bluuebunny/crossref_metadata_2025 huggingface dataset:
Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images
Authors: Arabi Hossein , Zaidi Habib
Published: June 2024

To 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

Dissoziative 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

Poultry 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

Children 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

This 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

Previous 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

Herein 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
Scaffolding Pre-service Science Teachers’ Problem-Solving Strategies in a Methane Gas Detector Task Within an Earthquake-Robotics PD Course
Authors: Cepni Salih , Aydin Mirac , Iryanti Mimin , Birisci Salih
Published: June 2024

Educational robotics (ER) has the potential to be a novel approach to teaching geohazards such as earthquakes at the college level. ER, which provides learners with problem-solving settings, requires proficiency in content knowledge and practical application to address ill-defined problems, challenging learners to master problem-solving strategies. Despite several efforts in the existing literature, it is necessary to scaffold the problem-solving strategies comprehensively. This qualitative study investigated the problem-solving strategies of nine pre-service science teachers aligned with a coding scheme containing problem-solving strategies not previously documented together. The participants were assigned to construct a methane gas detector with Tinkercad to mitigate post-earthquake explosion risks for rescue teams in an online robotics-integrated earthquake professional development (PD) course. Qualitative data, including artifacts, observations, and interviews, were analyzed using deductive coding. The results indicated that participants predominantly employed trial and error, expert opinion, and case-based reasoning. They rarely utilized heuristics and intuition and did not use capacity evaluation, prediction, or sketching strategies. Furthermore, the study synthesized different problem-solving strategies into a comprehensive framework, which was used as a coding scheme. This framework helps to clarify problem-solving mechanisms in an ER context, offering a structured approach.
Local neighborhood encodings for imbalanced data classification
Authors: Koziarski Michał , Woźniak Michał
Published: June 2024

This paper aims to propose Local Neighborhood Encodings (LNE)-a hybrid data preprocessing method dedicated to skewed class distribution balancing. The proposed LNE algorithm uses both over- and undersampling methods. The intensity of the methods is chosen separately for each fraction of minority and majority class objects. It is selected depending on the type of neighborhoods of objects of a given class, understood as the number of neighbors from the same class closest to a given object. The process of selecting the over- and undersampling intensities is treated as an optimization problem for which an evolutionary algorithm is used. The quality of the proposed method was evaluated through computer experiments. Compared with SOTA resampling strategies, LNE shows very good results. In addition, an experimental analysis of the algorithms behavior was performed, i.e., the determination of data preprocessing parameters depending on the selected characteristics of the decision problem, as well as the type of classifier used. An ablation study was also performed to evaluate the influence of components on the quality of the obtained classifiers. The evaluation of how the quality of classification is influenced by the evaluation of the objective function in an evolutionary algorithm is presented. In the considered task, the objective function is not de facto deterministic and its value is subject to estimation. Hence, it was important from the point of view of computational efficiency to investigate the possibility of using for quality assessment the so-called proxy classifier, i.e., a classifier of low computational complexity, although the final model was learned using a different model. The proposed data preprocessing method has high quality compared to SOTA, however, it should be noted that it requires significantly more computational effort. Nevertheless, it can be successfully applied to the case as no very restrictive model building time constraints are imposed.
Analysis of 12-lead ECGs for SARS-CoV-2 detection using deep learning techniques
Authors: Auriemma Citarella Alessia , De Marco Fabiola , Di Biasi Luigi , Di Chiara Luca , Tortora Genoveffa
Published: June 2024

The spread of the COVID-19 pandemic is expected to be uncontrollable by 2020. The main precautions to avoid virus spread have been the introduction of surgical masks or FFP2, sanitization of the hands, and maintaining social distancing. Due to their reliability, molecular tampons are the main detection and prevention methods known as the “Gold Standard”. However, these methods can be particularly uncomfortable. In this case, the analysis of electrocardiogram traces appears to be an alternative method for detecting COVID-19. The dataset used is made up of 1937 images from a study conducted in Pakistan that were preprocessed to train six different neural networks, including MobileNetV2, ResNet-18, ResNet-50, AlexNet, SqueezeNet, and an ad hoc defined neural network. The results show high classification performance, with an accuracy close to 98.94%, as reached by the Resnet-18 network. Moreover, significant attention was devoted to analyzing confusion matrices, revealing the capacity of the networks to identify distinctive features indicative of COVID-19 within ECG data. Finally, it is suggested that in nearly all experiments, including those with low performance, COVID-19 patients are correctly classified, further enhancing the diagnostic potential of ECGs data and DL approach.
An Agent-based Approach to Study Spatial Structure Effects on Estimated Distance Deterrence in Commuting
Authors: Azad Gholami Azam , Thorsen Inge , Ubøe Jan
Published: June 2024

We provide an experimental design to study how estimates of the distance deterrence parameter in a standard doubly-constrained gravity model respond to different patterns of spatial structure. The design is defined as an agent-based simulation framework that simultaneously considers the response of commuting and migration to changes in the spatial structure, accounting for labor and housing market issues. The agents are equipped with a utility function and are expected to maximize their utilities corresponding to their disposable income net of commuting costs and house value. We study how the estimates of the distance deterrence parameter respond to where the central business district is located, to the clustering of local and basic sector jobs, to changes in the road transportation network, and to the compactness in the system of towns in the geography.
The crypto world trades at tea time: intraday evidence from centralized exchanges across the globe
Authors: Brauneis Alexander , Mestel Roland , Theissen Erik
Published: June 2024

It is a stylized fact that trading activity, volatility and liquidity in equity and other financial markets follow specific intraday patterns. These patterns are to a large extent determined by institutional features such as exchange trading hours or batch settlement procedures. We analyze the intraday patterns that emerge when these institutional constraints are absent. We compile a large sample of 1940 currency pairs traded on 38 cryptocurrency exchanges located on five continents. These exchanges operate 24 h a day, seven days a week, and settle trades instantly. We find that there are pronounced time-of-day patterns in trading activity, volatility and liquidity. These patterns are remarkably similar across exchanges, time zones and cryptocurrency pairs. Specifically, trading activity, volatility and illiquidity all peak between 16:00 and 17:00 Coordinated Universal Time (UTC), i.e. during U.K. tea time. We find that characteristics of the exchanges (such as their locations) and of the traded currency pairs (e.g. whether two pairs share a common currency) explain some, but not all of the commonality in intraday patterns.
On substituent effect in 1,n–homodisubstituted polyenes
Authors: Graff Marek , Ostrowski Sawomir , Dobrowolski Jan Cz.
Published: June 2024

The all-trans and all-cis polyenes homodisubstituted at the ends were calculated at the B3LYP/6-31G** level. The disubstitution gives rise to three end-types of the conformers: trans-trans, trans-cis, and cis-cis, denoted as EE, EZ, and ZZ. The symmetry of the EE or ZZ all-cis isomers depended on the double bond parity. Twelve substituents used: H, BeH, BH2, BF2, Br, CH3, Cl, CN, F, NH2, NO2, OH, and SiH3 were chosen to exhibit different σ- and π-electron donating and electron withdrawing properties. For polyenes composed up to ca. 20 C-atoms, the π-electron donating and withdrawing character of the end groups matters and differently acting substituents play significantly different roles. Unexpectedly, the intramolecular interactions between the substituents and the neighboring chain CH groups near appeared more decisive for the compound’s stability than the substituent electron donating/withdrawing properties. The substituent-chain interplay was consonant in the all-trans and all-cis polyenes. Still, they were always more destabilizing in the latter than in all-trans isomers.
Governing for the Common Good: The Possibilities of Sociocracy in Nonprofit Organizations
Authors: King Daniel , Griffin Martyn
Published: June 2024

Within nonprofit organizational studies, there has been a long-standing interest in democratic governance as ways of building political participation, civic skills and fostering inclusion. While established approaches to democratic governance have many benefits, existing research points to numerous challenges, including apathy and oligarchization. This paper explores an alternative form of democratic governance, sociocracy. Sociocracy, sometimes called dynamic governance, is organized around four key elements: circular hierarchy, consent-based decision-making, double linking, and practices to foster inclusivity and voice, a unique blend which distinguishes it from other forms of democratic governance. This article explores the implications on workplace democracy that a nonprofit organization experienced when limiting it. We find that sociocracy offers many benefits, including empowering members and reducing the risk of domination, and also highlights the many challenges that can accompany the implementation of sociocracy, particularly how four forms of inequality contribute to those challenges.
Characterization, in vitro bioaccessibility and antidiabetic activities of maltodextrin and lecithin nanomaterials loaded with ellagic aci﻿d
Authors: Okumuş Emine , Bakkalbaşı Emre , Gümüş Ayşegül
Published: June 2024

Pomegranate peel is one of the wastes generated in large quantities in the food industry and ellagic acid is a natural bioactive phenolic compound in pomegranate peel. In this study, high-purity and low-cost ellagic acid was produced by acid hydrolyze process from pomegranate peel and characterization, in vitro bioaccessibility, antioxidant, and antidiabetic activity of the ellagic acid nanoencapsulated with maltodextrin and soy lecithin were studied. The extract yield and purity of ellagic acid was 6.47 and 88.74%, respectively. Results showed that lecithin coated samples were more stable with − 43.16 mV zeta potential and had higher encapsulation efficiency (99.29%) compared to maltodextrin. However, coated samples with maltodextrin had higher production efficiency (91.40%) and loading capacity (54.70%). The thermal resistance of ellagic acid after the nanoencapsulation process was significantly raised. The ellagic acid nanoencapsulated with maltodextrin had higher bioaccessibility (80.22%) and solubility in biological buffers (64.00-98.20%). The α-amylase and α-glucosidase inhibitiory activities of the ellagic acid decreased after the encapsulation process. However, the α-amylase inhibitory activities of the nanoencapsulated materials were higher than acarbose as a positive control. As a result, thermal resistance, solubility in a biological medium, and bioaccessibility of ellagic acid after nanoencapsulation with maltodextrin can be increased. Thus, the use of ellagic acid nanoencapsulated with maltodextrin in the food, cosmetic, and pharmacological industries can be diversified.
Hardy Spaces and Canonical Kernels on Quadric CR Manifolds
Authors: Boggess Albert , Brooks Jennifer , Raich Andrew
Published: June 2024

CR functions on an embedded quadric M always extend holomorphically to $$M+i\Gamma _M$$

M
+
i

Γ
M

where $$\Gamma _M$$

Γ
M

is the closure of the convex hull of the image of the Levi form. When $$\Gamma _M$$

Γ
M

is a closed polygonal cone, we show that the Bergman kernel on the interior of $$M+i\Gamma _M$$

M
+
i

Γ
M

is a derivative of the Szegö kernel. Moreover, we develop the $$L^p$$

L
p

Hardy space theory which turns out to be particularly robust. We provide examples that show that it is unclear how to formulate a corresponding relationship between the Bergman and Szegö kernels on a wider class of quadrics.
Implications of the WTO and EU fuel taxation reforms for a European fishing fleet: effects, side-effects and alternative approaches from an input–output analysis of the Galician (northwest Spain) fleet
Authors: Rodríguez-Rodríguez Gonzalo , Sánchez-Llamas Eduardo , Martínez-Cabrera Helena , Ballesteros Hugo M.
Published: June 2024

The World Trade Organization (WTO) and European Union (EU) have both put forward tax reforms that impact fisheries’ fuel costs, which are similar in the object of taxation but quite different in the externalities addressed, goals, scope, exemptions or tax rates, creating an opportunity to compare the effects and discuss the alternatives. These proposals are the result of long and intense debates in political, social and academic spheres. However, certain aspects remain unclear. First of all, the number of empirical studies is still limited, particularly considering that fisheries governance is subject to heterogeneous management systems and institutional contexts creating room for different potential outputs. Second, coastal states have no fiscal competence beyond 12 miles, which limits the scope of the reforms to small scale fleets (SSF). All in all, the economic consequences for the fleets remain unclear. To this end, we will build on the Galician fishing sector (NW Spain) input–output tables to discuss the direct impacts, effects and side effects of these reforms on a representative European fleet. Flaws identified in both initiatives mean that targeted externalities will not be corrected and, moreover, additional ones, such as distributional effects penalising SSF or fuel spillovers, could be boosted. Alternative policy approaches are discussed.
Advancing Customer Feedback Systems with Blockchain
Authors: Ballandies Mark C. , Holzwarth Valentin , Sunderland Barry , Pournaras Evangelos , Brocke Jan vom
Published: June 2024

Organizations have to adjust to changes in the ecosystem, and customer feedback systems (CFS) provide important information to adapt products and services to changing customer preferences. However, current systems are limited to single-dimensional rating scales and are subject to self-selection biases. The work contributes design principles for CFS and implements a CFS that advances current systems by means of contextualized feedback according to specific organizational objectives. The authors apply Design Science Research (DSR) methodology and report on a longitudinal DSR journey considering multiple stakeholder values by utilizing value-sensitive design methods. They conducted expert interviews, design workshops, demonstrations, and a four-day experiment in an organizational setup, involving 132 customers of a major Swiss library. In the process, the identified design principles and the implemented software artifact were validated qualitatively and quantitatively, leading to conclusions for their efficient instantiation. The authors found that i) blockchain technology can afford four design principles of effective CFS. Also, ii) combining DSR with value-sensitive design methods explicitly provides rationale for design principles in the form of identified important values. Moreover, iii) combining DSR with value-sensitive design methods makes the construction of software artifacts more efficient it terms of design time by restricting the design space of a software artifact to those options that align with stakeholder values. The findings of this work thus extend the knowledge about the design of CFS and offer both researchers a theoretical contribution to reasoning about design principles and managers and decision makers a guide for the efficient design of software artifacts.
Estimating stream sediment loads to assess management options for a Southern Appalachian mountain lake
Authors: Wilcox Jeffrey D. , Stark Karen Ann , Svetlov Ruth
Published: June 2024

Enka Lake is a 25-ha reservoir in the Southern Appalachian mountains surrounded by the Biltmore Lake residential neighborhood. The aesthetic and recreational value of Enka Lake can be negatively affected by turbid water and sediment buildup, resulting from stormwater originating upstream in its 15.38 km2 watershed. In this study, we monitored streamflow and sediment loads for nearly 2 years to better understand sedimentation dynamics and evaluate management options. We collected nearly 500 water samples throughout the watershed, focusing on two tributaries that provide the majority of streamflow into Enka Lake. During baseflow conditions, those tributaries are very clear (turbidity ranging from 1 to 20 NTU), but sediment-laden stormflow (turbidity > 1000 NTU) from dirt roads, gravel driveways, and poorly vegetated yards is common and, during extreme storm events, causes lake discoloration. Discharge-sediment data pairs were used to construct sediment rating curves and estimate sediment fluxes through these tributaries. The predicted sediment fluxes were tested with high-frequency sampling during and after three storms in different seasons; predicted values underestimated the peak sediment fluxes (0–400%) but generally matched total measured sediment loads. Estimates of annual sediment load from the two tributaries (540–900 tonnes/year) suggest that a dredging effort commissioned by the neighborhood association in 2016 may have removed only about 1–3 years’ worth of sediment. Installing sediment forebays or other retention structures is likely the preferred sediment management strategy moving forward (over a regular dredging schedule) since they can be maintained more easily without affecting lake levels. Designing these retention structures will benefit from the estimates of stormflow and sediment fluxes, and the study design presented here—including the help of community-based volunteer samplers—provides a model that could be used at other sites in the Southern Appalachians and elsewhere.
Experimental investigations on laboratory samples regarding the connection of spectral induced polarization to heterogeneity of hydraulic conductivity
Authors: Herold Robert , Beisembina Gulmira , Dietrich Peter , Börner Frank
Published: June 2024

Knowledge of the heterogeneity of aquifers caused by sedimentary processes is essential for preferential flow path and flow time estimations due to the spreading of contaminants and ground water protection. Spectral Induced Polarization (SIP) method is a useful geophysical method that can be used to predict hydraulic properties from surface measurements, as there is a close connection between electrical and hydraulic parameters of rocks. Thus, the influence of aquifer heterogeneities such as sedimentary structures on the relationship between SIP parameters and hydraulic conductivity has been subject of some studies and is still an area of interest. In this paper we present the results of laboratory measurements on defined heterogeneous sand samples with the aim to improve the understanding of that connection. More specifically we investigated the dependence of hydraulic conductivity ($$K$$
K
), as well as several SIP parameters like quadrature conductivity ($${\upsigma }^{{\prime}{\prime}}$$

σ

′
′

), in-phase conductivity ($${\upsigma }{^\prime}$$

σ

′

), phase shift ($$\varphi$$
φ
) and chargeability spectra ($$m(\tau )$$

m
(
τ
)

), which were calculated by Debye decomposition, on the composition of samples. For the laboratory experiments a measurement cell was designed to carry out hydraulic flow experiments and 4-electrodes SIP measurements on samples in the decimeter scale. The samples consisted of two different sands with different grain size distributions. The two sands were combined in varying volume shares and geometries as well as in parallel and serial orientation relative to hydraulic and electrical current flow. The experimental results show that the SIP parameters and $$K$$
K
are clearly dependent on the volume share of the sand components. In terms of preferential flow paths, known correlations to hydraulic conductivity could be reproduced, however the SIP parameters showed no dependency on the orientation of hydraulic heterogeneities. The results indicate that in samples, where the porosity and thus also the electrical conductivity amplitude are approximately homogeneous and only the grain surface area and hydraulic conductivity vary, the mean electrical parameters determined from the SIP data do not provide any information for recognising preferential flow within the scope of the measurement accuracy.
Reac4Cat-Ontology: Harnessing the Power of Ontological Description Logic in Catalysis Research as a Practical Approach to Knowledge Inferences
Authors: Behr Alexander S. , Borgelt Hendrik , Kockmann Norbert
Published: June 2024

Maximizing the use of digitally captured data is a key requirement for many of the late adopters of digital infrastructure. One of the newcomers is the chemical industry in the area of digitized laboratories. Here, tools and services that satisfy individual needs still need to be developed and distributed within the community. This work explores the potential of using graph databases — specifically those modeled via ontological knowledge graphs — to describe complex data linkages and draw logical conclusions. While knowledge graphs are not widely utilized in catalysis research, this study introduces a methodology to highlight their usability for semantic description and integration into diverse value chains with contact to the domain of (bio)chemistry and catalysis.

A demonstration is performed how ontologies and their knowledge graphs can be applied to perform essential functions of semantic annotation to chemical reactions, which are difficult to model relational. Traditional data description methods can be neglected using description logic, showing how logical inferences at the machine level can enrich data. This work also illustrates the seamless integration of this enhanced data into process simulations, connecting semantic description with practical applications. The immediate benefits for catalysis research are emphasized and the development of new tools and services envisioned. By clarifying how these graphs can be integrated into existing workflows, researchers are empowered to make the most of digitally acquired data in catalytic processes. This practical methodology lays the foundation for improved decision-making and innovation, fostering advancements in the field of catalysis research.
Disseminated septic arthritis caused by Ureaplasma urealyticum in an immunocompromised patient with hypogammaglobulinemia after rituximab therapy
Authors: Oguni Kohei , Fukushima Shinnosuke , Otsuka Yuki , Soejima Yoshiaki , Kawaguchi Marina , Sazumi Yosuke , Fujimori Takumi , Iio Koji , Umakoshi Noriyuki , Yamada Kazuki , Hagiya Hideharu , Otsuka Fumio
Published: June 2024

Purpose

Ureaplasma urealyticum is a rare pathogen associated with septic arthritis that predominantly affects patients with hypogammaglobulinemia. Bacterial identification of fastidious organisms is challenging because they are undetectable by routine culture testing. To the best of our knowledge, this is the first report of septic arthritis induced by U. urealyticum infection in Japan.

Case description

We describe the case of a 23-year-old Japanese female with secondary hypogammaglobulinemia (serum immunoglobulin level < 500 mg/dL), identified 8 years after treatment with rituximab. The patient presented with persistent fever and polyarthritis that were unresponsive to ceftriaxone and prednisolone. Contrast-enhanced computed tomography and gallium-67 scintigraphy revealed effusion and inflammation in the left sternoclavicular, hip, wrist, knee, and ankle joints. Although Gram staining and bacterial culture of the drainage fluid from the left hip joint were negative, the condition exhibited characteristics of purulent bacterial infection. The patient underwent empirical treatment with doxycycline, and her symptoms promptly resolved. Subsequent 16S ribosomal RNA (rRNA) gene sequencing of the joint fluid confirmed the presence of U. urealyticum, leading to the diagnosis of septic arthritis. Combination therapy with doxycycline and azithromycin yielded a favorable recovery from the inflammatory status and severe arthritic pain.

Conclusion

This case highlights U. urealyticum as a potential causative agent of disseminated septic arthritis, particularly in patients with hypogammaglobulinaemia. The 16S rRNA gene analysis proved beneficial for identifying pathogens in culture-negative specimens, such as synovial fluid, in suspected bacterial infections.
Use of CT for Dysphagia Evaluation: Advantages and Disadvantages in the Study of Swallowing
Authors: Inamoto Yoko , Ueha Rumi , Gonzalez-Fernandez Marlis
Published: June 2024

Purpose of Review

Swallowing computed tomography (CT) allows dynamic three-dimensional visualization of swallowing. This review article summarizes the advantages and disadvantages of performing swallowing CT.

Recent Findings

Visualization of swallowing-related structures three-dimensionally supports the use of swallowing CT in clinical, research, and educational settings. Several morphological and kinematic analyses can be performed to study swallowing physiology and pathophysiology. However, radiation dose, reclining posture during scanning, and lower time resolution compared to videofluoroscopy are disadvantages of this technology. Technological advances may mitigate these disadvantages in the future.

Swallowing CT can be used in clinical, educational, and research applications while recognizing that limitations exist to the use of this modality.
Community-Scale Problem-Solving: Reflections on a Decade of Infrastructure Development in the MGI
Authors: Long Benjamin , Sousa Amaral Guillaume , Dessauw Philippe , Bouhanni Hamza
Published: June 2024

This paper describes the need for a unified representation of Materials Genome Initiative (MGI) problem-solving processes. An approach for solving this problem in the form of pattern-languages is proposed as well as a means for realizing them through existing infrastructure and community development activities. As part of this strategy, key lifecycle questions related to the importance of community-based modeling and sustainability are considered. Central to the entire paper are two observations: a) that MGI itself, as well as its domains, communities, and infrastructure, are complex systems; and b) that scalability of these complex systems is maximized through robust modeling. These ideas are discussed as a strategy toward community-centered growth, unification, and sustainability in MGI ecosystem-scale problem-solving and infrastructure development.
Denial of Personal Racial Discrimination and Its Impact Among People of Color Who Use Substances: Implications for Measuring Racial Discrimination in Substance Use Research
Authors: Oh Hans , Sami Mojgan , Blevins Brittany , Hanson Hannah , Herzig Emma , Ho Catherine , Lee Ryan , Wong Kelly , Huh Jimi
Published: June 2024

Background

Even though racism is pervasive, some people of color may deny experiencing racial discrimination or may report being unaffected by it. This study examines the contexts and factors that may contribute to these responses among people who use substances.

Methods

We conducted seven focus groups (5–9 participants per group, total N = 43) among Black, Latino, and Asian American adults between the ages of 21 to 44 years old who reported current use of two or more of the following substances: alcohol, cigarettes, e-cigarettes, or cannabis. Data were analyzed using reflexive thematic analysis.

Results

Across all three ethno-racial groups, we found some respondents minimized or denied personal experiences of racial discrimination or hesitated to identify their experiences as racial discrimination, which in turn led to respondents to express uncertainty about seeing any sort of connection between racial discrimination and substance use. Themes included a minority comparison effect; a drowning out effect; diversity and racial composition of context; passing as White; and covertness of racism. Also, there were contradictions in accounts, and responses often depended on orienting cues.

Conclusions

While researchers continue to find associations between racial discrimination and substance use, some people of color may not acknowledge this connection. Recommendations include aligning definitions of racism between academic and public/popular discourse; updating measures to keep up with the evolving forms of racism using context-specific examples; combining subjective measures of racial discrimination with objective measures of racism; and dialoguing with the public to raise awareness around how racism is defined.
Changes in body composition in early breast cancer patients treated with aromatase inhibitors
Authors: Pedersini R. , Schivardi G. , Laini L. , Zamparini M. , Bonalumi A. , di Mauro P. , Bosio S. , Amoroso V. , Villa N. , Alberti A. , Di Meo N. , Gonano C. , Zanini B. , Laganà M. , Ippolito G. , Rinaudo L. , Farina D. , Castellano M. , Cappelli C. , Simoncini E. L. , Cosentini D. , Berruti A.
Published: June 2024

Purpose

The aim of the study was to analyze the modification of total and regional body composition in early breast cancer patients treated with aromatase inhibitors (AIs).

Methods

This is a prospective, single-center, observational, longitudinal study. Four-hundred and twenty-eight patients treated with adjuvant aromatase inhibitors were enrolled at the Medical Oncology and Breast Unit of Spedali Civili Hospital in Brescia from September 2014 to June 2022. Several body composition parameters including total and regional fat and lean body mass were investigated with dual-energy X-ray absorptiometry (DXA) scan at baseline and after 18 months of treatment with aromatase inhibitors.

Results

A significant increase in fat body mass (mean + 7.2%, 95% confidence interval [CI]: 5.5;8.9%) and a reduction in lean body mass (mean −3.1%, 95% CI −3.9; −2.4) were documented in this population. The changes in fat and lean body mass varied considerably according to different body districts ranging between + 3.2% to + 10.9% and from−1.3% to −3.9%, respectively.

Conclusion

Aromatase inhibitor adjuvant therapy in early breast cancer is associated with changes in body composition, with a wide variability among different body districts, leading to a risk of sarcopenic obesity. Supervised physical exercise that focuses on single body parts that may display detrimental variations may be beneficial for AIs treated patients.
A novel fractional neural grey system model with discrete q-derivative
Authors: Xu Zhenguo , Liu Caixia , Liang Tingting
Published: June 2024

The challenge of predicting time series with limited data has evolved over time due to nonlinearity, complexity, and limited information. It can be perceived as a mapping of dynamical systems in one-dimensional space. This article proposes a neural grey system to tackle this challenge. The system enhances its ability to fit nonlinearity by employing polynomials, captures complexity through a fractional-order cumulant operator, and resolves information-poor uncertainty by utilizing grey system modeling techniques. The model effectively integrates research findings from neural computing, uncertainty theory, and complexity theory at a theoretical level. It accurately describes dynamic processes of complex systems. Additionally, we have reduced the complexity of calculations in the algorithm design. We selected a dataset of total retail sales of consumer goods to test the model’s validity and applicability. Our experiments demonstrate that the newly proposed grey forecasting model can effectively forecast time series with small samples, offering good forecasting outcomes and generalization ability.
Government spending, multipliers, and public debt sustainability: an empirical assessment for OECD countries
Authors: Ciaffi Giovanna , Deleidi Matteo , Capriati Michele
Published: June 2024

This paper aims to quantify the linear and non-linear effects of government expenditure on output and public debt sustainability using the Local Projections (LP) approach on a dataset of 14 OECD countries considered for the 1981–2017 period. By employing the Blanchard and Perotti strategy to identify fiscal policy shocks, we show that government spending multipliers are close to one in linear models and that expansionary fiscal policy stimuli reduce the public debt-to-GDP ratio. When considering non-linearities using the smooth transition LP model, findings show that a fiscal policy shock leads to higher multipliers and a stronger reduction in the public debt-to-GDP ratio in economic phases characterised by a high rather than a low debt-to-GDP ratio. All results are confirmed even when government spending expectations are considered.
Collective actors and potential alliances for eco-social policies in Germany
Authors: Cremer Julia C.
Published: June 2024

To break the vicious circle of mutually reinforcing climate and social crises, there is a rapidly growing literature around integrated public policy instruments called eco-social policies. The actors potentially drive such policies in public debate and the policy process remains under-researched. This paper elaborates on the positioning of various non-state actors on eco-social policies and potential alliance-building in Germany. It derives hypotheses from the existing literature on the role of trade unions, social- and environmental non-governmental organizations, and social movements in eco-social transformation. It tests them empirically on a dataset of public communication and uses a combination of computer-assisted text-analytic tools, namely text mining and structural topic modeling. The findings indicate that although eco-social themes as cross-cutting issues offer good conditions for alliance-building, actors differ widely in their potential to do so. Based on actors’ eco-social broadness and transformational intensity, the paper finds four clusters: a status quo supporter, an activist, a pragmatic, and an eco-social alliance cluster and discusses possible strategies for them. The findings highlight the relevance of service sector unions and social non-governmental organizations as they could act as a bridge to connect social and ecological interests. The paper’s contribution to the Special Issue is twofold: It addresses the policy dimension by examining what issues actors raise about ecological and social concerns. In terms of politics, it gains insights into actors’ positioning and their strategic alliance-building. Both shed light on the potential for eco-social change.