bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction
Authors: Salehin Mohammad Musfequs 
Published: July 2024

AbstractGendered vulnerabilities and violence are intrinsically linked with forced migration. This chapter introduces the book’s main aim, which is to examine the gendered dynamics of violence experienced by the Rohingya people, first during the conflict in the Rakhine State of Myanmar, then after their forced migration to Bangladesh. It provides a brief overview of the context and the imperative to investigate the gendered dimension of Rohingya forced migration, emphasising the nature and extent of gendered vulnerabilities and violence, both in the country of origin and in the country of destination, as a continuous process. Additionally, it delineates the methodology employed in the research conducted for this book.
Gender, Violence, and Vulnerabilities in Forced Migration: A Multi-dimensional Approach
Authors: Salehin Mohammad Musfequs 
Published: July 2024

AbstractViolence is intertwined with a gendered logic, encompassing physical, sexual, structural, cultural, and symbolic dimensions. However, this association becomes even more complex within the context of refugees and forced migration. This chapter delves into the conceptual and theoretical framework surrounding multidimensional gendered vulnerabilities and violence in the context of forced migration. It explores key concepts integral to my discussion, including Galtung’s conceptualisations of violence (direct, structural, and cultural violence), Bourdieu’s theory of symbolic violence, and sexual and gender-based violence (SGBV) and vulnerabilities. Additionally, this chapter examines intersectionality and the continuum of violence framework to deepen our understanding of gendered vulnerabilities and violence in forced migration contexts.
Sequence-based Learning
Authors: Loeffler Christoffer , Ott Felix , Ott Jonathan , Oppelt Maximilian P. , Feigl Tobias 
Published: July 2024

AbstractLearning from time series data is an essential component in the AI landscape given the ubiquitous time-dependent data in real-world applications. To motivate the necessity of learning from time series data, we first introduce different applications, data sources, and properties. These can be as diverse as irregular and (non-)continuous time series data as well as streaming and spatio-temporal data. To introduce the mechanics of learning from time series data, we elaborate on the most renowned convolutional, recurrent and transformer architectures for learning from time series. Then, we discuss essential characteristics of learning with time series. Therefore, we explain deep metric learning, which learns feature representations that capture the similarity between time series data.We further describe time series similarity learning to extract representations that allow comparison between sequences of spatio-temporal data. In addition, we discuss the interpretability of learning methods on time series data that target safety, non-discrimination, and fairness.
Learning from Experience
Authors: Mutschler Christopher , Kontes Georgios , Rietsch Sebastian 
Published: July 2024

AbstractReinforcement Learning (RL) is one of the branches of Machine Learning (ML) that aims to learn from the interaction with an environment. In contrast to approaches such as supervised or unsupervised learning, where data samples usually are assigned to a ground truth label (supervised learning) or where they follow some stationary distribution (unsupervised learning), in RL, the agent is learning in direct interaction with the environment. This also defines what data is being collected as a result of which actions are being executed. The agent is hence learning from experience. While more traditionally, RL was focused purely on continuously arriving data, lately also approaches that resort to a given data pool of past environment interactions have gained more and more interest. This chapter covers the basics of RL and discusses the latest research in interactive environments, learning with available data or knowledge, and challenges that arise from the actual deployment of agents to the real world.
Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications
Authors: Wittenberg Thomas , Lang Thomas , Eixelberger Thomas , Gruber Roland 
Published: July 2024

AbstractFor the development, training, and validation of machine learning (ML) and deep learning (DL) based methods, such as, e.g., image analysis, prediction of critical events, extraction or reconstruction of information from disrupted data streams, searching similarities in data collections, or planning of procedures, a lot of data is needed. Additionally to this data (images, bio-signals, vital-signs, text records, machine states, trajectories, antenna data, ...) adequate supplementary information about the meaning encoded in the data is required. Only with this additional information – the meaning or knowledge – a tight relation between the raw data and the human-understandable concepts – the semantics – from the real world can be established. Nevertheless, as the amount of data needed to develop robust ML or DL methods is strongly increasing, the assessment and acquisition of the related knowledge becomes more and more challenging. Within this chapter, an overview of concepts of knowledge acquisition applied to the different examples of applications is described and evaluated. Six main groups of knowledge acquisition related to AI-based technologies have been identified, namely (1) manual annotation methods, (2) data augmentation, (3) generative networks or simulation techniques, (4) synchronized sensors, (5)Active Learning approaches, and (6) explicit knowledge modeling using semantic networks.
Das Mathematische Forschungsinstitut Oberwolfach: Vom „Reichsinstitut für Mathematik“ zur internationalen „sozialen Forschungsinfrastruktur“ (1944–1960er Jahre)
Authors: Remmert Volker 
Published: July 2024

ZusammenfassungDas Mathematische Forschungsinstitut Oberwolfach (MFO) ist seit 2005 Mitglied der Leibniz-Gemeinschaft und genießt international höchstes Ansehen. Es wurde im Herbst 1944 gegründet als „Reichsinstitut für Mathematik“, dessen Aufgaben sehr umfassend waren, aber völlig im zeitgenössischen institutionellen Rahmen blieben. In den 1950er und 1960er Jahren entwickelte das MFO sich zu einem Tagungszentrum, das zunehmend auch international ausstrahlte, d. h. hin zu einer „sozialen Forschungsinfrastruktur“ wie sie in der deutschen Forschungslandschaft der 1950er und 1960er Jahre völlig neu war. Dieser Wandel der institutionellen Identität des MFO zwischen 1944 und dem Beginn der 1960er Jahre lässt sich an den neuen Organisationsformen des Forschungsbetriebs am MFO ablesen, d. h. insbesondere an der Entwicklung und Bedeutung des wissenschaftlichen Programms des MFO und der verwendeten Forschungsinstrumente. Die neuen Formen des Forschungsbetriebs gingen einher mit einer komplexen Finanzierungsproblematik: einerseits bedingt durch die wegfallende Finanzierung bei Kriegsende, andererseits dadurch befördert, dass das MFO keinen kanonischen Platz in der gängigen Praxis der Forschungsförderung hatte. Im Fokus des Beitrags stehen die Strategien, die bis Mitte der 1960er Jahre entwickelt wurden, um die materielle Existenz des MFO zu sichern (frz. Militärregierung, Landesministerien, Bundesministerien, DFG und MPG, Thyssen und Volkswagen Stiftung). Im Jahr 1963 war die institutionelle Sicherung des MFO schließlich weitgehend abgeschlossen.
Modelling green knowledge production and environmental policies with semiparametric panel data regression models
Authors: Musolesi Antonio , Golinelli Davide , Mazzanti Massimiliano 
Published: July 2024

Abstract
This paper introduces a novel semiparametric econometric framework for policy evaluation and estimates a green knowledge production function for a large, 30-year panel dataset of high-income countries. Due to the substantial uncertainty in the data-generating process and the potential presence of nonlinearities and latent common factors, the paper explores semiparametric panel specifications that go beyond interactive fixed effects fully parametric models. The findings suggest that (i) the semiparametric additive specification with individual time trends is the preferred model, (ii) threshold effects and nonlinearities are salient features of the data that parametric specifications fail to capture, and (iii) the impact of environmental policy is noteworthy and exhibits clear heterogeneity when modelled as a nonparametric function of specific knowledge inputs. The evidence reveals a significant nonlinear policy inducement effect stemming from R&D investments.
An approximation algorithm for multiobjective mixed-integer convex optimization
Authors: Lammel Ina , Küfer Karl-Heinz , Süss Philipp 
Published: July 2024

AbstractIn this article we introduce an algorithm that approximates the nondominated sets of multiobjective mixed-integer convex optimization problems. The algorithm constructs an inner and outer approximation of the front exploiting the convexity of the patches for problems with an arbitrary number of criteria. In the algorithm, the problem is decomposed into patches, which are multiobjective convex problems, by fixing the integer assignments. The patch problems are solved using (simplicial) Sandwiching. We identify parts of patches that are dominated by other patches and ensure that these patch parts are not refined further. We prove that the algorithm converges and show a bound on the reduction of the approximation error in the course of the algorithm. We illustrate the behaviour of our algorithm using some numerical examples and compare its performance to an algorithm from literature.
Chronobiological variables predict non-response to serotonin and noradrenaline reuptake inhibitors in fibromyalgia: a cross-sectional study
Authors: Krupa Anna J. , Chrobak Adrian A. , Sołtys Zbigniew , Korkosz Mariusz , Nowakowski Jarosław , Dudek Dominika , Siwek Marcin 
Published: July 2024

AbstractAvailable data shows associations between chronotype, circadian rhythms, sleep quality and fibromyalgia (FM) presentation. However, no studies have explored links between the chronobiological variables and effectiveness of pharmacotherapy. We aimed to assess the chronotypes, circadian rhythms, sleep-wake cycle and sleep quality in FM and their links to treatment response to serotonin and noradrenalin reuptake inhibitors (SNRI). 60 FM patients: 30 responsive to SNRI (FM T[+]), 30 non-responsive to SNRI (FM T[-]) and 30 healthy controls participated. Subjects were assessed by physician and with questionnaire tools: Composite Scale of Morningness, Biological Rhythms Interview of Assessment in Neuropsychiatry, Sleep-Wake Pattern Assessment Questionnaire, Pittsburgh Sleep Quality Index and Fibromyalgia Impact Questionnaire. ANOVA analysis and simple logistic regressions were used to examine the relationships between chronological variables and response to SNRI. FM T[-] vs. FM T[+] presented lower morning affect (11.50[95%CI 9.96-13.04] vs. 14.00[95%CI 12.42-15.57];p=0.04), anytime wakeability (2.27[95%CI 1.4-3.13] vs. 4.03[95%CI 2.99-5.08];p=0.013) worse overall (11.40[95%CI 9.92-12.88] vs. 7.97[95%CI 6.75-9.19];p=0.002) and subjective (1.70[95%CI 1.30-2.01] vs. 1.17[95%CI 0.94-1.39];p=0.008) sleep quality, higher circadian rhythm disruptions (55.47[95%CI 52.32-58.62] vs. 44.97[95%CI 41.31-48.62];p<0.001), sleep disturbances (1.63[95%CI 1.38-1.68] vs. 1.30[95%CI 1.1-1.5];p=0.04), sleeping-medication use (1.80[95%CI 1.27-2.32] vs. 0.70[95%CI 0.28-1.12];p=0.003). Levels of morningness (AIC=82.91,OR=0.93,p=0.05), morning affect (AIC=81.901,OR=0.86,p=0.03) diurnal dysrhythmia (AIC=69.566,OR=1.14,p<0.001), anytime wakeability (AIC=80.307,OR=0.76,p=0.015), overall sleep quality (AIC=74.665, OR=1.31,p=0.002) subjective sleep quality (AIC=79.353, OR=2.832,p=0.01) and disturbances (AIC=82.669,OR=2.54,p=0.043), sleep medication use (AIC=77.017, OR=1.9,p=0.003) and daytime disfunction (AIC=82.908, OR=1.971,p=0.049) were predictors of non-response to SNRI. Chronobiological variables vary between FM T[+] and FM T[-] and are predictors of non-response to SNRI.
Diverse and ecologically unique mesophotic coral ecosystems in the central Indian Ocean
Authors: Diaz C. , Howell K. L. , Hosegood P. , Bolton A. , Foster N. L. 
Published: July 2024

AbstractResearch on Mesophotic Coral Ecosystems (MCEs; 30–150 m) has grown exponentially in the last few decades, highlighting their rich diversity and extensive distribution. However, they are still largely underexplored compared to shallow-water coral reefs and frequently remain under-protected and under-represented in marine spatial planning. One reason for the imbalance between the high ecological value of MCEs and the limited levels of protection may be that baseline data on MCEs are largely missing to date, yet are crucial to provide evidence-based information for management actions. Here, we present data on the alpha and beta diversity of the benthic communities within MCEs in the Chagos Archipelago, Indian Ocean. Using imagery collected from Remotely Operated Vehicle surveys, benthic invertebrate megafauna were surveyed along the entire depth gradient from shallow to lower mesophotic depths (15–160 m). The diversity of the benthic communities decreased with increasing depth, from shallow water to the lower mesophotic zone. Nevertheless, the deepest parts of MCEs in the Archipelago displayed higher species richness values than several other shallow subtropical regions. In addition, the benthic communities showed high dissimilarity along the depth gradient, indicating that the key driver of community composition change with depth is species turnover (species replacement), revealing the uniqueness of MCEs. This study presents novel findings on MCEs in the central Indian Ocean, demonstrating that they host a high and unique benthic diversity, and highlighting the need to protect these ecosystems to preserve the overall biodiversity of coral reefs.
Comparison of outcomes in small bowel surgery for Crohn’s disease: a retrospective NSQIP review
Authors: Aras Oguz AZ. , Patel Apar S. , Satchell Emma K. , Serniak Nicholas J. , Byrne Raphael M. , Cagir Burt 
Published: July 2024

Abstract
Introduction
Despite advances in medical therapy, approximately 33% of Crohn’s disease (CD) patients will need surgery within 5 years after initial diagnosis. Several surgical approaches to CD have been proposed including small bowel resection, strictureplasty, and combined surgery with resection plus strictureplasty. Here, we utilize the American College of Surgeons (ACS) national surgical quality registry (NSQIP) to perform a comprehensive analysis of 30-day outcomes between these three surgical approaches for CD.

Methods
The authors queried the ACS-NSQIP database between 2015 and 2020 for all patients undergoing open or laparoscopic resection of small bowel or strictureplasty for CD using CPT and IC-CM 10. Outcomes of interest included length of stay, discharge disposition, wound complications, 30-day related readmission, and reoperation.

Results
A total of 2578 patients were identified; 87% of patients underwent small bowel resection, 5% resection with strictureplasty, and 8% strictureplasty alone. Resection plus strictureplasty (combined surgery) was associated with the longest operative time (p = 0.002). Patients undergoing small bowel resection had the longest length of hospital stay (p = 0.030) and the highest incidence of superficial/deep wound infection (44%, p = 0.003) as well as the highest incidence of sepsis (3.5%, p = 0.03). Small bowel resection was found to be associated with higher odds of wound complication compared to combined surgery (OR 2.09, p = 0.024) and strictureplasty (1.9, p = 0.005).

Conclusion
Our study shows that various surgical approaches for CD are associated with comparable outcomes in 30-day related reoperation and readmission, or disposition following surgery between all three surgical approaches. However, small bowel resection displayed higher odds of developing post-operative wound complications.
Regulatory T cells inhibit FoxP3 to increase the population of tumor initiating cells in hepatocellular carcinoma
Authors: Liu Chang , Tu Yi-jun , Cai Hong-Yang , Pan Yan-yan , Wu Yuan-yuan , Zhang Li 
Published: July 2024

Abstract
Purpose
Tumor initiating cells (TICs) or cancer stem cells (CSCs) are considered to be the main culprit of hepatocellular carcinoma (HCC) initiation and progression, nevertheless the mechanism by which tumor microenvironment maintains the HCC ‘stemness’ is not fully understood. This study aims to investigate the effect of regulatory T cells (Tregs) on the TICs characteristics of HCC.

Methods
Immunocytochemistry, flow cytometry, real-time PCR, western blot, in vitro sphere-formation, and in vivo tumorigenesis assay were used to detect HCC ‘stemness’. Additionally, after forced expression or inhibition of FoxP3, β-catenin expression and HCC ‘stemness’ were investigated.

Results
Tregs enhanced the ‘stemness’ of HCC cells by upregulating TIC-related markers CD133, Oct3/4, Sox2, c-Myc, Klf4, Nanog, CD13, EpCAM, and inducting epithelial to mesenchymal transition (EMT), increasing TICs ratio, as well as promoting tumorigenic ability. Moreover, β-catenin and c-Myc were upregulated in HCC cells after co-cultured with Tregs. HCC ‘stemness’ was inhibited after treatment with Wnt/β-catenin pathway inhibitor. Furthermore, forced expression of FoxP3 resulted in increased GSK3β, decreased β-catenin and TIC ratio in HCC. In contrast, FoxP3 interference reduced GSK3β, enhanced β-catenin and TIC ratio of HCC.

Conclusion
This study, for the first time, demonstrated that Tregs increased the population of TICs in HCC by inhibiting FoxP3 as well as promoting β-catenin expression.
„Diagnostics beyond bacteria“ – Wundabstriche zur Analyse biomolekularer Krankheitsmuster in chronischen Wunden
Authors: Rembe Julian-Dario , Garabet Waseem , Lackmann Jan-Wilm , Augustin Matthias , Dissemond Joachim , Scharf Sebastian Alexander , Rommerskirchen Anna , Wienemann Tobias , Ibing Wiebke , Schelzig Hubert , Stuermer Ewa K. 
Published: July 2024

Zusammenfassung
Hintergrund
Patientenindividuelle Therapieansätze sind in der Versorgung von Menschen mit chronischen Wunden wichtig. Die Heterogenität zugrunde liegender Krankheitsprofile ebenso wie die Diversität des Wundmilieus erschweren generalisierbare Ansätze. Während molekulare Analyseverfahren wie Hochdurchsatzverfahren (OMICS) zunehmend verbreiteter und besser verfügbar sind, hat die Analyse von objektiven biomolekularen Krankheitsmustern weitestgehend keinen Eingang in die alltägliche Diagnostik und Therapiesteuerung gefunden.

Fragestellung
Die Nutzung von Wundabstrichen für die Analyse von Biomarkern und Krankheitsmustern in kutanen Wunden wurde evaluiert.

Material und Methoden
Es erfolgte die Analyse einer Probekohorte aus dem multizentrischen „Wound-BIOME“-Projekt. Das Projekt zielt auf die Erforschung des lokalen Wundmilieus verschiedener Entitäten, Heilungstendenzen und Regenerationsstadien auf biomolekularer Ebene ab. Hierzu wurde ein alltagstaugliches Protokoll zur Probengewinnung und -handhabung entwickelt und erprobt. Des Weiteren wurden Machbarkeitsanalysen für Multiplex-Immunoassays, Proteomics und Metagenomanalysen (Mikrobiom) durchgeführt.

Ergebnisse
Trotz Heterogenität zwischen den Proben konnten ausreichende Mengen Protein und mikrobielle DNA mit adäquater Qualität extrahiert werden, um biomolekulare Muster zu analysieren. Erste Analysen von deskriptiven Proteinsignaturen und mikrobiellen Taxonomie zeigen vielversprechende Ansätze für zukünftige individuelle, objektive Diagnostik und gezielte therapeutische Interventionen.

Schlussfolgerung
Standardisierte Wundabstriche zeigen sich insgesamt gut geeignet, um Probenmaterial für die Analyse des komplexen Wundmilieus mittels verschiedener Hochdurchsatzverfahren zu gewinnen.
Peer victimization in early adolescence and maladjustment in adulthood
Authors: Kretschmer Tina , van der Ploeg Rozemarijn , Kaufman Tessa 
Published: July 2024

AbstractStudies report different effect sizes for associations between peer victimization and later maladjustment. A possible origin of this heterogeneity is the length of the interval between victimization and maladjustment assessments. Effect sizes might also vary as a function of reporter. Longitudinal data from TRAILS, a study of adolescents followed from age 11 to 29 (baseline n = 2229) were used to test whether peer victimization assessed from adolescents themselves, their parents, teachers, and peers predicted internalizing symptoms, thought problems, and somatic complaints at six follow-ups with a temporal distance of up to 19 years. Neither self- nor peer-reported victimization predicted later maladjustment. In contrast, parent-reported victimization stably predicted adult maladjustment. Teacher-reported victimization also predicted maladjustment but associations were weaker and largely non-significant when parent reports were accounted for simultaneously. Parent-reported peer victimization has traditionally played a minor role in bullying research as parents are usually not present when victimization occurs. The results of this study however suggest that parents should be listened to when talking about their offspring being victimized.
Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis
Authors: Alnaggar Omar Abdullah Murshed Farhan , Jagadale Basavaraj N. , Saif Mufeed Ahmed Naji , Ghaleb Osamah A. M. , Ahmed Ammar A. Q. , Aqlan Hesham Abdo Ahmed , Al-Ariki Hasib Daowd Esmail 
Published: July 2024

AbstractIn healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs.