bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the coupling of non-metallic heteroatoms to CO2 from a Conceptual DFT perspective
Authors: Ferrer Maxime , Elguero José , Alkorta Ibon , Azofra Luis Miguel 
Published: June 2024

Abstract
Context
A Conceptual DFT (CDFT) study has been carry out to analyse the coupling reactions of the simplest amine (CH3NH2), alcohol (CH3OH), and thiol (CH3SH) compounds with CO2 to form the corresponding adducts CH3NHCO2H, CH3OCO2H, and CH3SCO2H. The reaction mechanism takes place in a single step comprising two chemical events: nucleophilic attack of the non-metallic heteroatoms to CO2 followed by hydrogen atom transfer (HAT). According to our calculations, the participation of an additional nucleophilic molecule as HAT assistant entails important decreases in activation electronic energies. In such cases, the formation of a six-membered ring in the transition state (TS) reduces the angular stress with respect to the non-assisted paths, characterised by four-membered ring TSs. Through the analysis of the energy and reaction force profiles along the intrinsic reaction coordinate (IRC), the ratio of structural reorganisation and electronic rearrangement for both activation and relaxation energies has been computed. In addition, the analysis of the electronic chemical potential and reaction electronic flux profiles confirms that the highest electronic activity as well as their changes take place in the TS region. Finally, the distortion/interaction model using an energy decomposition scheme based on the electron density along the reaction coordinate has been carried out and the relative energy gradient (REG) method has been applied to identify the most important components associated to the barriers.

Methods
The theoretical calculation were performed with Gaussian-16 scientific program. The B3LYP-D3(BJ)/aug-cc-pVDZ level was used for optimization of the minima and TSs. IRC calculations has also been carried out connecting the TS with the associated minima. Conceptual-DFT (CDFT) calculations have been carried out with the Eyringpy program and in-house code. The distortion/interaction model along the reaction coordinate have used the decomposition scheme of Mandado et al. and the analysis of the importance of each components have been done with the relative energy gradient (REG) method.
Oral quinolones versus intravenous β-lactam for the treatment of acute focal bacterial nephritis: a retrospective cohort study
Authors: Aceituno L. , Nuñez-Conde A. , Serra-Pladevall J. , Viñado B. , Castella E. , Escolà-Vergé Laura , Pigrau C. , Falcó V. , Len y O. 
Published: June 2024

Abstract
Background
Evidence regarding the best antibiotic regimen and the route of administration to treat acute focal bacterial nephritis (AFBN) is scarce. The aim of the present study was to compare the effectiveness of intravenous (IV) β-lactam antibiotics versus oral quinolones.

Methods
This is a retrospective single centre study of patients diagnosed with AFBN between January 2017 and December 2018 in Hospital Universitari Vall d’Hebron, Barcelona (Spain). Patients were identified from the diagnostic codifications database. Patients treated with oral quinolones were compared with those treated with IV β-lactam antibiotics. Therapeutic failure was defined as death, relapse, or evolution to abscess within the first 30 days.

Results
A total of 264 patients fulfilled the inclusion criteria. Of those, 103 patients (39%) received oral ciprofloxacin, and 70 (26.5%) IV β-lactam. The most common isolated microorganism was Escherichia coli (149, 73.8%) followed by Klebsiella pneumoniae (26, 12.9%). Mean duration of treatment was 21.3 days (SD 7.9). There were no statistical differences regarding therapeutic failure between oral quinolones and IV β-lactam treatment (6.6% vs. 8.7%, p = 0.6). Out of the 66 patients treated with intravenous antibiotics, 4 (6.1%) experienced an episode of phlebitis and 1 patient (1.5%) an episode of catheter-related bacteraemia.

Conclusions
When susceptible, treatment of AFBN with oral quinolones is as effective as IV β-lactam treatment with fewer adverse events.
Do we really apply evidence-based-recommendations to spine surgery? Results of an international survey
Authors: Bozkurt Ismail , Holt Matthew W. , Robinson Eric C. , Chaurasia Bipin , Zileli Mehmet 
Published: June 2024

Abstract
Objective
This international survey investigated Evidence-Based Medicine (EBM) in spine surgery by measuring its acceptance among spine surgeons. It assessed their understanding of EBM and how they apply it in practice by analyzing responses to various clinical scenarios..

Materials and methods
Following the CHERRIES guidelines, an e-survey was distributed to multiple social media forums for neurosurgeons and orthopedic surgeons on Facebook, LinkedIn, and Telegram and circulated further through email via the authors’ network. Three hundred participants from Africa, Asia, Europe, North America, and Oceania completed the survey.

Results
Our study revealed that 67.7% (n = 203) of respondents used EBM in their practice, and 97.3% (n = 292) believed training in research methodology and EBM was necessary for the practice of spine surgery. Despite this endorsement of using EBM in spine surgery, we observed varied responses to how EBM is applied in practice based on example scenarios. The responders who had additional training tended to obey EBM guidelines more than those who had no additional training. Most surgeons responded as always or sometimes prescribing methylprednisolone to patients with acute spinal cord injury. Other significant differences were identified between geographical regions, training, practice settings, and other factors.

Conclusions
Most respondents used EBM in practice and believed training in research methodology and EBM is necessary for spine surgery; however, there were significant variations on how to use them per case. Thus, the appropriate application of EBM in clinical settings for spinal surgery must be further studied.
Exponential expansions for approximation of probability distributions
Authors: Gambaro Anna Maria 
Published: June 2024

AbstractThis work analyses expansions of exponential form for approximating probability density functions, through the utilization of diverse orthogonal polynomial bases. Notably, exponential expansions ensure the maintenance of positive probabilities regardless of the degree of skewness and kurtosis inherent in the true density function. In particular, we introduce novel findings concerning the convergence of this series towards the true density function, employing mathematical tools of functional statistics. In particular, we show that the exponential expansion is a Fourier series of the true probability with respect to a given orthonormal basis of the so called Bayesian Hilbert space. Furthermore, we present a numerical technique for estimating the coefficients of the expansion, based on the first n exact moments of the corresponding true distribution. Finally, we provide numerical examples that effectively demonstrate the efficiency and straightforward implementability of our proposed approach.
ModelXGlue: a benchmarking framework for ML tools in MDE
Authors: López José Antonio Hernández , Cuadrado Jesús Sánchez , Rubei Riccardo , Di Ruscio Davide 
Published: June 2024

AbstractThe integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue . This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE.
Using reactive links to propagate changes across engineering models
Authors: Raţiu Cosmina-Cristina , Assunção Wesley K. G. , Herac Edvin , Haas Rainer , Lauwerys Christophe , Egyed Alexander 
Published: June 2024

AbstractCollaborative model-driven development is ade factopractice to create software-intensive systems in several domains (e.g., aerospace, automotive, and robotics). However, when multiple engineers work concurrently, keeping all model artifacts synchronized and consistent is difficult. This is even harder when the engineering process relies on a myriad of tools and domains (e.g., mechanic, electronic, and software). Existing work tries to solve this issue from different perspectives, such as using trace links between different artifacts or computing change propagation paths. However, these solutions mainly provide additional information to engineers, still requiring manual work for propagating changes. Yet, most modeling tools are limited regarding the traceability between different domains, while also lacking the efficiency and granularity required during the development of software-intensive systems. Motivated by these limitations, in this work, we present a solution based on what we call “reactive links”, which are highly granular trace links that propagate change between property values across models in different domains, managed in different tools. Differently from traditional “passive links”, reactive links automatically propagate changes when engineers modify models, assuring the synchronization and consistency of the artifacts. The feasibility, performance, and flexibility of our solution were evaluated in three practical scenarios, from two partner organizations. Our solution is able to resolve all cases in which change propagation among models were required. We observed a great improvement of efficiency when compared to the same propagation if done manually. The contribution of this work is to enhance the engineering of software-intensive systems by reducing the burden of manually keeping models synchronized and avoiding inconsistencies that potentially can originate from collaborative engineering in a variety of tool from different domains.
Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images
Authors: Arabi Hossein , Zaidi Habib 
Published: June 2024

AbstractTo develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation. To this end, three different datasets were employed used for brain tumor and hippocampus delineation from MR images (BraTS and Decathlon datasets, respectively) and kidney segmentation from CT images (Decathlon dataset). Since data augmentation techniques are also aimed at enhancing the performance of deep learning methods, a deformable data augmentation technique was proposed and compared with contrastive learning and self-learning frameworks. The segmentation accuracy for the three datasets was assessed with and without applying data augmentation, contrastive learning, and self-learning to individually investigate the impact of these techniques. The self-learning and deformable data augmentation techniques exhibited comparable performance with Dice indices of 0.913 ± 0.030 and 0.920 ± 0.022 for kidney segmentation, 0.890 ± 0.035 and 0.898 ± 0.027 for hippocampus segmentation, and 0.891 ± 0.045 and 0.897 ± 0.040 for lesion segmentation, respectively. These two approaches significantly outperformed the contrastive learning and the original model with Dice indices of 0.871 ± 0.039 and 0.868 ± 0.042 for kidney segmentation, 0.872 ± 0.045 and 0.865 ± 0.048 for hippocampus segmentation, and 0.870 ± 0.049 and 0.860 ± 0.058 for lesion segmentation, respectively. The combination of self-learning with deformable data augmentation led to a robust segmentation model with no outliers in the outcomes. This work demonstrated the beneficial impact of self-learning and deformable data augmentation on organ and lesion segmentation, where no additional training datasets are needed.
Aktuelle Forschungsergebnisse zur Neuropsychologie dissoziativer Anfälle
Authors: Jungilligens J. , Hoppe C. , Reuner G. 
Published: June 2024

ZusammenfassungDissoziative Anfälle sind komplexe und oftmals nicht ausreichend verstandene Symptome an der Grenze von körperlichen zu psychischen Phänomenen. Dies ist sowohl im Kinder- und Jugend- als auch im Erwachsenenbereich ein Problem, da ohne ein ausreichendes Krankheitsverständnis Diagnosevermittlung und Therapieplanung oft unbefriedigend verlaufen. In diesem narrativen Review wird eine Übersicht über aktuelle Forschungsergebnisse zur Neuropsychologie dissoziativer Anfälle gegeben. Dabei werden sowohl klinische als auch grundlagenwissenschaftliche Aspekte betrachtet und jeweils für den Kinder- und Jugend- sowie den Erwachsenenbereich beleuchtet. In der aktuellen Grundlagenforschung zu dissoziativen Anfällen wird v. a. die Rolle von Emotionen, Arousal-Zuständen und der Wahrnehmung von Körpersignalen untersucht. Dabei werden neben Fragebogen- und Verhaltensstudien auch experimentelle neurowissenschaftliche Methoden genutzt. In diesem Beitrag wird zudem ein aktueller, integrierender Ansatz für die Rolle von Emotionen vorgestellt. Aus der Übersicht zur Neuropsychologie dissoziativer Anfälle lassen sich neben Aspekten des Krankheitsverständnisses auch Hinweise auf Therapieansätze ableiten. Zudem wird in dieser Forschung die physische Realität des Psychischen deutlich.
“Lights out” poultry production and pandemic influenza
Authors: Sparrow Robert , Degeling Chris , Mayes Christopher 
Published: June 2024

AbstractPoultry production makes a substantial contribution to global food security, providing energy, protein, and essential micro-nutrients to humans. Modern intensive poultry farming systems are challenged by the evolution of Highly Pathogenic Avian Influenza strains. The presence of avian influenza in poultry flocks poses a significant risk of an avian origin influenza that is easily transmittable between human beings evolving. By reducing contact between humans and fowl, the use of automation in poultry production has the potential to improve biosecurity and thus reduce the risk of pandemic influenza. Many poultry facilities are already highly automated. The rapid rate of progress in robotics and AI suggests that “lights out”—fully automated—poultry production systems may soon be possible. In this paper we consider the ethical and policy issues that would be raised by lights-out poultry production. There is a strong animal and human welfare case for reducing the risk of pandemic influenza via increased use of automation. However, lights-out farming looks to be the ultimate endpoint of dynamics already present in industrial agriculture, which led to the dangers of zoonotic infection from animal agriculture in the first place. Whether nations should respond to that risk by doubling down on industrial models of animal production and embracing fully automated farms or by reconsidering the current model of animal agriculture altogether is, we suggest, both the most important, and the most difficult, question posed by the prospect of lights out farms.
An Expanded Conceptual Framework for Understanding Irritability in Childhood: The Role of Cognitive Control Processes
Authors: Elvin Olivia M. , Modecki Kathryn L. , Waters Allison M. 
Published: June 2024

AbstractChildren prone to irritability experience significant functional impairments and internalising and externalising problems. Contemporary models have sought to elucidate the underlying mechanisms in irritability, such as aberrant threat and reward biases to improve interventions. However, the cognitive control processes that underlie threat (e.g., attention towards threats) and reward (e.g., attention towards reward-related cues) biases and the factors which influence the differential activation of positive and negative valence systems and thus leading to maladaptive activation of cognitive control processes (i.e., proactive and reactive control) are unclear. Thus, we aim to integrate extant theoretical and empirical research to elucidate the cognitive control processes underlying threat and reward processing that contribute to irritability in middle childhood and provide a guiding framework for future research and treatment. We propose an expanded conceptual framework of irritability that includes broad intraindividual and environmental vulnerability factors and propose proximal ‘setting’ factors that activate the negative valence and positive valence systems and proactive and reactive cognitive control processes which underpin the expression and progression of irritability. We consider the implications of this expanded conceptualisation of irritability and provide suggestions for future research.
Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel
Authors: Boresta Marco , Giovannelli Tommaso , Roma Massimo 
Published: June 2024

AbstractThis paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.
Assessing complementarity and substitution effects of cultural events in rural communities: insights from a Mediterranean island
Authors: Meleddu Marta , Pulina Manuela 
Published: June 2024

AbstractPrevious studies do not systematically investigate complementarity and substitution effects of cultural participation and consumption in rural areas. Fairs, fiestas, and festivals, often performed in rural communities, have a substantial role in economic development. Nowadays, smartphones and the integration of global positioning system technology in mobile phones trace quantitative big data useful for studying complex systems and spaces. The present research analyses cultural events in the rural area of Gennargentu-Mandrolisai Mountain Community located in the Mediterranean island of Sardinia (Italy). This paper contributes to the literature at least in two ways. Methodologically, it analyses a complex cultural product that combines tangible and intangible cultural heritage. It explores complementarity and substitution effects between events within a coopetition setting. Empirically, it investigates consumption behaviour and tracks the inter-regional/inter-national cultural flows. An econometric analysis of a unique big dataset evaluates the supply-side market conditions that influence demand-side spill-over effects. This study has practical relevance and provides a valuable reference for policymakers in inner areas.
Poly(glycerol sebacate)-based soft-tissue-mimicked active layers for triboelectric nanogenerators
Authors: Timusk Martin , Nirwan Viraj P. , Lapčinskis Linards , Sandberg Anett , Trei Annika , Maimets Toivo , Godiņa Daniela , Rižikovs Jānis , Fahmi Amir , Šutka Andris 
Published: June 2024

AbstractHerein poly(glycerol sebacate) (PGS) has been synthesized and characterized to identify it’s potential as an active triboelectric layer in implantable and biodegradable TENG devices. The implantable and biodegradable TENG devices require excellent triboelectric properties, as well as the ability to mimic the mechanical properties of the surrounding tissues and exhibiting low cytotoxicity and eventual degradation and removal by bio-absorption. Two-step synthesis of PGS was carried out by using polycondensation between glycerol and sebacic acid in 1:1 molar ratio, followed by film preparation by spray-coating of the prepolymer solution. Crosslinking conditions were elaborated to yield good triboelectric performance together with low cytotoxicity. Triboelectric surface charge density of up to 0.188 nC cm−2 was obtained, exceeding the values of common triboelectric materials such as PDMS and PTFE by 2.89 and 3.76 times, respectively, when tested under identical contact-separation parameters. The mechanical properties of PGS can be tuned by varying the crosslinking degree to mimic soft tissues. In this context, in vitro studies on human skin fibroblasts revealed cell viability up to 78%. Furthermore, the cell viability is found to be strongly related to the crosslinking time of the PGS. The results show significantly lower cytotoxicity as compared to, for example, poly(sorbitol sebacate) and poly(hexanediol-co-citric acid). The flexibility and the confirmed biocompatibility beside the unique mechanical and physio-chemical collective properties of the of the polymer demonstrates the potential use of PGS for the next generation of implantable TENG devices.
Graphical abstract