bluuebunny/crossref_metadata_2025_split huggingface dataset:
An intelligent biosensing platform using phase space reconstruction‐assisted convolutional neural network for drug‐induced cardiotoxicity assessment
Authors: Yang Wenjian , Guo Xinyu , Wu Ruochen , Wu Yue , Fan Minzhi , Lv Bihu , Zhang Diming , Zhu Zhijing 
Published: June 2024

AbstractDrug‐induced cardiotoxicity often leads to patient deaths and drug recalls. Interdigitated electrode (IDE)‐based cellular impedance detection instrument has been integrated with intelligent algorithms to screen cardiotoxicity based on cardiomyocytes. These intelligent biosensing systems generally employ traditional machine learning methods to assess drug‐induced cardiotoxicity by analyzing cardiomyocytes mechanical beating signals. However, modern deep learning methods with robustness and flexibility have not been integrated in IDE‐based platform to screen cardiotoxicity. Here, for the first time, we implemented deep convolutional neural network (CNN) to analyze cardiomyocytes mechanical beating signals for cardiotoxicity assessment. This method can eliminate the feature engineering procedures, such as manual design and extraction of signal features required by traditional machine learning methods. To facilitate two‐dimensional (2‐D) CNN analysis, we utilized phase space reconstruction to convert one‐dimensional beating signals into 2‐D image representations as well as take into account nonlinear dynamic information. The phase space reconstruction‐assisted convolutional neural network (PSRCNN) is capable of accurately categorizing drug‐induced cardiotoxicities and predicting cardiotoxicity levels. It obtains accuracies ranging from 0.82 to 0.99 when recognizing the cardiotoxicity of newly developed drugs, which are represented by drugs that are not used during model training. Furthermore, we explore in depth to compare the performance of CNN with other traditional machine learning methods. The PSRCNN method can achieve higher accuracies when compared to other methods. The PSRCNN‐based biosensing platform is highly potential in improving the efficiency and accuracy of high‐throughput screening of newly developed drugs for cardiotoxicity during drug discovery.
Health‐related quality of life is a predictor of readmission following emergency laparotomy
Authors: í Soylu Lív , Hansen Jannick Brander , Kvist Madeline , Burcharth Jakob , Kokotovic Dunja 
Published: June 2024

AbstractBackgroundHealth‐related quality of life (HRQoL) is a multidimensional concept used to examine the impact of patient‐perceived health status on quality of life. Patients' perception of illness affects outcomes in both medical and elective surgical patients; however, not much is known about how HRQoL effects outcomes in the emergency surgical setting. This study aimed to examine if patient‐reported HRQoL was a predictor of unplanned readmission after emergency laparotomy.MethodsThis study included 215 patients who underwent emergency laparotomy at the Copenhagen University Hospital, Herlev, between August 1, 2021, and July 31, 2022. Patient‐reported HRQoL was assessed with the EuroQol group EQ5D index (EQ5D5L descriptive system and EQ‐VAS). The population was followed from 0 to 180 days after discharge, and readmissions and days alive and out of hospital were registered. A Cox proportional hazard model was used to examine HRQoL and the risk of readmission within 30 and 180 days.ResultsWithin 30 days, 28.4% of patients were readmitted; within 180 days, the number accumulated to 45.1%. Low self‐evaluated HRQoL predicted 180‐day readmission and was significantly associated with fewer days out of hospital within both 90 and 180 days. Low HRQoL and discharge with rehabilitation were independent risk factors for short‐ (30‐day) and long‐term (180‐day) emergency readmission.ConclusionPatient‐perceived quality of life is an independent predictor of 180‐day readmission, and the number of days out of hospital was correlated to self‐reported HRQoL.
Using ML-Based Models in Simulation of CPPSs: A Case Study of Smart Meter Production
Authors: Hranisavljevic Nemanja , Westermann Tom , Kroke Philip , Waschkies Carsten 
Published: June 2024

AbstractSimulation models have proven successful in various CPPS tasks such as optimization, diagnosis or reconfiguration. However, creating these models is a costly process. This paper describes an approach which uses: 1) recorded data to automatically learn timed automata models of system components; and 2) manual logic based on prior knowledge that extends and enables the utilization of the learned models for simulation. Experiments in a smart meter production facility show: 1) a successful detection of a suboptimal configuration; 2) the identification of causes of a decrease in productivity; and 3) a correct assessment of possible actions after a disturbance has occurred.
Hyperon semileptonic decays in QCD sum rules
Authors: Zhang Sheng-Qi , Zhang Xuan-Heng , Qiao Cong-Feng 
Published: June 2024

Abstract
We investigate the hyperon semileptonic decays within the framework of QCD sum rules. The flavor SU(3) symmetry breaking effects are analyzed via the relevant form factors and corresponding branching fractions. Employing the z-series parameterization to capture the q2 dependence of form factors, we calculate the hyperon semileptonic decay rates and confront them with the recent experimental measurements. Moreover, we calculate as well the non-standard tensor form factors, which involve certain new physics beyond the standard model.
Tropological sigma models
Authors: Albrychiewicz Emil , Ellers Kai-Isaak , Valiente Andrés Franco , Hořava Petr 
Published: June 2024

Abstract
With the use of mathematical techniques of tropical geometry, it was shown by Mikhalkin some twenty years ago that certain Gromov-Witten invariants associated with topological quantum field theories of pseudoholomorphic maps can be computed by going to the tropical limit of the geometries in question. Here we examine this phenomenon from the physics perspective of topological quantum field theory in the path integral representation, beginning with the case of the topological sigma model before coupling it to topological gravity. We identify the tropicalization of the localization equations, investigate its geometry and symmetries, and study the theory and its observables using the standard cohomological BRST methods. We find that the worldsheet theory exhibits a nonrelativistic structure, similar to theories of the Lifshitz type. Its path-integral formulation does not require a worldsheet complex structure; instead, it is based on a worldsheet foliation structure.
How does pharmacological and toxicological knowledge evolve? A case study on hydrogen cyanide in German pharmacology and toxicology textbooks from 1878 to 2020
Authors: Ludwig Laureen , Seifert Roland 
Published: June 2024

AbstractLittle is known about how pharmacological and toxicological knowledge evolves. The aim of this study was to investigate the changes in the presentation of the poison hydrogen cyanide in sixteen German-language pharmacology and toxicology textbooks from 1878 to 2020. The categories of structure, molecular mechanism of action, occurrence, effects, resorption, areas of application, lethal dose, acute symptoms of intoxication, treatment of hydrogen cyanide poisoning, and recommended therapeutic preparations were evaluated. The knowledge on the structure, lethal dosage, and occurrence of hydrogen cyanide has remained constant. In contrast, knowledge on molecular mechanism of action and recommended preparations of the poison has changed dramatically. Until 1944, the binding of hydrogen cyanide to hemoglobin was considered the mechanism of action, whereas from 1951 onwards, the interaction of hydrogen cyanide with the Fe3+ of cytochrome oxidase was described. The number of preparations containing hydrogen cyanide decreased into obsolescence until 1951. The areas of application of hydrogen cyanide also show a change, as from 1919 onwards, mainly industrial areas of application of the poison are described instead of medical ones, and from 1951 onwards, criminalistic areas of application are also mentioned. Thus, pharmacological and toxicological knowledge develops non-linearly, molecular mechanism and uses being the most dynamic areas, whereas the knowledge about hydrogen cyanide’s chemical structure, lethal dose, and occurrence remained constant. Older pharmacology and toxicology textbooks were better than newer ones at discussing changes in scientific concepts. Pharmacology and toxicology textbooks also mostly failed to discuss the misuse of hydrogen cyanide (Zyklon B) during the Nazi regime, missing an important opportunity to showcase the ethical responsibility of pharmacology and toxicology. Thus, future pharmacology and toxicology textbooks should improve on discussing the development of pharmacological and toxicological concepts and the ethical responsibility of the discipline.
Challenges in Chemical Vapour Deposition of Graphene on Metallurgical Alloys Exemplified for NiTi Shape Memory Alloys
Authors: Fickl Bernhard , Heinzle Samuel , Gstöttenmayr Stephanie , Emri Dorian , Blazevic Filip , Artner Werner , Dipolt Christian , Eder Dominik , Bayer Bernhard C. 
Published: June 2024

AbstractAtomically-thin two-dimensional (2D) materials like graphene have been suggested as ultimately thin corrosion barriers and functional coatings for modern metallurgical alloys. The challenges of chemical vapour deposition (CVD) of such 2D materials, particularly graphene, on modern metallurgical alloys are discussed and reviewed here, focusing on the key problems with the metallurgical alloys’ often limited catalytic activity towards 2D materials growth and the key need to preserve the metallurgical alloys’ bulk properties during the high temperature 2D materials CVD processes. Using graphene CVD on NiTi (Nitinol) shape memory alloys as a case study, we illustrate the constraints arising from low catalytic activity and tendency to form oxides due the Ti in the NiTi alloy in terms of graphene growth results. We show that, by using a scalable low-temperature CVD process at 650 to 750 °C, we can deposit fully covering carbon films on the NiTi, albeit at limited structural quality. Notably, we also demonstrate that our CVD process does not degrade the bulk microstructure of the NiTi during carbon deposition and, importantly, leaves the crystallographic shape memory effect evolution intact. This underscores the potential of CVD for depositing graphene films on NiTi alloys while emphasizing the necessity for further exploration of CVD conditions to achieve high-quality graphene deposits akin to those on prior widely investigated dedicated (often sacrificial) high-purity metal substrates such as Ni.
With me it is exactly the same: second stories and their argumentative function in child talk
Authors: Wala Joanna , Hannken-Illjes Kati , Bose Ines , Kurtenbach Stephanie 
Published: June 2024

AbstractConversation circles in kindergartens can foster discursive abilities such as argumentation. This paper analyzes argumentation in conversation circles in a kindergarten with respect to the function of narrative argumentation. We focus on second stories as a specific narrative form that is characterized by relating in content and form to a before told narrative thereby displaying similarity and alignment. In this paper, we will analyze videographed conversation circles in a kindergarten. Our methodological approach is interactional and qualitative, informed by conversation analysis and narrative analysis. We will argue that preschool children use second stories for argumentative purposes in cooperative argumentative exchanges and through them produce interactional alignment.
Concordance of HIV Prevention Advocacy Reports and its Associations with HIV Protective Behaviors
Authors: Malika Nipher , Green Harold D. , Bogart Laura M. , Matovu Joseph K.B. , Klein David J. , Okoboi Steven , Gwokyalya Violet , Ninsiima Susan , Wagner Glenn J. 
Published: June 2024

AbstractPeer advocacy can promote HIV protective behaviors, but little is known about the concordance on prevention advocacy(PA) reports between people living with HIV(PLWH) and their social network members. We examined prevalence and correlates of such concordance, and its association with the targeted HIV protective behavior of the social network member. Data were analyzed from 193 PLWH(index participants) and their 599 social network members(alters). Kappa statistics measured concordance between index and alter reports of PA in the past 3 months. Logistic and multinomial regressions evaluated the relationship between advocacy concordance and alter condom use and HIV testing behavior and correlates of PA concordance. Advocacy concordance was observed in 0.3% of index-alter dyads for PrEP discussion, 9% for condom use, 18% for HIV testing, 26% for care engagement, and 49% for antiretroviral use discussions. Fewer indexes reported condom use(23.5% vs. 28.1%;$${ \chi }^{2}$$=3.7, p=0.05) and HIV testing(30.5% vs. 50.5%; $${\chi }^{2}$$=25.3, p<0.001) PA occurring. Condom advocacy concordance was higher if the index and alter were romantic partners(OR=3.50; p=0.02), and lower if the index was 10 years younger than the alter(OR=0.23; p = 0.02). Alters had higher odds of using condoms with their main partner when both reported condom advocacy compared to dyads where neither reported advocacy(OR=3.90; p<0.001) and compared to dyads where only the index reported such advocacy(OR = 3.71; p=0.01). Age difference and relationship status impact advocacy agreement, and concordant perceptions of advocacy are linked to increased HIV protective behaviors. Alters’ perceptions may be crucial for behavior change, informing strategies for improving advocacy.
Robust and privacy-preserving collaborative training: a comprehensive survey
Authors: Yang Fei , Zhang Xu , Guo Shangwei , Chen Daiyuan , Gan Yan , Xiang Tao , Liu Yang 
Published: June 2024

AbstractIncreasing numbers of artificial intelligence systems are employing collaborative machine learning techniques, such as federated learning, to build a shared powerful deep model among participants, while keeping their training data locally. However, concerns about integrity and privacy in such systems have significantly hindered the use of collaborative learning systems. Therefore, numerous efforts have been presented to preserve the model’s integrity and reduce the privacy leakage of training data throughout the training phase of various collaborative learning systems. This survey seeks to provide a systematic and comprehensive evaluation of security and privacy studies in collaborative training, in contrast to prior surveys that only focus on one single collaborative learning system. Our survey begins with an overview of collaborative learning systems from various perspectives. Then, we systematically summarize the integrity and privacy risks of collaborative learning systems. In particular, we describe state-of-the-art integrity attacks (e.g., Byzantine, backdoor, and adversarial attacks) and privacy attacks (e.g., membership, property, and sample inference attacks), as well as the associated countermeasures. We additionally provide an analysis of open problems to motivate possible future studies.
Environmental Racism and Climate (In)Justice in the Anthropocene: Addressing the Silences and Erasures in Management and Organization Studies
Authors: Ergene Seray , Banerjee Subhabrata Bobby , Ergene Erim 
Published: June 2024

AbstractIn this paper, we are situated in postcolonial, decolonial, and feminist epistemologies to study environmental racism in the Anthropocene—a new geological epoch where human activity has changed the functioning of the earth. Drawing from critiques of the Anthropocene, the concept of racial capitalism, as well as environmental justice and racism scholarship, we show how proposed solutions to the climate crisis overlook and may even exacerbate racial injustices faced by communities of color. We contend that a climate justice agenda that is grounded on racial justice is necessary for our scholarship to develop a racially just management and organization studies (MOS). To accomplish this agenda, we propose three shifts: from studying elite institutions to researching grassroots organizations concerned with climate and racial justice, from uncritical endorsement of global technologies to studying local adaptation by communities of color, and from offering decontextualized climate solutions to unraveling racial histories that can help us address racial and climate injustices. We discuss the implications of these shifts for management research and education and argue that MOS cannot afford to ignore climate justice and racial justice—they are both inextricably linked, and one cannot be achieved without the other.
Socially Engaged Art Approaches to CSCW with Young People in Rurban Communities
Authors: Murray Maria , Slingerland Geertje , Pantidi Nadia , McCarthy John 
Published: June 2024

Abstract
The rapidly expanding rural community (often called rurban) is a new place for CSCW with unique sociogeographic characteristics that give rise to the need for adapted participatory practices. Socially Engaged Art (SEA) offers pluralistic and critical approaches to participative rurban CSCW to meet this need. This paper provides a case study of SEA-informed CSCW in an Irish rurban community. An online digital art summer school was delivered to young residents of Northrock using freely available digital collaboration and creation tools. Young people in rurban communities are navigating personal, social and political issues in a complex and evolving environment. In this summer school, SEA was applied to explore these issues through the creation and sharing of digital art on participant experiences and hopes for the future. The summer school hoped to promote critical thinking, confrontational dialogue and greater mutual understanding. We found that rapid creation and critique of a range of digital art expressions of social issues accessed nuanced and contradictory experiences, bringing them into dialogue with each other while supporting mutual understanding and new perspectives on rurban place and identity as they evolve. We propose integrating SEA into CSCW with young people in liminal and transitional communities such as the rurban to explore complex lived experiences in pursuit of more equitable futures and sustainable community expansion. We also draw attention to the usefulness of readily available digital and online tools in supporting CSCW in creative workshop situations.
Robust explainer recommendation for time series classification
Authors: Nguyen Thu Trang , Le Nguyen Thach , Ifrim Georgiana 
Published: June 2024

AbstractTime series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition, sports analytics and general sensing. In this area, interest in explanability has been growing as explanation is key to understand the data and the model better. Recently, a great variety of techniques (e.g., LIME, SHAP, CAM) have been proposed and adapted for time series to provide explanation in the form of saliency maps, where the importance of each data point in the time series is quantified with a numerical value. However, the saliency maps can and often disagree, so it is unclear which one to use. This paper provides a novel framework to quantitatively evaluate and rank explanation methods for time series classification. We show how to robustly evaluate the informativeness of a given explanation method (i.e., relevance for the classification task), and how to compare explanations side-by-side. The goal is to recommend the best explainer for a given time series classification dataset. We propose AMEE, a Model-Agnostic Explanation Evaluation framework, for recommending saliency-based explanations for time series classification. In this approach, data perturbation is added to the input time series guided by each explanation. Our results show that perturbing discriminative parts of the time series leads to significant changes in classification accuracy, which can be used to evaluate each explanation. To be robust to different types of perturbations and different types of classifiers, we aggregate the accuracy loss across perturbations and classifiers. This novel approach allows us to recommend the best explainer among a set of different explainers, including random and oracle explainers. We provide a quantitative and qualitative analysis for synthetic datasets, a variety of time-series datasets, as well as a real-world case study with known expert ground truth.
Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction
Authors: Hadadi Fatemeh , Dawes Joshua H. , Shin Donghwan , Bianculli Domenico , Briand Lionel 
Published: June 2024

AbstractWith the increasing complexity and scope of software systems, their dependability is crucial. The analysis of log data recorded during system execution can enable engineers to automatically predict failures at run time. Several Machine Learning (ML) techniques, including traditional ML and Deep Learning (DL), have been proposed to automate such tasks. However, current empirical studies are limited in terms of covering all main DL types—Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and transformer—as well as examining them on a wide range of diverse datasets. In this paper, we aim to address these issues by systematically investigating the combination of log data embedding strategies and DL types for failure prediction. To that end, we propose a modular architecture to accommodate various configurations of embedding strategies and DL-based encoders. To further investigate how dataset characteristics such as dataset size and failure percentage affect model accuracy, we synthesised 360 datasets, with varying characteristics, for three distinct system behavioural models, based on a systematic and automated generation approach. Using the F1 score metric, our results show that the best overall performing configuration is a CNN-based encoder with Logkey2vec. Additionally, we provide specific dataset conditions, namely a dataset size $$>350$$

>
350

or a failure percentage $$>7.5\%$$

>
7.5
%

, under which this configuration demonstrates high accuracy for failure prediction.