bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cybergrooming Victimization Among Young People: A Systematic Review of Prevalence Rates, Risk Factors, and Outcomes
Authors: Schittenhelm Catherine , Kops Maxime , Moosburner Maeve , Fischer Saskia M. , Wachs Sebastian 
Published: September 2024

AbstractAdolescence is marked by a unique blend of factors, including adolescents’ exploration of their emerging sexuality and growing engagement with digital media. As adolescents increasingly navigate online spaces, cybergrooming victimization has emerged as a significant concern for the development and protection of young people. Yet, there is a lack of systematic analyses of the current state of research. To this end, the present systematic review aimed to integrate existing quantitative research on prevalence rates, risk factors, and outcomes of cybergrooming victimization, informed by an adaptation of the General Aggression Model. Studies providing self-reported data on cybergrooming victimization of people between the ages of 5 and 21 were included. A total of 34 studies met all inclusion criteria, with most focusing on adolescence. Reported prevalence rates were characterized by strong heterogeneity, which could largely be attributed to the underlying methodology. Overall, the included studies showed that at least one in ten young people experiences cybergrooming victimization. Findings further indicated that various factors, for example, being a girl, being older, engaging in risky behavior, displaying problematic Internet use, reporting lower mental well-being, and experiencing other types of victimization, are positively associated with cybergrooming victimization. However, most studies’ cross-sectional designs did not allow for an evidence-based classification into risk factors, outcomes, and co-occurrences, so findings were embedded in the proposed model based on theoretical considerations. In addition, there is a noted lack of studies that include diverse samples, particularly younger children, LGBTQIA+ youth, and young people with special educational needs. These findings emphasize that cybergrooming victimization is a prevalent phenomenon among young people that requires prevention and victim support addressing multiple domains.
Unlocking the potential of low-melting-point alloys integrated extrusion additive manufacturing: insights into mechanical behavior, energy absorption, and electrical conductivity
Authors: Jin Liuchao , Zhai Xiaoya , Zhang Kang , Jiang Jingchao 
Published: September 2024

AbstractAdditive manufacturing is a commonly used manufacturing method in complex part fabrication, instant assemblies, part consolidation, mass customization and personalization, on-demand manufacturing, lightweight, and topological optimization due to its advantage of lower costs, flexibility to learn and use, reduced raw material wastage, digital design integration, high efficiency, environmental-friendliness. However, the current metal 3D printing, which is mainly fabricated layer by layer using laser, is expensive to manufacture metal parts. Therefore, in this paper, a low-cost high-quality metal manufacturing process called low-melting-point alloys (LMPAs) integrated extrusion additive manufacturing will be examined. This manufacturing process can fabricate complex metal structures and integrated circuits with simple fused deposition modeling, which is a cost-effective method for producing these objects. LMPAs with different melting points are used for performance comparison to find out the optimized mechanical behavior, energy absorption properties, and electrical conductivity. Our investigation into LMPAs integrated extrusion additive manufacturing has revealed significant findings. Tensile tests conducted on LMPAs with varying melting points have illuminated distinct mechanical behaviors. Notably, lower melting points contribute to increased ductility but reduced stiffness, while higher melting points result in greater stiffness but diminished ductility. These results emphasize the importance of tailoring LMPA selection based on specific application requirements. Furthermore, our examination of lattice and triply periodic minimal surface structures has demonstrated consistent energy absorption properties across different manufacturing temperatures, highlighting the adaptability and versatility of LMPAs for energy absorption applications. Additionally, our electrical conductivity assessments have shown that LMPAs with melting points of $${{47}^{\circ }C}$$

47

∘

C

and $${{120}^{\circ }}\hbox {C}$$

120

∘

C

exhibit higher electrical conductivity, making them suitable for applications requiring good electrical conduction properties. These findings collectively underscore the significance of LMPAs in additive manufacturing, offering valuable insights for material selection and applications in various domains.
Additive manufacturing of flexible thermoplastic polyurethane (TPU): enhancing the material elongation through process optimisation
Authors: Marco Viccica , Massimo Giordano , Manuela Galati 
Published: September 2024

AbstractThermoplastic polyurethane (TPU) is used to produce elastomeric parts with superior wear/abrasion resistance, toughness, shock absorption properties, and flexibility, even at low temperatures. The production of this material through additive manufacturing (AM) techniques has been increasing because of the possibility of tuning the mechanical properties using structural design and build process parameters. Despite the data being limited, TPU produced by AM, mainly based on material extrusion, is much stiffer than the corresponding produced by conventional manufacturing, and, therefore, it shows a limited elongation. This study presents the mechanical characterization of TPU produced by the infrared light powder bed fusion (PBF-IrL) system (HP multi-jet fusion), which has recently been introduced. The properties are compared with TPU produced by open (3ntrA4) and closed (Markforged) material extrusion (MEX) systems. For the open FDM, the effects of the processing conditions are investigated to improve the material elongation and UTS with respect to the data reported in the literature for AM and conventional manufacturing. For this reason, an extensive and comprehensive review has been carried out. Compared to material extrusion, PBF-IrL TPU specimens showed higher Young’s modulus, but poorer A%. Considering the samples produced by MEX and compared to previous results in the literature, the properties obtained in this study are superior both in terms of UTS and A%.
Theorem of the alternative for a system of mappings in generalized complete metric spaces
Authors: Khantwal Deepak 
Published: September 2024

AbstractThe aim of the paper is to establish some theorems of the alternative for a system of mappings in generalized complete metric spaces. Our results extend and generalize the well-known results of Daiz and Margolis (Bull Am Math Soc 74:305–309, 1968), Matkowski (Bull Acad Polon Sci Sér Sci Math Astron Phys 21:323–324, 1973), Jleli and Samet (J Inequal Appl 38:8, 2014), and many others. We also present some illustrative examples to validate our results.
Projected Changes in Rainfall Extremes over West African Cities Under Specific Global Warming Levels Using CORDEX and NEX-GDDP Datasets
Authors: Klutse Nana Ama Browne , Abiodun Babatunde J. , Quagraine Kwesi A. , Nkrumah Francis , Abaton Abayomi A. , Adekoke Jimmy , Sylla Mouhamadou Bamba , Berkoh-Oforiwaa Rebecca , Koffi Hubert A. , Essien Patrick 
Published: September 2024

AbstractWe analyze long-term extreme events in daily precipitation for the period of 1950–2005 and future projections from 2006 to 2100 based on representative concentration pathways (RCP8.5) over West Africa, including twelve major cities of the subregion. The study uses two datasets from the Coordinated Regional Downscaling Experiments (CORDEX) and NASA Earth eXchange Global Daily Downscaled Projections (NEX–GDDP), for their potential to simulate extreme rainfall characteristics over West Africa. CORDEX datasets are dynamically downscaled from 9 General Circulation Model (GCM) and the NEX-GDDP is statistically downscaled from 21 GCM simulations from CMIP5. The ensemble members of each dataset were used for the analysis and evaluated with eight observations. The projected changes in the rainfall indices were analyzed at four Global Warming Levels (GWLs) under the RCP8.5 climate-forcing scenario. The projections from the CORDEX and NEX-GDDP ensembles were compared. Self-Organizing Map (SOM) analysis was used to classify the projected changes from the simulations into twelve groups, based on their characteristics. Both CORDEX and NEX-GDDP models perform well in simulating the frequency and intensities of daily rainfall characteristics for important parts of the domain. However, substantial differences exist between the models and their relative performance varies with the indices. In all cases, NEX-GDDP outperforms CORDEX in stimulating the extreme indices. On the projections, CORDEX showed a wider spread than the NEX–GDDP for RTOT and vice versa for R97.5pTOT for all the GWLs.
Influence of Protected Areas in the Carbon Cycle: A Machine Learning Approach for Future Forecasting
Authors: Lago-González Pedro , Acuña Alonso Carolina , Álvarez Xana 
Published: September 2024

AbstractProtected Areas (PAs) are destined to the conservation of natural resources, whose quantity and quality are menaced by Climate Change and anthropogenic pressure. The objective of this research was to quantify and forecast the Carbon Absorption of PAs, attending these factors. We used the Net Primary Productivity (NPP) and implemented machine learning algorithms, as Random Forests (RF), Gradient Boosting Trees (GBT), and Multilayer Perceptrons (MLPNN), to forecast it in four differentiated PAs of Galicia (NW Spain): the Central Massif, the Sil Canyons, Fragas do Eume, and the Tambre River. Two testing stages were carried, one in the areas where the model was trained, and another in the whole territory. Finally, we set several scenarios based on projections SSP2-4.5 and SSP 5-8.5 in and land use changes. GBT was the most accurate algorithm, with a Root Mean Squared Error (RMSE) of 0.05 kgC/m2 (5.7% of the average NPP) and correlation of 0.9. RF obtained an error of 0.07 and correlation of 80%, and MLPNN 0.06 and 86%, respectively. The GBT obtained a RMSE of 0.04 kgC/m2 and R2 of 0.95 in the first test. These results were worsened in the second test, with an RMSE of 0.09 kgC/m2 and 71% of correlation. For the SSP2-4.5 scenarios, a decrease around 7% can be expected, barely influenced by the land use. On the other hand, the SSP5-8.5 is expected to record a decrease of 5% in the NPP, with no significant differences between the land uses, but with a significant trend. All the pilot sites stick to this trend, except for the Tambre River, which was forecasted to record a slight increase of the NPP (~ 3%). The prediction and analysis of future scenarios can help the management of the territory, focusing on the mitigation of the effects of Climate Change in PAs.
Graphical Abstract
Characterisation of the complete chloroplast genome of Solanum tuberosum cv. White Lady
Authors: Frank Krisztián , Nagy Erzsébet , Taller János , Wolf István , Polgár Zsolt 
Published: September 2024

AbstractPotato (Solanum tuberosum) is considered worldwide as one of the most important non-cereal food crops. As a result of its adaptability and worldwide production area, potato displays a vast phenotypical variability as well as genomic diversity. Chloroplast genomes have long been a core issue in plant molecular evolution and phylogenetic studies, and have an important role in revealing photosynthetic mechanisms, metabolic regulations and the adaptive evolution of plants. We sequenced the complete chloroplast genome of the Hungarian cultivar White Lady, which is 155 549 base pairs (bp) in length and is characterised by the typical quadripartite structure composed of a large- and small single-copy region (85 991 bp and 18 374 bp, respectively) interspersed by two identical inverted repeats (25 592 bp). The genome consists of 127 genes of which 82 are protein-coding, eight are ribosomal RNAs and 37 are transfer RNAs. The overall gene content and distribution of the genes on the White Lady chloroplast was the same as found in other potato chloroplasts. The alignment of S. tuberosum chloroplast genome sequences resulted in a highly resolved tree, with 10 out of the 13 nodes recovered having bootstrap values over 90%. By comparing the White Lady chloroplast genome with available S. tuberosum sequences we found that gene content and synteny are highly conserved. The new chloroplast sequence can support further studies of genetic diversity, resource conservation, evolution and applied agricultural research. The new sequence can support further potato genetic diversity and evolutionary studies, resource conservation, and also applied agricultural research.
Revaluating the Sustainable Development Thesis: exploring the moderating influence of Technological Innovation on the impact of Foreign Direct Investment (FDI) on Green Growth in the OECD Countries
Authors: Ketchoua Germain Stephane , Arogundade Sodiq , Mduduzi Biyase 
Published: September 2024

AbstractThe Sustainable Development Goals (SDGs) advocate for advancing green growth, a concept that balances economic prosperity with environmental protection. At the core of this vision are principles of sustainable consumption, production, and energy usage, all aimed at mitigating climate change and safeguarding ecosystems. This study investigates how technological innovation influences the relationship between Foreign Direct Investment (FDI) and green growth in OECD member countries. Specifically, we examine two facets of green growth: production-based CO2 productivity and demand-based CO2 productivity. We employ empirical analyses using the EKC and STIRPAT framework, which includes Ordinary Least Squares (OLS), smoothed instrumental-variables quantile regression (SIVQR), and System GMM methodologies, to uncover significant insights. Our analysis reveals that FDI impedes green growth, while technological innovation is pivotal in enhancing it. This pattern holds steady across various time frames and renewable energy sources. Furthermore, our findings indicate that combining FDI and technological advancement leads to heightened production-based CO2 productivity but diminished demand-based CO2 productivity. We also identify the presence of an environmental Kuznets curve for production-based CO2 productivity. Adding to significant scientific value by demonstrating how technological innovation moderates FDI's impact on green growth in OECD countries, we advocate for fostering collaborative partnerships between foreign investors and local innovators to leverage global expertise while advancing green objectives. Additionally, policy interventions should focus on stimulating demand for eco-friendly products and services to bolster demand-based CO2 productivity.
The creative agency of large language models: a philosophical inquiry
Authors: Ukpaka Paschal Mmesoma 
Published: September 2024

AbstractThis paper explores the difficult question of whether Large Language Models (LLMs) are intrinsically creative. Because they can independently create original content, LLMs are often seen as creative agents. Contrary to the belief that LLMs are creative, this paper argues that LLMs are not creative for two reasons. First, LLMs are not creative because they lack an essential component of creativity, which is the first-person experience of the world. Secondly, LLMs are not creative because they are not the principal authors of their creative output, for they lack the subjective awareness and intentionality necessary to be regarded as authors, and their output is a collaborative effort of the AI model, data providers, and other stakeholders. Since they are not full-fledged authors in a traditional sense, they are not creative.
Ethical considerations of use of hold-out sets in clinical prediction model management
Authors: Chislett Louis , Aslett Louis J. M. , Davies Alisha R. , Vallejos Catalina A. , Liley James 
Published: September 2024

AbstractClinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative prediction: predictions inform interventions which influence the outcome they were trying to predict, leading to a potential underestimation of risk in some patients if a model is updated on this data. One suggested resolution to this is the use of hold-out sets, in which a set of patients do not receive model derived risk scores, such that a model can be safely retrained. We present an overview of clinical and research ethics regarding potential implementation of hold-out sets for clinical prediction models in health settings. We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss informed consent, clinical equipoise, and truth-telling. We present illustrative cases of potential hold-out set implementations and discuss statistical issues arising from different hold-out set sampling methods. We also discuss differences between hold-out sets and randomised control trials, in terms of ethics and statistical issues. Finally, we give practical recommendations for researchers interested in the use hold-out sets for clinical prediction models.
Decoding environmental impact with image-based CO2 emission analytics
Authors: Kan Jie-Yuan , Feng Yu-Xi , Yao Yi-Na , Sun Ren-Da , Xu Yong , Xu Ming 
Published: September 2024

AbstractAs per the regulations of Intergovernmental Panel on Climate Change (IPCC), the country’s carbon emission data hold primacy. Nevertheless, establishing and utilizing a database pose a daunting challenge. This study presents the “Carbon Seer System,” a novel software prototype designed to inform and educate users about the carbon footprint during their production and in daily life. The development process involved a three-stage user interview process, ensuring system’s alignment with user needs and preferences. The system employs machine learning and visual recognition technology, including convolutional neural networks (CNNs), feature pyramid networks, and self-attention mechanisms, enabling users to analyze captured images for carbon emission factors. It automatically identifies the carbon emission factors of industrial products, energy producers, household producers, waste treatment, and transportation. A unique “group detection” method allows for the simultaneous analysis of multiple objects in a single image, enhancing user convenience. Additionally, the software features a carbon footprint tracker and a carbon sink dashboard, providing users with insights into their carbon emissions and the efforts needed for offsetting. The study concludes that the “Carbon Seer System” represents a significant step towards individual enablement at understanding and actively participating in a low-carbon lifestyle.
Sexual and reproductive health focus group discussions with Syrian men and women living as refugees in the Bekaa Valley, Lebanon
Authors: Gordon Anna C. T. , Mamluk Loubaba 
Published: September 2024

Abstract
Background
Conflict in Syria since 2011 led to over one million Syrians fleeing to Lebanon, predominantly from economically disadvantaged areas with low literacy and high child marriage rates. Over 90% live in extreme poverty, in informal tented settlements with minimal access to education, healthcare or employment. Displacement and poverty have further increased early marriages and unplanned pregnancies, and curtailed access to sexual and reproductive healthcare (SRH) in the Bekaa valley. This is exacerbated by increasing rates of sexual and gender-based violence (SGBV), intimate partner violence and domestic violence.

Study design
We aimed to explore SRH beliefs and practices and teach on key SRH topics through focus group discussions (FGD) and questionnaires, co-designed with Syrian practitioners, conducted with Syrian men and women. FGD were recorded, transcribed and thematically analysed. Questionnaires collected demographics and explored SRH beliefs and practices.

Findings
24 FGD with 203 participants, 72.4% female and 27.6% men. 90.1% participants were married with an average age-gap of 6.3 years between partners. Teenage marriage rates were 55.6% for women, and 47.4% delivered their first child before the age of 20. 43.6% participants were not using any contraception. Findings demonstrate the impacts of SRH cultural norms and changes due to displacement, financial crisis, and increased exposure to technology and NGOs.

Conclusions
SRH is multifaceted and contested, requiring systemic improvements in access to care, employment and education. This small but important proof-of-concept study demonstrates the possibility of engaging men and women in SRH discussion; paramount to empowering communities and challenging intergenerational SGBV.
Effects of Mesozooplankton Growth and Reproduction on Plankton and Organic Carbon Dynamics in a Marine Biogeochemical Model
Authors: Clerc Corentin , Bopp Laurent , Benedetti Fabio , Knecht Nielja , Vogt Meike , Aumont Olivier 
Published: September 2024

AbstractMarine mesozooplankton play an important role for marine ecosystem functioning and global biogeochemical cycles. Their size structure, varying spatially and temporally, heavily impacts biogeochemical processes and ecosystem services. Mesozooplankton exhibit size changes throughout their life cycle, affecting metabolic rates and functional traits. Despite this variability, many models oversimplify mesozooplankton as a single, unchanging size class, potentially biasing carbon flux estimates. Here, we include mesozooplankton ontogenetic growth and reproduction into a 3‐dimensional global ocean biogeochemical model, PISCES‐MOG, and investigate the subsequent effects on simulated mesozooplankton phenology, plankton distribution, and organic carbon export. Utilizing an ensemble of statistical predictive models calibrated with a global set of observations, we generated monthly climatologies of mesozooplankton biomass to evaluate the simulations of PISCES‐MOG. Our analyses reveal that the model and observation‐based biomass distributions are consistent ( = 0.40, total epipelagic biomass: 137 TgC from observations vs. 232 TgC in the model), with similar seasonality (later bloom as latitude increases poleward). Including ontogenetic growth in the model induced cohort dynamics and variable seasonal dynamics across mesozooplankton size classes and altered the relative contribution of carbon cycling pathways. Younger and smaller mesozooplankton transitioned to microzooplankton in PISCES‐MOG, resulting in a change in particle size distribution, characterized by a decrease in large particulate organic carbon (POC) and an increase in small POC generation. Consequently, carbon export from the surface was reduced by 10%. This study underscores the importance of accounting for ontogenetic growth and reproduction in models, highlighting the interconnectedness between mesozooplankton size, phenology, and their effects on marine carbon cycling.