*$-strong closedness of the class of Brownian unitaries.

Published
2024

14. The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol

Author

Subbotko, Konstanty, Jablonski, Wojciech, and Bilinski, Piotr

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization error, which can severely damage the process of obtaining the final architecture. In our work, we first study the risk of discretization error and show how it affects an unregularized supernet. Then, we present that penalizing high entropy, a common technique of architecture regularization, can hinder the supernet's performance. Therefore, to robustify the DNAS framework, we introduce a novel single-stage searching protocol, which is not reliant on decoding a continuous architecture. Our results demonstrate that this approach outperforms other DNAS methods by achieving 75.3% in the searching stage on the Cityscapes validation dataset and attains performance 1.1% higher than the optimal network of DCNAS on the non-dense search space comprising short connections. The entire training process takes only 5.5 GPU days due to the weight reuse, and yields a computationally efficient architecture. Additionally, we propose a new dataset split procedure, which substantially improves results and prevents architecture degeneration in DARTS., Comment: Published in CVPR-NAS 2024 workshop

Published
2024

20. Convergence of power sequences of operators via their stability

Author

Jabłoński, Zenon Jan, Jung, Il Bong, Kubrusly, Carlos, and Stochel, Jan

Subjects
Mathematics - Functional Analysis
Abstract

This paper is concerned with the convergence of power sequences and stability of Hilbert space operators, where "convergence" and "stability" refer to weak, strong and norm topologies. It is proved that an operator has a convergent power sequence if and only if it is a (not necessarily orthogonal) direct sum of an identity operator and a stable operator. This reduces the issue of convergence of the power sequence of an operator $T$ to the study of stability of $T$. The question of when the limit of the power sequence is an orthogonal projection is investigated. Among operators sharing this property are hyponormal and contractive ones. In particular, a hyponormal or a contractive operator with no identity part is stable if and only if its power sequence is convergent. In turn, a unitary operator has a weakly convergent power sequence if and only if its singular-continuous part is weakly stable and its singular-discrete part is the identity. Characterizations of the convergence of power sequences and stability of subnormal operators are given in terms of semispectral measures.

Published
2024

21. Criteria for algebraic operators to be unitary

Author

Jablonski, Zenon Jan, Jung, Il Bong, and Stochel, Jan

Subjects
Mathematics - Functional Analysis
Abstract

Criteria for an algebraic operator $T$ on a complex Hilbert space $\mathcal{H}$ to be unitary are established. The main one is written in terms of the convergence of sequences of the form $\{\|T^nh\|\}_{n=0}^{\infty}$ with $h\in \mathcal{H}$. Related questions are also discussed., Comment: 9 pages

Published
2024

22. Leveraging Data Augmentation for Process Information Extraction

Author

Neuberger, Julian, Doll, Leonie, Engelmann, Benedict, Ackermann, Lars, and Jablonski, Stefan

Subjects
Computer Science - Computation and Language
Abstract

Business Process Modeling projects often require formal process models as a central component. High costs associated with the creation of such formal process models motivated many different fields of research aimed at automated generation of process models from readily available data. These include process mining on event logs, and generating business process models from natural language texts. Research in the latter field is regularly faced with the problem of limited data availability, hindering both evaluation and development of new techniques, especially learning-based ones. To overcome this data scarcity issue, in this paper we investigate the application of data augmentation for natural language text data. Data augmentation methods are well established in machine learning for creating new, synthetic data without human assistance. We find that many of these methods are applicable to the task of business process information extraction, improving the accuracy of extraction. Our study shows, that data augmentation is an important component in enabling machine learning methods for the task of business process model generation from natural language text, where currently mostly rule-based systems are still state of the art. Simple data augmentation techniques improved the $F_1$ score of mention extraction by 2.9 percentage points, and the $F_1$ of relation extraction by $4.5$. To better understand how data augmentation alters human annotated texts, we analyze the resulting text, visualizing and discussing the properties of augmented textual data. We make all code and experiments results publicly available., Comment: Accepted at BPMDS 2024 (https://sites.google.com/view/bpmds/), to be printed

Published
2024

23. Paleoecology and paleobiogeography of the latest Miocene site of Shuitangba, Zhaotong, China

Author

Su, DF, Kelley, J, Flynn, LJ, Ji, XP, Deng, CL, Deng, T, Li, P, Li, Z, Sanders, WJ, Stidham, TA, Sun, F, Wang, X, Wang, Y, Youlatos, D, and Jablonski, NG

Subjects
Physical Geography and Environmental Geoscience, Earth Sciences, History, Heritage and Archaeology, Climate Change Science, Geology, Ecology, Evolutionary Biology, Paleontology, Physical geography and environmental geoscience, Archaeology
Abstract

Apart from northern and central China, the fossil record of the latest Miocene and Early Pliocene of Asia is not well documented and the record of South China during this interval is especially poor. Shuitangba, a site in Yunnan Province, offers a rare window into the paleoenvironment of the latest Miocene in southwestern China. Over 2400 vertebrate and macrobotanical specimens have been recovered from the site. The faunal assemblage is dominated by aquatic avian taxa and many of the mammalian taxa are those that indicate densely vegetated, water-margin habitats. Pollen and carpological remains indicate a temperate to subtropical, broad-leaved/coniferous forest around standing water, with more open areas containing grasses and herbs. Analyses of clay minerals, chemical weathering, and enamel stable isotopes suggest that Shuitangba was warmer and more humid than today, possibly with more pronounced seasonality. Results of community structure analysis indicate that the Shuitangba mammalian community was different from those of other Late Miocene Chinese sites in the high proportions of aquatic-dependent mammalian taxa. While Shuitangba shared mammalian faunal elements with other Late Miocene sites in Yunnan, it was still faunally distinct. Further, Shuitangba was depauperate in its mammalian fauna, which may have been a result of the depositional setting rather than a true reflection of regional faunal diversity.

Published
2024

27. A Pilot Study of a Human-Readable Robotic Process Automation Language

Author

Gago, Piotr, Jabłoński, Daniel, Voitenkova, Anna, Debelyi, Ihor, Skorupska, Kinga, Grzeszczuk, Maciej, and Kopeć, Wiesław

Subjects
Computer Science - Human-Computer Interaction
Abstract

In this paper, we explore the usability of a custom eXtensible Robotic Language (XRL) we proposed. To evaluate the user experience and the interaction with the potential XRL-based software robot, we conducted an exploratory study comparing the notation of three business processes using our XRL language and two languages used by the leading RPA solutions. The results of our exploratory study show that the currently used XML-based formats perform worse in terms of conciseness and readability. Our new XRL language is promising in terms of increasing the readability of the language, thus reducing the time needed to automate business processes., Comment: 12 pages

Published
2023

28. Trace element bioaccumulation, tissue distribution, and elimination in odontocetes stranded in Florida and Georgia, USA over a 15-year period (2007-2021).

Author

Page, Annie, Hay, Clara, Marks, Wendy, Bennett, Baylin, Gribble, Matthew, Noke Durden, Wendy, Stolen, Megan, Jablonski, Teresa, Gordon, Nadia, Kolkmeyer, Trip, Jiang, Mingshun, Pegg, Nicole, Brown, Hunter, and Burton, Steve

Subjects
Cadmium, Heavy metals, Kogia, Mercury, Toxins, Whale
Abstract

Odontocetes obtain nutrients including essential elements through their diet and are exposed to heavy metal contaminants via ingestion of contaminated prey. We evaluated the prevalence, concentration, and tissue distribution of essential and non-essential trace elements, including heavy metal toxicants, in tissue (blubber, kidney, liver, skeletal muscle, skin) and fecal samples collected from 90 odontocetes, representing nine species, that stranded in Georgia and Florida, USA during 2007-2021. Samples were analyzed for concentrations of seven essential (cobalt, copper, iron, manganese, molybdenum, selenium, zinc) and five non-essential (arsenic, cadmium, lead, mercury, thallium) elemental analytes using inductively-coupled plasma mass spectrometry. Rissos dolphins (Grampus griseus) and short-finned pilot whales (Globicephala macrorhynchus) had the highest median concentrations of mercury, cadmium, and lead, while dwarf sperm whales (Kogia sima) had the lowest. Adult pygmy and dwarf sperm whales that stranded in 2019-2021 had higher concentrations of arsenic, copper, iron, lead, manganese, selenium, thallium, and zinc compared to those that stranded in 2010-2018, suggesting an increasing risk of exposure over time. The highest concentrations of many elements (e.g., cadmium, cobalt, copper, manganese, molybdenum, thallium, zinc) were in fecal samples, illustrating the usefulness of this noninvasively collected sample. Aside from fecal samples, hepatic tissues had the highest concentrations of iron, manganese, mercury, molybdenum, and selenium in most species; renal tissues had the highest concentrations of cadmium; skin had the highest concentrations of zinc; and copper, arsenic, and lead concentrations were primarily distributed among the liver and kidneys. Phylogenetic differences in patterns of trace element concentrations likely reflect species-specific differences in diet, trophic level, and feeding strategies, while heterogeneous distributions of elemental analytes among different organ types reflect differences in elemental biotransformation, elimination, and storage. This study illustrates the importance of monitoring toxic contaminants in stranded odontocetes, which serve as important sentinels of environmental contamination, and whose health may be linked to human health.

Published
2024

37. Measurement of beauty production via non-prompt charm hadrons in p–Pb collisions at sNN = 5.02 TeV

Author

Acharya, S., Adamová, D., Agarwal, A., Aglieri Rinella, G., Aglietta, L., Agnello, M., Agrawal, N., Ahammed, Z., Ahmad, S., Ahn, S. U., Ahuja, I., Akindinov, A., Akishina, V., Al-Turany, M., Aleksandrov, D., Alessandro, B., Alfanda, H. M., Alfaro Molina, R., Ali, B., Alici, A., Alizadehvandchali, N., Alkin, A., Alme, J., Alocco, G., Alt, T., Altamura, A. R., Altsybeev, I., Alvarado, J. R., Alvarez, C. O. R., Anaam, M. N., Andrei, C., Andreou, N., Andronic, A., Andronov, E., Anguelov, V., Antinori, F., Antonioli, P., Apadula, N., Aphecetche, L., Appelshäuser, H., Arata, C., Arcelli, S., Arnaldi, R., Arneiro, J. G. M. C. A., Arsene, I. C., Arslandok, M., Augustinus, A., Averbeck, R., Averyanov, D., Azmi, M. D., Baba, H., Badalà, A., Bae, J., Baek, Y. W., Bai, X., Bailhache, R., Bailung, Y., Bala, R., Balbino, A., Baldisseri, A., Balis, B., Banerjee, D., Banoo, Z., Barbasova, V., Barile, F., Barioglio, L., Barlou, M., Barman, B., Barnaföldi, G. G., Barnby, L. S., Barreau, E., Barret, V., Barreto, L., Bartels, C., Barth, K., Bartsch, E., Bastid, N., Basu, S., Batigne, G., Battistini, D., Batyunya, B., Bauri, D., Bazo Alba, J. L., Bearden, I. G., Beattie, C., Becht, P., Behera, D., Belikov, I., Bell Hechavarria, A. D. C., Bellini, F., Bellwied, R., Belokurova, S., Beltran, L. G. E., Beltran, Y. A. V., Bencedi, G., Bensaoula, A., Beole, S., Berdnikov, Y., Berdnikova, A., Bergmann, L., Besoiu, M. G., Betev, L., Bhaduri, P. P., Bhasin, A., Bhattacharjee, B., Bianchi, L., Bielčík, J., Bielčíková, J., Bigot, A. P., Bilandzic, A., Biro, G., Biswas, S., Bize, N., Blair, J. T., Blau, D., Blidaru, M. B., Bluhme, N., Blume, C., Boca, G., Bock, F., Bodova, T., Bok, J., Boldizsár, L., Bombara, M., Bond, P. M., Bonomi, G., Borel, H., Borissov, A., Borquez Carcamo, A. G., Botta, E., Bouziani, Y. E. M., Bratrud, L., Braun-Munzinger, P., Bregant, M., Broz, M., Bruno, G. E., Buchakchiev, V. D., Buckland, M. D., Budnikov, D., Buesching, H., Bufalino, S., Buhler, P., Burmasov, N., Buthelezi, Z., Bylinkin, A., Bysiak, S. A., Cabanillas Noris, J. C., Cabrera, M. F. T., Cai, M., Caines, H., Caliva, A., Calvo Villar, E., Camacho, J. M. M., Camerini, P., Canedo, F. D. M., Cantway, S. L., Carabas, M., Carballo, A. A., Carnesecchi, F., Caron, R., Carvalho, L. A. D., Castillo Castellanos, J., Castoldi, M., Catalano, F., Cattaruzzi, S., Ceballos Sanchez, C., Cerri, R., Chakaberia, I., Chakraborty, P., Chandra, S., Chapeland, S., Chartier, M., Chattopadhay, S., Chattopadhyay, S., Chattopadhyay, S., Chen, M., Cheng, T., Cheshkov, C., Chibante Barroso, V., Chinellato, D. D., Chizzali, E. S., Cho, J., Cho, S., Chochula, P., Chochulska, Z. A., Choudhury, D., Christakoglou, P., Christensen, C. H., Christiansen, P., Chujo, T., Ciacco, M., Cicalo, C., Ciupek, M. R., Clai, G., Colamaria, F., Colburn, J. S., Colella, D., Colocci, M., Concas, M., Conesa Balbastre, G., Conesa del Valle, Z., Contin, G., Contreras, J. G., Coquet, M. L., Cortese, P., Cosentino, M. R., Costa, F., Costanza, S., Cot, C., Crochet, P., Cruz-Torres, R., Cui, P., Czarnynoga, M. M., Dainese, A., Dange, G., Danisch, M. C., Danu, A., Das, P., Das, P., Das, S., Dash, A. R., Dash, S., De Caro, A., de Cataldo, G., de Cuveland, J., De Falco, A., De Gruttola, D., De Marco, N., De Martin, C., De Pasquale, S., Deb, R., Del Grande, R., Dello Stritto, L., Deng, W., Devereaux, K. C., Dhankher, P., Di Bari, D., Di Mauro, A., Diab, B., Diaz, R. A., Dietel, T., Ding, Y., Ditzel, J., Divià, R., Djuvsland, Ø., Dmitrieva, U., Dobrin, A., Dönigus, B., Dubinski, J. M., Dubla, A., Dupieux, P., Dzalaiova, N., Eder, T. M., Ehlers, R. J., Eisenhut, F., Ejima, R., Elia, D., Erazmus, B., Ercolessi, F., Espagnon, B., Eulisse, G., Evans, D., Evdokimov, S., Fabbietti, L., Faggin, M., Faivre, J., Fan, F., Fan, W., Fantoni, A., Fasel, M., Feliciello, A., Feofilov, G., Fernández Téllez, A., Ferrandi, L., Ferrer, M. B., Ferrero, A., Ferrero, C., Ferretti, A., Feuillard, V. J. G., Filova, V., Finogeev, D., Fionda, F. M., Flatland, E., Flor, F., Flores, A. N., Foertsch, S., Fokin, I., Fokin, S., Follo, U., Fragiacomo, E., Frajna, E., Fuchs, U., Funicello, N., Furget, C., Furs, A., Fusayasu, T., Gaardhøje, J. J., Gagliardi, M., Gago, A. M., Gahlaut, T., Galvan, C. D., Gangadharan, D. R., Ganoti, P., Garabatos, C., Garcia, J. M., García Chávez, T., Garcia-Solis, E., Gargiulo, C., Gasik, P., Gaur, H. M., Gautam, A., Gay Ducati, M. B., Germain, M., Gernhaeuser, R. A., Ghosh, C., Giacalone, M., Gioachin, G., Giri, S. K., Giubellino, P., Giubilato, P., Glaenzer, A. M. C., Glässel, P., Glimos, E., Goh, D. J. Q., Gonzalez, V., Gordeev, P., Gorgon, M., Goswami, K., Gotovac, S., Grabski, V., Graczykowski, L. K., Grecka, E., Grelli, A., Grigoras, C., Grigoriev, V., Grigoryan, S., Grosa, F., Grosse-Oetringhaus, J. F., Grosso, R., Grund, D., Grunwald, N. A., Guardiano, G. G., Guernane, R., Guilbaud, M., Gulbrandsen, K., Gumprecht, J. J. W. K., Gündem, T., Gunji, T., Guo, W., Gupta, A., Gupta, R., Gupta, R., Gwizdziel, K., Gyulai, L., Hadjidakis, C., Haider, F. U., Haidlova, S., Haldar, M., Hamagaki, H., Han, Y., Hanley, B. G., Hannigan, R., Hansen, J., Haque, M. R., Harris, J. W., Harton, A., Hartung, M. V., Hassan, H., Hatzifotiadou, D., Hauer, P., Havener, L. B., Hellbär, E., Helstrup, H., Hemmer, M., Herman, T., Hernandez, S. G., Herrera Corral, G., Herrmann, S., Hetland, K. F., Heybeck, B., Hillemanns, H., Hippolyte, B., Hobus, I. P. M., Hoffmann, F. W., Hofman, B., Hong, G. H., Horst, M., Horzyk, A., Hou, Y., Hristov, P., Huhn, P., Huhta, L. M., Humanic, T. J., Hutson, A., Hutter, D., Hwang, M. C., Ilkaev, R., Inaba, M., Innocenti, G. M., Ippolitov, M., Isakov, A., Isidori, T., Islam, M. S., Iurchenko, S., Ivanov, M., Ivanov, M., Ivanov, V., Iversen, K. E., Jablonski, M., Jacak, B., Jacazio, N., Jacobs, P. M., Jadlovska, S., Jadlovsky, J., Jaelani, S., Jahnke, C., Jakubowska, M. J., Janik, M. A., Janson, T., Ji, S., Jia, S., Jiang, T., Jimenez, A. A. P., Jonas, F., Jones, D. M., Jowett, J. M., Jung, J., Jung, M., Junique, A., Jusko, A., Kaewjai, J., Kalinak, P., Kalweit, A., Karasu Uysal, A., Karatovic, D., Karatzenis, N., Karavichev, O., Karavicheva, T., Karpechev, E., Karwowska, M. J., Kebschull, U., Keidel, R., Keil, M., Ketzer, B., Khade, S. S., Khan, A. M., Khan, S., Khanzadeev, A., Kharlov, Y., Khatun, A., Khuntia, A., Khuranova, Z., Kileng, B., Kim, B., Kim, C., Kim, D. J., Kim, E. J., Kim, J., Kim, J., Kim, J., Kim, M., Kim, S., Kim, T., Kimura, K., Kirkova, A., Kirsch, S., Kisel, I., Kiselev, S., Kisiel, A., Kitowski, J. P., Klay, J. L., Klein, J., Klein, S., Klein-Bösing, C., Kleiner, M., Klemenz, T., Kluge, A., Kobdaj, C., Kohara, R., Kollegger, T., Kondratyev, A., Kondratyeva, N., Konig, J., Konigstorfer, S. A., Konopka, P. J., Kornakov, G., Korwieser, M., Koryciak, S. D., Koster, C., Kotliarov, A., Kovacic, N., Kovalenko, V., Kowalski, M., Kozhuharov, V., Kozlov, G., Králik, I., Kravčáková, A., Krcal, L., Krivda, M., Krizek, F., Krizkova Gajdosova, K., Krug, C., Krüger, M., Krupova, D. M., Kryshen, E., Kučera, V., Kuhn, C., Kuijer, P. G., Kumaoka, T., Kumar, D., Kumar, L., Kumar, N., Kumar, S., Kundu, S., Kurashvili, P., Kurepin, A., Kurepin, A. B., Kuryakin, A., Kushpil, S., Kuskov, V., Kutyla, M., Kuznetsov, A., Kweon, M. J., Kwon, Y., La Pointe, S. L., La Rocca, P., Lakrathok, A., Lamanna, M., Landou, A. R., Langoy, R., Larionov, P., Laudi, E., Lautner, L., Laveaga, R. A. N., Lavicka, R., Lea, R., Lee, H., Legrand, I., Legras, G., Lehrbach, J., Lejeune, A. M., Lelek, T. M., Lemmon, R. C., León Monzón, I., Lesch, M. M., Lesser, E. D., Lévai, P., Li, M., Li, X., Liang-gilman, B. E., Lien, J., Lietava, R., Likmeta, I., Lim, B., Lim, S. H., Lindenstruth, V., Lindner, A., Lippmann, C., Liu, D. H., Liu, J., Liveraro, G. S. S., Lofnes, I. M., Loizides, C., Lokos, S., Lömker, J., Lopez, X., López Torres, E., Lotteau, C., Lu, P., Lu, Z., Lugo, F. V., Luhder, J. R., Lunardon, M., Luparello, G., Ma, Y. G., Mager, M., Maire, A., Majerz, E. M., Makariev, M. V., Malaev, M., Malfattore, G., Malik, N. M., Malik, Q. W., Malik, S. K., Malinina, L., Mallick, D., Mallick, N., Mandaglio, G., Mandal, S. K., Manea, A., Manko, V., Manso, F., Manzari, V., Mao, Y., Marcjan, R. W., Margagliotti, G. V., Margotti, A., Marín, A., Markert, C., Martinengo, P., Martínez, M. I., Martínez García, G., Martins, M. P. P., Masciocchi, S., Masera, M., Masoni, A., Massacrier, L., Massen, O., Mastroserio, A., Matonoha, O., Mattiazzo, S., Matyja, A., Mazuecos, A. L., Mazzaschi, F., Mazzilli, M., Mdhluli, J. E., Melikyan, Y., Melo, M., Menchaca-Rocha, A., Mendez, J. E. M., Meninno, E., Menon, A. S., Menzel, M. W., Meres, M., Miake, Y., Micheletti, L., Mihaylov, D. L., Mikhaylov, K., Minafra, N., Miśkowiec, D., Modak, A., Mohanty, B., Mohisin Khan, M., Molander, M. A., Monira, S., Mordasini, C., Moreira De Godoy, D. A., Morozov, I., Morsch, A., Mrnjavac, T., Muccifora, V., Muhuri, S., Mulligan, J. D., Mulliri, A., Munhoz, M. G., Munzer, R. H., Murakami, H., Murray, S., Musa, L., Musinsky, J., Myrcha, J. W., Naik, B., Nambrath, A. I., Nandi, B. K., Nania, R., Nappi, E., Nassirpour, A. F., Nath, A., Nath, S., Nattrass, C., Naydenov, M. N., Neagu, A., Negru, A., Nekrasova, E., Nellen, L., Nepeivoda, R., Nese, S., Nicassio, N., Nielsen, B. S., Nielsen, E. G., Nikolaev, S., Nikulin, S., Nikulin, V., Noferini, F., Noh, S., Nomokonov, P., Norman, J., Novitzky, N., Nowakowski, P., Nyanin, A., Nystrand, J., Oh, S., Ohlson, A., Okorokov, V. A., Oleniacz, J., Onnerstad, A., Oppedisano, C., Ortiz Velasquez, A., Otwinowski, J., Oya, M., Oyama, K., Pachmayer, Y., Padhan, S., Pagano, D., Paić, G., Paisano-Guzmán, S., Palasciano, A., Panebianco, S., Pantouvakis, C., Park, H., Park, H., Park, J., Parkkila, J. E., Patley, Y., Patra, R. N., Paul, B., Pei, H., Peitzmann, T., Peng, X., Pennisi, M., Perciballi, S., Peresunko, D., Perez, G. M., Pestov, Y., Petersen, M. T., Petrov, V., Petrovici, M., Piano, S., Pikna, M., Pillot, P., Pinazza, O., Pinsky, L., Pinto, C., Pisano, S., Płoskoń, M., Planinic, M., Pliquett, F., Plociennik, D. K., Poghosyan, M. G., Polichtchouk, B., Politano, S., Poljak, N., Pop, A., Porteboeuf-Houssais, S., Pozdniakov, V., Pozos, I. Y., Pradhan, K. K., Prasad, S. K., Prasad, S., Preghenella, R., Prino, F., Pruneau, C. A., Pshenichnov, I., Puccio, M., Pucillo, S., Qiu, S., Quaglia, L., Ragoni, S., Rai, A., Rakotozafindrabe, A., Ramello, L., Rami, F., Rasa, M., Räsänen, S. S., Rath, R., Rauch, M. P., Ravasenga, I., Read, K. F., Reckziegel, C., Redelbach, A. R., Redlich, K., Reetz, C. A., Regules-Medel, H. D., Rehman, A., Reidt, F., Reme-Ness, H. A., Rescakova, Z., Reygers, K., Riabov, A., Riabov, V., Ricci, R., Richter, M., Riedel, A. A., Riegler, W., Riffero, A. G., Rignanese, M., Ripoli, C., Ristea, C., Rodriguez, M. V., Rodríguez Cahuantzi, M., Rodríguez Ramírez, S. A., Røed, K., Rogalev, R., Rogochaya, E., Rogoschinski, T. S., Rohr, D., Röhrich, D., Rojas Torres, S., Rokita, P. S., Romanenko, G., Ronchetti, F., Rosas, E. D., Roslon, K., Rossi, A., Roy, A., Roy, S., Rubini, N., Rudolph, J. A., Ruggiano, D., Rui, R., Russek, P. G., Russo, R., Rustamov, A., Ryabinkin, E., Ryabov, Y., Rybicki, A., Ryu, J., Rzesa, W., Sabiu, B., Sadovsky, S., Saetre, J., Šafařík, K., Saha, S. K., Saha, S., Sahoo, B., Sahoo, R., Sahoo, S., Sahu, D., Sahu, P. K., Saini, J., Sajdakova, K., Sakai, S., Salvan, M. P., Sambyal, S., Samitz, D., Sanna, I., Saramela, T. B., Sarkar, D., Sarma, P., Sarritzu, V., Sarti, V. M., Sas, M. H. P., Sawan, S., Scapparone, E., Schambach, J., Scheid, H. S., Schiaua, C., Schicker, R., Schlepper, F., Schmah, A., Schmidt, C., Schmidt, H. R., Schmidt, M. O., Schmidt, M., Schmidt, N. V., Schmier, A. R., Schotter, R., Schröter, A., Schukraft, J., Schweda, K., Scioli, G., Scomparin, E., Seger, J. E., Sekiguchi, Y., Sekihata, D., Selina, M., Selyuzhenkov, I., Senyukov, S., Seo, J. J., Serebryakov, D., Serkin, L., Šerkšnytė, L., Sevcenco, A., Shaba, T. J., Shabetai, A., Shahoyan, R., Shangaraev, A., Sharma, B., Sharma, D., Sharma, H., Sharma, M., Sharma, S., Sharma, S., Sharma, U., Shatat, A., Sheibani, O., Shigaki, K., Shimomura, M., Shin, J., Shirinkin, S., Shou, Q., Sibiriak, Y., Siddhanta, S., Siemiarczuk, T., Silva, T. F., Silvermyr, D., Simantathammakul, T., Simeonov, R., Singh, B., Singh, B., Singh, K., Singh, R., Singh, R., Singh, R., Singh, S., Singh, V. K., Singhal, V., Sinha, T., Sitar, B., Sitta, M., Skaali, T. B., Skorodumovs, G., Smirnov, N., Snellings, R. J. M., Solheim, E. H., Song, J., Sonnabend, C., Sonneveld, J. M., Soramel, F., Soto-hernandez, A. B., Spijkers, R., Sputowska, I., Staa, J., Stachel, J., Stan, I., Steffanic, P. J., Stellhorn, T., Stiefelmaier, S. F., Stocco, D., Storehaug, I., Strangmann, N. J., Stratmann, P., Strazzi, S., Sturniolo, A., Stylianidis, C. P., Suaide, A. A. P., Suire, C., Sukhanov, M., Suljic, M., Sultanov, R., Sumberia, V., Sumowidagdo, S., Szymkowski, M., Taghavi, S. F., Taillepied, G., Takahashi, J., Tambave, G. J., Tang, S., Tang, Z., Tapia Takaki, J. D., Tapus, N., Tarasovicova, L. A., Tarzila, M. G., Tassielli, G. F., Tauro, A., Tavira García, A., Tejeda Muñoz, G., Telesca, A., Terlizzi, L., Terrevoli, C., Thakur, S., Thomas, D., Tikhonov, A., Tiltmann, N., Timmins, A. R., Tkacik, M., Tkacik, T., Toia, A., Tokumoto, R., Tomassini, S., Tomohiro, K., Topilskaya, N., Toppi, M., Torres, V. V., Torres Ramos, A. G., Trifiró, A., Triloki, T., Triolo, A. S., Tripathy, S., Tripathy, T., Trubnikov, V., Trzaska, W. H., Trzcinski, T. P., Tsolanta, C., Tu, R., Tumkin, A., Turrisi, R., Tveter, T. S., Ullaland, K., Ulukutlu, B., Upadhyaya, S., Uras, A., Urioni, M., Usai, G. L., Vala, M., Valle, N., van Doremalen, L. V. R., van Leeuwen, M., van Veen, C. A., van Weelden, R. J. G., Vande Vyvre, P., Varga, D., Varga, Z., Vargas Torres, P., Vasileiou, M., Vasiliev, A., Vázquez Doce, O., Vazquez Rueda, O., Vechernin, V., Vercellin, E., Vergara Limón, S., Verma, R., Vermunt, L., Vértesi, R., Verweij, M., Vickovic, L., Vilakazi, Z., Villalobos Baillie, O., Villani, A., Vinogradov, A., Virgili, T., Virta, M. M. O., Vodopyanov, A., Volkel, B., Völkl, M. A., Voloshin, S. A., Volpe, G., von Haller, B., Vorobyev, I., Vozniuk, N., Vrláková, J., Wan, J., Wang, C., Wang, D., Wang, Y., Wang, Y., Wegrzynek, A., Weiglhofer, F. T., Wenzel, S. C., Wessels, J. P., Wiechula, J., Wikne, J., Wilk, G., Wilkinson, J., Willems, G. A., Windelband, B., Winn, M., Wright, J. R., Wu, W., Wu, Y., Xiong, Z., Xu, R., Yadav, A., Yadav, A. K., Yamaguchi, Y., Yang, S., Yano, S., Yeats, E. R., Yin, Z., Yoo, I.-K., Yoon, J. H., Yu, H., Yuan, S., Yuncu, A., Zaccolo, V., Zampolli, C., Zanone, F., Zardoshti, N., Zarochentsev, A., Závada, P., Zaviyalov, N., Zhalov, M., Zhang, B., Zhang, C., Zhang, L., Zhang, M., Zhang, M., Zhang, S., Zhang, X., Zhang, Y., Zhang, Z., Zhao, M., Zherebchevskii, V., Zhi, Y., Zhou, D., Zhou, Y., Zhu, J., Zhu, S., Zhu, Y., Zugravel, S. C., and Zurlo, N.

Published
2024
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38. Indonesia-Germany MathCityMap Training: Shifting Mobile Math Trails Teacher Training to a Hybrid Environment

Author

Cahyono, Adi Nur, Masrukan, Mulyono, Ludwig, Matthias, Jablonski, Simone, and Oehler, Deng-Xin Ken

Abstract

The purpose of this study is to explore how MathCityMap training for teachers can be designed and implemented in a hybrid setting. Design research has been carried out involving teachers from 34 provinces in Indonesia. A four-day model training program for indoor and outdoor activities in a hybrid setting has been designed. The core concept of designing modeling tasks and math trails, as well as the introduction to MathCityMap, were delivered in a hybrid mode between Indonesia and Germany. The activity continued with the development of MathCityMap Trails in city parks. Post-training activities were conducted in their respective places of origin, in schools, and in city parks. Participants have successfully designed trails in both their schools and the city parks where they live supported using Learning Management System. The findings of the MathCityMap team's review indicate that most of the trails fulfilled the requirements. The trainee teachers then disseminate their knowledge and experience to their colleagues. They also used MathCityMap Trails for classroom learning as well as other activities such as MathCityMap competitions and public educational activities. The hybrid environment helps to accelerate the attainment of training objectives while also broadening the program's reach and network.

Published
2023

39. Real Objects as a Reason for Mathematical Reasoning -- A Comparison of Different Task Settings

Author

Simone Jablonski

Abstract

In this article, the goal is to describe students' mathematical reasoning in the context of different settings of problem-solving tasks. The core of the tasks are real objects, which are presented to the students with the help of photos, a 3D model or in the environment itself. With reference to the experiential learning theory and relations to problem-solving and modelling, theoretical potentials for mathematical reasoning emerge. In a qualitative study with 19 secondary school students these are empirically tested. The evaluation of the video recordings of the students' solution processes are coded with the help of qualitative content analysis, among others with references to problem-solving and linguistic categories of conclusive speech acts. The results show that mathematical reasoning can be observed especially in the work with photos and that the work with real objects generally evokes reasoning activities in the area of planning and exploration of strategies.

Published
2023

40. Measurement of beauty production via non-prompt charm hadrons in p–Pb collisions at s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 5.02 TeV

Author

The ALICE collaboration, S. Acharya, D. Adamová, A. Agarwal, G. Aglieri Rinella, L. Aglietta, M. Agnello, N. Agrawal, Z. Ahammed, S. Ahmad, S. U. Ahn, I. Ahuja, A. Akindinov, V. Akishina, M. Al-Turany, D. Aleksandrov, B. Alessandro, H. M. Alfanda, R. Alfaro Molina, B. Ali, A. Alici, N. Alizadehvandchali, A. Alkin, J. Alme, G. Alocco, T. Alt, A. R. Altamura, I. Altsybeev, J. R. Alvarado, C. O. R. Alvarez, M. N. Anaam, C. Andrei, N. Andreou, A. Andronic, E. Andronov, V. Anguelov, F. Antinori, P. Antonioli, N. Apadula, L. Aphecetche, H. Appelshäuser, C. Arata, S. Arcelli, R. Arnaldi, J. G. M. C. A. Arneiro, I. C. Arsene, M. Arslandok, A. Augustinus, R. Averbeck, D. Averyanov, M. D. Azmi, H. Baba, A. Badalà, J. Bae, Y. W. Baek, X. Bai, R. Bailhache, Y. Bailung, R. Bala, A. Balbino, A. Baldisseri, B. Balis, D. Banerjee, Z. Banoo, V. Barbasova, F. Barile, L. Barioglio, M. Barlou, B. Barman, G. G. Barnaföldi, L. S. Barnby, E. Barreau, V. Barret, L. Barreto, C. Bartels, K. Barth, E. Bartsch, N. Bastid, S. Basu, G. Batigne, D. Battistini, B. Batyunya, D. Bauri, J. L. Bazo Alba, I. G. Bearden, C. Beattie, P. Becht, D. Behera, I. Belikov, A. D. C. Bell Hechavarria, F. Bellini, R. Bellwied, S. Belokurova, L. G. E. Beltran, Y. A. V. Beltran, G. Bencedi, A. Bensaoula, S. Beole, Y. Berdnikov, A. Berdnikova, L. Bergmann, M. G. Besoiu, L. Betev, P. P. Bhaduri, A. Bhasin, B. Bhattacharjee, L. Bianchi, J. Bielčík, J. Bielčíková, A. P. Bigot, A. Bilandzic, G. Biro, S. Biswas, N. Bize, J. T. Blair, D. Blau, M. B. Blidaru, N. Bluhme, C. Blume, G. Boca, F. Bock, T. Bodova, J. Bok, L. Boldizsár, M. Bombara, P. M. Bond, G. Bonomi, H. Borel, A. Borissov, A. G. Borquez Carcamo, E. Botta, Y. E. M. Bouziani, L. Bratrud, P. Braun-Munzinger, M. Bregant, M. Broz, G. E. Bruno, V. D. Buchakchiev, M. D. Buckland, D. Budnikov, H. Buesching, S. Bufalino, P. Buhler, N. Burmasov, Z. Buthelezi, A. Bylinkin, S. A. Bysiak, J. C. Cabanillas Noris, M. F. T. Cabrera, M. Cai, H. Caines, A. Caliva, E. Calvo Villar, J. M. M. Camacho, P. Camerini, F. D. M. Canedo, S. L. Cantway, M. Carabas, A. A. Carballo, F. Carnesecchi, R. Caron, L. A. D. Carvalho, J. Castillo Castellanos, M. Castoldi, F. Catalano, S. Cattaruzzi, C. Ceballos Sanchez, R. Cerri, I. Chakaberia, P. Chakraborty, S. Chandra, S. Chapeland, M. Chartier, S. Chattopadhay, S. Chattopadhyay, M. Chen, T. Cheng, C. Cheshkov, V. Chibante Barroso, D. D. Chinellato, E. S. Chizzali, J. Cho, S. Cho, P. Chochula, Z. A. Chochulska, D. Choudhury, P. Christakoglou, C. H. Christensen, P. Christiansen, T. Chujo, M. Ciacco, C. Cicalo, M. R. Ciupek, G. Clai, F. Colamaria, J. S. Colburn, D. Colella, M. Colocci, M. Concas, G. Conesa Balbastre, Z. Conesa del Valle, G. Contin, J. G. Contreras, M. L. Coquet, P. Cortese, M. R. Cosentino, F. Costa, S. Costanza, C. Cot, P. Crochet, R. Cruz-Torres, P. Cui, M. M. Czarnynoga, A. Dainese, G. Dange, M. C. Danisch, A. Danu, P. Das, S. Das, A. R. Dash, S. Dash, A. De Caro, G. de Cataldo, J. de Cuveland, A. De Falco, D. De Gruttola, N. De Marco, C. De Martin, S. De Pasquale, R. Deb, R. Del Grande, L. Dello Stritto, W. Deng, K. C. Devereaux, P. Dhankher, D. Di Bari, A. Di Mauro, B. Diab, R. A. Diaz, T. Dietel, Y. Ding, J. Ditzel, R. Divià, Ø. Djuvsland, U. Dmitrieva, A. Dobrin, B. Dönigus, J. M. Dubinski, A. Dubla, P. Dupieux, N. Dzalaiova, T. M. Eder, R. J. Ehlers, F. Eisenhut, R. Ejima, D. Elia, B. Erazmus, F. Ercolessi, B. Espagnon, G. Eulisse, D. Evans, S. Evdokimov, L. Fabbietti, M. Faggin, J. Faivre, F. Fan, W. Fan, A. Fantoni, M. Fasel, A. Feliciello, G. Feofilov, A. Fernández Téllez, L. Ferrandi, M. B. Ferrer, A. Ferrero, C. Ferrero, A. Ferretti, V. J. G. Feuillard, V. Filova, D. Finogeev, F. M. Fionda, E. Flatland, F. Flor, A. N. Flores, S. Foertsch, I. Fokin, S. Fokin, U. Follo, E. Fragiacomo, E. Frajna, U. Fuchs, N. Funicello, C. Furget, A. Furs, T. Fusayasu, J. J. Gaardhøje, M. Gagliardi, A. M. Gago, T. Gahlaut, C. D. Galvan, D. R. Gangadharan, P. Ganoti, C. Garabatos, J. M. Garcia, T. García Chávez, E. Garcia-Solis, C. Gargiulo, P. Gasik, H. M. Gaur, A. Gautam, M. B. Gay Ducati, M. Germain, R. A. Gernhaeuser, C. Ghosh, M. Giacalone, G. Gioachin, S. K. Giri, P. Giubellino, P. Giubilato, A. M. C. Glaenzer, P. Glässel, E. Glimos, D. J. Q. Goh, V. Gonzalez, P. Gordeev, M. Gorgon, K. Goswami, S. Gotovac, V. Grabski, L. K. Graczykowski, E. Grecka, A. Grelli, C. Grigoras, V. Grigoriev, S. Grigoryan, F. Grosa, J. F. Grosse-Oetringhaus, R. Grosso, D. Grund, N. A. Grunwald, G. G. Guardiano, R. Guernane, M. Guilbaud, K. Gulbrandsen, J. J. W. K. Gumprecht, T. Gündem, T. Gunji, W. Guo, A. Gupta, R. Gupta, K. Gwizdziel, L. Gyulai, C. Hadjidakis, F. U. Haider, S. Haidlova, M. Haldar, H. Hamagaki, Y. Han, B. G. Hanley, R. Hannigan, J. Hansen, M. R. Haque, J. W. Harris, A. Harton, M. V. Hartung, H. Hassan, D. Hatzifotiadou, P. Hauer, L. B. Havener, E. Hellbär, H. Helstrup, M. Hemmer, T. Herman, S. G. Hernandez, G. Herrera Corral, S. Herrmann, K. F. Hetland, B. Heybeck, H. Hillemanns, B. Hippolyte, I. P. M. Hobus, F. W. Hoffmann, B. Hofman, G. H. Hong, M. Horst, A. Horzyk, Y. Hou, P. Hristov, P. Huhn, L. M. Huhta, T. J. Humanic, A. Hutson, D. Hutter, M. C. Hwang, R. Ilkaev, M. Inaba, G. M. Innocenti, M. Ippolitov, A. Isakov, T. Isidori, M. S. Islam, S. Iurchenko, M. Ivanov, V. Ivanov, K. E. Iversen, M. Jablonski, B. Jacak, N. Jacazio, P. M. Jacobs, S. Jadlovska, J. Jadlovsky, S. Jaelani, C. Jahnke, M. J. Jakubowska, M. A. Janik, T. Janson, S. Ji, S. Jia, T. Jiang, A. A. P. Jimenez, F. Jonas, D. M. Jones, J. M. Jowett, J. Jung, M. Jung, A. Junique, A. Jusko, J. Kaewjai, P. Kalinak, A. Kalweit, A. Karasu Uysal, D. Karatovic, N. Karatzenis, O. Karavichev, T. Karavicheva, E. Karpechev, M. J. Karwowska, U. Kebschull, R. Keidel, M. Keil, B. Ketzer, S. S. Khade, A. M. Khan, S. Khan, A. Khanzadeev, Y. Kharlov, A. Khatun, A. Khuntia, Z. Khuranova, B. Kileng, B. Kim, C. Kim, D. J. Kim, E. J. Kim, J. Kim, M. Kim, S. Kim, T. Kim, K. Kimura, A. Kirkova, S. Kirsch, I. Kisel, S. Kiselev, A. Kisiel, J. P. Kitowski, J. L. Klay, J. Klein, S. Klein, C. Klein-Bösing, M. Kleiner, T. Klemenz, A. Kluge, C. Kobdaj, R. Kohara, T. Kollegger, A. Kondratyev, N. Kondratyeva, J. Konig, S. A. Konigstorfer, P. J. Konopka, G. Kornakov, M. Korwieser, S. D. Koryciak, C. Koster, A. Kotliarov, N. Kovacic, V. Kovalenko, M. Kowalski, V. Kozhuharov, G. Kozlov, I. Králik, A. Kravčáková, L. Krcal, M. Krivda, F. Krizek, K. Krizkova Gajdosova, C. Krug, M. Krüger, D. M. Krupova, E. Kryshen, V. Kučera, C. Kuhn, P. G. Kuijer, T. Kumaoka, D. Kumar, L. Kumar, N. Kumar, S. Kumar, S. Kundu, P. Kurashvili, A. Kurepin, A. B. Kurepin, A. Kuryakin, S. Kushpil, V. Kuskov, M. Kutyla, A. Kuznetsov, M. J. Kweon, Y. Kwon, S. L. La Pointe, P. La Rocca, A. Lakrathok, M. Lamanna, A. R. Landou, R. Langoy, P. Larionov, E. Laudi, L. Lautner, R. A. N. Laveaga, R. Lavicka, R. Lea, H. Lee, I. Legrand, G. Legras, J. Lehrbach, A. M. Lejeune, T. M. Lelek, R. C. Lemmon, I. León Monzón, M. M. Lesch, E. D. Lesser, P. Lévai, M. Li, X. Li, B. E. Liang-gilman, J. Lien, R. Lietava, I. Likmeta, B. Lim, S. H. Lim, V. Lindenstruth, A. Lindner, C. Lippmann, D. H. Liu, J. Liu, G. S. S. Liveraro, I. M. Lofnes, C. Loizides, S. Lokos, J. Lömker, X. Lopez, E. López Torres, C. Lotteau, P. Lu, Z. Lu, F. V. Lugo, J. R. Luhder, M. Lunardon, G. Luparello, Y. G. Ma, M. Mager, A. Maire, E. M. Majerz, M. V. Makariev, M. Malaev, G. Malfattore, N. M. Malik, Q. W. Malik, S. K. Malik, L. Malinina, D. Mallick, N. Mallick, G. Mandaglio, S. K. Mandal, A. Manea, V. Manko, F. Manso, V. Manzari, Y. Mao, R. W. Marcjan, G. V. Margagliotti, A. Margotti, A. Marín, C. Markert, P. Martinengo, M. I. Martínez, G. Martínez García, M. P. P. Martins, S. Masciocchi, M. Masera, A. Masoni, L. Massacrier, O. Massen, A. Mastroserio, O. Matonoha, S. Mattiazzo, A. Matyja, A. L. Mazuecos, F. Mazzaschi, M. Mazzilli, J. E. Mdhluli, Y. Melikyan, M. Melo, A. Menchaca-Rocha, J. E. M. Mendez, E. Meninno, A. S. Menon, M. W. Menzel, M. Meres, Y. Miake, L. Micheletti, D. L. Mihaylov, K. Mikhaylov, N. Minafra, D. Miśkowiec, A. Modak, B. Mohanty, M. Mohisin Khan, M. A. Molander, S. Monira, C. Mordasini, D. A. Moreira De Godoy, I. Morozov, A. Morsch, T. Mrnjavac, V. Muccifora, S. Muhuri, J. D. Mulligan, A. Mulliri, M. G. Munhoz, R. H. Munzer, H. Murakami, S. Murray, L. Musa, J. Musinsky, J. W. Myrcha, B. Naik, A. I. Nambrath, B. K. Nandi, R. Nania, E. Nappi, A. F. Nassirpour, A. Nath, S. Nath, C. Nattrass, M. N. Naydenov, A. Neagu, A. Negru, E. Nekrasova, L. Nellen, R. Nepeivoda, S. Nese, N. Nicassio, B. S. Nielsen, E. G. Nielsen, S. Nikolaev, S. Nikulin, V. Nikulin, F. Noferini, S. Noh, P. Nomokonov, J. Norman, N. Novitzky, P. Nowakowski, A. Nyanin, J. Nystrand, S. Oh, A. Ohlson, V. A. Okorokov, J. Oleniacz, A. Onnerstad, C. Oppedisano, A. Ortiz Velasquez, J. Otwinowski, M. Oya, K. Oyama, Y. Pachmayer, S. Padhan, D. Pagano, G. Paić, S. Paisano-Guzmán, A. Palasciano, S. Panebianco, C. Pantouvakis, H. Park, J. Park, J. E. Parkkila, Y. Patley, R. N. Patra, B. Paul, H. Pei, T. Peitzmann, X. Peng, M. Pennisi, S. Perciballi, D. Peresunko, G. M. Perez, Y. Pestov, M. T. Petersen, V. Petrov, M. Petrovici, S. Piano, M. Pikna, P. Pillot, O. Pinazza, L. Pinsky, C. Pinto, S. Pisano, M. Płoskoń, M. Planinic, F. Pliquett, D. K. Plociennik, M. G. Poghosyan, B. Polichtchouk, S. Politano, N. Poljak, A. Pop, S. Porteboeuf-Houssais, V. Pozdniakov, I. Y. Pozos, K. K. Pradhan, S. K. Prasad, S. Prasad, R. Preghenella, F. Prino, C. A. Pruneau, I. Pshenichnov, M. Puccio, S. Pucillo, S. Qiu, L. Quaglia, S. Ragoni, A. Rai, A. Rakotozafindrabe, L. Ramello, F. Rami, M. Rasa, S. S. Räsänen, R. Rath, M. P. Rauch, I. Ravasenga, K. F. Read, C. Reckziegel, A. R. Redelbach, K. Redlich, C. A. Reetz, H. D. Regules-Medel, A. Rehman, F. Reidt, H. A. Reme-Ness, Z. Rescakova, K. Reygers, A. Riabov, V. Riabov, R. Ricci, M. Richter, A. A. Riedel, W. Riegler, A. G. Riffero, M. Rignanese, C. Ripoli, C. Ristea, M. V. Rodriguez, M. Rodríguez Cahuantzi, S. A. Rodríguez Ramírez, K. Røed, R. Rogalev, E. Rogochaya, T. S. Rogoschinski, D. Rohr, D. Röhrich, S. Rojas Torres, P. S. Rokita, G. Romanenko, F. Ronchetti, E. D. Rosas, K. Roslon, A. Rossi, A. Roy, S. Roy, N. Rubini, J. A. Rudolph, D. Ruggiano, R. Rui, P. G. Russek, R. Russo, A. Rustamov, E. Ryabinkin, Y. Ryabov, A. Rybicki, J. Ryu, W. Rzesa, B. Sabiu, S. Sadovsky, J. Saetre, K. Šafařík, S. K. Saha, S. Saha, B. Sahoo, R. Sahoo, S. Sahoo, D. Sahu, P. K. Sahu, J. Saini, K. Sajdakova, S. Sakai, M. P. Salvan, S. Sambyal, D. Samitz, I. Sanna, T. B. Saramela, D. Sarkar, P. Sarma, V. Sarritzu, V. M. Sarti, M. H. P. Sas, S. Sawan, E. Scapparone, J. Schambach, H. S. Scheid, C. Schiaua, R. Schicker, F. Schlepper, A. Schmah, C. Schmidt, H. R. Schmidt, M. O. Schmidt, M. Schmidt, N. V. Schmidt, A. R. Schmier, R. Schotter, A. Schröter, J. Schukraft, K. Schweda, G. Scioli, E. Scomparin, J. E. Seger, Y. Sekiguchi, D. Sekihata, M. Selina, I. Selyuzhenkov, S. Senyukov, J. J. Seo, D. Serebryakov, L. Serkin, L. Šerkšnytė, A. Sevcenco, T. J. Shaba, A. Shabetai, R. Shahoyan, A. Shangaraev, B. Sharma, D. Sharma, H. Sharma, M. Sharma, S. Sharma, U. Sharma, A. Shatat, O. Sheibani, K. Shigaki, M. Shimomura, J. Shin, S. Shirinkin, Q. Shou, Y. Sibiriak, S. Siddhanta, T. Siemiarczuk, T. F. Silva, D. Silvermyr, T. Simantathammakul, R. Simeonov, B. Singh, K. Singh, R. Singh, S. Singh, V. K. Singh, V. Singhal, T. Sinha, B. Sitar, M. Sitta, T. B. Skaali, G. Skorodumovs, N. Smirnov, R. J. M. Snellings, E. H. Solheim, J. Song, C. Sonnabend, J. M. Sonneveld, F. Soramel, A. B. Soto-hernandez, R. Spijkers, I. Sputowska, J. Staa, J. Stachel, I. Stan, P. J. Steffanic, T. Stellhorn, S. F. Stiefelmaier, D. Stocco, I. Storehaug, N. J. Strangmann, P. Stratmann, S. Strazzi, A. Sturniolo, C. P. Stylianidis, A. A. P. Suaide, C. Suire, M. Sukhanov, M. Suljic, R. Sultanov, V. Sumberia, S. Sumowidagdo, M. Szymkowski, S. F. Taghavi, G. Taillepied, J. Takahashi, G. J. Tambave, S. Tang, Z. Tang, J. D. Tapia Takaki, N. Tapus, L. A. Tarasovicova, M. G. Tarzila, G. F. Tassielli, A. Tauro, A. Tavira García, G. Tejeda Muñoz, A. Telesca, L. Terlizzi, C. Terrevoli, S. Thakur, D. Thomas, A. Tikhonov, N. Tiltmann, A. R. Timmins, M. Tkacik, T. Tkacik, A. Toia, R. Tokumoto, S. Tomassini, K. Tomohiro, N. Topilskaya, M. Toppi, V. V. Torres, A. G. Torres Ramos, A. Trifiró, T. Triloki, A. S. Triolo, S. Tripathy, T. Tripathy, V. Trubnikov, W. H. Trzaska, T. P. Trzcinski, C. Tsolanta, R. Tu, A. Tumkin, R. Turrisi, T. S. Tveter, K. Ullaland, B. Ulukutlu, S. Upadhyaya, A. Uras, M. Urioni, G. L. Usai, M. Vala, N. Valle, L. V. R. van Doremalen, M. van Leeuwen, C. A. van Veen, R. J. G. van Weelden, P. Vande Vyvre, D. Varga, Z. Varga, P. Vargas Torres, M. Vasileiou, A. Vasiliev, O. Vázquez Doce, O. Vazquez Rueda, V. Vechernin, E. Vercellin, S. Vergara Limón, R. Verma, L. Vermunt, R. Vértesi, M. Verweij, L. Vickovic, Z. Vilakazi, O. Villalobos Baillie, A. Villani, A. Vinogradov, T. Virgili, M. M. O. Virta, A. Vodopyanov, B. Volkel, M. A. Völkl, S. A. Voloshin, G. Volpe, B. von Haller, I. Vorobyev, N. Vozniuk, J. Vrláková, J. Wan, C. Wang, D. Wang, Y. Wang, A. Wegrzynek, F. T. Weiglhofer, S. C. Wenzel, J. P. Wessels, J. Wiechula, J. Wikne, G. Wilk, J. Wilkinson, G. A. Willems, B. Windelband, M. Winn, J. R. Wright, W. Wu, Y. Wu, Z. Xiong, R. Xu, A. Yadav, A. K. Yadav, Y. Yamaguchi, S. Yang, S. Yano, E. R. Yeats, Z. Yin, I.-K. Yoo, J. H. Yoon, H. Yu, S. Yuan, A. Yuncu, V. Zaccolo, C. Zampolli, F. Zanone, N. Zardoshti, A. Zarochentsev, P. Závada, N. Zaviyalov, M. Zhalov, B. Zhang, C. Zhang, L. Zhang, M. Zhang, S. Zhang, X. Zhang, Y. Zhang, Z. Zhang, M. Zhao, V. Zherebchevskii, Y. Zhi, D. Zhou, Y. Zhou, J. Zhu, S. Zhu, Y. Zhu, S. C. Zugravel, and N. Zurlo

Subjects
Heavy Ion Experiments, Heavy Quark Production, QCD, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The production cross sections of D0, D+, and Λ c + $$ {\Lambda}_{\textrm{c}}^{+} $$ hadrons originating from beauty-hadron decays (i.e. non-prompt) were measured for the first time at midrapidity in proton–lead (p–Pb) collisions at the center-of-mass energy per nucleon pair of s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 5.02 TeV. Nuclear modification factors (R pPb) of non-prompt D0, D+, and Λ c + $$ {\Lambda}_{\textrm{c}}^{+} $$ are calculated as a function of the transverse momentum (p T) to investigate the modification of the momentum spectra measured in p–Pb collisions with respect to those measured in proton–proton (pp) collisions at the same energy. The R pPb measurements are compatible with unity and with the measurements in the prompt charm sector, and do not show a significant p T dependence. The p T-integrated cross sections and p T-integrated R pPb of non-prompt D0 and D+ mesons are also computed by extrapolating the visible cross sections down to p T = 0. The non-prompt D-meson R pPb integrated over p T is compatible with unity and with model calculations implementing modification of the parton distribution functions of nucleons bound in nuclei with respect to free nucleons. The non-prompt Λ c + $$ {\Lambda}_{\textrm{c}}^{+} $$ /D0 and D+ /D0 production ratios are computed to investigate hadronisation mechanisms of beauty quarks into mesons and baryons. The measured ratios as a function of p T display a similar trend to that measured for charm hadrons in the same collision system.

Published
2024
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41. Association of time to start of enteral nutrition and outcome in cats with hepatic lipidosis

Author

Olivia P. Wallace, Sara A. Jablonski, Jennifer S. Thomas, Jack H. Bock III, and Daniel K. Langlois

Subjects
fatty liver, feeding tube, prognosis, steatosis, Veterinary medicine, SF600-1100
Abstract

Abstract Background Enteral nutrition (EN) is essential for management of hepatic lipidosis (HL) in cats. Objectives To determine if time to start of EN and other clinicopathologic variables are associated with outcome in cats with HL. Animals Forty‐eight cats with HL. Methods Retrospective study. Information retrieved from medical records and client communications included clinicopathologic findings, time to start of EN, initial % of resting energy requirements provided, type of feeding tube, duration of hospitalization, and 3‐month survival. Variables were compared between surviving and nonsurviving cats and between cats fed ≤12 hours and >12 hours after hospital admission. Multivariable statistical testing was performed to further investigate variables of interest. Results Seventeen of 25 (68%) cats fed ≤12 hours and 13 of 23 (57%) of cats fed >12 hours after hospital admission survived (P = .55). Only increasing age (odds ratio [OR], 1.313; 95% confidence interval [CI], 1.032‐1.671; P = .03) and the presence of ascites (OR, 6.415; 95% CI, 1.354‐30.395; P = .02) were associated with death in multivariable analysis. Hospitalization duration (median, interquartile range [IQR]) was shorter in cats fed >12 hours (2.8 days; IQR, 2.1‐3.8 days) as compared with cats fed ≤12 hours (4.8 days; IQR, 3.6‐6.2 days) after hospital admission (P

Published
2024
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42. A comparison between in vitro and randomized in situ models for remineralization of artificial enamel lesions

Author

Anahita Jablonski-Momeni, Jenna Lentz, Boris Jablonski, Andreas Kiesow, and Maria Morawietz

Subjects
Enamel, Demineralization, Remineralization, Dentifrice, Fluoride, In-situ, Medicine, Science
Abstract

Abstract The randomized study aimed to evaluate the comparability of in situ (iS) and in vitro (iV) study protocols regarding remineralization of artificial enamel lesions. Two toothpastes (group A: 1450 ppm sodium fluoride, group B: placebo 0 ppm F-), were investigated. IV, a pH-cycling model with toothpaste slurry treatment was applied for 10d. IS, remineralization was performed in 9 participants wearing splints with embedded enamel samples for 10 and 21d, randomly allocated to groups A and B. Samples were scanned by X-ray micro-computed tomography (μCT) and grayscale value line profiles corresponding to mineral density (rel.ΔZ) were analyzed. Statistical analyses were performed using MedCalc Statistical Software, v22.021. T-Test for dependent and independent data and analysis of variance (ANOVA) were used for further analyses (α = 0.05). Rel.ΔZ of fluoride treated samples (A) were iV = 40.2%, iS 10d = 11.5% and iS 21d = 46.1% (p > 0.05). Rel.ΔZ of placebo treated samples (B) were: iV = − 6.2%, iS 10d = 25.2% and iS 21d = 11.0% (p > 0.05). Remineralization potential of both toothpastes was significantly different regarding iV (p

Published
2024
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43. Semi-simple Lie algebras are determined by their Iwasawa subalgebras

Author

Epstein, Jonathan and Jablonski, Michael

Subjects
Mathematics - Representation Theory, Mathematics - Differential Geometry, 2020: 53C25, 53C30, 22E25, 22E46
Abstract

Using tools from the geometry of Einstein solvmanifolds, we give a geometric argument that a semi-simple Lie algebra (of non-compact type) is completely determined by its Iwasawa subalgebra. Furthermore, we produce an algebraic procedure for recovering the semi-simple (of non-compact type) from its Iwasawa subalgebra., Comment: 7 pages, main theorem improved in new version

Published
2023

44. The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning

Author

Guleria, Shan, Schwartz, Benjamin, Sharma, Yash, Fernandes, Philip, Jablonski, James, Adewole, Sodiq, Srivastava, Sanjana, Rhoads, Fisher, Porter, Michael, Yeghyayan, Michelle, Hyatt, Dylan, Copland, Andrew, Ehsan, Lubaina, Brown, Donald, and Syed, Sana

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods: An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results: When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

Published
2023

45. A high-resolution asymmetric von Hamos spectrometer for low-energy X-ray spectroscopy at the CRYRING@ESR electron cooler

Author

Jagodziński, P., Banaś, D., Pajek, M., Kubala-Kukuś, A., Jabłoński, Ł., Stabrawa, I., Szary, K., Sobota, D., Warczak, A., Gumberidze, A., Beyer, H. F., Lestinsky, M., Weber, G., Stöhlker, Th., and Trassinelli, M.

Subjects
Physics - Atomic Physics
Abstract

We present research program and project for high-resolution wavelength-dispersive spectrometer dedicated to low-energy X-ray spectroscopy at the electron cooler of the CRYRING@ESR storage ring, which is a part of the international Facility for Antiproton and Ion Research (FAIR) currently being built in Darmstadt. Due to the unique shape of the electorn-ion recombination X-ray source, resulting from the overlapping of the electron and ion beams in the electron cooler, the spectrometer can work in the specific asymmetric von Hamos (AvH) geometry. In order to completely eliminate the influence of Doppler effect on the measured X-ray energies, two asymmetric von Hamos spectrometers will be installed next to the dipole magnets on both sides of the electron cooler to detect blue/red (0$^{\circ}$/180$^{\circ}$) shifted X-rays, e.g. emitted in the radiative recombination (RR) process. The X-ray-tracing Monte-Carlo simulations show that the proposed AvH spectrometer will allow to determine with sub-meV precision, the low-energy X-rays (5-10 keV) emitted from stored bare or few-electron heavy ions interacting with cooling electrons. This experimental precision will enable accurate studies of the quantum electrodynamics (QED) effects in mid-Z H- and He-like ions.

Published
2023
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48. Convergence of power sequences of B-operators with applications to stability

Author

Chavan, Sameer, Jabłoński, Zenon Jan, Jung, Il Bong, and Stochel, Jan

Subjects
Mathematics - Functional Analysis, Primary 47A08, Secondary 47B20
Abstract

The B-operators (abbreviation for Brownian-type operators) are upper triangular 2x2 block matrix operators that satisfy certain algebraic constraints. The purpose of this paper is to characterize the weak, the strongand the uniform stability of B-operators, respectively. This is achieved by giving equivalent conditions for the convergence of powers of a B-operator in each of the corresponding topologies. A more subtle characterization is obtained for B-operators with subnormal (2,2) entry. The issue of the strong stability of the adjoint of a B-operator is also discussed., Comment: 15 pages

Published
2023

49. Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text

Author

Neuberger, Julian, Ackermann, Lars, and Jablonski, Stefan

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Process-aware information systems offer extensive advantages to companies, facilitating planning, operations, and optimization of day-to-day business activities. However, the time-consuming but required step of designing formal business process models often hampers the potential of these systems. To overcome this challenge, automated generation of business process models from natural language text has emerged as a promising approach to expedite this step. Generally two crucial subtasks have to be solved: extracting process-relevant information from natural language and creating the actual model. Approaches towards the first subtask are rule based methods, highly optimized for specific domains, but hard to adapt to related applications. To solve this issue, we present an extension to an existing pipeline, to make it entirely data driven. We demonstrate the competitiveness of our improved pipeline, which not only eliminates the substantial overhead associated with feature engineering and rule definition, but also enables adaptation to different datasets, entity and relation types, and new domains. Additionally, the largest available dataset (PET) for the first subtask, contains no information about linguistic references between mentions of entities in the process description. Yet, the resolution of these mentions into a single visual element is essential for high quality process models. We propose an extension to the PET dataset that incorporates information about linguistic references and a corresponding method for resolving them. Finally, we provide a detailed analysis of the inherent challenges in the dataset at hand., Comment: Currently under review for CoopIS23

Published
2023
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50. Qualitative and quantitative assessment of infraoccluded deciduous teeth: a systematic review

Author

Teresa Temming, Susanne Waldmann, Anahita Jablonski-Momeni, and Heike Korbmacher-Steiner

Subjects
Tooth ankylosis, Deciduous teeth, Infraocclusion, Alveolar bone growth, Systematic review, Specialties of internal medicine, RC581-951
Abstract

Abstract Background Infraocclusion of deciduous teeth is a frequent dental anomaly caused by ankylosis accompanied by local growth disturbance. During puberal growth spurt an increasing progression of infraocclusion is detected. The clinical classification of ankylosed deciduous teeth varies considerably among scientific studies. The aim of this paper is to present an up-to-date overview of the variety of methods. Methods The systematic literature search followed the PRISMA guidelines and included the analysis of the following databases and study registries: PubMed (MEDLINE), the Cochrane Library, Web of Science, Embase.com and ClinicalTrials.gov from database inception until September 23, 2024. Studies that investigated at least one ankylosed deciduous tooth per participant in a quantitative or qualitative manner were included. Studies that evaluated only histological data were excluded. Controlled and uncontrolled clinical studies, retrospective studies, observational studies and cross-sectional studies were included. The studies were restricted to English and German languages. Case reports, case series, comments, expert opinions, letters to the editor, literature reviews and studies enrolling less than 10 patients or 10 infraoccluded teeth in total were excluded. Results Of 5645 records, 42 papers qualified for the final analysis. The evaluation of infraoccluded deciduous teeth was mainly (n = 37) performed by quantitative and semiquantitative assessment of the extent of infraposition at the occlusal level. The measurement reference differed considerably. Fewer studies have analyzed ankylosed deciduous teeth at the alveolar level by examining the contour of the alveolar ridge (n = 7) or the height of the alveolar process (n = 5). Even fewer studies (n = 4) have performed qualitative analysis at the skeletal level by evaluating the influence of the vertical skeletal growth pattern on the incidence of ankylosed deciduous teeth. Conclusions To carry out a comprehensive evaluation of infraoccluded deciduous teeth, an assessment of the occlusal, alveolar and potentially skeletal levels is advisable. Radiographic investigations i.e. panoramic radiographs are therefore essential as a supplement to clinical examination. There is a need for standardization and objectification of the methods for the classification of infraoccluded deciduous teeth to give a general recommendation of clinical performance. Registration This systematic review has been registered in the international prospective register of systematic reviews (PROSPERO) under the registration number: CRD42024555842.

Published
2024
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