bluuebunny/crossref_metadata_2025_split huggingface dataset:
Green public procurement as an effective way for sustainable development: A systematic literature review and bibliometric analysis
Authors: Ortega Carrasco Pablo , Iannone Fabio , Ferrón Vílchez Vera , Testa Francesco 
Published: October 2024

AbstractGreen public procurement (GPP) has emerged as a strategic tool for sustainable development. However, the drivers and barriers to effective GPP implementation remain unclear. Additionally, the growing volume of GPP research necessitates interlinking with sustainability concepts, such as sustainable development, to guide future research. This paper presents a systematic literature review and bibliometric analysis of GPP research between 2003 and 2023 to identify key themes, trends and research gaps. A dataset of 201 documents retrieved from the Web of Science database was analysed using CitNetExplorer and VOSViewer. By analysing prior literature on the effect of GPP on the achievement of sustainable development, we have found, among others concerns, that: (1) richer qualitative analysis on GPP is needed; (2) there is a gap in understanding localised factors influencing GPP in developing countries; and (3) the transformative role of digitalisation in promoting social and environmental outcomes in GPP is an essential factor. These findings will inform policymakers, practitioners and researchers on the effective implementation of GPP for sustainable development.
Empowering marginalised communities: Leveraging indigenous knowledge for sustainable energy development in South Africa
Authors: Mawere Joshua , Mukonza Ricky Munyaradzi 
Published: October 2024

AbstractAmidst South Africa's volatile energy landscape—characterised by political turbulence, economic instability and Eskom's chronic financial and operational challenges—the imperative for transformative reform is both urgent and indispensable. Municipalities are confronted with deteriorating infrastructure, recurrent power disruptions and widespread electricity theft, which collectively exacerbate the national energy crisis. This paper investigates the integration of indigenous knowledge systems within the country's energy sector as a strategic response to these persistent challenges, promoting the empowerment of marginalised communities and advancing environmental sustainability. Anchored in the theoretical frameworks of environmental justice and indigenous rights, the paper advocates for Afrocentric paradigms such as traditional resource governance and community‐driven energy solutions. Employing thematic analysis of secondary data, it evaluates the viability and prospective benefits of this paradigm shift. The paper posits that the inclusion of indigenous knowledge in energy practices not only presents tangible solutions to the energy crisis but also cultivates socio‐economic empowerment and reinforces environmental stewardship.
From sustainable development towards resilience: Does digital finance matter in enhancing ecological resilience?
Authors: Jiang Wei , Jiang Nana 
Published: October 2024

AbstractDigital finance, as a representative of the digital economy, provides unprecedented opportunities for enhancing the adaptability and resilience of ecosystems. However, there is currently no literature that integrates digital finance and urban ecological resilience into the same research framework. In light of this, we utilize panel data from 2011 to 2019 to investigate the impact of digital finance on ecological resilience. Our results indicate that (1) Digital finance can effectively enhance ecological resilience, a conclusion corroborated by both theoretical frameworks and empirical evidence. Specifically, the coverage breadth and usage depth of digital finance can effectively enhance ecological resilience, while the digitization level has not shown a significant impact. (2) Green technological innovation, improvements in energy efficiency, and the agglomeration of producer services are identified as effective channels through which digital finance promotes ecological resilience. (3) The impact of digital finance on ecological resilience exhibits significant regional heterogeneity, with more pronounced effects observed in cities with advanced traditional financial systems, non‐resource‐based cities, and central cities. These findings will help policymakers better understand the relationship between digital finance and urban ecological resilience, and formulate targeted policies that leverage digital finance development to enhance ecological resilience.
A Hybrid Machine Learning‐Based Data‐Centric Cybersecurity Detection in the 5G‐Enabled IoT
Authors: Zeng Lingcheng , An Yunzhu , Zhou Heng , Luo Qifeng , Lin Yuede , Zhang Zhiqiang 
Published: October 2024

ABSTRACTBy increasing smart objects as high‐potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data‐centric infrastructure for detecting safe device‐to‐device communications by supporting security and privacy issues. As a 5G‐enabled communication technology, vehicle‐to‐vehicle (V2V) communication provides a wireless exchange information connection between smart vehicles, intelligent devices, and a cloud‐edge computing environment. This connection should be established with a safe and secured run‐time protection system to avoid many critical anomalies and misbehavior problems. Detecting run‐time malicious transformations with data‐centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G‐enabled communication technology. This paper provides a hybrid genetic algorithm‐based ensemble bagged trees (GA‐EBT) algorithm for a data‐centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. In this hybrid algorithm, GA provides a beneficial performance on the training accuracy factor based on a single‐objective fitness function and EBT supports a high‐degree prediction and training process using multiple decision tree models with bootstrapped samples of the training data. For the evaluation of the proposed algorithm, four real test cases are applied for messaging injection attacks in the V2V environments in comparison to the state‐of‐the‐art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve an optimal high rate accuracy factor of 99.999, precision and recall factors of 100%, and an F1‐Score factor of 100% to detect unexpected cyber‐attacks for the V2V communications in the IoT environment.
An Efficient Algorithm for Exact Segmentation of Large Compositional and Categorical Time Series
Authors: Truong Charles , Runge Vincent 
Published: October 2024

ABSTRACTChange‐point detection, also known as signal segmentation, is an essential preprocessing step in many applications, ranging from industrial monitoring to bioinformatics. In short, it consists in finding the temporal boundaries of homogeneous regimes in long and non‐stationary time series. While this area of research is active, most existing methods are designed for Euclidean data. However, in many practical scenarios, the collected time series are compositional, meaning that each observation belongs to the probability simplex (the set of non‐negative vectors whose components sum to one). In this work, we propose an algorithm detecting change‐points in large compositional signals with an underlying piecewise stationary model. We cast the change‐point detection task as a discrete optimization problem, whose solution is shown to converge to the true change‐points. We introduce a new and time‐efficient dynamic programming algorithm that solves exactly this problem. To limit the number of operations, we describe a novel pruning rule that allows us to reduce the set of candidate change‐point indices. Our method is tested on a thorough simulation study, which confirms its efficiency. Additionally, we apply our method to a human activity segmentation task, highlighting the necessity for such novel techniques compared to standard algorithms.
Clinicopathological, Histopathological, and Immunohistochemical Analysis of Mandibular Fibrosarcoma in a DSH Cat: A Case Report
Authors: Ahmadi‐hamedani Mahmood , Moslemi Hamid Reza , Khaligh Sahar Ghaffari , Sedghi Mehdi , Sattari Kimia 
Published: October 2024

ABSTRACTA 4‐year‐old white male DSH cat was presented to the Veterinary Hospital on 18 December 2023, with a history of lethargy, loss of appetite, and salivation. During the inspection of the oral cavity, unilateral swelling was observed on the ventral side of the jaw. Before any therapeutic intervention, a cell blood count (CBC) test and FNA cytology were conducted. A five‐day course of adjunctive treatment, including ceftriaxone (5.5 mg/kg via IV) and clindamycin (25 mg/kg via IV), was given due to a suspicion of infection. Pantoprazole, metoclopramide (administered at 1 mg/kg via IV and IM), and duphalyte 500 mL (10 mL/kg IV) were used concurrently to alleviate nausea and stimulate appetite. Following a lack of improvement, a radical excision procedure was performed on tooth number 304 after the 5‐day treatment to excise the mass for histopathology and immunohistochemical analysis. The observation of mitotic bodies, pleomorphism, necrosis, and haemorrhage were consistent with malignancy, and squamous cell carcinoma (SCC) was suspected. Immunohistochemical staining was positive for the vimentin marker, the S‐100 protein, and desmin. This report describes a rare case of oral fibrosarcoma in a DSH cat in Iran.
Towards a Multi-dimensional Health Data Analysis Framework
Authors: Rabbi Fazle , Fatemi Bahareh , Mukhiya Suresh Kumar , Lamo Yngve 
Published: October 2024

AbstractHealthcare processes need to be streamlined to offer better healthcare services. Data analysis can be crucial in reducing costs, optimizing processes, and analyzing treatment effectiveness. However, data analysis in healthcare is complex due to the variety and complexity of patient data. This paper proposes a multi-dimensional comparative analysis method that offers healthcare professionals a lens to delve into healthcare datasets from various perspectives. The paper discusses the importance of comparative analysis in healthcare illustrated by two examples on how we can understand the pattern of comorbidity and how we can analyze the effectiveness of internet delivered psychological interventions. The paper presents a multi-dimensional comparative analysis framework covering various use cases in analysing healthcare data. The framework allows healthcare professionals to compare and contrast healthcare data across multiple dimensions, including clinical dimensions such as diagnosis, outcome measures, time dimension, patient dimensions (engagement, involvement), cost dimension, and other relevant factors. This approach offers a more insightful understanding of healthcare data and facilitates informed decision-making in healthcare practices.
CRISP-PCCP – A Development Methodology Supporting FDA Approval for Machine Learning Enabled Medical Devices
Authors: Pechmann Ludwig , Potdevin Yannik , Brehmer Kai , Nowotka Dirk , Leucker Martin 
Published: October 2024

AbstractThe U.S. Food and Drug Administration (FDA) is the regulatory body that ensures the safety, efficacy, and security of medical devices and software in the healthcare sector in the U.S. However, its guidelines and regulations often set a global benchmark, influencing medical device standards in Europe and other regions. The FDA recently published a draft guidance, the Predetermined Change Control Plan (PCCP), aiming to support medical device manufacturers with the release of continual learning Machine Learning-Enabled Device Software Functions (ML-DSF). Such ML-DSFs are intended to change after initial market approval. We present a systematic process to support the implementation of the PCCP. Building upon the Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology (CRISP-ML(Q)), we present an approach that a manufacturer may use to identify and evaluate the impact of anticipated changes to ML-DSF. Our process also indicates a forecast, whether the anticipated change would be accepted by the FDA as a part of the PCCP.
From Explanation Correctness to Explanation Goodness: Only Provably Correct Explanations Can Save the World
Authors: Schwammberger Maike 
Published: October 2024

AbstractExplainability Engineering gets evermore important in the era of self-learning and automated systems. We motivate the necessity for interdisciplinary research to engineer verifiably correct and good explanations: Systems engineering research must ensure that correct and machine-understandable explanations can be derived from system specifications and social sciences research must ensure that a context-dependent and stakeholder-tailored explanation can be provided in a fitting manner. We describe our first steps in the direction of a holistic and interdisciplinary explainability engineering process for tackling these challenges.
Safeguarding AI-Based Software Development and Verification using Witnesses (Position Paper)
Authors: Beyer Dirk 
Published: October 2024

AbstractThis position paper accompanies a presentation and discussion at AISoLA 2023 on the topic of how (generative) AI influences software engineering, namely, the development and verification of software programs. We outline a few opportunities and challenges that are posed by the application of AI. AI-based techniques can be an efficient method to produce software code. Not only this, AI can also be efficient in producing invariants that help proving correctness of software programs. But unfortunately, the results generated by AI-based approaches are often still imprecise or wrong: Code produced with the help of AI often does not satisfy the specification, and AI-generated invariants are often not helpful to prove the correctness of the program. The solution is to safeguard the process by independently checking the results using verification witnesses and witness validation. The goal of this paper is to convince readers that software witnesses are important and that independent result validation is of utmost importance to ensure correctness.
Digitised Historical Sources and Non-digital Humanists: An Interdisciplinary Challenge?
Authors: Le Roux Maelle , Gasperini Anna 
Published: October 2024

AbstractThe digitisation of sources has opened new perspectives for humanities scholars. Digitisation allowed a larger access to sources, removing some financial and geographical limits, and the use of digital tools provided new perspectives for humanities scholars, who are able to read the sources differently. However, working with digitised sources also created new challenges that humanities scholars are not always equipped to overcome.The ‘MedIcal Literature and Communication about Child Health’ (MILC) project uses historical medical books for a non-specialist audience to analyse discourses on children’s health in England, France and Italy between 1850 and 1914. Despite being born a non-digital humanities project, with a focus on manual qualitative analysis and a combination of history and literature methods, it took a digital turn when using digitised sources, with issues of digitisation and Optical Character Recognition (OCR) among others. The team working on the project is composed of three humanities scholars, with limited computer science skills. This required us to find digital humanities and in general IT tools adapted to our skillset, and suited to our needs. These tools did not always fit all our needs, and often presented issues in terms of accessibility and compatibility with the general standards of digital humanities.Using examples from the issues faced by this project, and from the solutions found, this paper will argue that the challenges encountered by humanities scholars are interdisciplinary, not only because they overcome the traditional disciplinary boundaries inside the humanities, but also because they mirror challenges that computer scientists are working to solve. This paper will also argue that collaboration is a necessity which would benefit both humanities scholars and computer scientists in their work on the improvement and development of new tools, with the help of AI for example. Using the work done by a team of non-digital humanities scholars, it will argue that accessibility is a central issue in digital humanities and in the creation of IT tools, which needs to be addressed.
Studienergebnisse: Gemeinsame Faktoren eines NTE-Berichts
Authors: Nadile Stefan 
Published: October 2024

ZusammenfassungIm Folgenden werden zunächst die Faktoren bestimmt, die die verschiedenen Berichte miteinander verbindet. Diese sind vor allem kulturell bedingte Eigenheiten, die das Erzählen von mitunter unglaublich scheinenden Ereignissen erschweren, ermöglichen oder begünstigen. Zunächst werden die Hauptkategorien des aus dem Datenmaterial generierten Kategoriensystems erörtert.
Zusammenfassung und Bewertung
Authors: Nadile Stefan 
Published: October 2024

ZusammenfassungDie Studienergebnisse werden in der Folge zusammengetragen und bewertet. Dabei werden die Typen zunächst einander gegenübergestellt, Haupt- und Subkategorien typenübergreifend dargestellt sowie auf Gemeinsamkeiten und Unterschiede eingegangen. Daraufhin werden dann jeweils weitere gemeinsame, typenunspezifische Faktoren zusammenfassend eingeordnet.
Quark mass effects in Higgs production
Authors: Czakon Michał , Eschment Felix , Niggetiedt Marco , Poncelet Rene , Schellenberger Tom 
Published: October 2024

Abstract
We examine the effect of finite top- and bottom-quark masses on the Higgs production cross section in the gluon-gluon fusion channel. We employ both $$ \overline{\textrm{MS}} $$

MS
¯

and on-shell renormalisation for the quark masses and provide a thorough comparison. Furthermore, we explore alternative treatments of quark masses, in particular in the four-flavour scheme, and investigate their impact on the cross section. Our work also presents novel predictions for differential cross sections in the Higgs rapidity. The results lead to a significant reduction of scale uncertainties, and our analysis enables us to offer well-grounded recommendations for future research in this area.
Low-virtuality splitting in the Standard Model
Authors: Nardi Filippo , Ricci Lorenzo , Wulzer Andrea 
Published: October 2024

Abstract
When the available collision energy is much above the mass of the particles involved, scattering amplitudes feature kinematic configurations that are enhanced by the much lower virtuality of some intermediate particle. Such configurations generally factorise in terms of a hard scattering amplitude with exactly on-shell intermediate particle, times universal factors. In the case of real radiation emission, such factors are splitting amplitudes that describe the creation or the annihilation — for initial or final state splittings — of the low-virtuality particle and the creation of the real radiation particles. We compute at tree-level the amplitudes describing all the splittings that take place in the Standard Model when the collision energy is much above the electroweak scale. Unlike previous results, our splitting amplitudes fully describe the low-virtuality kinematic regime, which includes the region of collinear splitting, of soft emission, and combinations thereof. The splitting amplitudes are compactly represented as little-group tensors in an improved bi-spinor formalism for massive spin-1 particles that automatically incorporates the Goldstone Boson Equivalence Theorem. Simple explicit expressions are obtained using a suitably defined infinite-momentum helicity basis representation of the spinor variables. Our results, combined with the known virtual contributions, could enable systematic predictions of the leading electroweak radiation effects in high-energy scattering processes, with particularly promising phenomenological applications to the physics of future colliders with very high energy such as a muon collider.
On the non-perturbative bulk Hilbert space of JT gravity
Authors: Iliesiu Luca V. , Levine Adam , Lin Henry W. , Maxfield Henry , Mezei Márk 
Published: October 2024

Abstract
What is the bulk Hilbert space of quantum gravity? In this paper, we resolve this problem in 2d JT gravity, both with and without matter, providing an explicit definition of a non-perturbative Hilbert space specified in terms of metric variables. The states are wavefunctions of the length and matter state, but with a non-trivial and highly degenerate inner product. We explicitly identify the null states, and discuss their importance for defining operators non-perturbatively. To highlight the power of the formalism we developed, we study the non-perturbative effects for two bulk linear operators that may serve as proxies for the experience of an observer falling into a two-sided black hole: one captures the length of an Einstein-Rosen bridge and the other captures the center-of-mass collision energy between two particles falling from opposite sides. We track the behavior of these operators up to times of order $$ {e}^{S_{\textrm{BH}}} $$

e

S
BH

, at which point the wavefunction spreads to the complete set of eigenstates of these operators. If these observables are indeed good proxies for the experience of an infalling observer, our results indicate an O(1) probability of detecting a firewall at late times that is self-averaging and universal.
Transverse dynamics of charmed hadrons in ultra-relativistic nuclear collisions
Authors: Andronic Anton , Braun-Munzinger Peter , Brunßen Hjalmar , Crkovská Jana , Stachel Johanna , Vislavicius Vytautas , Völkl Martin 
Published: October 2024

Abstract
Transverse momentum pT spectra and anisotropic flow distributions are studied for charmonia and charmed hadrons produced in Pb-Pb collisions and measured with the ALICE detector at the CERN Large Hadron Collider (LHC). The investigations are performed within the framework of the Statistical Hadronization Model with the transverse dynamics evaluated using predictions from relativistic viscous hydrodynamics as implemented in the computer codes MUSIC and FluiduM. With this essentially parameter-free approach good agreement is obtained for pT spectra in the range pT < 10 GeV/c. The observed wide distribution in pT of anisotropic flow coefficients v2 and v3 for charmonia is also well reproduced, while their magnitude is generally somewhat over predicted. This finding may be connected to a difference in spatial distribution between light and charmed hadrons due to a different diffusion of light and heavy quarks in the hot fireball.