bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding as Usability and Context-Sensitivity to Interests
Authors: Søndergaard Andreas 
Published: December 2023

AbstractIs understanding subject to a factivity constraint? That is, must the agent’s representation of some subject matter be accurate in order for her to understand that subject matter? ‘No’, I argue in this paper. As an alternative, I formulate a novel manipulationist account of understanding. Rather than correctly representing, understanding, on this account, is a matter of being able to manipulate a representation of the world to satisfy contextually salient interests. This account of understanding is preferable to factivism, I argue, mainly for simplicity reasons. While it explains the intuitive data about understanding as successfully as factivist accounts, it is simpler by virtue of reducing the value truth bestows on understanding to that of usability.
Implication of community-level ecophysiological parameterization to modelling ecosystem productivity: a case study across nine contrasting forest sites in eastern China
Authors: Fang Minzhe , Cheng Changjin , He Nianpeng , Si Guoxin , Sun Osbert Jianxin 
Published: December 2023

AbstractParameterization is a critical step in modelling ecosystem dynamics. However, assigning parameter values can be a technical challenge for structurally complex natural plant communities; uncertainties in model simulations often arise from inappropriate model parameterization. Here we compared five methods for defining community-level specific leaf area (SLA) and leaf C:N across nine contrasting forest sites along the North–South Transect of Eastern China, including biomass-weighted average for the entire plant community (AP_BW) and four simplified selective sampling (biomass-weighted average over five dominant tree species [5DT_BW], basal area weighted average over five dominant tree species [5DT_AW], biomass-weighted average over all tree species [AT_BW] and basal area weighted average over all tree species [AT_AW]). We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites, with deviations ranging from 28.0 to 73.3%. In addition, there were only slight deviations (< 10%) between the whole plant community sampling (AP_BW) predicted NPP and the four simplified selective sampling methods, and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site. The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling, and will support the choice of parameterization methods.
The potential for an old-growth forest to store carbon in the topsoil: A case study at Sasso Fratino, Italy
Authors: Chiti Tommaso , Benilli Nicola , Mastrolonardo Giovanni , Certini Giacomo 
Published: December 2023

AbstractThere is considerable interest devoted to old-growth forests and their capacity to store carbon (C) in biomass and soil. Inventories of C stocks in old-growth forests are carried out worldwide, although there is a lack of information on their actual potential for C sequestration. To further understand this, soil organic carbon (SOC) was measured in one of Italy’s best-preserved old-growth forests, the Sasso Fratino Integral Nature Reserve. This reserve is on the World Heritage List along with other ancient beech forests of Europe, and it is virtually untouched due to the steepness of the terrain, even before legal constraints were imposed. Although the sandstone-derived soils are often shallow, they are rich in organic matter. However, no quantification had been carried out. By systematically sampling the topsoil across the forest, we accurately determined the average amount of SOC (62.0 ± 16.9 Mg ha–1) and nitrogen (4.0 ± 1.2 Mg ha–1) in the top 20 cm. Using the CENTURY model, future dynamics of SOC stocks were predicted to 2050 according to two climate scenarios, A1F1 and B2, the first of high concern and the second more optimistic. The model projected an increase of 0.2 and 0.3 Mg ha–1 a–1 by 2030 under the A1F1 and B2 scenarios, respectively, suggesting that the topsoil in old-growth forests does not reach equilibrium but continues accumulating SOC. However, from 2030 to 2050, a decline in SOC accumulation is predicted, indicating SOC net loss at high altitudes under the worst-case scenario. This study confirms that soils in old-growth forests play a significant role in carbon sequestration. It also suggests that climate change may affect the potential of these forests to store SOC not only in the long term but also in the coming years.
Genome wide investigation of Hsf gene family in Phoebe bournei: identification, evolution, and expression after abiotic stresses
Authors: Liao Wenhai , Tang Xinghao , Li Jingshu , Zheng Qiumian , Wang Ting , Cheng Shengze , Chen Shiping , Cao Shijiang , Cao Guangqiu 
Published: December 2023

AbstractHeat shock transcription factors (Hsfs) have important roles during plant growth and development and responses to abiotic stresses. The identification and function of Hsf genes have been thoroughly studied in various herbaceous plant species, but not woody species, especially Phoebe bournei, an endangered, unique species in China. In this study, 17 members of the Hsf gene family were identified from P. bournei using bioinformatic methods. Phylogenetic analysis indicated that PbHsf genes were grouped into three subfamilies: A, B, and C. Conserved motifs, three-dimensional structure, and physicochemical properties of the PbHsf proteins were also analyzed. The structure of the PbHsf genes varied in the number of exons and introns. Prediction of cis-acting elements in the promoter region indicated that PbHsf genes are likely involved in responses to plant hormones and stresses. A collinearity analysis demonstrated that expansions of the PbHsf gene family mainly take place via segmental duplication. The expression levels of PbHsf genes varied across different plant tissues. On the basis of the expression profiles of five representative PbHsf genes during heat, cold, salt, and drought stress, PbHsf proteins seem to have multiple functions depending on the type of abiotic stress. This systematic, genome-wide investigation of PbHsf genes in P. bournei and their expression patterns provides valuable insights and information for further functional dissection of Hsf proteins in this endangered, unique species.
Global vs Blow-Up Solutions and Optimal Threshold for Hyperbolic ODEs with Possibly Singular Nonlinearities
Authors: Cassani Daniele , Miyasita Tosiya 
Published: December 2023

AbstractWe consider a hyperbolic ordinary differential equation perturbed by a nonlinearity which can be singular at a point and in particular this includes MEMS type equations. We first study qualitative properties of the solution to the stationary problem. Then, for small value of the perturbation parameter as well as initial value, we establish the existence of a global solution by means of the Lyapunov function and we show that the omega limit set consists of a solution to the stationary problem. For strong perturbations or large initial values, we show that the solution blows up. Finally, we discuss the relationship between upper bounds of the perturbation parameter for the existence of time-dependent and stationary solutions, for which we establish an optimal threshold.
Preoperative dental screening can reduce periprosthetic infections of hip and knee endoprostheses in the first month after surgery: results of a cohort study
Authors: Fenske Fabian , Kujat Benjamin , Krause Leah , Meyer Stephan , Sander Anna-Katharina , Repmann Jaqueline , Neuhaus Michael , Haak Rainer , Roth Andreas , Lethaus Bernd , Ziebolz Dirk , Schmalz Gerhard 
Published: December 2023

Abstract
Purpose
The oral cavity and, in particular, potential oral foci might pose a risk of periprosthetic joint infection (PJI). The aim of this cohort study was to determine whether practical preoperative dental screening would reduce the prevalence of early PJI in the first month after surgery.

Methods
Patients attending a specialized endoprosthesis implantation clinic between 2018 and 2022 were recruited. Two groups were examined. The test group consisted of patients attending the clinic between 2020 and 2022 and who were referred to their family dentist using a standardized form. The comparison group consisted of patients who were treated in the clinic between 2018 and 2020. They were not referred to their family dentist. The two groups were compared for the prevalence of PJI. Univariate analysis followed by multiple logistic regression was performed to confirm risk factors for PJI in this cohort.

Results
2560 individuals (test group: 1227, comparison group: 1333) were included. The prevalence of PJI was significantly lower in the test group (0.8% vs. 1.8%, p = 0.04). Multiple logistic regression with PJI as the dependent variable showed that a dental referral was a strong predictor of a lower prevalence of PJI (OR: 0.43, CI95 0.205–0.917, p = 0.03). Male gender was also strongly associated with a higher frequency of PJI (OR: 2.68, CI95 1.32–5.42, p = 0.01). Age (OR: 1.06, CI95 1.01–1.10, p = 0.01) and BMI (OR: 1.11, CI95 1.05–1.17, p < 0.01) had little effect on the risk of PJI.

Conclusion
Dental referral using a standardized form can reduce the prevalence of early PJI. Accordingly, orthopedists and dentists should collaborate in this practical way.
Modeling Synchronization for Detecting Collaborative Learning Process Using a Pedagogical Conversational Agent: Investigation Using Recurrent Indicators of Gaze, Language, and Facial Expression
Authors: Hayashi Yugo 
Published: December 2023

AbstractConstructive interactions and knowledge integration activities are methods commonly used for learning; however, establishing successful coordination becomes a hurdle in computer-mediated collaborations. The development of systems to facilitate communication activities in such situations has been attempted, but models are still required for capturing learners’ interactions and detecting their quality. This study explored several types of verbal and nonverbal behaviors of learners that can be implemented while designing tutoring systems to effectively capture their interaction processes in scenarios where learners engage in collaborative learning mediated by a pedagogical conversational agent (PCA). This study focused on the degree of behavior recurrence of each speaker, which is considered suitable for observing levels of effectiveness. Specifically, this study focused on three indicators—gaze synchronization, language conformance, and emotional matching through facial expression—to establish a system-based index for measuring learners’ collaborative processes such as synchronization. This study experimentally examined the relationship between these indicators and the performance and process of collaborative learning among 44 learners while using PCA for facilitation. Subsequently, numerous dependent variables in the collaborative learning process were predicted using the three proposed indicators. However, no significant correlation was established between learning performance and the indicators used. These findings show that the recurrence of indicators is useful for estimating the collaborative learning process and that these indicators can be used in the development of learning support systems to trace learners’ achievements in successful interactions.
Recent Advances in Patterning Strategies for Full-Color Perovskite Light-Emitting Diodes
Authors: Lee Gwang Heon , Kim Kiwook , Kim Yunho , Yang Jiwoong , Choi Moon Kee 
Published: December 2023

AbstractMetal halide perovskites have emerged as promising light-emitting materials for next-generation displays owing to their remarkable material characteristics including broad color tunability, pure color emission with remarkably narrow bandwidths, high quantum yield, and solution processability. Despite recent advances have pushed the luminance efficiency of monochromic perovskite light-emitting diodes (PeLEDs) to their theoretical limits, their current fabrication using the spin-coating process poses limitations for fabrication of full-color displays. To integrate PeLEDs into full-color display panels, it is crucial to pattern red–green–blue (RGB) perovskite pixels, while mitigating issues such as cross-contamination and reductions in luminous efficiency. Herein, we present state-of-the-art patterning technologies for the development of full-color PeLEDs. First, we highlight recent advances in the development of efficient PeLEDs. Second, we discuss various patterning techniques of MPHs (i.e., photolithography, inkjet printing, electron beam lithography and laser-assisted lithography, electrohydrodynamic jet printing, thermal evaporation, and transfer printing) for fabrication of RGB pixelated displays. These patterning techniques can be classified into two distinct approaches: in situ crystallization patterning using perovskite precursors and patterning of colloidal perovskite nanocrystals. This review highlights advancements and limitations in patterning techniques for PeLEDs, paving the way for integrating PeLEDs into full-color panels.
Study on the fault slip rule and the rockburst mechanism induced by mining the panel through fault
Authors: Kong Peng , Wang Changxiang , Xing Luyi , Liang Min , He Jin 
Published: December 2023

AbstractWhen mining near the fault can cause the fault to slip and release energy, resulting in rockburst and posing a significant safety risk to coal mine production. This paper proposes a numerical simulation method, based on FLAC3D numerical simulation, for calculating the dynamic response of fault slip below the impact of mining. The release patterns of energy from fault slip are compared between mining via the fault from the hanging wall and mining via the fault from the footwall. The dynamic response characteristics of coal and rock mass under the disturbance of fault slip induced dynamic loading are revealed. This research suggests that the fault slip seismic moment (M0) of the panel mined via the fault from footwall is substantially higher than from hanging wall. Moreover, the disturbance of the footwall panel mining via the fault leads to a more severe disturbance of the surrounding rock mass, resulting in a higher risk of rockburst. Plastic failure of the rock surrounded in the mining area under the strong dynamic loading of fault slip leads to a significant reduction in peak abutment stress, releasing tremendous energy. When the disturbance caused by fault slip dynamic load is relatively small, the maximum abutment stress increases, and the dynamic load increases the energy storage of surrounding rock. Based on energy theory, an energy criterion for the occurrence of fault slip type rockburst has been proposed. The results suggest that strong fault slip dynamic load has an obvious inducing effect on rockburst, and the stronger the dynamic load and greater static load of the rock, the more the manifestation of rockburst.
Mechanism of surrounding rock failure in impact stress and in-situ stress in circular tunnel
Authors: Zhou Jun , Zhao Guangming , Meng Xiangrui , Dong Chunliang , Qiao Yang , Yu Meilu 
Published: December 2023

AbstractWith depth increase of many mines, the damage of surrounding rock by high ground stress cannot be ignored under the blasting excavation method. In view of the strong disturbance of surrounding rock caused by dynamic excavation and unloading under high initial rock stress conditions, based on the elastic unloading theory, the analytical solution of dynamic excavation and unloading stress is given using the residue theorem and Laplace inverse transform. The stress field distribution under the coupling effect of blasting load and initial rock stress is described. By making a rock model and implementing biaxial loading using a drop hammer to simulate the impact load, radial cracks generated by the impact stress wave and circumferential cracks generated by unloading during excavation were captured, and the stress change curve during excavation was monitored. The monitoring curve and theoretical curve have consistency in trend. Under the coupling effect of blasting load and initial stress, surrounding rock will generate tensile stress and broken, and the unloading effect of initial stress occurs after the blasting load, which causes tensile damage to the surrounding rock. The stress field curve plotted by the theory explains well the crack propagation caused by the coupling effect of blasting load and initial stress.
Evaluation of adsorption and corrosion inhibition properties of Solanum Macrocarpon leaves extract on mild steel in sulphuric acid solutions
Authors: Ezugha Stephen Ifeanyi , Aralu Chiedozie Chukwuemeka 
Published: December 2023

AbstractCorrosion of mild steel (MS) in industries has become a menace that has led to the use of organic green inhibitors from plant origin, which is seen as a cheap, eco-friendly substitute for inorganic inhibitors. This work employed Solanum macrocarpon (SM) methanol leaf extract as a green inhibitor using a gravimetric method at 303–323 K, respectively. The phytochemical screening was done using standard methods to identify the phytochemical compounds in the leave extract. The Fourier transform infrared (FTIR) analysis was also done to elucidate the functional groups that contain heteroatoms responsible for the inhibition efficiency. The effectiveness of the inhibition efficiency increased with concentration and decreased with rising temperature. The results demonstrated that Solanum macrocarpon leaf methanol extract is an effective mild steel corrosion inhibitor in 0.5 M H2SO4. 95% optimum inhibition efficiency (I.E) was observed at 0.5% w/v concentration for 303 K. The inhibition potential was attributed to the phytochemical compounds in the leaf extract, which contain polar functional groups and hetero-atoms in their structures. The $${E}_{a}$$

E
a

and $$\Delta {G}_{ads}$$

Δ

G

ads

showed that the adsorption mechanism followed physisorption. The results showed the potential use of SM methanol extract as a corrosion inhibitor in mild steel, which can decrease corrosion in industries.
The cross-sectional stock return predictions via quantum neural network and tensor network
Authors: Kobayashi Nozomu , Suimon Yoshiyuki , Miyamoto Koichi , Mitarai Kosuke 
Published: December 2023

AbstractIn this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains the lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests capability of model’s capturing non-linearity between input features.
A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment
Authors: Elahi Mahboob , Afolaranmi Samuel Olaiya , Martinez Lastra Jose Luis , Perez Garcia Jose Antonio 
Published: December 2023

AbstractDriven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/retrofitting. As reported in the 2022 McKinsey Global Survey (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products.
Higher-order soliton solutions for the Sasa–Satsuma equation revisited via $$\bar{\partial }$$ method
Authors: Kuang YongHui , Mao Bolin , Wang Xin 
Published: December 2023

AbstractIn optics, the Sasa–Satsuma equation can be used to model ultrashort optical pulses. In this paper higher-order soliton solutions for the Sasa–Satsuma equation with zero boundary condition at infinity are analyzed by $$\bar{\partial }$$

∂

¯

method. The explicit determinant form of a soliton solution which corresponds to a single $$p_{l}$$

p
l

-th order pole is given. Besides the interaction related to one simple pole and the other one double pole is considered.