bluuebunny/crossref_metadata_2025_split huggingface dataset:
Universal framework for assessing the environmental impact of marine non-indigenous species in different situations of data availability
Authors: Kotta Jonne , Ojaveer Henn , Kaasik Ants , Szava-Kovats Robert , Nurkse Kristiina , Outinen Okko , Einberg Heli , Zaiko Anastasija , Lehtiniemi Maiju , Orav-Kotta Helen , Barboza Francisco R. 
Published: June 2024

AbstractCurrent methods for assessing the environmental impacts of marine non-indigenous species (NIS) are limited by insufficient data, an over-reliance on expert judgement and too coarse a spatial resolution, which hampers accurate local management. However, advances in data-driven analyses offer significant potential for developing more comprehensive and accurate frameworks for assessing the impacts of NIS in marine ecosystems. This study fills this major gap in NIS management by proposing a comprehensive and practical framework that integrates systematic reviews, meta-analyses, species distribution modelling, and expert judgement to assess NIS impacts across varying levels of information availability. The framework also recommends complementary, under-utilised data sources, and tools to reduce significantly existing information constraints. The framework presented in this study not only advances scientific understanding of NIS impacts by providing a scalable and adaptable framework for assessing NIS impacts in different ecological contexts, but also provides practical tools for environmental managers implementing legislation on NIS. We recognise that although our data-driven approach to NIS management is best handled by specialists, maximising its potential requires making the information accessible and user-friendly to a broader audience. This can be achieved through digital tools that simplify and facilitate the understanding of these assessments for environmental managers.
Research Trends in STEM Clubs: A Content Analysis
Authors: Öndeş Rabia Nur 
Published: June 2024

Abstract
To identify the research trends in studies related to STEM Clubs, 56 publications that met the inclusion and extraction criteria were identified from the online databases ERIC and WoS in this study. These studies were analysed by using the descriptive content analysis research method based on the Paper Classification Form (PCF), which includes publishing years, keywords, research methods, sample levels and sizes, data collection tools, data analysis methods, durations, purposes, and findings. The findings showed that, the keywords in the studies were used under six different categories: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables). Case studies were frequently employed, with middle school students serving as the main participants in sample groups ranging from 11–15, 16–20, and 201–250. Surveys, questionnaires, and observations were the primary methods of data collection, and descriptive analysis was commonly used for data analysis. STEM Clubs had sessions ranging from 2 to 16 weeks, with each session commonly lasting 60 to 120 min. The study purposes mainly focused on four themes: the impact of participation on various aspects such as attitudes towards STEM disciplines, career paths, STEM major selection, and academic achievement; the development and implementation of a sample STEM Club program, including challenges and limitations; the examination of students' experiences, perceptions, and factors influencing their involvement and choice of STEM majors; the identification of some aspects such as attitudinal effects and non-academic skills; and the comparison of STEM experiences between in-school and out-of-school settings. The study results mainly focused on three themes: the increase in various aspects such as academic achievement, STEM major choice, engagement in STEM clubs, identity, interest in STEM, collaboration-communication skills; the design of STEM Clubs, including sample implementations, design principles, challenges, and factors affecting their success and sustainability; and the identification of factors influencing participation, motivation, and barriers. Overall, this study provides a comprehensive understanding of STEM Clubs, leading the way for more targeted and informed future research endeavours.
Mineralization of clofibric acid by persulfate-promoted catalytic subcritical water oxidation process using CoFe2O4@SiO2 catalyst
Authors: Görmez Özkan 
Published: June 2024

AbstractThe design and use of innovative treatment processes are very important in preventing the possible toxic effects of organic pollutants in aquatic environments. One of these methods is the subcritical water oxidation method, which has been used recently. In the current study, the mineralization of clofibric acid (CFA) was carried out under more effective and mild conditions using persulfate (PS) as an oxidant and CoFe2O4@SiO2 catalyst by the subcritical water oxidation (sub-CWO) process. Characterization of the synthesized catalyst was performed through XRD, FTIR, TEM and SEM–EDS analyses. In the CFA oxidation with persulfate-promoted catalytic Sub-CWO process, optimum working conditions was determined as 15 mM PS, 40 min, 383 K, and 0.3 g L−1 catalyst dosage using the response surface method and Box–Behnken design. The catalyst's efficiency remained relatively stable after three cycles under optimal conditions, resulting in a 97% total organic carbon (TOC) removal. Decomposition products were determined and a degradation mechanism was proposed.
Determinants of housing prices: Serbian Cities’ perspective
Authors: Marinković Srđan , Džunić Marija , Marjanović Ivana 
Published: June 2024

AbstractThis study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.
Customized Green Energetic Tetra(Imidazole)Copper(II) Nitrate (Cu-Im) Complex/Ammonium Nitrate Co-crystal: A Novel Reactive Halogen-Free Oxidizer with Superior Stability and Decomposition Kinetics
Authors: Attwa Mohamed , Tantawy Hesham , Elbasuney Sherif 
Published: June 2024

AbstractAmmonium nitrate (AN) is the most promising affordable green oxidizer for solid propellants; however, its endothermic phase transitions and endothermic decomposition restrict its applications. This work shaded the light on multifunctional energetic complex tetra(imidazole)copper (II) nitrate (Cu-Im) with combustion enthalpy of 15.57 KJ/g as a high energy dense material, phase stabilizer, and catalyst for AN-based propellant. The Cu-Im complex was synthesized via a green simple solvent-free melt-assisting technique. As-synthesized Cu-Im complex demonstrated a highly pure crystalline structure with thermal stability up to 200 °C. Cu-Im complex was integrated into AN via an evaporative crystallization technique. The phase transitions and the thermal behavior of the developed Cu-Im complex/AN cocrystal were investigated via DSC and TGA. The Cu-Im complex stabilized the phase transition of AN up to 88.48 °C via an evolved strong hydrogen bonding system within the Cu-Im complex/AN cocrystal. Moreover, the energetic Cu-Im complex offered a significant decrease in the endothermic phase transition peaks associated with IV↔II, and II↔I by 38.81%, and 25.28% respectively. Additionally, AN strong endothermic melting was decreased by 45.32%. The Cu-Im complex converts the AN endothermic thermal decomposition peak with + 1400 J/g into an intensive exothermic peak with the release of -2241 J/g. The Cu-Im complex exhibited a superior catalytic effect via the decrease in AN decomposition temperature by 48.85 °C. Cu-Im complex offered a remarkable reduction in apparent activation energy of AN decomposition by 56.24% and 48.12% via Kissinger’s model and integral iso-conversional nonlinear Vyazovkin method (VYA) respectively. The superior catalytic performance of the Cu-Im complex was attributed to the evolution of CuO nanoparticles during its decomposition. The electron-deficient Lewis acid copper ions have a large affinity to electron lone pair of nitrogen; this effect could support the conversion of NH3 to N2 and H2. This catalytic action could boost the decomposition enthalpy of AN. Consequently, the developed Cu-Im/AN cocrystal could be considered a potential green substitute for perchlorate-based oxidizers.
Cationic Iridium(III) Complexes Bearing Different Bulky N,N′-Chelating Ligands
Authors: Wong Wai-Yeung , Chau Nga-Yuen , Wang Qiwei , Jiang Lu , Li Junlong , Sun Zelin 
Published: June 2024

AbstractSix cationic iridium(III) complexes bearing different bulky N,N′-chelating ligands were successfully synthesized and characterized by spectroscopic, electrochemical, thermal and photophysical methods. Because the molecular quenching and self-aggregation within molecules would deteriorate the device efficiency, the bulky molecules can suppress these problems and improve the device performance. The best doped organic light-emitting diodes based on these charged Ir(III) complex E1 exhibited Lmax of 26,800 cd/m2, ηL, max of 35.0 cd/A and ηP, max of 18.5 lm/W, which also showed a small efficiency roll-off in the range of 100 to 1000 cd/m2.
Graphical Abstract
Hop, walk or bound? Limb proportions in kangaroos and the probable locomotion of the extinct genus Protemnodon
Authors: Jones Billie , Janis Christine M. 
Published: June 2024

AbstractKangaroos (Macropodoidea) display a diversity of locomotor modes, from bounding quadrupedally to hopping bipedally; but hopping has a body mass limit, which was exceeded by a number of extinct taxa. In the Pleistocene, a variety of “giant” kangaroos existed, both within the extinct subfamily Sthenurinae and the extant subfamily Macropodinae (both within the family Macropodidae). Sthenurines have been previously considered to have a type of locomotion (bipedal striding) different from extant kangaroos, but the primary locomotor mode of the large species of the extinct “giant” macropodine genus Protemnodon, closely related to extant large kangaroos, has undergone little question and has been assumed to be hopping. Here, the association between limb proportions and locomotor mode across Macropodoidea is assessed by examination of functional limb indices. We show that large (> 100 kg) Protemnodon species are unlike any other known macropodoids; their position in this functional morphospace, along with previously published evidence on humeral morphology, supports a prior hypothesis of a primarily quadrupedal mode of locomotion, likely some sort of bounding.
Process Evaluation of Individual Placement and Support and Participatory Workplace Intervention to Increase the Sustainable Work Participation of People with Work Disabilities
Authors: Oude Geerdink E. , Huysmans M. A. , van Kempen H. , Maarleveld J. M. , van Weeghel J. , Anema J. R. 
Published: June 2024

AbstractPurposeThis study is a process evaluation of the use of Individual Placement and Support (IPS) and Participatory Workplace Intervention (PWI) to increase the work participation of people with work disabilities. We ran the evaluation alongside a randomized controlled trial (RCT), to investigate whether and to what extent IPS and PWI were executed according to protocol.MethodsThe study population consisted of clients with work disabilities, and their job coaches who were employed by the municipality of a large city in the Netherlands. Data were collected between September 2019 and November 2022 using registration forms, accompanied by researchers’ notes and logbooks.ResultsFor IPS the dose delivered was reasonable and the IPS fidelity measurement score was fair. The job search focused on paid work for almost all clients and was based on their wishes as indicated in the protocol, but integration of employment services with (health) care was often lacking. A minority of the clients who were assigned to PWI received the intervention, often because the client did not start work within the follow-up period and a workplace was a requirement to apply the intervention.ConclusionThe results of this study show that IPS was executed reasonably and with a fair fidelity, which indicated implementation was sufficient to find an effect on work participation in the RCT. PWI was barely realized in practice and no conclusions regarding the fidelity could be drawn. We therefore conclude that we cannot expect PWI to have any effect on work participation in the RCT.
Discrete-time graph neural networks for transaction prediction in Web3 social platforms
Authors: Dileo Manuel , Zignani Matteo 
Published: June 2024

AbstractIn Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or posting, to specific relations given by crypto-token transfers facilitated by the blockchain. In this dynamic and intertwined networked context, modeled as a financial network, our main goals are (i) to predict whether a pair of users will be involved in a financial transaction, i.e. the transaction prediction task, even using textual information produced by users, and (ii) to verify whether performances may be enhanced by textual content. To address the above issues, we compared current snapshot-based temporal graph learning methods and developed T3GNN, a solution based on state-of-the-art temporal graph neural networks’ design, which integrates fine-tuned sentence embeddings and a simple yet effective graph-augmentation strategy for representing content, and historical negative sampling. We evaluated models in a Web3 context by leveraging a novel high-resolution temporal dataset, collected from one of the most used Web3 social platforms, which spans more than one year of financial interactions as well as published textual content. The experimental evaluation has shown that T3GNN consistently achieved the best performance over time and for most of the snapshots. Furthermore, through an extensive analysis of the performance of our model, we show that, despite the graph structure being crucial for making predictions, textual content contains useful information for forecasting transactions, highlighting an interplay between users’ interests and economic relationships in Web3 platforms. Finally, the evaluation has also highlighted the importance of adopting sampling methods alternative to random negative sampling when dealing with prediction tasks on temporal networks.
Flowable resin-based composites modified with chlorhexidine-loaded mesoporous silica nanoparticles induce superior antibiofilm properties
Authors: Shrestha Barsha , Aati Sultan , Maria Rajan Sheetal , Fawzy Amr 
Published: June 2024

AbstractClinical failure of dental resin-composite restorations is mainly due to bacterial-mediated secondary caries formation. Therefore, the development of a flowable resin-composite material having inherent antibacterial properties is crucial to enhance the durability of dental restorations. Herein, dental flowable resin-composite material was modified with chlorhexidine-loaded mesoporous silica nanoparticles (CHX-MSN) to induce in situ antibacterial properties against S. mutans. Mesoporous silica nanoparticles loaded with chlorhexidine (CHX-MSN) were formulated and characterized for drug-loading/encapsulation efficiency, morphology by electron microscopy, and infrared spectral analysis. CHX-MSN were incorporated into the flowable composite material at different concentrations of 1, 5, and 10% (w/w) and examined at two time points (baseline and 3 months in artificial saliva). The CHX-MSN modified composites exhibited an initial CHX release burst followed by a steady release up to 30 days. The antimicrobial efficacy of the modified composites was evaluated by crystal violet assay, MTT assay, and confocal laser scanning microscopy. In addition to measuring the degree of conversion and cytotoxicity, the mechanical properties were characterized by surface microhardness and flexural strength. The modified composites demonstrated a significant increase in antimicrobial properties compared to the unmodified control (p < 0.05) which is dependent on the concentration of the CHX-MSN nanoparticles. In addition, the modified composites possessed acceptable biocompatibility without adversely affecting mechanical properties and degree of conversion up to 5% addition of CHX-MSN nanoparticles. This study introduced a protocol to develop resin-based flowable dental composite material having superior antibacterial property against cariogenic biofilms aiming for enhancing clinical longevity of dental restorations.
Graphical Abstract
Defining the role of surgery for patients with multiple brain metastases
Authors: Ersoy Tunc Faik , Brainman Daniel , Coras Roland , Berger Björn , Weissinger Florian , Grote Alexander , Simon Matthias 
Published: June 2024

Abstract
Purpose
To better define the role of surgery, we investigated survival and functional outcomes in patients with multiple brain metastases.

Methods
Pertinent clinical and radiological data of 131 consecutive patients (156 surgeries) were analyzed retrospectively.

Results
Surgical indications included mass effect (84.6%) and need for tissue acquisition (44.9%, for molecularly informed treatment: 10 patients). Major (i.e. CTCAE grade 3–5) neurological, surgical and medical complication were observed in 6 (3.8%), 12 (7.7%), and 12 (7.7%) surgical cases. Median preoperative and discharge KPS were 80% (IQF: 60–90%). Median overall survival (mOS) was 7.4 months. However, estimated 1 and 2 year overall survival rates were 35.6% and 25.1%, respectively. Survival was dismal (i.e. mOS ≤ 2.5 months) in patients who had no postoperative radio- and systemic therapy, or who incurred major complications. Multivariate analysis with all parameters significantly correlated with survival as univariate parameters revealed female sex, oligometastases, no major new/worsened neurological deficits, and postoperative radio- and systemic therapy as independent positive prognostic parameters. Univariate positive prognostic parameters also included histology (best survival in breast cancer patients) and less than median (0.28 cm3) residual tumor load.

Conclusions
Surgery is a reasonable therapeutic option in many patients with multiple brain metastases. Operations should primarily aim at reducing mass effect thereby preserving the patients’ functional health status which will allow for further local (radiation) and systemic therapy. Surgery for the acquisition of metastatic tissue (more recently for molecularly informed treatment) is another important surgical indication. Cytoreductive surgery may also carry a survival benefit by itself.
Explicit frictional stick–slip dynamics of elastic contact problem incorporating the LuGre model
Authors: Hu Han , Zhu Xiaosong , Batou Anas , Ouyang Huajiang 
Published: June 2024

AbstractDynamical contact problem with friction is of continuous concern and have been investigated over the years. The effective and accurate description of the development of the friction force is the key to this problem. However, the stick–slip phenomenon, as typical dynamical behaviour, has rarely been considered when developing contact algorithms for frictional contact problems of elastic bodies. The conventionally used Coulomb’s model, with differentiating the static and kinetic coefficient of friction, can lead to inaccurate results due to errors generated when switch between the stick or slip states. In this work, a three-dimensional mortar-based frictional contact algorithm is proposed in an explicit scheme. The proposed algorithm incorporates the LuGre model to account for dynamical frictional behaviours such as stick–slip. The effectiveness and accuracy of the proposed algorithm are validated through several simplified two-dimensional cases. The sticking time ratio and the evolution of the global coefficient of friction with time of a three-dimensional contact problem with a randomly rough contact interface are investigated as an application.
A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans
Authors: Li Yuelin , Wang Zonghu , Li Yuru , Du Jiewen , Gao Xiangrui , Li Yuanpeng , Lai Lipeng 
Published: June 2024

Abstract
Purpose:
Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.

Methods:
This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound ($$f_{up}$$

f

up

), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a “bottom-up” PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform’s accuracy.

Results:
Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0$$\%$$
%
accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5$$\%$$
%
accuracy.

Conclusion:
The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.