bluuebunny/crossref_metadata_2025 huggingface dataset:
Wasserstein barycenter regression: application to the joint dynamics of regional GDP and life expectancy in Italy
Authors: Levantesi Susanna , Nigri Andrea , Pagnottoni Paolo , Spelta Alessandro
Published: July 2024

We propose to investigate the joint dynamics of regional gross domestic product and life expectancy in Italy through Wasserstein barycenter regression derived from optimal transport theory. Wasserstein barycenter regression has the advantage of being flexible in modeling complex data distributions, given its ability to capture multimodal relationships, while maintaining the possibility of incorporating uncertainty and priors, other than yielding interpretable results. The main findings reveal that regional clusters tend to emerge, highlighting inequalities in Italian regions in economic and life expectancy terms. This suggests that targeted policy actions at a regional level fostering equitable development, especially from an economic viewpoint, might reduce regional inequality. Our results are validated by a robustness check on a human mobility dataset and by an illustrative forecasting exercise, which confirms the model’s ability to estimate and predict joint distributions and produce novel empirical evidence.
Versatile time-window sliding machine learning techniques for stock market forecasting
Authors: Zhan Zeqiye , Kim Song-Kyoo
Published: July 2024

The stock market which is a critical instrument in the modern financial system consistently attracts a significant number of individuals and financial institutions. The amalgamation of machine learning with stock forecasting has seen increased interest due to the rising prominence of machine learning. Among numerous machine learning models, the long short-term memory (LSTM) is favored by many researchers for its superior performance in long time series. This paper presents an innovative approach that integrates LSTM with versatile sliding window techniques to enhance prediction results and training performance. Moreover, the attached experiments incorporate convolutional filters and combined bivariate performance measures, which are invaluable methodologies for enhancing stock forecasting systems. These significant contributions are expected to be beneficial for researchers operating within this domain.
Mis-Out and Mis-In Examples: The Case of Rational Numbers
Authors: Tirosh Dina , Tsamir Pessia
Published: July 2024

This paper focuses on the definitions and the mis-out and mis-in examples of rational numbers that four prospective elementary teachers presented while working on rational number assignments. The participants were first asked to respond, individually, to an Individual Rational Number Assignment, consisting of items aiming at detecting their personal concept definitions of rational numbers and identifying the entities that they regarded as rational numbers. Then, to share their work with another prospective teacher, to identify similarities and differences in their responses, and to list issues that were raised during the individual or pair work, that they would like to discuss in class. The data exposed a tendency to provide one definition of rational numbers, to identify the term “rational” with “natural”, not to include a clarification that a rational number is a number, and a controversy regarding including (or not including) a statement that b ≠ 0 in the definition. Other observations related to a tendency not to categorize negative numbers (and perhaps also zero) as rational numbers and an inconsistency between their responses to the question “what is a rational number?” and their classification of examples of rational numbers. Recommendations for topics for discussion with prospective teachers, in light of the responses to the assignments, are suggested and methodological issues for considerations are proposed.
The influence of artificial intelligence as a tool for future economies on accounting procedures: empirical evidence from Saudi Arabia
Authors: Mgammal Mahfoudh Hussein
Published: July 2024

This study investigates the transformative influence of artificial intelligence (AI) on accounting procedures in Saudi Arabia by examining accounting professionals’ attitudes, understanding, and practices regarding AI implementation. Using a questionnaire-based survey distributed among accounting professionals in Saudi Arabia, data analysis was conducted using the partial least squares (PLS) technique. We find significant direct relationships between AI awareness and usage, AI engagement and accountants, and the impact of AI and accounting procedures. This finding suggests that accountants who are knowledgeable about and utilize AI are more likely to be engaged in AI, leading to positive changes in accounting procedures. Moreover, the robust positive relationship between AI’s impact on accounting procedures and accounting efficiency indicates a significant positive influence. The outcomes revealed that AI engagement and impact played significant mediating roles in these relationships. These findings suggest that, while AI awareness and usage alone can lead to improved accounting outcomes, the effect is mediated by the level of engagement with AI and its impact. We provide compelling evidence that AI positively affects the accounting profession. Accountants who are aware of and use AI are more likely to engage in and experience positive changes in their accounting procedures. We provide theoretical insights into the influence of AI on accounting procedures, offer valuable resources to academics, and suggest areas for future research. It equips practitioners with effective strategies for incorporating AI into their practices, highlighting the harnessing of AI’s transformative potential of AI while addressing potential challenges.
Cyclic Refiner: Object-Aware Temporal Representation Learning for Multi-view 3D Detection and Tracking
Authors: Guo Mingzhe , Zhang Zhipeng , Jing Liping , He Yuan , Wang Ke , Fan Heng
Published: July 2024

We propose a unified object-aware temporal learning framework for multi-view 3D detection and tracking tasks. Having observed that the efficacy of the temporal fusion strategy in recent multi-view perception methods may be weakened by distractors and background clutters in historical frames, we propose a cyclic learning mechanism to improve the robustness of multi-view representation learning. The essence is constructing a backward bridge to propagate information from model predictions (e.g., object locations and sizes) to image and BEV features, which forms a circle with regular inference. After backward refinement, the responses of target-irrelevant regions in historical frames would be suppressed, decreasing the risk of polluting future frames and improving the object awareness ability of temporal fusion. We further tailor an object-aware association strategy for tracking based on the cyclic learning model. The cyclic learning model not only provides refined features, but also delivers finer clues (e.g., scale level) for tracklet association. The proposed cycle learning method and association module together contribute a novel and unified multi-task framework. Experiments on nuScenes show that the proposed model achieves consistent performance gains over baselines of different designs (i.e., dense query-based BEVFormer, sparse query-based SparseBEV and LSS-based BEVDet4D) on both detection and tracking evaluation. Codes and models will be released.
Metabolic insights into HIV/TB co-infection: an untargeted urinary metabolomics approach
Authors: Olivier Cara , Luies Laneke
Published: July 2024

Introduction

Amid the global health crisis, HIV/TB co-infection presents significant challenges, amplifying the burden on patients and healthcare systems alike. Metabolomics offers an innovative window into the metabolic disruptions caused by co-infection, potentially improving diagnosis and treatment monitoring.

Aim

This study uses untargeted metabolomics to investigate the urinary metabolic signature of HIV/TB co-infection, enhancing understanding of the metabolic interplay between these infections.

Methods

Urine samples from South African adults, categorised into four groups — healthy controls, TB-positive, HIV-positive, and HIV/TB co-infected — were analysed using GCxGC-TOFMS. Metabolites showing significant differences among groups were identified through Kruskal-Wallis and Wilcoxon rank sum tests.

Results

Various metabolites (n = 23) were modulated across the spectrum of health and disease states represented in the cohorts. The metabolomic profiles reflect a pronounced disruption in biochemical pathways involved in energy production, amino acid metabolism, gut microbiome, and the immune response, suggesting a bidirectional exacerbation between HIV and TB. While both diseases independently perturb the host’s metabolism, their co-infection leads to a unique metabolic phenotype, indicative of an intricate interplay rather than a simple additive effect.

Conclusion

Metabolic profiling revealed a unique metabolic landscape shaped by HIV/TB co-infection. The findings highlight the potential of urinary differential metabolites for co-infection, offering a non-invasive tool for enhancing diagnostic precision and tailoring therapeutic interventions. Future research should focus on expanding sample sizes and integrating longitudinal analyses to build upon these foundational insights, paving the way for metabolomic applications in combating these concurrent pandemics.
A Look into the Future: The Impact of Metaverse on Traditional Theories and Thinking in International Business
Authors: Saridakis George , Khan Zaheer , Knight Gary , Idris Bochra , Mitra Jay , Khan Huda
Published: July 2024

Recent advances in technology (e.g., the Internet of Things, Augmented Reality, Virtual Reality, Cloud Services, Artificial Intelligence, and Blockchain), the shift towards digital products, and the increased use of digitalisation by firms have enhanced consumers’ experience and transformed how companies operate, create products/services, and offer value to multiple stakeholders on a global scale. These technological innovations have led to the phenomenon known as the Metaverse. The Metaverse does not refer to any one type of technology but is a broad (and often speculative) shift in how we interact with different technologies in the same space. In the context of International Business (IB), the recent emergence of Metaverse appears to make geographical, sectoral, and operational barriers less relevant, raising complex questions about how current IB theories can explain the world-spanning, sectorally fluid, and centrifugal behaviour of firms in the era of digital globalisation. In this paper, we obtain a critical understanding of the important opportunities and challenges that the Metaverse and the fluidity of digital technologies bring to the IB field. Specifically, we discuss how traditional theories can be effectively utilised to explain firms’ internationalisation, and adapted to reflect the new technological era. We propose a framework for new approaches to IB research to help advance research on Metaverse and IB, which can provide important opportunities for future work in this field.
Scanning Acoustic Microscopy Characterization of Cold-Sprayed Coatings Deposited on Grooved Substrates
Authors: Koller Martin , Cizek Jan , Janovská Michaela , Ševčík Martin , Kondas Jan , Singh Reeti , Seiner Hanuš
Published: July 2024

The effect of non-planar substrate surface on homogeneity and quality of cold-sprayed (CS) deposits was studied by scanning acoustic microscopy (SAM). Fe coatings were cold-sprayed onto Al substrates containing artificially introduced grooves of square- and trapezoid-shaped geometries, with flat or cylindrical bottoms. The Al substrates were either wrought or cold-sprayed, to comprehend their prospective influence on the Fe coatings buildup. SAM was then used to assess morphological properties of the materials from the cross-view and top-view directions. The microstructure below the surface of the studied samples was visualized by measuring the amplitudes of the reflection echoes and the velocity of the ultrasonic waves. The SAM analysis revealed that the regions of coating imperfections around the grooves are larger than what is suggested by standard scanning electron microscopy (SEM) observations. Furthermore, we found that the seemingly non-influenced coating regions that appear perfectly homogeneous and dense in SEM do, in fact, possess heterogeneous microstructure associated with the individual CS nozzle passes.
The missed crossing vessel during open pyeloplasty: a potential advantage of the robot-assisted approach in children
Authors: Abdulfattah Suhaib , Zirel Laura , Mittal Sameer , Srinivasan Arun , Shukla Aseem R.
Published: July 2024

Objective

To investigate whether the panoramic view offered by robot-assisted laparoscopic pyeloplasty (RALP) reduces the likelihood of missing a crossing vessel compared to open pyeloplasty in cases where initial pyeloplasty fails.

Methods

A single institution redo-pyeloplasty database was reviewed for children treated between January 2012 to July 2023. Clinical history, imaging and operative details were reviewed to identify the etiology for the redo procedure.

Results

Cohort consisted of 45 patients undergoing a redo RALP during the study period. 29 of 45 patients had an initial open surgical approach, whereas 16 had an initial RALP. 10 patients were noted to have a missed crossing vessel on redo pyeloplasty – 9 had an initial open approach whereas 1 had an initial RALP (p<0.0001).

Conclusions

RALP may reduce the risk of missing a crossing vessel due to the panoramic view of the surgical field intrinsic to an intraperitoneal RALP approach.
Meaning without Agency: The Establishment of Meaningful Time Relations as Prerequisite for the Emergence of Biosemiosis
Authors: Kusters Constantijn-Alexander
Published: July 2024

This article reexamines meaning, agency, and interpretation by challenging the view that they require primary or secondary agency. Using Paul Ricœur’s narrative temporality, it explores Terrence Deacon’s autogenic theory, reinterpreting it as a narrative process with non-agentic meaning by distinguishing between distended and displaced temporal relations. Distended relations pertain to agency and biosemiosis, while displaced relations involve the meaning found not in the entity but the processes which gave it a functionally historicized existence. Applying Ricœur’s analysis of temporal aporia and Deacon’s concept of zero, the article suggests that meaning in Deacon’s model mirrors the normative process of narrative interpretation. It emphasizes that primary agency requires meaningful organization for agentic action to have a self for which decisions matter, concluding that meaning, life, and primary agency are grounded in already existing displaced temporal relations resultant from proto-interpretative relations not bounded within an organism.
Changes and correlates of household food insecurity during COVID-19: a repeated cross-sectional survey of low-income households in peri-urban Peru
Authors: Pradeilles Rebecca , Eymard-Duvernay Sabrina , Pareja Rossina , Holdsworth Michelle , Landais Edwige , Creed-Kanashiro Hilary M. , Rousham Emily K.
Published: July 2024

National lockdowns and containment measures to control the spread of COVID-19 led to increased unemployment, lower household incomes and reduced access to affordable and nutritious foods globally. This study aimed to examine changes and correlates of household food insecurity experience and mitigation strategies adopted in peri-urban Peru during the COVID-19 pandemic. Low income households with children age < 2 years in Lima and Huánuco participated in three repeated cross-sectional surveys from 2020 to 2022 (n = 759). We assessed changes in household food insecurity experience using the Food Insecurity Experience Scale. Correlates of moderate-severe food insecurity were analysed using univariate and multivariable linear mixed-effect regressions. We also assessed perceived impacts of the pandemic on livelihoods, coping strategies and receipt of financial or food assistance. Moderate-severe food insecurity was 47.0% in 2020 (survey 1) decreasing to 31.1% in 2022 (survey 3). In adjusted analyses, food insecurity was higher in households with perceived reduced income (β = 12.69 [6.82; 18.56]); in the lower socio-economic status (SES) tertiles (compared to the relatively highest SES tertile; middle tertile (β = 20.91 [9.89; 31.93]), lowest tertile (β = 39.37 [28.35; 50.40]); in households with ≥ 2 children < 5 years (β = 8.78 [2.05; 15.50]); and in Lima (compared to Huánuco; β = 10.47 [1.27; 19.67]). Food insecurity improved more among the relatively lowest SES compared to the relatively highest SES households between survey 1 and 3 (interaction p = 0.007). In conclusion, almost half of households experienced moderate-severe food insecurity mid-pandemic with greater risk observed in the most socio-economically disadvantaged households. The inequality gap in food insecurity associated with SES narrowed over time likely due to household coping strategies and reduced poverty.
Conceptualizing Automated Decision-Making in Organizational Contexts
Authors: Boos Anna Katharina
Published: July 2024

Despite growing interest in automated (or algorithmic) decision-making (ADM), little work has been done to conceptually clarify the term. This article aims to tackle this issue by developing a conceptualization of ADM specifically tailored to organizational contexts. It has two main goals: (1) to meaningfully demarcate ADM from similar, yet distinct algorithm-supported practices; and (2) to draw internal distinctions such that different ADM types can be meaningfully distinguished. The proposed conceptualization builds on three arguments: First, ADM primarily refers to the automation of practical decisions (decisions to φ) as opposed to cognitive decisions (decisions that p). Second, rather than referring to algorithms as literally making decisions, ADM refers to the use of algorithms to solve decision problems at an organizational level. Third, since algorithmic tools by nature primarily settle cognitive decision problems, their classification as ADM depends on whether and to what extent an algorithmically generated output p has an action triggering effect—i.e., translates into a consequential action φ. The examination of precisely this p-φ relationship, allows us to pinpoint different ADM types (suggesting, offloading, superseding). Taking these three arguments into account, we arrive at the following definition: ADM refers to the practice of using algorithms to solve decision problems, where these algorithms can play a suggesting, offloading, or superseding role relative to humans, and decisions are defined as action triggering choices.
Synthetic Socio-Technical Systems: Poiêsis as Meaning Making
Authors: Bisconti Piercosma , McIntyre Andrew , Russo Federica
Published: July 2024

With the recent renewed interest in AI, the field has made substantial advancements, particularly in generative systems. Increased computational power and the availability of very large datasets has enabled systems such as ChatGPT to effectively replicate aspects of human social interactions, such as verbal communication, thus bringing about profound changes in society. In this paper, we explain that the arrival of generative AI systems marks a shift from ‘interacting through’ to ‘interacting with’ technologies and calls for a reconceptualization of socio-technical systems as we currently understand them. We dub this new generation of socio-technical systems synthetic to signal the increased interactions between human and artificial agents, and, in the footsteps of philosophers of information, we cash out agency in terms of ‘poiêsis’. We close the paper with a discussion of the potential policy implications of synthetic socio-technical system.
Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder
Authors: Öztürk Demet , Aydoğan Sena , Kök İbrahim , Akın Bülbül Işık , Özdemir Selda , Özdemir Suat , Akay Diyar
Published: July 2024

Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.
A discursive field of contested ethics: Reporting the UK’s blue economy in the making
Authors: Winder Gordon
Published: July 2024

This paper analyses newspaper coverage of ‘blue economy’ in the UK 2012–2020. It elaborates on Germond-Duret and Germond’s (2022) finding that UK media published a dominant development discourse concerning the UK’s marine space without reflecting on the risks of growth agendas. Articles from UK print media featuring ‘blue economy’ were compiled from LexisNexis then analysed to establish patterns of publishing, the investors, investments and projects reported, the experts cited and their statements on blue economy. Analysis reveals no sustained coverage, little that communicated with a national audience, little investigative reporting, and no predominance of London-based newspapers in news production. Reporting on blue economy was sporadic until a surge in 2020 that coincided with efforts to secure investments through local growth plans. Newspapers registered this buzz of activity, collecting statements from actors who used business ethics to express the legitimacy of their planning to build what they called a ‘blue economy’ even though this looked more like ‘blue growth’. A second group of actors including the Scottish Government announced plans to build a ‘sustainable blue economy’. These actors used the ethics of regional development, achieving net zero and protecting, enhancing or restoring ocean environments. Journalists reported critical views on these plans: inadequate attention to environmental protection, investment in activities seen as environmentally unsustainable or investments with limited regional development. Thus, analysis of statements on ‘blue economy’ reveals a discursive field featuring a confusion of contested ethical claims. This paper identifies the UK’s fragmented newspaper landscape and new architecture of public–private enterprise partnerships as important contexts shaping media engagement with blue economy.
Dysglycemia, gender, and cognitive performance in older persons living with mild cognitive impairment: findings from a cross-sectional, population-based study
Authors: Boccardi Virginia , Travaglini Emma Giulia , Sciacca Emanuela , Mancinetti Francesca , Murasecco Ilenia , Guazzarini Anna Giulia , Bastiani Patrizia , Ruggiero Carmelinda , Mecocci Patrizia
Published: July 2024

Objective

This study aims to examine the relationship between dysglycemia - also known as pre-diabetes or impaired glucose tolerance- and cognitive abilities in an older population living Mild Cognitive Impairment (MCI) and stratified by gender.

Study design

This is a retrospective study with data gathered from a large Italian clinical-based database.

Main outcome measures

The evaluation of cognitive performances by the Mini-Mental State Examination and the Addenbrooke’s Cognitive Examination Revised rating scale as tests of screening and a comprehensive neuropsychological evaluation of several cognitive areas.

Results

The study comprised 682 subjects (445 F/237 M) with a mean age of 76.08 ± 9.03 (range: 66–93) years. In all population, subjects with dysglycemia 193 (28.3%) had significantly poorer performance in memory (p = 0.006) and logic reasoning (p = 0.007) when compared with subjects without dysglycemia. The linear regression analyses revealed significant differences in the correlates of cognitive domains between gender groups. Independent of multiple covariates, women with dysglycemia showed worse performances in attention and short-term memory domains as compared with men. Even in the absence of dysglycemia women were more likely to show lower score in screening test of general cognition and attention.

Conclusions

Our findings suggest that dysglycemia in older individuals with MCI is associated with declines in specific cognitive domains, potentially influenced by gender. Implementing a comprehensive approach involving risk stratification and preventive strategies may be more effective in averting further cognitive decline in this high-risk population.
The growth rate of multicolor Ramsey numbers of 3-graphs
Authors: Bradač Domagoj , Fox Jacob , Sudakov Benny
Published: July 2024

The q-color Ramsey number of a k-uniform hypergraph G,  denoted r(G; q), is the minimum integer N such that any coloring of the edges of the complete k-uniform hypergraph on N vertices contains a monochromatic copy of G. The study of these numbers is one of the most central topics in combinatorics. One natural question, which for triangles goes back to the work of Schur in 1916, is to determine the behavior of r(G; q) for fixed G and q tending to infinity. In this paper, we study this problem for 3-uniform hypergraphs and determine the tower height of r(G; q) as a function of q. More precisely, given a hypergraph G, we determine when r(G; q) behaves polynomially, exponentially or double exponentially in q. This answers a question of Axenovich, Gyárfás, Liu and Mubayi.
Exploration of a new AlCoCrNiNb high-entropy alloy: in situ alloying of a CoCrMo, M247, and Nb powder mixture via laser powder bed fusion
Authors: Tsai Meng-Hsiu , Cheng Chia-Ping , Fu Ho-Chung , Chiba Akihiko , Yamanaka Kenta
Published: July 2024

High-entropy alloys (HEAs) have compelling advantages, such as high strength and corrosion resistance, but they remain underexplored owing to the limited availability of certain prealloyed and elemental powders. In this study, an AlCoCrNiNb HEA was fabricated in situ via laser powder bed fusion (LPBF) using a powder mixture of commercial CoCrMo, Mar M247, and Nb elemental powders. The powder mixture was blended for 24 h using a horizontal blending machine, to obtain similar chemical compositions in the middle and top layers of the blended powder. However, local Nb aggregation was observed in the bottom layer owing to the particle-size effect. X-ray diffraction and chemical composition analyses revealed that the in situ alloyed AlCoCrNiNb HEA specimen obtained via LPBF was a homogeneous solid solution with a face-centered cubic structure. HEA exhibits a fine-grained morphology, and its maximum microhardness is approximately 970 HV. These characteristics are typical of rapid solidification and sluggish diffusion. These results underscore the effectiveness of using commercial prealloyed and elemental powders for fabricating AlCoCrNiNb HEAs through LPBF-based in situ alloying, thus advancing the development of HEAs. Thermodynamic calculations were performed to support these outcomes.
Two-sample testing with local community depth
Authors: Evans Ciaran , Berenhaut Kenneth S.
Published: July 2024

In this paper, we investigate nonparametric two-sample hypothesis testing with local community depth, a measure of data depth calculated from pairwise distances between observations. Unlike many common “center-outwards” measures of data depth, local community depth captures local features of a distribution such as multimodality and can adapt to modes with different scales. This gives local community depth greater power to detect scale changes in multimodal distributions, and importantly the method is parameter-free. The local depth employed supplements existing adaptations of depth to local regions and is computationally cheaper in high dimensions than many classic depths such as halfspace depth, regression depth, simplicial depth, and Mahalanobis depth. We test hypotheses by using local community depth in the Liu–Singh test and give conditions under which our method is consistent against fixed alternatives (and has a limiting null distribution). In simulations and an application to diagnosing dengue fever, we show that local community depth outperforms other depths when the underlying distribution is multimodal or asymmetric, and is comparable to other methods when the data is unimodal and symmetric.
A bridge between influence models and control methods
Authors: Sadaf Abida , Mathieson Luke , Bródka Piotr , Musial Katarzyna
Published: July 2024

Understanding how influence is seeded and spreads through social networks is an increasingly important study area. While there are many methods to identify seed nodes that are used to initialize a spread of influence, the idea of using methods for selecting driver nodes from the control field in the context of seed selection has not been explored yet. In this work, we present the first study of using control approaches as seed selection methods. We employ a Minimum Dominating Set to develop a candidate set of driver nodes. We propose methods based upon driver nodes (i.e. Driver-Random, Driver-Degree, Driver-Closeness, Driver-Betweenness, Driver-Degree-Closeness-Betweenness, Driver-Kempe, Driver-Ranked) for selecting seeds from this set. These methods make use of centrality measures to rank the driver nodes in terms of their potential as seed nodes. We compare proposed methods to existing approaches using the Linear Threshold model on both real and synthetic networks. Our experiment results show that the proposed methods consistently outperform the benchmarks. We conclude that using driver nodes as seeds in the influence spread results in faster and thus more effective spread than when applying traditional methods.
AU3-GAN: A Method for Extracting Roads from Historical Maps Based on an Attention Generative Adversarial Network
Authors: Zhao Yao , Wang Guangxia , Yang Jian , Li Tingting , Li Ziwei
Published: July 2024

In recent years, the integration of deep learning technology based on convolutional neural networks with historical maps has made it possible to automatically extract roads from these maps, which is highly important for studying the evolution of transportation networks. However, the similarity between roads and other features (such as contours, water systems, and administrative boundaries) poses a significant challenge to the feature extraction capabilities of convolutional neural networks (CNN). Additionally, CNN require a large quantity of labelled data for training, which can be a complex issue for historical maps. To address these limitations, we propose a method for extracting roads from historical maps based on an attention generative adversarial network. This approach leverages the unique architecture and training methodology of the generative adversarial network to augment datasets by generating data that closely resembles real samples. Meanwhile, we introduce an attention mechanism to enhance UNet3 + and achieve accurate historical map road segmentation images. We validate our method using the Third Military Mapping Survey of Austria-Hungary and compare it with a typical U-shaped network. The experimental results show that our proposed method outperforms the direct use of the U-shaped network, achieving at least an 18.26% increase in F1 and a 7.62% increase in the MIoU, demonstrating its strong ability to extract roads from historical maps and provide a valuable reference for road extraction from other types of historical maps.
Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling
Authors: Mukta Md. Saddam Hossain , Ahmed Jubaer , Raiaan Mohaimenul Azam Khan , Fahad Nur Mohammad , Islam Muhammad Nazrul , Imtiaz Nafiz , Islam Md. Adnanul , Ali Mohammed Eunus , Azam Sami
Published: July 2024

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value ) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group.
Inference and prioritization of tissue-specific regulons in Arabidopsis and Oryza
Authors: Dai Honggang , Fan Yaxin , Mei Yichao , Chen Ling-Ling , Gao Junxiang
Published: July 2024

A regulon refers to a group of genes regulated by a transcription factor binding to regulatory motifs to achieve specific biological functions. To infer tissue-specific gene regulons in Arabidopsis, we developed a novel pipeline named InferReg. InferReg utilizes a gene expression matrix that includes 3400 Arabidopsis transcriptomes to make initial predictions about the regulatory relationships between transcription factors (TFs) and target genes (TGs) using co-expression patterns. It further improves these anticipated interactions by integrating TF binding site enrichment analysis to eliminate false positives that are only supported by expression data. InferReg further trained a graph convolutional network with 133 transcription factors, supported by ChIP-seq, as positive samples, to learn the regulatory logic between TFs and TGs to improve the accuracy of the regulatory network. To evaluate the functionality of InferReg, we utilized it to discover tissue-specific regulons in 5 Arabidopsis tissues: flower, leaf, root, seed, and seedling. We ranked the activities of regulons for each tissue based on reliability using Borda ranking and compared them with existing databases. The results demonstrated that InferReg not only identified known tissue-specific regulons but also discovered new ones. By applying InferReg to rice expression data, we were able to identify rice tissue-specific regulons, showing that our approach can be applied more broadly. We used InferReg to successfully identify important regulons in various tissues of Arabidopsis and Oryza, which has improved our understanding of tissue-specific regulations and the roles of regulons in tissue differentiation and development.
Iron administered in the neonatal period changed memory, brain monoamine levels, and BDNF mRNA expression in adult Sprague–Dawley rats
Authors: Rogóż Zofia , Kamińska Kinga , Lorenc-Koci Elżbieta , Wąsik Agnieszka
Published: July 2024

Background

Iron is one of the key microelements in the mammalian body and is the most abundant metal in the brain. Iron, a very important chemical element in the body of mammals, is the most abundant metal in the brain. It participates in many chemical reactions taking place in the central nervous system acting as a cofactor in key enzymatic reactions involved in neurotransmitter synthesis and degradation, dendritic arborization, and myelination. Moreover, iron accumulation in the brain has been implicated in the pathogenesis of neurogenerative disorders.

Material and methods

The aim of our study was to assess the influence of iron administered orally (30 mg/kg) to rats in the neonatal period (p12-p14) by testing the performance of rats in the open field and social interaction tests, and by evaluating the recognition memory, monoamine levels in some brain structures, and BDNF mRNA expression. The behavioral and biochemical tests were performed in adult p88-p92 rats.

Results

Iron administered to rats in the neonatal period induced long-term deficits in behavioral tests in adult rats. It reduced the exploratory activity in the open field test. In the social interaction test, it induced deficits in the parameters studied, and decreased memory retention. Moreover, iron changed the brain monoamine levels in some studied brain structures and decreased the expression of BDNF mRNA in the hippocampus.

Conclusions

All earlier and our present results indicated that iron administered to rats in the neonatal period induced an increase in oxidative stress which resulted in a change in the brain monoamine levels and decreased BDNF mRNA expression which may play a role in iron-induced memory impairment in adult rats.
Energy-absorbing limitations of hard hat safety helmets in mitigating trauma from falling objects
Authors: Ptak Mariusz , Dymek Mateusz , Wdowicz Daniel , Szumiejko Adrianna , Kwiatkowski Artur
Published: July 2024

The aim of the study was to analyze the effectiveness of hard hat helmets in mitigating head injuries from high-energy falling objects through a real-world case study, advanced numerical simulations and an uncertainty study. The study aims to answer the following research questions: (a) to what extent would the use of the protective helmet limit the kinetic energy of the falling construction prop, (b) whether the hard hat helmet would be damaged, and if so, to what extent, according to the helmet standards? A fatal construction accident involving a falling prop impact on the victim’s head was reconstructed using multi-body dynamics simulations and finite element analysis (FEA) based on uncertainty-based determination of initial conditions. The study quantified the impact energy, helmet damage and its energy-absorbing capabilities, and potential injury reduction compared to scenarios without a helmet. While the helmet absorbed significant energy (245% of the standard requirement) and reduced the Head Injury Criterion by 8–11%, the high impact energy ultimately proved fatal. This study highlights the limitations of hard hat helmets in extreme scenarios with high kinetic energy impacts. While helmets offer valuable protection, unrealistic expectations should not be placed on their ability to prevent all head injuries. The study not only enhances our understanding of the biomechanics of head injuries in such incidents but also provides practical implications for safety protocols and regulations.
A Māori data governance assessment of the NZ COVID Tracer app
Authors: Sterling Rogena , Kukutai Tahu , Chambers Tim , Chen Andrew Tzer-Yeu
Published: July 2024

Purpose

During the COVID-19 pandemic, the NZ COVID Tracer App (NZCTA) was released as a digital intervention to support contact tracing processes in Aotearoa New Zealand. This paper examines whether NZCTA met the data governance requirements of the Indigenous Māori people. Māori are an interesting case study as they have unique Treaty and data sovereignty rights, and a higher risk of COVID-related mortality.

Methods

The NZCTA was assessed against 24 criteria drawn from the Māori Data Governance Model. The assessment drew on documentary sources and the authors’ knowledge of NZCTA and contact tracing process. Each criteria was assessed as ‘met’, ‘partially met’ or ‘not met’.

Results

Our retrospective assessment showed a mixed performance against the Māori Data Governance Model, with NZCTA only fulfilling seven of the 24 model criteria and failing to meet nine.

Conclusion

There is significant room for improvement in future digital health interventions for Māori. Much work remains to be done in the Aotearoa public sector to uphold Māori data sovereignty and address systemic barriers to genuine partnership with Māori.
Teaching responsible creativity: a path to ethical innovation
Authors: Rebecchi Kevin , Todd Lubart , Hagège Hélène
Published: July 2024

Education today is evolving to foster essential psychosocial skills like creativity, critical thinking, and responsibility, equipping students for 21st-century challenges. However, it is crucial to examine the motivations driving these changes and their alignment with broader societal goals beyond merely economic interests.

This article explores the ethical dimensions of creativity, recognizing its potential for both positive innovation and harm. It emphasizes the neutrality of creativity and the importance of evaluating the intentions and consequences behind creative acts, not just their outcomes. We introduce and define the concept of ‘‘responsible creativity,’’ characterized by the ability to generate original ideas, behaviors, or objects while considering the moral and legal repercussions of one's actions, both in the present and future. This concept is anchored in the ethics of conviction and responsibility, promoting a fundamental commitment to avoid harm and enhance well-being. Our model of responsible creativity incorporates qualities such as benevolence, flexibility, positivity, reliability, accountability, reflexivity, and wisdom. We advocate for nurturing this type of creativity in educational settings through innovative approaches such as board and video games, philosophy workshops, wisdom education, and mindfulness meditation. These pedagogical methods aim to cultivate future leaders who are ethically aware and capable of using their creativity to positively impact society and the environment. By navigating the interplay of creativity and ethics, this article provides valuable insights and practical guidance for educators and policymakers striving to create a more ethically conscious and innovative educational landscape.
Analysis of the socio-environmental impacts of an environmental disaster caused by a minining company in the city of Maceió, Brazil
Authors: de Lima Filho Walter Araujo , de Almeida Levino Natallya , Guarnieri Patrícia
Published: July 2024

In mid-2018, several neighbourhoods in Maceió, Brazil, were affected by soil subsidence in the region. Since then, unprecedented developments and consequences have been observed in the affected areas. Against this backdrop, the study aims to analyses the socio-environmental impacts suffered by the five neighbourhoods affected by subsidence from rock salt extraction in the municipality of Maceió, Alagoas, Brazil. We collected secondary data from documents (Geological Survey studies, information made available by the Federal Public Prosecutor's Office, Compensation Plan, Terms of Agreement, Socio-urban Action Plan, Environmental Impact Study, Study of the Economic Impact of the Disaster) to assess the socio-environmental impacts caused by the socio-environmental disaster. These data were analysed using Checklist, Interaction Matrix, and Delphi. The results highlight the complexity of the effects since they were observed from a multidisciplinary approach. In addition, it is clear that these impacts were relevant and important from the specialists' point of view and, when analysed quantitatively and qualitatively,such as impacts on identity and cultural aspects, change in standard of living, and overload in the provision of public facilities, which had a high degree of impact when evaluated in this study. The study also concluded that it is pertinent to take measures to mitigate the impacts indicated as possible through integration between public authorities and the mining company. Upon its completion, a technical product in the form of a document containing the identified mitigation measures was submitted to the competent authorities. These results can be relevant for practitioners and policymakers involved in decisions related to mining operations.
Physical Mechanisms Sustaining Silica Production Following the Demise of the Diatom Phase of the North Atlantic Spring Phytoplankton Bloom During EXPORTS
Authors: Brzezinski Mark A. , Johnson Leah , Estapa Margaret , Clevenger Samantha , Roca‐Martí Montserrat , Romanelli Elisa , Buck Kristen N. , Jenkins Bethany D. , Jones Janice L.
Published: July 2024

Each spring, the North Atlantic experiences one of the largest open‐ocean phytoplankton blooms in the global ocean. Diatoms often dominate the initial phase of the bloom with succession driven by exhaustion of silicic acid. The North Atlantic was sampled over 3.5 weeks in spring 2021 following the demise of the main diatom bloom, allowing mechanisms that sustain continued diatom contributions to be examined. Diatom biomass was initially relatively high with biogenic silica concentrations up to 2.25 μmol Si L−1. A low initial silicic acid concentration of 0.1–0.3 μM imposed severe Si limitation of silica production and likely limited the diatom growth rate. Four storms over the next 3.5 weeks entrained silicic acid into the mixed layer, relieving growth limitation, but uptake limitation persisted. Silica production was modest and dominated by the >5.0 μm size fraction although specific rates were highest in the 0.6–5.0 μm size fraction over most of the cruise. Silica dissolution averaged 68% of silica production. The resupply of silicic acid via storm entrainment and silica dissolution supported a cumulative post‐bloom silica production that was 32% of that estimated during the main bloom event. Diatoms contributed significantly to new and to primary production after the initial bloom, possibly dominating both. Diatom contribution to organic‐carbon export was also significant at 40%–70%. Thus, diatoms can significantly contribute to regional biogeochemistry following initial silicic acid depletion, but that contribution relies on physical processes that resupply the nutrient to surface waters.
Rift to Post‐Rift Tectonostratigraphy of the Sverdrup Basin in Relation to Onset of the High Arctic Large Igneous Province (HALIP) in the Early Cretaceous, Arctic Canada
Authors: Hadlari T.
Published: July 2024

A summary of the Jurassic‐Cretaceous rift to breakup tectonostratigraphy of the onshore Sverdrup Basin is correlated to the offshore Amerasia Basin in order to reconstruct a tectonic setting for the High Arctic Large Igneous Province (HALIP). The rift climax from the Canadian rifted margin is correlated with hyper‐extension of the continent‐ocean‐transition zone. Hyper‐extension of the continental lithosphere can accommodate plate motions of Arctic Alaska‐Chukotka away from the Canadian Arctic Islands and Lomonosov Ridge between ∼155 Ma and 135–133 Ma. After lithospheric breakup at ∼135–133 Ma, correlation of the post‐rift stage to the seafloor spreading anomalies M10n to M4n that are associated with oceanic crustal domains can accommodate plate motions from 135–133 Ma to 128 Ma. The uncertainties associated with the earliest magmas of HALIP overlap with the uncertainties on the timing of the latest seafloor spreading. The first main pulse of HALIP in the Aptian at 124–120 Ma post‐dates seafloor spreading and so HALIP was emplaced in a tectonic setting that closely resembles the present state of the south and eastern Amerasia Basin. At the paleogeographic center of the HALIP, the Alpha Ridge complex is consistent with the magmatic character and history of similar Cretaceous oceanic plateau in terms of volume and duration.