bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid music recommendation with graph neural networks
Authors: Bevec Matej , Tkalčič Marko , Pesek Matevž 
Published: August 2024

AbstractModern music streaming services rely on recommender systems to help users navigate within their large collections. Collaborative filtering (CF) methods, that leverage past user–item interactions, have been most successful, but have various limitations, like performing poorly among sparsely connected items. Conversely, content-based models circumvent the data-sparsity issue by recommending based on item content alone, but have seen limited success. Recently, graph-based machine learning approaches have shown, in other domains, to be able to address the aforementioned issues. Graph neural networks (GNN) in particular promise to learn from both the complex relationships within a user interaction graph, as well as content to generate hybrid recommendations. Here, we propose a music recommender system using a state-of-the-art GNN, PinSage, and evaluate it on a novel Spotify dataset against traditional CF, graph-based CF and content-based methods on a related song prediction task, venturing beyond accuracy in our evaluation. Our experiments show that (i) our approach is among the top performers and stands out as the most well rounded compared to baselines, (ii) graph-based CF methods outperform matrix-based CF approaches, suggesting that user interaction data may be better represented as a graph and (iii) in our evaluation, CF methods do not exhibit a performance drop in the long tail, where the hybrid approach does not offer an advantage.
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation
Authors: Li Lin , Qiu Jianing , Spratling Michael 
Published: August 2024

Abstract
Deep neural networks are vulnerable to adversarial examples. Adversarial training (AT) is an effective defense against adversarial examples. However, AT is prone to overfitting which degrades robustness substantially. Recently, data augmentation (DA) was shown to be effective in mitigating robust overfitting if appropriately designed and optimized for AT. This work proposes a new method to automatically learn online, instance-wise, DA policies to improve robust generalization for AT. This is the first automated DA method specific for robustness. A novel policy learning objective, consisting of Vulnerability, Affinity and Diversity, is proposed and shown to be sufficiently effective and efficient to be practical for automatic DA generation during AT. Importantly, our method dramatically reduces the cost of policy search from the 5000 h of AutoAugment and the 412 h of IDBH to 9 h, making automated DA more practical to use for adversarial robustness. This allows our method to efficiently explore a large search space for a more effective DA policy and evolve the policy as training progresses. Empirically, our method is shown to outperform all competitive DA methods across various model architectures and datasets. Our DA policy reinforced vanilla AT to surpass several state-of-the-art AT methods regarding both accuracy and robustness. It can also be combined with those advanced AT methods to further boost robustness. Code and pre-trained models are available at: https://github.com/TreeLLi/AROID.
Non-thermal plasma-catalytic processes for CO2 conversion toward circular economy: fundamentals, current status, and future challenges
Authors: Mukhtar Ahmad , Saqib Sidra , Mohotti Dinithi , Ndeddy Aka Robinson , Hossain Mokter , Agyekum-Oduro Ekow , Wu Sarah 
Published: August 2024

AbstractPractical and energy-efficient carbon dioxide (CO2) conversion to value-added and fuel-graded products and transitioning from fossil fuels are promising ways to cope with climate change and to enable the circular economy. The carbon circular economy aims to capture, utilize, and minimize CO2 emissions as much as possible. To cope with the thermodynamic stability and highly endothermic nature of CO2 conversion via conventional thermochemical process, the potential application of non-thermal plasma (NTP) with the catalyst, i.e., the hybrid plasma catalysis process to achieve the synergistic effects, in most cases, seems to promise alternatives under non-equilibrium conditions. This review focuses on the NTP fundamentals and comparison with conventional technologies. A critical review has been conducted on the CO2 reduction with water (H2O), methane (CH4) reduction with CO2 to syngas (CO + H2), CO2 dissociation to carbon monoxide (CO), CO2 hydrogenation, CO2 conversion to organic acids, and one-step CO2–CH4 reforming to the liquid chemicals. Finally, future challenges are discussed comprehensively, indicating that plasma catalysis has immense investigative areas.
Social consequences of wood-based innovations: a generic analysis of sectoral differences in Austria
Authors: Mair-Bauernfeind Claudia , Boiger Theresa , Asada Raphael , Stern Tobias 
Published: August 2024

Abstract
Purpose
To model the overall social consequences of changing wood utilization, a system perspective is needed that encompasses the entire wood utilization system in a defined region. The aim of this study was to analyze the social performance of wood-based industries in Austria using sector-specific data and to use less disaggregated data to depict social risks in the resource extraction phase. Additionally, the social consequences of innovations in terms of the social performance of a sector and potential side effects on other wood-based industries were analyzed.

Methods
Differences in the sectoral social performance of forestry and wood-based industries in Austria were analyzed using sectoral data for 11 different social indicators (e.g., occupational injuries, woman in managerial positions) collected at official sites in Austria. To calculate the overall social performance of the sector, sectoral data from Austria need to be combined with data from other sources representing the value chain (e.g., from resource extracting countries). This enables the social consequences of a change to be analyzed by including the social indicators in the system dynamics model WOODSIM. The WOODSIM model depicts the Austrian wood utilization system, allowing the user to model direct and indirect effects of introducing an innovation in a particular industry on the social performance of industries in the wood utilization system.

Results and discussion
The results show that social risks can differ depending on the sectoral context even within the same country (e.g., occupational injuries in wood harvesting compared to textile production). The most dangerous sectors (in terms of injuries) are forestry and construction (34 and 3 times higher than Austrian average, respectively). Including the risks of resource extraction affects the social performance of the industries. Surprisingly, the median for Austria is 1434 accidents per 100,000 employees, whereas it is only 592 for all countries combined. Modeling the social consequences with system dynamics reveals that some innovations can result in bigger improvements in social performance than others, mostly due to the existence of more globalized value chains.

Conclusions
This work illustrates the importance of including sectoral information when performing generic social life cycle assessments and models the social consequences of an innovation for the first time using system dynamics modeling. To avoid overestimating positive effects when analyzing consequences, a systems perspective must be taken. Better and more disaggregated data are needed to depict the social performance of sectors more accurately.
Development of metacognitive monitoring and control skills in elementary school: a latent profile approach
Authors: van Loon Mariëtte , Roebers Claudia M. 
Published: August 2024

AbstractThis study aims to understand individual differences between children in metacognitive monitoring and control processes and the developmental trajectories of metacognition over one year. Three indicators of procedural metacognition were used: monitoring accuracy (discrimination of confidence judgments between correct and incorrect test responses), effective restudy selections, and accuracy of response maintenance/withdrawal decisions. These indicators were measured for two tasks (text comprehension and Kanji memory) at two measurement points one year apart. Participants were 151 second graders (M age 7.61 years) and 176 fourth graders (M age 9.62 years). With latent profile analyses, distinct metacognition profiles were found for both grade levels at both measurement points. Children showed heterogeneity in the proficiency of metacognition but also in the extent to which metacognitive skills were generalizable across the two tasks. For second-grade children, being low at metacognition at the first measurement point was not associated with extra risks for low metacognition one year later. However, for fourth graders, children with low metacognitive skills appeared likely to stay low in metacognition over time and particularly showed ineffective restudy decisions. This indicates that they seemed at risk for a longer-term metacognitive deficiency. Findings may improve understanding of the heterogeneity of metacognition and support distinguishing typical from at-risk metacognitive development.
Comparing the effects of a collaboration script and collaborative reflection on promoting knowledge about good collaboration and effective interaction
Authors: Strauß Sebastian , Tunnigkeit Isis , Eberle Julia , Avdullahu Arlind , Rummel Nikol 
Published: August 2024

AbstractA key challenge in CSCL research is to find ways to support learners in becoming effective collaborators. While the effectiveness of external collaboration scripts is well established, there is a need for research into support that acknowledges learners’ autonomy during collaboration. In the present study, we compare an external collaboration script and a reflection scaffold to a control condition and examine their effects on learners’ knowledge about effective collaboration and on their groups’ interaction quality. In an experimental study that employed a 1× three-factorial design, 150 university students collaborated in groups of three to solve two information pooling problems. These groups either received an external collaboration script during collaboration, no support during collaboration but a reflection scaffold before beginning to collaborate on the second problem, or no support for their collaboration. Multilevel modeling suggests that learners in the reflection condition gained more knowledge about effective collaboration than learners who collaborated guided by an external collaboration script or learners who did not receive any support. However, we found no effect of the script or the reflection scaffold on the quality of interaction in the subsequent collaboration. Explorative analyses suggest that learners acquired knowledge particularly about those interactions that are required for solving information pooling tasks (e.g., sharing information). We discuss our findings by contrasting the design of the external collaboration script and the reflection scaffold to identify potential mechanisms behind scripting and collaborative reflection and to what extent these forms of support foster collaboration skills and engagement in productive interaction.
Guided growth surgery for angular deformity of the knee: one centres experience
Authors: Moore Dave M. , Turner Henry , Boughton Oliver , Noonan Maria , Noel Jacques , Kiely Pat , O’Toole Pat , Kelly Paula M. , Moore David P. 
Published: August 2024

Abstract
Background
Angular deformity in the lower extremity can result in pain, gait disturbance, cosmetic deformity and joint degeneration. Up until the introduction of guided growth, which has since become the widely accepted treatment for frontal plane angular angular deformity around the knee in skeletally immature patients, treatment consisted of staples, corrective osteotomy or an angular epiphysiodesis. Guided growth modulation uses the tension band principle with the goal of treatment being to normalise the lower limb mechanical axis resulting in lower morbidity than previous treatments. In order to assess the success of this procedure we reviewed our results in an attempt to identify patients who may not benefit from this elegant procedure.

Methods
We performed a retrospective review of prospectively collected surgical records and diagnostic imaging in our paediatric tertiary national referral centre to identify all patients who had guided growth surgery for coronal plane angular deformity of the knee from 2007 to 2023. We noted the patient demographics, diagnosis, peri-operative experience and outcome. All patients were followed until skeletal maturity, until their hardware was removed or at least 2 years.

Results
Two hundred thirty-six patients were assessed for eligibility. Of the 282 treated knees which met the criteria for final assessment 55 (19.5%) were unsuccessful. Complications were few but included infection and metal-work prominence. Procedures that were less likely to be successfully included growth disturbances following trauma (18.8% failure) or infection (40%), tumour (66.6%), mucopolysaccharidoses type I (15.7%), spondyloepiphyseal dysplasia (25%) or Blount’s disease (60%). Idiopathic angular deformity showed an 89.5% success rate with guided growth.

Conclusion
In our hands, guided growth had an 80.5% success rate when all diagnoses were considered. We continue to advocate the use of guided growth as a successful treatment option for skeletally immature patients with limb deformity however caution should be employed when considering its use in certain patient groups.
Level of Evidence: Level III, retrospective cohort study.
Prognostic and Predictive Models in Myelofibrosis
Authors: Mora Barbara , Bucelli Cristina , Cattaneo Daniele , Bellani Valentina , Versino Francesco , Barbullushi Kordelia , Fracchiolla Nicola , Iurlo Alessandra , Passamonti Francesco 
Published: August 2024

Abstract
Purpose of Review
Myelofibrosis (MF) includes prefibrotic primary MF (pre-PMF), overt-PMF and secondary MF (SMF). Median overall survival (OS) of pre-PMF, overt-PMF and SMF patients is around 14 years, seven and nine years, respectively. Main causes of mortality are non-clonal progression and transformation into blast phase.

Recent Findings
Discoveries on the impact of the biological architecture on OS have led to the design of integrated scores to predict survival in PMF. For SMF, OS estimates should be calculated by the specific MYSEC-PM (MYelofibrosis SECondary-prognostic model). Information on the prognostic role of the molecular landscape in SMF is accumulating. Crucial treatment decisions for MF patients could be now supported by multivariable predictive algorithms. OS should become a relevant endpoint of clinical trials.

Summary
Prognostic models guide prediction of OS and treatment planning in MF, therefore, their timely application is critical in the personalized approach of MF patients.
The CNN Effect and South Korea’s Humanitarian Response to North Korea: A Media-Driven Policy?
Authors: Yeger Shay , Podoler Guy 
Published: August 2024

AbstractThis study explores the intersection of media and policy in inter-Korean relations, focusing on the impact of South Korean media on the government’s response to the North Korean famine. Examining the famine through the “CNN effect” and Robinson’s “policy-media interaction model,” we move beyond simplistic cause-and-effect narratives and assess the media’s potential to drive policy change in a unique context: a divided nation marked by both deep animosity and shared cultural ties. Our analysis reveals that the South Korean media predominantly conveyed informative narratives rather than emotional ones during the coverage of the North Korean famine. We argue that, consistent with the interaction model, the media primarily functioned to solidify societal consensus on humanitarian aid, rather than directly shaping government policy. Robinson’s framework supports this finding, suggesting that media influence diminishes when policy goals are clearly defined, as they were for South Korea’s response to the famine. However, this study also addresses the limitations of Robinson’s model, noting that it may oversimplify media behavior by neglecting crucial cultural, political, and historical factors. Ultimately, this study sheds light on the nuanced dynamics of media, policy, and inter-Korean relations in the face of a humanitarian crisis.
Research-Practice Partnerships for the Development of School Mental Health Interventions: An Introduction to the Special Issue
Authors: Lawson Gwendolyn M. , Owens Julie Sarno 
Published: August 2024

AbstractThe rising prevalence of mental health challenges among youth has created a pressing need for effective, feasible, equitable, and contextually relevant interventions. Educators and school mental health professionals face critical challenges in helping students overcome such barriers to school success. This makes the need for school-based intervention development research particularly that conducted in the context of collaborative research-practice partnerships, greater than ever. Despite the critical importance of iterative intervention development work, such work often receives less in attention in the published literature compared to studies about the outcomes of interventions. The goal of this special issue is to highlight innovative and rigorous research that describes the process of iteratively developing school mental health services in partnership with educators. Each paper in the special issue describes how education partners (and others including students, families, and other community partners) contributed to the development of an intervention or implementation strategy (i.e., a method or technique to enhance intervention adoption, implementation, or sustainment), how data informed iterations of the intervention or strategy, considerations related to contextual appropriateness, and lessons learned related to community-partnered school-based intervention development. In this introduction paper, we provide a context for this work and highlight innovations across papers in the special issue.
Antibacterial activity of natural flavones against bovine mastitis pathogens: in vitro, SAR analysis, and computational study
Authors: Haj Hasan Ahlam , Preet Gagan , Astakala Rishi Vachaspathy , Al-Adilah Hanan , Oluwabusola Emmanuel Tope , Ebel Rainer , Jaspars Marcel 
Published: August 2024

AbstractBovine mastitis is a worldwide disease affecting dairy cattle and causes major economic losses in the dairy industry. Recently, the emergence of microbial resistance to the current antibiotics complicates the treatment protocol which necessitates antibiotic stewardship and further research to find new active compounds. Recently, phytobiotics have gained interest in being used as an alternative to antibiotics in the poultry industry as an antibiotic stewardship intervention. This study evaluated the in vitro antibacterial activity of 16 flavonoids against bovine mastitis pathogens. Two flavones: 2-(4-methoxyphenyl)chromen-4-one (1) and 2-(3-hydroxyphenyl)chromen-4-one (4) showed inhibition of the growth of Klebsiella oxytoca with MIC values range (25–50 µg mL− 1) followed by a structure-activity relationship (SAR) study indicating that the presence of a hydroxyl group at C-3` or methoxy at C-4` increases the activity against Klebsiella oxytoca while the presence of hydroxyl group at C-7 decreases the activity. Furthermore, a structure-based drug development approach was applied using several in silico tools to understand the interactions of active flavones at the active site of the DNA gyrase protein. Compound (4) showed a higher docking score than quercetin (standard) which is known to have antibacterial activity by inhibiting the DNA gyrase. In addition, the structure-based pharmacophores of compound (4) and quercetin showed similar pharmacophoric features and interactions with DNA gyrase. Based on our findings, compounds (1) and (4) are promising for further study as potential anti-microbial phytochemicals that can have a role in controlling bovine mastitis as well as to investigate their mechanism of action further.
Hierarchical Service Composition via Blockchain-enabled Federated Learning
Authors: Huang Li , Zhao Lu , Liu Yansong , Zhao Yao 
Published: August 2024

AbstractIn recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) and intelligent applications. Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. However, the vast array of available CSs presents a challenge in efficiently addressing specified QoS requirements, turning CSC into a recognized NP-hard problem. Existing solutions, often involving third-party brokers, struggle with scalability in large-scale systems and overlook crucial security concerns. To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. HSC comprises an initialization phase and two selection layers. The first selection layer enables each CSP to efficiently select services using a pre-trained model, while the second selection layer employs a blockchain-based QoS-aware mechanism for the final composition result, addressing privacy concerns. HSC introduces a novel framework, collaborative service selection methods, and a smart selection algorithm, demonstrating remarkable composition efficiency in extensive simulations compared to the baseline approach.
Exploring factors influencing the microfinance program sustainability
Authors: Gunawan Indra , Nurchasanah  
Published: August 2024

AbstractEnsuring the sustainability of development interventions such as microfinance programs is essential for making sure that the financial access is available for the poor in long term. However, many project-based microfinance have faced sustainability issues. Therefore, exploring the factors affecting the sustainability of the programs is essential. Using institutionalist view of microfinance sustainability and considering project implementation aspects, this study aims to explore the factors that influence the sustainability of project-based microfinance institutions (MFIs). Quantitative approach by using a statistical tool is used to analyse the survey of the 85 microfinance bankers from 65 Microfinance institutions (MFIs). The result showed that project-based microfinance sustainability is influenced by local project characteristics, community support and project management. It implies that project implementation and its sustainability is concomitant. Project implementation that considers local community and is supported by project management could hold an important role in the sustainability of the project. Furthermore, developing sustainability strategies in planning and implementation stages is crucial. Hence, international development project actors should take them into account.