bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework
Authors: Xia Yanqing , Deng Yili , Tao Xuanyu , Zhang Sainan , Wang Chengliang 
Published: February 2024

AbstractIn the post-pandemic era, there has been a heightened global focus on the mental health challenges facing individuals, with a particular emphasis on innovative and effective therapeutic approaches. Despite the extensive body of research within the realm of art therapy addressing individual psychological healing, the potential of digital mediums in this field has been largely overlooked. This gap is especially pronounced in studies targeting the unique demographic of Generation Z, known as ‘digital natives.’ This study aims to investigate the role of engagement in online digital exhibitions as a potential restorative intervention for enhancing the mental well-being of Generation Z users. Grounded in the Stimulus-Organism-Response (S-O-R) framework and the theory of restorative environments, this research examines the psychological responses of Generation Z participants to online digital art exhibitions, particularly from the perspective of website aesthetics. The impact of these responses on users’ place attachment and loyalty behaviors is also explored. Utilizing a structural equation modeling approach, an online digital art exhibition was deployed on the ZEPETO app, a platform popular among Chinese Generation Z users. Participants were subsequently invited to partake in an online survey post-exhibition, yielding a dataset of 332 valid responses. The findings reveal that: (1) the four design elements of website aesthetics (coherence, novelty, interactivity, immersion) significantly influence the perceived restoration among Generation Z users, with immersion being the most influential factor; (2) perceived restoration and place attachment are crucial predictors of loyalty behavior; (3) perceived restoration has a positive impact on the place attachment of Generation Z users towards online digital art exhibitions. This study demonstrates that online digital art exhibitions can facilitate an emotional healing journey for Generation Z, contributing to the alleviation of psychological stress and the promotion of psychological well-being. Moreover, digital technology exhibitions have the potential to transcend human creativity and imagination, offering a unique and promising pathway for future research and practices in design related to emotional healing.
Individual entrepreneurial orientation for entrepreneurial readiness
Authors: Adeniyi Adeshina Olushola , Gamede Vangeli , Derera Evelyn 
Published: February 2024

AbstractLow business start-ups due to poor entrepreneurial competence among the youth has continued to attract the interest of entrepreneurship educators and practitioners. Previous investigations have explored individual entrepreneurial orientation, with little attention given to entrepreneurial readiness of students from science and technology colleges in Nigeria. This research shortcoming forms the motivation for this study. The study aims to explore the effect of Individual Entrepreneurial Orientation (IEO) components on students’ entrepreneurial readiness for business start-ups. The philosophical approach is framed within the positivist perspective, with a survey of 289 exit-level students as the sample size. The Kaiser–Meyer–Olkin test and Bartlett’s test were performed to measure the sample adequacy. Pearson’s correlation and regression analysis were conducted to validate the hypotheses. The results indicated that IEO risk-taking shows insignificant association with the students’ entrepreneurial readiness, while IEO innovation and IEO proactivity show significant association with the students’ entrepreneurial readiness. The study further reveals that there is no gender difference in the students’ entrepreneurial readiness as influenced by IEO towards starting a business. Managerial implication suggests the promotion and development of an entrepreneurial mindset with practical translations.
Valence shell electronically excited states of norbornadiene and quadricyclane
Authors: Cooper Joseph C. , Holland David M. P. , Ingle Rebecca A. , Bonanomi Matteo , Faccialà Davide , De Oliveira Nelson , Abid Abdul R. , Bachmann Julien , Bhattacharyya Surjendu , Borne Kurtis , Bosch Michael , Centurion Martin , Chen Keyu , Forbes Ruaridh J. G. , Lam Huynh V. S. , Odate Asami , Rudenko Artem , Venkatachalam Anbu S. , Vozzi Caterina , Wang Enliang , Weber Peter M. , Ashfold Michael N. R. , Kirrander Adam , Rolles Daniel 
Published: February 2024

The absolute photoabsorption cross sections of norbornadiene (NBD) and quadricyclane (QC), two isomers with chemical formula C7H8 that are attracting much interest for solar energy storage applications, have been measured from threshold up to 10.8 eV using the Fourier transform spectrometer at the SOLEIL synchrotron radiation facility. The absorption spectrum of NBD exhibits some sharp structure associated with transitions into Rydberg states, superimposed on several broad bands attributable to valence excitations. Sharp structure, although less pronounced, also appears in the absorption spectrum of QC. Assignments have been proposed for some of the absorption bands using calculated vertical transition energies and oscillator strengths for the electronically excited states of NBD and QC. Natural transition orbitals indicate that some of the electronically excited states in NBD have a mixed Rydberg/valence character, whereas the first ten excited singlet states in QC are all predominantly Rydberg in the vertical region. In NBD, a comparison between the vibrational structure observed in the experimental 11B1–11A1 (3sa1 ← 5b1) band and that predicted by Franck–Condon and Herzberg–Teller modeling has necessitated a revision of the band origin and of the vibrational assignments proposed previously. Similar comparisons have encouraged a revision of the adiabatic first ionization energy of NBD. Simulations of the vibrational structure due to excitation from the 5b2 orbital in QC into 3p and 3d Rydberg states have allowed tentative assignments to be proposed for the complex structure observed in the absorption bands between ∼5.4 and 7.0 eV.
Effects of straw mulching, liming, and soil texture on ammonia volatilisation: a study of conventional and enhanced efficiency fertilisers
Authors: Cassim Bruno Maia Abdo Rahmen , Cordioli Vitor Rodrigues , Chilante Guilherme Frelo , Mazzi Fabrício Linares , Muniz Antonio Saraiva , Inoue Tadeu Takeyoshi , Batista Marcelo Augusto 
Published: February 2024

Context In no-tillage agriculture, maintenance of soil cover combined with liming without incorporation increases nitrogen (N) loss via ammonia (NH3) volatilisation, decreasing the efficiency of nitrogen fertilisers. Aims To quantify N losses by NH3 volatilisation from conventional and enhanced efficiency fertilisers applied to a clayey and a sandy loam soil subjected or not to lime (CaCO3) application and straw mulching. Methods Two laboratory experiments were carried out; one using a clayey soil, and the other using a sandy loam soil. Both experiments followed a 4 × 2 × 2 factorial design with four N sources (urea, urea-NBPT, urea-formaldehyde, and ammonium sulfate), absence and presence of liming, and absence and presence of Brachiaria ruziziensis straw mulching. NH3 volatilisation was measured using closed flasks containing filter paper soaked with sulfuric acid and quantified by titration with sodium hydroxide. Key results NH3 volatilisation was up to 62% of the N applied. Losses due to NH3 volatilisation from both soil types decreased in the following order of treatment: liming + straw mulching > straw mulching only > liming only. Urea-formaldehyde and ammonium sulfate were the most efficient in reducing NH3 emissions. However, when ammonium sulfate was applied to a clayey soil after liming, it resulted in higher NH3 emissions than conventional urea. Conclusions Urea-formaldehyde showed better performance in reducing NH3 losses due to greater stability in the presence of straw or liming. Implications Soils with straw and limestone can lead to large NH3 volatilisation losses if urea conventional is broadcast.
Highly stable and fast response photodetector based on double perovskite Cs2AgBiCl6 crystals
Authors: Han Zhengyu , Dai Mengjia , Zeng Zhichen , Ye Chunhui , Dai Rucheng , Wang Zhongping , Sun Xiaoyu , Zhang Zengming 
Published: February 2024

Abstract
Photodetectors have attracted extensive research interest owing to their widespread applications. Although perovskite crystals with outstanding optoelectronic properties have proved to be promising candidates for photodetectors, the development is hindered by the instability of perovskite crystals. In this work, we fabricated the photodetector based on high quality Cs2AgBiCl6 crystals. Under 415 nm excitation (0.72 mW cm−2) at voltage of 20 V, the photodetector exhibits good responsivity of 4.45 μA W−1 and detectivity of 2.75 × 108 Jones, respectively. Also, the photodetector shows fast response with rise time 57 ms (

τ
r

) and fall times of 57 ms (

τ
1

) and 832 ms (

τ
2

). Furthermore, the photodetector demonstrates high stability and maintains its good performance even after exposure to vacuum, high temperature and humid conditions.
A novel algorithm for complex transfer conditions in bearing fault diagnosis
Authors: Dong Jingchuan , Su Depeng , Jiang Hongyu , Gao Yubo , Chen Tao 
Published: February 2024

Abstract
Transfer learning in bearing fault diagnosis can effectively improve model generalization and accelerate the practical application of fault diagnosis algorithms. However, previous algorithms primarily focused on simple transfer conditions like known target domain data or the same device. In industrial practice, the conditions for algorithm transfer are more complex. Therefore, cross-domain fault diagnosis under complex transfer conditions is a challenging task with significant practical value. This paper proposes a new bearing fault diagnosis algorithm based on attention mechanism and feature enhancement, which provides better feature extraction capabilities. The main approach involves performing deep aliasing on deep features and training the model to identify domain-invariant classification features under extreme conditions for effective fault diagnosis. Additionally, our network performs well in handling low signal-to-noise ratio problems. Extensive experiments were conducted on three different bearing case studies to validate the effectiveness of the proposed method, showing superior performance compared to other deep transfer learning methods.
Chest x-ray diagnosis via spatial-channel high-order attention representation learning
Authors: Gao Xinyue , Jiang Bo , Wang Xixi , Huang Lili , Tu Zhengzheng 
Published: February 2024

Abstract

Objective. Chest x-ray image representation and learning is an important problem in computer-aided diagnostic area. Existing methods usually adopt CNN or Transformers for feature representation learning and focus on learning effective representations for chest x-ray images. Although good performance can be obtained, however, these works are still limited mainly due to the ignorance of mining the correlations of channels and pay little attention on the local context-aware feature representation of chest x-ray image. Approach. To address these problems, in this paper, we propose a novel spatial-channel high-order attention model (SCHA) for chest x-ray image representation and diagnosis. The proposed network architecture mainly contains three modules, i.e. CEBN, SHAM and CHAM. To be specific, firstly, we introduce a context-enhanced backbone network by employing multi-head self-attention to extract initial features for the input chest x-ray images. Then, we develop a novel SCHA which contains both spatial and channel high-order attention learning branches. For the spatial branch, we develop a novel local biased self-attention mechanism which can capture both local and long-range global dependences of positions to learn rich context-aware representation. For the channel branch, we employ Brownian Distance Covariance to encode the correlation information of channels and regard it as the image representation. Finally, the two learning branches are integrated together for the final multi-label diagnosis classification and prediction. Main results. Experiments on the commonly used datasets including ChestX-ray14 and CheXpert demonstrate that our proposed SCHA approach can obtain better performance when comparing many related approaches. Significance. This study obtains a more discriminative method for chest x-ray classification and provides a technique for computer-aided diagnosis.
Conversion of sugarcane biomass into sustainable fabrics: softening of fibers using alkali and silicone softener treatment
Authors: Qadir Muhammad Bilal , Alsaiari Mabkhoot , Ali Zulfiqar , Afzal Ali , Khaliq Zubair , Irfan Muhammad , Rahman Abdul , Jalalah Mohammed , Harraz Farid A 
Published: February 2024

Abstract
This study addresses environmental concerns related to sugarcane biomass as an industrial fuel source by exploring its potential for textile applications. Bagasse undergoes sequential alkali-H2O2 treatment, followed by varying concentrations of silicone softener (50 g l−1 − 100 g l−1 − 150g l−1). The goal is to enhance fiber fineness and softness. Comprehensive physical and chemical characterization reveals significant alterations in treated fibers, impacting surface morphology, crystallinity, linear density, and moisture regain. Results indicate a decline in fiber linear density from 59.47tex to 48.84tex, thus improved fineness, moisture regain initial from 6.9% to 4.7%, reduced crystallinity, and enhanced mechanical strength with silicone softener treatment. Treated fibers show promise as a sustainable alternative to conventional cotton, emphasizing the importance of sugarcane biomass for eco-friendly textile manufacturing.
Multi-objective optimization and evolution of dissimilar welding process between Cr-Mo steel and austenitic stainless steel for power plant application
Authors: Benlamnouar Mohamed Farid , Bensaid Nabil , Saadi Tahar , Badji Riad 
Published: February 2024

Abstract
In this study, response surface methodology (RSM) was employed to optimize the welding parameters’ effects on mechanical properties of dissimilar welds between Cr-Mo steel grade (P11) and austenitic stainless steel (AISI 316LN). To determine the best welding parameters, variance analysis (ANOVA), desirability function, and perturbation analysis were used to create regression models and identify the significant parameters influencing tensile strength and hardness gaps in the weld joints. The results indicated that welding speed is the most significant parameter affecting both the austenitic hardness gap and tensile strength, while gas flow has the most significant impact on the hardness gap of Cr-Mo steel. Furthermore, welding speed positively influences the mechanical properties of dissimilar weld, whereas welding current has a slight negative effect on tensile strength. The optimum welding parameters were found to be 130 A for welding current, 70 mm min−1 for welding speed, and 13 l min−1 for welding gas flow, resulting in hardness gap values of 18.10 HV (Stainless steel side), 27.38 HV (Cr-Mo steel side), and a tensile strength of 453.90 MPa. The optimum parameter effect is concentrated at the weld interfaces between the fusion zone and the heat-affected zone. This effect led to limitations in grain coarsening, a reduction in the martensite and delta ferrite phase percentages, a slight increase in the bainite ratio, and a decrease in carbide precipitations. As a result, a homogenization of strain distribution in the optimum weld was achieved, leading to ductile fracture in Cr-Mo steel.
Research in methodologies for modelling the oral cavity
Authors: Shahid Muhammad Suhaib , French Andrew P , Valstar Michel F , Yakubov Gleb E 
Published: February 2024

Abstract
The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a ‘complete model’ which includes consideration of a full set of facial features (i.e. not only mouth) as well as synergistic stimuli such as audio and facial thermal data. 3D modelling technologies capable of accurately and efficiently capturing a complete representation of the mouth for an individual have broad applications in the study of oral actions, due to their cost-effectiveness and time efficiency. This review delves into the field of clinical phonetics to classify oral actions pertaining to both speech and non-speech movements, identifying how the various vocal organs play a role in the articulatory and masticatory process. Vitaly, it provides a summation of 12 articulatory recording methods, forming a tool to be used by researchers in identifying which method of recording is appropriate for their work. After addressing the cost and resource-intensive limitations of existing methods, a new system of modelling is proposed that leverages external to internal correlation modelling techniques to create a more efficient models of the oral cavity. The vision is that the outcomes will be applicable to a broad spectrum of oral functions related to physiology, health and wellbeing, including speech, oral processing of foods as well as dental health. The applications may span from speech correction, designing foods for the aging population, whilst in the dental field we would be able to gain information about patient’s oral actions that would become part of creating a personalised dental treatment plan.
A 3D-printed phantom for stereotactic body radiation therapy simulation
Authors: Yu Ying-Hao , Yen Tsung-Yu , Hung Shih-Kai , Chen Shi-He , Wang Kuei-Ying 
Published: February 2024

Abstract
In modern radiation therapy for lung cancer, examining the uncertainty between tumor motion and beam delivery is vitally important. To lower the radiation dose delivery to the patient’s normal tissue, narrowing the irradiation field margin to hit the tumor accurately is critical. Thus we proposed a phantom that simulates the thorax and lung tumor’s motions by employing a 3D printing technique. The lung tumor is controlled by a linear miniature Delta robot arm, with a maximum displacement of 20 mm in each direction. When we simulated the thoracic breathing movements at 12 mm in A–P (Anterior-Posterior), the control errors were within 10%. The average tracking errors of the prosthetic tumor were within 1.1 mm. Therefore, the 3D-printed phantom with a robot arm can provide a reliable simulation for training and dosimetry measurement before lung radiotherapy, especially SBRT.
A two-step Rayleigh-Schrödinger Brillouin-Wigner approach to transition energies
Authors: Delafosse Loris , Hussein Amr , Yalouz Saad , Robert Vincent 
Published: February 2024

Abstract
Perturbative methods are attractive to describe the electronic structure of molecular systems because of their low-computational cost and systematically improvable character. In this work, a two-step perturbative approach is introduced combining multi-state Rayleigh-Schrödinger (effective Hamiltonian theory) and state-specific Brillouin-Wigner schemes to treat degenerate configurations and yield an efficient evaluation of multiple energies. The first step produces model functions and an updated definition of the perturbative partitioning of the Hamiltonian. The second step inherits the improved starting point provided in the first step, enabling then faster processing of the perturbative corrections for each individual state. The here-proposed two-step method is exemplified on a model-Hamiltonian of increasing complexity.
Experimental study on strain and stress of steady device in a catenary for a high-speed railway
Authors: Pan Like , Chen Liming , Peng Peihuo , He Fan , Zhang Haibo , Xing Tong , Yang Caizhi 
Published: February 2024

Abstract
Steady device is an important component in a catenary for a high-speed railway. It plays an important role in ensuring the current collection quality and stable and reliable operation of the train. In this paper, the strain distributions of positioner tube, column and pin sleeve in the positioning device are measured by strain gauges in our laboratory, and the variation laws of axial and transverse strains, and axial stresses of the three key components under different horizontal and oblique loads, and lifting heights of positioner are studied and analyzed. It is found that the strains and axial stresses of all measuring points basically have a linear relationship with the horizontal load, which are little affected by the oblique load. There is an ‘arch’ change law between these strains/stresses and the lifting height of positioner. For the stresses of two measuring points at the same section, the tensile stress is generally higher than the compressive stress. The maximum tensile stress occurs on positioner tube, which is the weakest part. This work provides the technical support for the material selection, structural design and detection of the positioning device.
Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks
Authors: Rezasefat Mohammad , Hogan James D 
Published: February 2024

Abstract
This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the multi-decoder CNN (MUDE-CNN) and the multiple encoder–decoder model with transfer learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder–decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing (AM)-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via AM. The temporal model evaluation demonstrated MTED-TL’s consistent superiority over MUDE-CNN, owing to transfer learning’s advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.