bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mineral- and Rock Type Localities in Romania and Their Potential Geoheritage Value
Authors: Gál Ágnes , Szakács Alexandru , Ionescu Corina , Kovacs Marinel 
Published: July 2024

AbstractA complete inventory of 16 mineral type localities and 1 rock type locality in Romania, as well as their assessed geoheritage characteristics and values are presented for the first time. Thirty-nine mineral species, one chemical element and one common rock type were discovered, described and named in Romania. Most of these are closely linked to traditional mining areas: the Baia Mare region, the Apuseni Mts. and the Banat region. Three mineral type localities are outstandingly rich: Săcărâmb/Nagyág in the southern Apuseni Mts. (8 new minerals), Baia Sprie/Felsőbánya in the Baia Mare region (6 new minerals), and Băița Bihor/Rézbánya in the Northern Apuseni Mts. (6 new minerals). The scientific, educational and touristic value and significance of the type localities regarded as „geosites” and „geodiversity sites” are discussed. Based on the quantitative assessment of the scientific, educational and touristic values, the type localities were hierarchically classified, Baia Sprie, Săcărâmb and Măgura Uroiului being the highest ranked. From the geoconservation perspective, site protection and valuation strategies at local and/or regional scales are envisaged.
Enamel matrix proteins in promoting saliva lubrication
Authors: Wang Hujun , Tang Yue , Qiu Haonan , Hu Jingyang , Su Yuan , Zheng Jing , Zhou Zhongrong 
Published: July 2024

AbstractAnti-wear performance of human enamel in the mouth is closely related to the lubrication of salivary pellicle. It is well known that the inorganic hydroxyapatite (HA) of the enamel plays an important role in the adsorption and pellicle-forming of salivary proteins on the enamel, but the role of enamel matrix proteins remains unclear. In this study, the adsorption and lubrication behavior of salivary proteins on original, heated, and deproteinated enamel surfaces was comparatively investigated using an atomic force microscopy and nano-indentation/scratch techniques. Compared with that on the original enamel surface, the adsorption and lubrication behavior of salivary proteins remains almost unchanged on the heated enamel surface (where the enamel matrix proteins are denatured but the size of HA crystalline nanoparticles keeps constant) but exhibits an obvious compromise on the deproteinated enamel surface (where the enamel matrix proteins are removed and agglomeration of HA crystallites occurs). The HA agglomeration weakens the electrostatic interaction of enamel surfaces with salivary proteins to cause a distinct negative influence on the adsorption and pellicle-forming of salivary proteins. Further, the negative effect is confirmed with a quartz crystal microbalance with dissipation. In summary, by regulating enamel nanostructure for appropriate electrostatic interactions between salivary proteins and enamel surfaces, the enamel matrix proteins play an essential role in the adsorption and pellicle-forming of salivary proteins on human enamel, and then contribute to saliva lubrication, which provides the enamel with an anti-wear mechanism. The findings will promote and assist the design of enamel-inspired anti-wear materials.
Nickel phosphorous trisulfide: A ternary 2D material with an ultra-low coefficient of friction
Authors: Deng Haoyu , Yu Tongtong , Du Changhe , Shen Ruilin , Zhao Yongkang , He Xinjian , Feng Yange , Zhang Liqiang , Wang Daoai 
Published: July 2024

AbstractUltra-low friction is crucial for the anti-friction, anti-wear, and long-life operation of nanodevices. However, very few two-dimensional materials can achieve ultra-low friction, and they have some limitations in their applications. Therefore, exploring novel materials with ultra-low friction properties is greatly significant. The emergence of ternary two-dimensional materials has opened new opportunities for nanoscale ultra-low friction. This study introduced nickel phosphorous trisulfide (NiPS3, referred to as NPS), a novel two-dimensional ternary material capable of achieving ultralow friction in a vacuum, into the large nanotribology family. Large-size and high-quality NPS crystals with up to 14 mm × 6 mm × 0.3 mm dimensions were grown using the chemical vapor transport method. The NPS nanosheets were obtained using mechanical exfoliation. The dependence of the NPS nanotribology on layer, velocity, and angle was systematically investigated using lateral force microscopy. Interestingly, the coefficient of friction (COF) of NPS with multilayers was decreased to about 0.0045 under 0.005 Pa vacuum condition (with load up to 767.8 nN), achieving the ultra-low friction state. The analysis of the frictional dissipation energy and adhesive forces showed that NPS with multilayers had minimum frictional dissipation energy and adhesive forces since the interlayer interactions were weak and the meniscus force was excluded under vacuum conditions. This study on the nanoscale friction of a ternary two-dimensional material lays a foundation for exploring the nanoscale friction and friction origin of other two-dimensional materials in the future.
Kosten von COVID-19 – Auswertungen von Routinedaten einer Unfallversicherung
Authors: Dulon Madeleine , Wendeler Dana , Nienhaus Albert 
Published: July 2024

Zusammenfassung
Hintergrund
Für Beschäftigte im Gesundheitswesen, die 2020 an COVID-19 erkrankten, wird die Entwicklung der Folgekosten über einen Zeitraum von 3,5 Jahren beschrieben.

Methodik
Auf Basis der Routinedaten der Berufsgenossenschaft für Gesundheitsdienst und Wohlfahrtspflege (BGW) werden Versicherungsfälle ausgewählt, die im Jahr 2020 an COVID-19 erkrankt waren. Für diese Fälle werden die Leistungen mit Buchungsdatum bis zum 30.06.2023 ausgewertet. Für die Analyse soziodemografischer Merkmale wird die Stichprobe entsprechend ihres Erkrankungsdatums der ersten und zweiten Pandemiewelle zugeordnet.

Ergebnisse
Die Auswertung basiert auf 47.697 Fällen. In der Gruppe aus der ersten Welle kommen Indikatoren für schwere Krankheitsverläufe signifikant häufiger vor, wie stationäre Behandlung, Zahlung von Verletztengeld und Anteil an Verstorbenen. Die Kosten belaufen sich über den betrachteten Buchungszeitraum auf 117,2 Mio. Euro mit einem Gipfel der jährlichen Aufwendungen in 2022. Den größten Anteil an den Gesamtkosten haben Verletztengeld und stationäre Behandlung (52,2 % bzw. 27,5 %). Bei beiden zeigen sich die deutlichsten Zuwächse in den Jahren 2021 und 2022. Für ambulante Heilbehandlungen nehmen die Kosten bezogen auf die Gesamtkosten kontinuierlich von 3,1 % in 2020 auf 14,9 % in 2023 zu. Dies ist auf eine Zunahme bei den physikalischen Therapien zurückzuführen.

Diskussion
Für Versicherte mit COVID-19 im Jahr 2020 entstehen auch im Jahr 2023 noch Kosten. Der überwiegende Anteil der Kosten entfällt auf Verletztengeld und stationäre Behandlung. Die Kosten für Maßnahmen zur Rehabilitation im ambulanten Versorgungsbereich nehmen in den Folgejahren nach der Erkrankung deutlich zu.
TCohPrompt: task-coherent prompt-oriented fine-tuning for relation extraction
Authors: Long Jun , Yin Zhuoying , Liu Chao , Huang Wenti 
Published: July 2024

AbstractPrompt-tuning has emerged as a promising approach for improving the performance of classification tasks by converting them into masked language modeling problems through the insertion of text templates. Despite its considerable success, applying this approach to relation extraction is challenging. Predicting the relation, often expressed as a specific word or phrase between two entities, usually requires creating mappings from these terms to an existing lexicon and introducing extra learnable parameters. This can lead to a decrease in coherence between the pre-training task and fine-tuning. To address this issue, we propose a novel method for prompt-tuning in relation extraction, aiming to enhance the coherence between fine-tuning and pre-training tasks. Specifically, we avoid the need for a suitable relation word by converting the relation into relational semantic keywords, which are representative phrases that encapsulate the essence of the relation. Moreover, we employ a composite loss function that optimizes the model at both token and relation levels. Our approach incorporates the masked language modeling (MLM) loss and the entity pair constraint loss for predicted tokens. For relation level optimization, we use both the cross-entropy loss and TransE. Extensive experimental results on four datasets demonstrate that our method significantly improves performance in relation extraction tasks. The results show an average improvement of approximately 1.6 points in F1 metrics compared to the current state-of-the-art model. Codes are released at https://github.com/12138yx/TCohPrompt.
Controlling carbon emissions through modeling and optimization: addressing an earth system and environment challenge
Authors: Shahid Iqra , Naqvi Rehana Ali , Yousaf M. , Siddiqui A. M. , Sohail A. 
Published: July 2024

AbstractThis study aims to analyze the trend of carbon dioxide CO2 emissions from various sources in Pakistan between 1990 and 2020 to effectively model the underlying dynamics of carbon emissions. The design and fitting of the model to historical emissions data reveal significant trends and patterns, highlighting the alarming increase in CO2 emissions. These findings underscore the necessity for robust policy interventions to mitigate emissions and achieve sustainable development goals (SDGs). This work can contribute to addressing the challenges of recent plans targeting global warming and the climate emergency. By controlling these parameters, the mean reversion can be managed, allowing for the control of the increasing rate of carbon emissions in regions threatened by climate change. The O-U model provides a valuable framework for understanding the stochastic nature of carbon emissions, offering insights into the persistence and variability of emission levels over time. The optimized parametric thresholds of the stochastic model, after synchronizing it with the real data, reveal that the challenge of carbon emissions cannot be naturally resolved and serious interventions are highly desired. These include robust policy measures to mitigate emissions, improve air quality, and combat climate change.
Next stop carbon dioxide removal? German climate policies and the risky road to negative emission technologies
Authors: Haas Tobias , Schoppek Dorothea Elena 
Published: July 2024

AbstractBoth the global and the German climate targets are ambitious given recent emission reduction rates. In addition to emission reduction measures, carbon dioxide removal has been increasingly discussed recently and initial measures have been developed to potentially scale-up carbon removals in order to meet net zero targets. In this article, we undertake a political economy-based analysis of the historical development and structural conditions of German climate policy measures and their enabling role for the emergence and strengthening of carbon dioxide removal policy options. We refer to regulation theory and the Modell Deutschland approach and argue that within this model the concept of ecological modernization has been established as the dominant approach to regulate society-nature relations. In light of the ambitious climate policy goals, such as net zero by 2045, ecological modernization reaches its current limits and must be recalibrated. Against this background, we conclude by discussing the risk of mitigation deterrence associated with carbon dioxide removals and considering alternative pathways, such as degrowth, to social ecological transformation.
A review on the application of biopolymers (xanthan, agar and guar) for sustainable improvement of soil
Authors: Kumar Sumit , Yadav Brahm Deo , Raj Rohit 
Published: July 2024

AbstractThis review explores the use of biopolymers as sustainable alternatives for soil improvement in geotechnical engineering. Specifically, focusing on three commonly used biopolymers: xanthan gum, agar gum, and guar gum, this review highlights their potential applications and effects on the geotechnical properties of different soil types. Xanthan gum exhibits improved impermeability and water storage capacity, making it suitable for anti-wind erosion, soil remediation, and grouting vegetation growth. Agar gum shows promise in liquefaction remediation with its gelation process and absence of chemical reactions during soil cementation. It is resistant to thermal degradation, oxidation, acid-alkaline environments, and salt concentrations. Its hygroscopicity and stability make it useful for soil stabilization. Guar gum enhances shear strength, reduces permeability, and improves soil stability, making it effective for mine tailing stabilisation, slope stability, and other geotechnical applications. Being a polymeric molecule, as it breaks down, it becomes more biopolymer-rich, making it more resistant to wetting and drying processes. This review discusses biopolymer-treated soils’ strengthening mechanisms, such as hydrogel formation and cross-links between soil particles. The utilisation of biopolymers offers advantages in terms of abundance, non-toxicity, and potential for reducing greenhouse gas emissions. The review also identifies the use of biopolymers in mine tailings. Although further research is needed to optimise their application and explore their full potential in sustainable improvement practices, This integration of biopolymers in soil engineering would provide a more environmentally friendly approach.
Perovskite multiple quantum wells: toward artificial construction and lasing
Authors: Chin Sang-Hyun 
Published: July 2024

AbstractHalide perovskites (HPs) are rising semiconductors in optoelectronics, such as photovoltaic and light-emitting applications. However currently, laser diode with HPs is still not realized yet. When coherent emissions are targeted as the applications, materials with lower lasing thresholds and increased stabilities must be ensured to realize the device. Perovskite multiple-quantum-wells (MQWs) can potentially ease the population inversion by confining photoexcitation, but there must be several more progress on fabrication process and structural design are required to realize electrically pumped laser. Hence, in this perspective, current research trend and progress in this field are summarized and some suggestions to design HP MQW lasers, whether self-assembled or artificially fabricated, ground on recent literature are casted.
Intervertebral disc creep behaviour through viscoelastic models: an in-vitro study
Authors: Sciortino Vincenza , Jansen Jan Ulrich , Cerniglia Donatella , Ingrassia Tommaso , Wilke Hans-Joachim 
Published: July 2024

AbstractThe intervertebral disc (IVD) is a complex biological structure that ensures the spine strength, stability, mobility, and flexibility. This is achieved due to its biphasic nature which is attained by its solid phase (annulus fibrosus) and fluid phases (nucleus pulposus). Hence, the IVD biomechanical response to long-term loads, which is critical as it affects hydration, and nutrients-water transport influencing disc height reduction, has been further explored and mathematically modelled in this paper. An in-vitro study was performed on seven human lumbar spine specimens (L4-5), to assess if the classical rheological models and Nutting's power law can model in a simple way the intermediate characteristics between solid and fluid of the IVD. Creep tests were conducted by applying a static compression load of 500 N for 15 min. A correlation analysis was done (Pearson, p < 0.05) between the model parameters and the maximum value of Disc Height Reduction, followed by a linear regression analysis. In summary, the long-term IVD mechanical behavior was modeled in a simple way, emphasizing that yet there is no mathematical certainty about this mechanical behavior. Hence, a future aim might be to develop intervertebral disc prostheses capable of replicating only the disc mechanical response, without necessarily considering the microscopic-level biological drivers. Therefore, a future goal is to fully understand and model the disc's mechanical response toward the design of new disc prostheses that would consider only the macroscopic aspect, without considering the biological aspects.
Graph representation learning for parameter transferability in quantum approximate optimization algorithm
Authors: Falla Jose , Langfitt Quinn , Alexeev Yuri , Safro Ilya 
Published: July 2024

AbstractThe quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. Optimal QAOA parameter concentration effects for special MaxCut problem instances have been observed, but a rigorous study of the subject is still lacking. Due to clustering of optimal QAOA parameters for MaxCut, successful parameter transferability between different MaxCut instances can be explained and predicted based on local properties of the graphs, including the type of subgraphs (lightcones) from which graphs are composed as well as the overall degree of nodes in the graph (parity). In this work, we apply five different graph embedding techniques to determine good donor candidates for parameter transferability, including parameter transferability between different classes of MaxCut instances. Using this technique, we effectively reduce the number of iterations required for parameter optimization, obtaining an approximate solution to the target problem with an order of magnitude speedup. This procedure also effectively removes the problem of encountering barren plateaus during the variational optimization of parameters. Additionally, our findings demonstrate that the transferred parameters maintain effectiveness when subjected to noise, supporting their use in real-world quantum applications. This work presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that QAOA can be substantially accelerated under both ideal and noisy conditions.
Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends
Authors: Valizadeh Alireza , Amirhosseini Mohammad Hossein 
Published: July 2024

AbstractMachine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively.
The importance of adjusting the processing parameters for the resulting material density of PBF-LB AISI 316L lattice structures
Authors: Szcześniak Krzysztof , Pawlak Andrzej , Dybała Bogdan , Kras Aleksander 
Published: July 2024

AbstractLattice structures are becoming more commonly used in the design of components for additive manufacturing. This is due to their ability to reduce the weight of manufactured parts, minimize material consumption, and achieve specific properties by modifying their geometry. As the applications of lattice structures continue to evolve, it is essential to determine whether the process parameters used in the PBF-LB (Laser Beam Powder Bed Fusion) process for manufacturing these structures should be the same as or different from those used for larger cross-sectional components. An analysis of the existing literature revealed insufficient data on this subject, which inspired this study. Experiments conducted using AISI 316L stainless steel showed that lattice structures can be produced with significantly lower volumetric energy density, while maintaining a high relative material density. In the experiment on lattice structures made of BCCZ and gyroid unit cells, a relative material density of over 99.5% was achieved with a volumetric energy density of approximately 33 J/mm3. These findings are significant for the fabrication of lattice structures. The lower volumetric energy density typically allows for greater geometric accuracy and reduced internal stresses. Furthermore, it has been proven that the nodes of the structure are critical places exposed to porosity formation.
Memory recall can increase authenticity and engagement: understanding the role of work context and memory function in autobiographical memories
Authors: Sutton Anna 
Published: July 2024

AbstractAuthenticity is positively associated with important psychological outcomes in the workplace, such as well-being and engagement, and is reliant on having a coherent sense of self. This coherent self is maintained by recalling autobiographical memories (AM). But the specific relationships between authenticity and memory functions are unclear. This study aims to first, identify differences in memory function and content in the recall of authentic and inauthentic moments and second, address how these AM functions are related to authenticity, engagement and well-being. Participants completed measures of authenticity, engagement and well-being before and after a memory recall task. For the recall task, they were randomly assigned to recall either an authentic, inauthentic, or control memory. Recall narratives were coded for memory functions (self-identity, social, directive) and for physical/social context features. Authentic moments, inauthentic moments and controls differed in terms of social and physical contexts. Authentic moments were most likely to involve being alone while inauthentic moments were most likely to happen at work. Authentic or inauthentic moments were both more likely than controls to involve work colleagues, with inauthentic moments also involving more distant acquaintances such as bosses or customers. In addition, recalling a memory, no matter what type, increased engagement and authentic self-awareness. While there was no difference between authentic or inauthentic moments in influencing these outcomes, individuals’ pre-recall levels of engagement and authenticity influenced the memory functions used. This taxonomy of (in)authentic moment recall can guide future research in authenticity and memory function. In addition, these findings begin to address the interactive role that authenticity plays in memory recall and its influence on other psychological outcomes.