bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comprehensive Density Functional Theory Analysis on Structural, Electronic, and Optical Properties of BiOF
Authors: Boran Yakup , Kara Hüsnü 
Published: July 2024

AbstractBased on density functional theory (DFT), the electronic, structural, and optical properties of bismuth oxyfluoride (BiOF) are investigated by using various exchange–correlation functionals. The local density approximation (LDA), Perdew-Burke-Ernzerhof generalized gradient approximation (PBE-GGA), Perdew-Burke-Ernzerhof generalized gradient approximation for solids (PBEsol), and Wu-Cohen (WC) functionals are implemented in Wien2k software. Modified Becke-Johnson (mBJ) and unmodified Becke-Johnson (umBJ) potentials are also applied to obtain an enhanced band gap. Spin–orbit coupling effect (SOC) is also taken into consideration due to the heavy Bi atom. When employing the PBE+umBJ potential, BiOF exhibits the most enhanced band gap energy, with a direct band gap energy of 3.72 eV. Moreover, optical properties such as dielectric functions, absorption coefficients, and optical conductivities are also calculated for photon energies up to ̴̴ 14 eV. In comparison to other available theoretical and experimental findings, the calculated results are in good agreement.
Relative tilting theory in abelian categories I: Auslander–Buchweitz–Reiten approximations theory in subcategories and cotorsion-like pairs
Authors: Argudín-Monroy Alejandro , Mendoza Hernández Octavio 
Published: July 2024

AbstractIn this paper, we introduce a special kind of relative (co)resolutions associated with a pair of classes of objects in an abelian category $$\mathcal {C}.$$

C
.

We will see that, by studying this relative (co)resolutions, we get a possible generalization of a part of the Auslander–Buchweitz approximation theory that is useful for developing n-$$\mathcal {X}$$
X
-tilting theory in Argudin Monroy and Mendoza Hernández (relative tilting theory in abelian categories II: n-$$\mathcal {X}$$
X
tilting theory. arXiv:2112.14873, 2021). With this goal, new concepts as $$\mathcal {X}$$
X
-complete and $$\mathcal {X}$$
X
-hereditary pairs are introduced as a generalization of complete and hereditary cotorsion pairs. These pairs appear in a natural way in the study of the category of representations of a quiver in an abelian category (Argudin Monroy and Mendoza Hernández in categories of quiver representations and relative cotorsion pairs. arXiv:2311.12774v1, 2023). Our main results will include an existence theorem for relative approximations, among other results related with closure properties of relative (co)resolution classes and relative homological dimensions which are essential in the development of n-$$\mathcal {X}$$
X
-tilting theory in Argudin Monroy and Mendoza Hernández (2021).
Model inductive bias enhanced deep reinforcement learning for robot navigation in crowded environments
Authors: Chen Man , Huang Yongjie , Wang Weiwen , Zhang Yao , Xu Lei , Pan Zhisong 
Published: July 2024

AbstractNavigating mobile robots in crowded environments poses a significant challenge and is essential for the coexistence of robots and humans in future intelligent societies. As a pragmatic data-driven approach, deep reinforcement learning (DRL) holds promise for addressing this challenge. However, current DRL-based navigation methods have possible improvements in understanding agent interactions, feedback mechanism design, and decision foresight in dynamic environments. This paper introduces the model inductive bias enhanced deep reinforcement learning (MIBE-DRL) method, drawing inspiration from a fusion of data-driven and model-driven techniques. MIBE-DRL extensively incorporates model inductive bias into the deep reinforcement learning framework, enhancing the efficiency and safety of robot navigation. The proposed approach entails a multi-interaction network featuring three modules designed to comprehensively understand potential agent interactions in dynamic environments. The pedestrian interaction module can model interactions among humans, while the temporal and spatial interaction modules consider agent interactions in both temporal and spatial dimensions. Additionally, the paper constructs a reward system that fully accounts for the robot’s direction and position factors. This system's directional and positional reward functions are built based on artificial potential fields (APF) and navigation rules, respectively, which can provide reasoned evaluations for the robot's motion direction and position during training, enabling it to receive comprehensive feedback. Furthermore, the incorporation of Monte-Carlo tree search (MCTS) facilitates the development of a foresighted action strategy, enabling robots to execute actions with long-term planning considerations. Experimental results demonstrate that integrating model inductive bias significantly enhances the navigation performance of MIBE-DRL. Compared to state-of-the-art methods, MIBE-DRL achieves the highest success rate in crowded environments and demonstrates advantages in navigation time and maintaining a safe social distance from humans.
A novel BWM-entropy-COPRAS group decision framework with spherical fuzzy information for digital supply chain partner selection
Authors: Gao Kai , Liu Tingting , Rong Yuan , Simic Vladimir , Garg Harish , Senapati Tapan 
Published: July 2024

AbstractThe transformation and upgrading of traditional supply chain models through digital technology receive widespread attention from the fields of circular economy, manufacturing, and sustainable development. Enterprises need to choose a digital supply chain partner (DSCP) during the process of digital transformation in uncertain and sustainable environments. Thus, the research constructs an innovative decision methodology for selecting the optimal DSCP to achieve digital transformation. The proposed methodology is propounded based upon the entropy measure, generalized Dombi operators, integrated weight-determination model, and complex proportional assessment (COPRAS) method under spherical fuzzy circumstances. Specifically, a novel entropy measure is proposed for measuring the fuzziness of spherical fuzzy (SF) sets, while generalized Dombi operators are presented for fusing SF information. The related worthwhile properties of these operators are discussed. Further, an integrated criteria weight-determination model is presented by incorporating objective weights obtained from the SF entropy-based method and subjective weights from the SF best worst method. Afterward, an improvement of the COPRAS method is proposed based on the presented generalized Dombi operators with SF information. Lastly, the practicability and validity of the proposed methodology are verified by an empirical study that selects an appropriate DSCP for a new energy vehicle enterprise to finish the goal of digital transformation. The sensitivity and comparative analysis are carried out to illustrate the stability, reliability, and superiority of the propounded methodology from multiple perspectives. The results and conclusions indicate that the propounded method affords a synthetic and systematic uncertain decision-making framework for identifying the optimal DSCP with incomplete weight information.
KnowledgeNavigator: leveraging large language models for enhanced reasoning over knowledge graph
Authors: Guo Tiezheng , Yang Qingwen , Wang Chen , Liu Yanyi , Li Pan , Tang Jiawei , Li Dapeng , Wen Yingyou 
Published: July 2024

AbstractLarge language models have achieved outstanding performance on various downstream tasks with their advanced understanding of natural language and zero-shot capability. However, they struggle with knowledge constraints, particularly in tasks requiring complex reasoning or extended logical sequences. These limitations can affect their performance in question answering by leading to inaccuracies and hallucinations. This paper proposes a novel framework called KnowledgeNavigator that leverages large language models on knowledge graphs to achieve accurate and interpretable multi-hop reasoning. Especially with an analysis-retrieval-reasoning process, KnowledgeNavigator searches the optimal path iteratively to retrieve external knowledge and guide the reasoning to reliable answers. KnowledgeNavigator treats knowledge graphs and large language models as flexible components that can be switched between different tasks without additional costs. Experiments on three benchmarks demonstrate that KnowledgeNavigator significantly improves the performance of large language models in question answering and outperforms all large language models-based baselines.
Numerical investigation of laser powder bed fusion of glass
Authors: Datsiou Kyriaki Corinna , Ashcroft Ian 
Published: July 2024

AbstractAdditive manufacturing of glass using laser powder bed fusion has been recently developed, demonstrating its potential to be applied in small scale applications such as flow reactors for the chemical engineering and pharmaceutical manufacturing industries. While previous research demonstrated that complex 3-dimensional shapes can be manufactured, built parts are often brittle, exhibit high porosity and lack transparency. This study employs a transient, heat transfer finite element analysis to shed light on the thermal response of laser—glass powder bed interaction and the impact of processing parameters. Through this understanding, the research seeks to identify practical strategies that can be employed to improve the quality and properties of the built parts. Bulk solid and powder soda lime silica glass properties are used as input in the model, while the laser heat flux and scan strategy, conversion of powder feedstock to bulk solid glass and heat losses from convection and radiation effects are introduced in the model through Fortran coding. The study showed that effective powder consolidation, resulting in well-defined geometrical features, is achieved for temperatures near the glass melting point. Additionally, uniform consolidation depths and widths can be achieved by increasing laser power, elevating substrate temperature and reducing scan speed within certain limits, whilst ensuring hatch spacing is below the corresponding single scan track width for unidirectional adjacent laser trajectories.
„Was hast du damit gemeint?“. Mittels Fragen explanative und argumentative Diskurse im schülergeleiteten Klassenrat ermöglichen
Authors: Kreuz Judith , Müller Lee Ann 
Published: July 2024

ZusammenfassungDer Beitrag beschäftigt sich mit den zentralen Praktiken des Fragens, Erklärens und Argumentierens im schülergeleiteten Klassenrat der Primarschule. Der Klassenrat ist ein schulisches Unterrichtssetting, das darauf abzielt, den Schülerinnen und Schülern (Mit‑)Verantwortung für die Gestaltung ihrer Klassengemeinschaft zu übergeben und sie darüber hinaus in ihren sozialen und sprachlichen Kompetenzen zu fördern. Eine wichtige Instanz stellt die Gesprächsleitung dar, da ihr die Aufgabe zuteil wird, das Gespräch zu steuern, das Rederecht zu verteilen und Partizipationsmöglichkeiten für ihre Mitschülerinnen und Mitschüler zu schaffen. Dabei können Fragen, die einerseits Informationen und andererseits Meinungen der Beteiligten einfordern, eine wichtige Rolle für die Realisierung dieser Aufgabe spielen. Der Beitrag geht anhand zweier Beispiele aus einem videografierten Längsschnitt empirisch den Fragen nach, welche Fragearten und -formen die Gesprächsleitenden verwenden, um die Mitschülerinnen und Mitschüler ko-konstruktiv in die Diskussionsrunden einzubinden (quantitativer Zugang mittels Codierungen) und welche Möglichkeiten die Schülerinnen und Schüler ergreifen, um Sachverhalte zu klären oder eigene Meinungen einzubringen (qualitativer Zugang mittels Gesprächsanalyse). Sowohl für diese explanativen als auch die argumentativen Äußerungen zeigt sich in freien Diskussionen („Kernphase“) eine hohe wechselseitige Orientierung an den Beiträgen anderer, die sich turnübergreifend im ko-konstruierten Erklären und explorativen Argumentieren niederschlägt. In routinisierten Diskussionsphasen hingegen, werden Beiträge eher solistisch hervorgebracht. Das gemeinsame Diskutieren verlangt Diskurskompetenzen auf Kontextualisierungsebene (z. B. Erkennen konditioneller Relevanzen zum Erklären), Vertextungsebene (z. B. Präzisierungen der Erkläraktivitäten anderer) und Markierungsebene (z. B. Verwendung von „weil“-Begründungen), welche das diskurserwerbsförderliche Potenzial von kindergeleiteten Klassenräten in Abhängigkeit verschiedener Rollen (z. B. Gesprächsleitung) und Themen (z. B. Konfliktlösung, Bestimmung eines neuen Klassenziels) unterstreichen.
A Flexural Ultrasonic Transducer for Inducing Acoustic Cavitation on Material Surfaces
Authors: Sugita Naohiro , Yamamoto Shinya , Tomioka Keita , Shinshi Tadahiko 
Published: July 2024

Abstract
Purpose
This paper proposes a flexural ultrasonic transducer specifically designed for surface treatment of materials with delicate surfaces such as skin by acoustic cavitation at low frequencies. The goal of this preliminary study is to assess the resonance frequencies and the output sound pressure of the proposed transducer and confirm generation of acoustic cavitation on the surface of an artificial skin phantom.

Methods
A transducer prototype was designed based on structural-acoustic simulation and fabricated. The proposed design employs a concave-shaped acoustic resonator with a spherical cavity, which is driven by flexural vibration of a piezoelectric ceramic disk actuator. The transducer prototype has compact dimensions of 15 mm in diameter and 8 mm in axial length, working at frequencies around flexural vibration modes of the piezoelectric disk with a thickness of 1 mm.

Results
The maximum sound pressure amplitude reached 125 kPa with an input voltage amplitude of 10 V at the second resonance frequency of 167 kHz, where the third axisymmetric eigenmode was excited. Despite enhancing the maximum pressure, the sound pressure outside the resonator attenuates because the near-field distance of the irradiated sound wave is smaller than the height of the resonator. This implies that the proposed method provides the cavitation effect on material surfaces, possibly minimizing the side effect of ultrasound irradiation on the underlying structure. Cavitation generation on a urethane gel surface was directly observed by using a high-speed video camera.

Conclusion
We confirmed that acoustic cavitation was generated and propelled to the target surface. It concludes that ultrasound irradiation using the proposed ultrasonic transducer could be a promising alternative for effective and safe ultrasound treatment of material surfaces.
Virtual Reality in Education and the Co-construction of Immediacy
Authors: Forsler Ingrid 
Published: July 2024

AbstractVirtual reality (VR) in educational settings is often promoted by commercial actors as a way to experience environments outside the classroom and a soon-to-be part of everyday teaching and learning. This study follows the development of an educational software package in a Swedish municipality that combines VR technology with 360° live footage from museums and science centers to enable students to visit these spaces from the classroom via their headsets. By focusing on the workarounds and configurations intuitively performed by teachers, students, museum staff, and technicians in this pilot project, different kinds of articulation work performed to make the technology fit with local conditions are identified, from hands-on repair and maintenance to the facilitation of interaction and presence. The collective effort put into making the technology disappear and create a feeling of unmediated experience or immediacy shows how global imaginaries about VR as an immersive technology are enacted by the participants, at the same time as the work put into the project made them challenge the idea of VR as a new everyday technology. This tension between the desire for immediacy and the hands-on work in the physical environment that goes into fulfilling these imaginaries points to the need for local production of educational technologies that recognizes their co-constructive, embodied, and situated nature.
Determination of the levels of selected essential and toxic metals in soil collected from the flower farm in Roshara Roses PLC (Bishoftu) of Ethiopia and assessment of associated human health risks
Authors: Desalegn Elizabeth , Getachew Negash , Tesfaye Gizaw , Tessema Merid 
Published: July 2024

AbstractThe overuse of pesticides and fertilizers to grow high-quality roses unintentionally introduces heavy metals into the soil which is detrimental to the human health. Thus, the aim of the current study is to examine the concentrations of the essential (Ca, Mg, Zn, Fe, Cu, Fe, Mn and Ni) and toxic metals (Cd and Pb) in the soil samples from the flower farm using Flame atomic absorption spectrometry (FAAS) and to assess the risk that heavy metals pose to human health. To obtain an optimum condition for the wet digestion of soil samples, the volume ratio of reagents, reflux time and temperature were optimized. The concentrations of Ca, Fe, Mn, Mg, Zn, Cr, Ni, Cu were found to be 3468, 4926, 450, 255, 74, 70,48.5 and 25 and 2150, 4862,472, 241, 71, 58, 37 and 12.5 mg/kg for the soil samples collected from the flower farm and road side, respectively. Cadmium and lead were not detected in the studied soil samples. Statistical analysis of variance (ANOVA) showed the concentrations of Ca and Cu in the two soil samples are significantly different. Correlation analysis showed there was strong positive association for Fe with Zn, Cr and Ni, Mg with Ca, Cu, Ni and Cr, Zn with Cr and Cu and Ni with (Cu, Zn and Cr). Values of hazard quotient (HQ) and hazard index (HI) for the heavy metals under the study in the all exposure pathways are below 1.0 indicating that none of them could be harmful to human health.
Research on reasonable coal pillar staggered distance in shallow multi-seam mining
Authors: Wang Qingxiong , Huang Qingxiang , He Yanpeng , Fan Dongling , Chen Sushe , Wang Wei 
Published: July 2024

Abstract
In shallow buried closely spaced multi-seam mining in Jurassic Coalfield in western China, due to specific reasons, it is inevitable that the lower and upper coal seam working faces and coal pillars are overlapped, resulting in stress concentration, uneven subsidence of the ground surface, the deformation and damage of the roadway intensified. In order to decrease surface damage and stress concentration, combined with physical simulation, engineering practices and theoretical analyses, reasonable coal pillar staggered distance in shallow multi-seam mining is investigated. The evolution properties of three-field (stress field, displacement field and fracture field) based on coal pillar staggered distance (CPSD) were analyzed, the determination method of rational CPSD was put up. Research findings reveal that concentrated stress results from upper and lower pillar stress superposition, and the lower pillar should be arranged in the low-stress zone. Through rational pillar arrangement, the uneven subsidence of surface is reduced, and the concentrated fractures of overlying strata and surface are decreased. For No. 1–2 and No. 2–2 coal seam mining, as CPSD is increased, subsidence above the upper coal pillar increases and the uneven subsidence of surface decreases, the concentrated stress reduced by 25%, the width of surface fractures reduced 81.1%. Consequently, rational CPSD on the basis of coupling control of three-field can be determined, the reasonable CPSD is 40–55 m. It can provide a method to realize underground safe mining and ground surface green mining in shallow multi-seam mining.
Explainable artificial intelligence and machine learning algorithms for classification of thyroid disease
Authors: Kumari Priyanka , Kaur Baljinder , Rakhra Manik , Deka Aniruddha , Byeon Haewon , Asenso Evans , Rawat Anil Kumar 
Published: July 2024

AbstractA common endocrine issue affecting millions globally is thyroid illness. For this ailment to be effectively treated and managed, an early and accurate diagnosis is essential. Machine learning algorithms have attracted a lot of attention recently in the healthcare industry and have the potential to improve thyroid disease diagnosis and categorization. The implementation of machine learning methods for the classification of thyroid disease is presented in this study. To create predictive models, the study makes use of a dataset that includes a variety of thyroid-related factors, including age, gender, and hormone levels. To evaluate the effectiveness of several machine learning techniques in classifying thyroid diseases, including random forest, support vector machines, XG-Boost, and ensemble classifier, they are implemented and compared. To ensure robust model performance, the methodology includes data preparation, feature selection, and model training, as well as strategies for hyperparameter adjustment and cross-validation. To assess the algorithms’ efficiency in differentiating between several thyroid illness classifications, such as hyperthyroidism, hypothyroidism, and the study measures the algorithms’ accuracy, precision, recall, F1-score, voting, and area under the ROC curve.
Predictive Assessment of the Interior Quality of Apartments Based on Multimodal Data with Variable Visual Input
Authors: Despotovic Miroslav , Stumpe Eric , Brunauer Wolfgang A. , Lehner Matthias , Zeppelzauer Matthias 
Published: July 2024

AbstractMultimodal analyses in the context of automated real estate valuation (AVM) offer the possibility of enriching the models with additional information, which benefits the accuracy of the models. However, this variety of data can overwhelm common machine learning models, which generally only process certain data modalities and only a fixed data quantity. This leads to a bottleneck in information processing, as in many cases much more information is available per observation, of which only a single selection or sample is used to train the algorithm and the remaining information is disregarded. We propose a multimodal network architecture that incorporates both textual and visual inputs and fuses their information. Furthermore, we introduce a training strategy that can take advantage of a variable number of input images for each real estate object. In our experiments, we test and compare several unimodal (baseline) models with our multimodal architecture. Our approach shows several advantages in terms of model performance over unimodal approaches. The results show the best performance for the multimodal model with a variable number of visual inputs, as well as improved prediction for the underrepresented classes of indoor quality, mitigating the effects of unbalanced data. With the presented approach, which efficiently combines and merges multiple data modalities, we have shown how such a method can be easily adapted to an AVM for the extraction of supplementary information.
A new approach to the similarity problem
Authors: Papapetros E. 
Published: July 2024

AbstractWe say that a $$C^*$$

C
∗

-algebra $${\mathcal {A}}$$
A
satisfies the similarity property ((SP)) if every bounded homomorphism $$u: {\mathcal {A}}\rightarrow {\mathcal {B}}(H),$$

u
:
A
→
B
(
H
)
,

where H is a Hilbert space, is similar to a $$*$$

∗

-homomorphism and that a von Neumann algebra $${\mathcal {M}}$$
M
satisfies the weak similarity property ((WSP)) if every $$\textrm{w}^*$$

w
∗

-continuous, unital and bounded homomorphism $$\pi : {\mathcal {M}}\rightarrow {\mathcal {B}}(H),$$

π
:
M
→
B
(
H
)
,

where H is a Hilbert space, is similar to a $$*$$

∗

-homomorphism. The similarity problem is known to be equivalent to the question of whether every von Neumann algebra is hyperreflexive. We improve on that by introducing the following hypothesis (EP): Every separably acting von Neumann algebra with a cyclic vector is hyperreflexive. We prove that under (EP), every separably acting von Neumann algebra satisfies (WSP) and we pass from the case of separably acting von Neumann algebras to all $$C^*$$

C
∗

-algebras.