bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sleep quality in cancer patients: a common metric for several instruments measuring sleep quality
Authors: Friedrich Michael , Schulte Thomas , Malburg Merle , Hinz Andreas 
Published: August 2024

Abstract
Purpose
Sleep problems are frequently observed in cancer patients. Multiple questionnaires for assessing sleep quality have been developed. The aim of this study was to present transfer rules that allow the conversion of the patients’ scores from one questionnaire to another. In addition, we anchored this common metric to the general population.

Methods
A sample of 1,733 cancer patients completed the following questionnaires: Pittsburgh Sleep Quality Index, Insomnia Sleep Index, Jenkins Sleep Scale, EORTC QLQ-C30, and the sleep scale of the EORTC QLQ-SURV100. The methods for establishing a common metric were based on Item Response Theory.

Results
The main result of the study is a figure that allows the conversion from one of the above-mentioned sleep scales into another. Furthermore, the scores of the questionnaires can be transferred to theta scores that indicate the position within the group of cancer patients and also to T scores that indicate the position in relation to the general population. The correlations between the sleep scales ranged between 0.70 and 0.85.

Conclusions
The conversion rules presented in the study enable researchers and clinicians to directly compare single scores or mean scores across studies using different sleep scales, to assess the degree of sleep problems with regard to the general population, and to relate cutoff scores from one questionnaire to another.
The mutual effect of nutrients on plant–herbivore interactions
Authors: Zeng Ming 
Published: August 2024

AbstractNutrients contained in plant tissues serve as the driving force behind plant and herbivore interactions. The nutrient level, including different kinds of elements and amount of fertilizer, can vary owing to variations in soil composition and this can directly impact plant defense response against herbivores. Plants take up nutrients via two pathways: (i) directly through the root hairs and epidermis and (ii) indirectly through the beneficial microbes that interact with plant roots, such as mycorrhizal fungi and rhizobacteria. The associated microbes, modifying plant nutrient composition, can indirectly affect plant and herbivore interactions. These direct and indirect nutrient uptake pathways adjust plant resistance or tolerance to herbivore attack, as evidenced by factors such as herbivore performance, plant nutrient status, biomass allocation, and compensatory growth. In turn, aboveground and belowground herbivory can exert an effect on nutrient exchange between plants and their associated microbes, primarily on the key resources such as carbon (C), nitrogen (N) and phosphorus (P). Furthermore, it shows that herbivory, in the presence of plant associated beneficial microbes, has no negative effect on plant N and C content. The molecular mechanisms underlying these ecological interactions are being systematically uncovered. In order to outline the research progress in this field, this review synthesizes the current scientific literature regarding the mutual effect of nutrients on plant–herbivore interactions. It aims to support efforts in maintaining the sustainability of ecosystem by optimizing plant defense strategies via managing nutrient variability.
An autonomous centreless approach to chunk-wise data partitioning
Authors: Gu Xiaowei 
Published: August 2024

AbstractIn this paper, a novel autonomous centreless algorithm is proposed for data partitioning. The proposed algorithm firstly constructs the nearest neighbour affinity graph and identifies the local peaks of data density to build micro-clusters. Unlike the vast majority of partitional clustering algorithms, the proposed algorithm does not rely on singleton prototypes, namely, centres or medoids of the micro-clusters to partition the data space. Instead, these micro-clusters are directly utilised to attract nearby data samples to form shape-free Voronoi tessellations, hence, being centreless and robust to noisy data. A fusion scheme is further implemented to fuse these data clouds with higher intra-cluster similarity together to attain a more compact partitioning of data. The proposed algorithm is able to perform data partitioning on a chunk-wise basis and is highly computationally efficient with the default distance measure. Therefore, it is suitable for both static data partitioning in offline scenarios and streaming data partitioning in online scenarios. Numerical examples on a variety of benchmark datasets demonstrate the efficacy of the proposed algorithm.
An explainable approach to deep learning from CT-scans for Covid identification
Authors: Soares Eduardo , Angelov Plamen , Zhang Ziyang 
Published: August 2024

AbstractThe Covid-19 disease has spread widely over the whole world since the beginning of 2020. Following the epidemic which started in Wuhan, China on January 30, 2020 the World Health Organization (WHO) declared a global health emergency and a pandemic. Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies to control the spread of this new disease. Recent findings have observed specific image patterns from computed tomography (CT) for patients infected by SARS-CoV-2 which are distinct from the other pulmonary diseases. In this paper, we propose an explainable-by-design that has an integrated image segmentation mechanism based on SLIC that improves the algorithm performance and the interpretability of the resulting model. In order to evaluate the proposed approach, we used the SARS-CoV-2 CT scan dataset that we published recently and has been widely used in the literature. The proposed Super-xDNN could obtain statistically better results than traditional deep learning approaches as DenseNet-201 and Resnet-152. Furthermore, it also improved the explainability and interpretability of its decision mechanism when compared with the xDNN basis approach that uses the whole image as prototype. The segmentation mechanism of Super-xDNN favored a decision structure that is more close to the human logic. Moreover, it also allowed the provision of new insights as a heat-map which highlights the areas with highest similarities with Covid-19 prototypes, and an estimation of the area affected by the disease.
Maize-grain zinc and iron concentrations as influenced by agronomic management and biophysical factors: a meta-analysis
Authors: Kihara Job , Sileshi Gudeta W , Bolo Peter , Mutambu Dominic , Senthilkumar Kalimuthu , Sila Andrew , Devkota Mina , Saito Kazuki 
Published: August 2024

AbstractHuman Zn and Fe deficiencies can be reduced through agronomic biofortification, but information on factors influencing maize grain-Zn and -Fe levels remain scanty. This analysis: (1) Establishes the global distribution of Zn and Fe concentrations in maize grain; (2) assess the contribution of different agronomic practices to the effectiveness of Zn fertilizers for increasing grain yields, and Zn and Fe levels in maize grain; and (3) identify key biophysical factors and metrics to more effectively guide agronomic biofortification of Zn. Using 5874 data points in 138 published papers from 34 countries, we estimated a 7.5% probability of grain-Zn concentrations exceeding the benchmark target of 38 mg kg−1. Using 3187 data points from 65 studies across 27 countries we estimated a 8.5% probability of grain-Fe concentrations exceeding the target of 60 mg kg−1. Our 70-paper meta-analysis revealed that applying Zn and/or Fe in combination with inorganic NPK fertilizer can increase maize-grain-Zn and-Fe concentrations by 31% (p < 0.01) relative to the control (NPK only). In 52% and 37.5% of the studies respectively, grain-Zn and -Fe levels showed significant and concomitant increase with grain-yield increases. Soil organic matter, pH, soil-available Zn, organic input applications, and N, Zn and Fe application rates and methods were among the key factors influencing grain Zn and Fe. We conclude there is substantial room for increasing maize-grain Zn and Fe concentrations, and applying Zn, especially in combined soil and foliar applications, gives substantial increases in grain-Zn and -Fe concentrations. This global review reveals large data gaps on maize-grain nutrient levels, and we call for routine collection of such information in future research.
Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells
Authors: Olatunji K. O. , Oladipo S. O. , Madyira D. M. , Sun Y. 
Published: August 2024

Abstract
The study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks.

Graphical Abstract
mint: Integrating scientific visualizations into virtual reality
Authors: Geringer Sergej , Geiselhart Florian , Bäuerle Alex , Dec Dominik , Odenthal Olivia , Reina Guido , Ropinski Timo , Weiskopf Daniel 
Published: August 2024

Abstract
We present an image-based approach to integrate state-of-the-art scientific visualization into virtual reality (VR) environments: the mint visualization/VR inter-operation system. We enable the integration of visualization algorithms from within their software frameworks directly into VR without the need to explicitly port visualization implementations to the underlying VR framework—thus retaining their capabilities, specializations, and optimizations. Consequently, our approach also facilitates enriching VR-based scientific data exploration with established or novel VR immersion and interaction techniques available in VR authoring tools. The separation of concerns enables researchers and users in different domains, like virtual immersive environments, immersive analytics, and scientific visualization, to independently work with existing software suitable for their domain while being able to interface with one another easily. We present our system architecture and inter-operation protocol (mint), an example of a collaborative VR environment implemented in the Unity engine (VRAUKE), as well as the integration of the protocol for the visualization frameworks Inviwo, MegaMol, and ParaView. Our implementation is publicly available as open-source software.

Graphical abstract
Changes in Neuroimmunological Synapses During Cerebral Ischemia
Authors: Bitar Lynn , Puig Berta , Oertner Thomas G. , Dénes Ádám , Magnus Tim 
Published: August 2024

AbstractThe direct interplay between the immune and nervous systems is now well established. Within the brain, these interactions take place between neurons and resident glial cells, i.e., microglia and astrocytes, or infiltrating immune cells, influenced by systemic factors. A special form of physical cell–cell interactions is the so-called “neuroimmunological (NI) synapse.” There is compelling evidence that the same signaling pathways that regulate inflammatory responses to injury or ischemia also play potent roles in brain development, plasticity, and function. Proper synaptic wiring is as important during development as it is during disease states, as it is necessary for activity-dependent refinement of neuronal circuits. Since the process of forming synaptic connections in the brain is highly dynamic, with constant changes in strength and connectivity, the immune component is perfectly suited for the regulatory task as it is in constant turnover. Many cellular and molecular players in this interaction remain to be uncovered, especially in pathological states. In this review, we discuss and propose possible communication hubs between components of the adaptive and innate immune systems and the synaptic element in ischemic stroke pathology.
Collaborative Inhibition: A Phenomenological Perspective
Authors: Gyollai Daniel 
Published: August 2024

AbstractThe tendency for people to remember less as members of a group than they would be capable of were they to remember alone is a phenomenon known as collaborative inhibition. The article offers a phenomenological account of this highly counterintuitive effect of group remembering. It argues that the mutual failure to live up to one’s potential does not warrant the standard, strongly negative views about the role of others in recall. Rather, the phenomenon may imply that sharedness itself becomes constitutive of the process in the sense that interaction partners co-determine what and how to remember. Drawing on phenomenological approaches to remembering and second-person engagement, the article argues that individuals participating in shared remembering co-construct their memories by reciprocally and dynamically incorporating each other’s perspectives, attitudes, and emotions about their shared past.
Gender differences in online education
Authors: Erdemli Candan , Vall Castelló Judit 
Published: August 2024

AbstractOnline learning resources have become extremely popular, particularly after the restrictions caused by the Covid-19 outbreak. In this paper, we use data for Spain at the individual level from an online math learning platform which is used by children from over 100 countries, to document the gender differences in the context of online learning. We quantify the gender gaps in effort and relative performance outcomes and analyze whether the gaps differ by the gender of the parent who mainly supervises the children. Our main results point toward significant gender gaps in the relative performance outcomes in favor of boys, while the evidence for the effort gender gaps is only significant when we compare the siblings of the opposite gender (controlling for parent fixed effects). Further, we find that living in municipalities with more egalitarian gender norms is associated with narrower or positive gender gaps in effort outcomes, while we do not find such differences in the relative performance outcomes. Taking into account the increase in the use of online learning tools and their progressive integration into the regular educational system, our results provide important information to minimize gender biases in these new settings.
AI, Radical Ignorance, and the Institutional Approach to Consent
Authors: Steinberg Etye 
Published: August 2024

AbstractMore and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this the problem of radical ignorance. Interestingly, radical ignorance exists in consent contexts other than AI, where it seems that individuals can provide informed consent. The article argues that radical ignorance can undermine informed consent in some contexts but not others because, under certain institutional, autonomy-protecting conditions, consent can be valid without being (perfectly) informed. By understanding these institutional conditions, we can formulate practical solutions to foster valid, albeit imperfectly informed consent across various decision contexts and within different institutions.
Multi-objective Optimization of Process Parameters for Surface Quality and Geometric Tolerances of AlSi10Mg Samples Produced by Additive Manufacturing Method Using Taguchi-Based Gray Relational Analysis
Authors: Işik Uğur , Demir Halil , Özlü Barış 
Published: August 2024

AbstractIn this study, it has been focused on examining the effects of production parameters on quality parameters such as surface roughness and geometric tolerances in the production of AlSi10Mg samples by the additive manufacturing method. The experimental design has been prepared according to the Taguchi L27 orthogonal array. As a result, in the production of samples, increasing laser power (P) contributed positively to surface roughness and diameter change, and increasing scanning distance (SD) negatively contributed to circularity change and concentricity. Further, it has been determined that increasing the scanning speed (SS) negatively affects the concentricity change of the produced samples. The optimum production parameters for surface roughness and diameter variation has been determined as A1B1C3. The optimum production parameters for circularity variation and concentricity have been determined as A3B3C1 and A3B1C1, respectively. According to the ANOVA analysis results, the most effective parameters for surface roughness, diameter change, circularity change and concentricity have been 53.22% P, 62.45% SD, 37.23% SS and 40.41% SD, respectively. Furthermore, as a result of the gray relationship analysis (GRA) performed for the output parameters, the optimum production parameter has been determined as A2B1C3.
Progressive and efficient verification for digital signatures: extensions and experimental results
Authors: Boschini Cecilia , Fiore Dario , Pagnin Elena , Torresetti Luca , Visconti Andrea 
Published: August 2024

AbstractDigital signatures are widely deployed to authenticate the source of incoming information, or to certify data integrity. Common signature verification procedures return a decision (accept/reject) only at the very end of the execution. If interrupted prematurely, however, the verification process cannot infer any meaningful information about the validity of the given signature. This limitation is due to the algorithm design solely, and it is not inherent to signature verification. In this work, we provide a formal framework to extract information from prematurely interrupted signature verification, independently of why the process halts: we propose a generic verification procedure that progressively builds confidence on the final decision. Our transformation builds on a simple but powerful intuition and applies to a wide range of existing schemes considered to be post-quantum secure, including some lattice-based and multivariate equations based constructions. We demonstrate the feasibility of our approach through an implementation on off-the-shelf resource-constrained devices. In particular, an intensive testing activity has been conducted measuring the increase of performance on three IoT boards—i.e., Arduino, Raspberry, and Espressif—and a consumer-grade laptop. While the primary motivation of progressive verification is to mitigate unexpected interruptions, we show that verifiers can leverage it in two innovative ways. First, progressive verification can be used to intentionally adjust the soundness of the verification process. Second, our transformation splits verification into a computationally intensive offline set-up (run once), and an efficient online verification that is faster than the original algorithm. We conclude showing how to tweak our compiler for progressive verification to work on a wide range of signatures with properties, on three real-life use cases, and in combination with efficient verification.
The effect of carbon coating on the arsenite sorption by magnetic carbon nanocomposites
Authors: Burbano A. A. , Lassalle V. L. , Horst M. F. , Gascó G. , Méndez A. 
Published: August 2024

Abstract
Arsenic pollution has emerged through anthropogenic activities and natural mineral leaching processes. This study aims to advance the use of magnetic carbon nanocomposites (MCNs) in the sorption of arsenic, studying the influence of feedstock and the presence of carbon coating on magnetic nanoparticles. Previous works have shown that post-pyrolysis treatment improves the stability of MCNs by reducing iron leaching due to the formation of a carbon coating that encapsulates the iron oxide nanoparticles. However, this carbon coating could influence the adsorption properties of MCNs. This investigation deals with arsenic adsorption by four MCNs prepared by co-precipitation of magnetite (Fe3O4) nanoparticle into four carbonaceous matrixes, followed by a post-pyrolysis treatment. The pristine carbonaceous matrixes used in the present work were commercial activated carbon (CAC), charcoal (CC), hydrochar from the orange residue (HCOR), and biochar from sunflower husk (BCSFH). Pristine carbonaceous materials and MCNs without post-pyrolyzed were also used as arsenic sorbents in water solutions. Additionally, kinetic studies were carried out to explore the sorption properties of different MCNs and pristine materials, concerning the removal efficiencies (expressed as a percentage) and adsorption capacities, determining the equilibrium time. The results demonstrated that the presence of magnetite increases the adsorption of arsenic, being higher in the case of materials obtained by direct co-precipitation than in materials subjected to a post-pyrolysis process. The presence of a carbon layerprotecting the magnetite slightly decreases the adsorption of arsenic.
The use of doping control data to administer sex-based eligibility regulations: an analysis of how the World Anti-Doping Agency and international sport federations violate data protection laws
Authors: Mazzucco Marcus , Brehaut Jensen 
Published: August 2024

AbstractThe World Anti-Doping Agency (WADA)’s World Anti-Doping Code (WADC) provides that anti-doping organizations, such as international sport federations (IFs), may use data from a doping control test to monitor compliance with sex-based eligibility regulations that regulate the serum testosterone levels of transgender and intersex athletes. This contemplated use of doping control data has been incorporated into the regulations of several IFs and is facilitated by WADA’s Anti-Doping Administration Management System (ADAMS)—a web-based database managed by WADA in Canada that contains analytical results from doping control tests and is accessible by anti-doping organizations. WADA’s collection, use and disclosure of personal information through ADAMS is subject to Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA). This paper examines WADA’s non-compliance with PIPEDA when it discloses doping control data in ADAMS to an IF for the purpose of the IF’s administration of sex-based eligibility regulations, and how a complaint about WADA’s data disclosures might be handled by Canada’s Privacy Commissioner. The paper also examines the application of the European Union’s General Data Protection Regulation to IFs that seek to use doping control data stored in or outside of ADAMS to administer sex-based eligibility regulations. The paper concludes with the perspective that data protection laws can be used to challenge the implementation of sex-based eligibility regulations, alongside other human rights-based legal strategies.