bluuebunny/crossref_metadata_2025_split huggingface dataset:
State-of-the-art on impact and explosion behaviour of concrete structures: report of RILEM TC 288-IEC
Authors: Cadoni Ezio , Pérez Caldentey Alejandro , Colombo Matteo , Dancygier Avraham N. , di Prisco Marco , Grisaro Hezi , Martinelli Paolo , Ožbolt Josko , Pająk Małgorzata , Weerheijm Jaap 
Published: February 2025

Abstract
Extreme loads can arise from accidents such as vehicle collisions or airplane crashes, as well as deliberate acts of terrorism or military attacks involving blasts and fragmentation. Blast overpressure can also occur accidentally, for example, from explosions of hazardous materials such as gas. Distinguishing between accidental and deliberate loads is crucial for designing appropriate protection measures. The repercussions of extreme loading events can be devastating, leading to injuries, loss of life, economic setbacks, and significant social disruption. These consequences result not only from the direct effects of impacts or explosions, but also from secondary factors such as structural collapse, which is particularly concerning due to its potential for widespread devastation and substantial losses. Efforts to enhance the protection of concrete structures have focused on understanding the properties of construction materials and how structures respond to impact and blast loads. This document presents a comprehensive overview of RILEM TC 288-IEC, aiming to provide essential guidance for designing concrete structures to withstand extreme dynamic loads. This emphasizes the importance of a thorough understanding and accurate modelling of loading scenarios and material behaviour. By implementing the strategies outlined in this document, engineers can enhance the safety and resilience of structures facing such challenges.
Glomerular Filtration Rate Measurement Utilizing Transdermal Detection Methodology
Authors: Dorshow Richard B. , Debreczeny Martin P. , Goldstein Stuart L. 
Published: February 2025

Background:
The well-known accuracy limitations of estimated GFR (eGFR) currently employed in clinical practice present barriers to optimal care for patients with, or at-risk for, decreased kidney function. A point-of-care glomerular filtration rate (GFR) measurement methodology has the potential to address these limitations. We prospectively assessed transdermal detection of the novel fluorescent GFR tracer agent relmapirazin in participants having normal or impaired kidney function across all human skin colors on the Fitzpatrick Skin Scale (FSS).

Methods:
A multi-center study comprising 74 participants with eGFR from normal to Stage 4 CKD was performed. Forty-six participants were FSS types I-III, and twenty-eight were FSS type IV-VI. A module containing an LED and photodetector to activate and collect transdermal relmapirazin fluorescence was attached adhesively to the upper chest of each participant. Relmapirazin (1.5mg/kg) was administered by intravenous push, and fluorescence emission was acquired for 12 hours. A two-compartment pharmacokinetic model fit the fluorescent intensity vs time data, and the fluorescence clearance rate (FCR) was extracted from the second (terminal) compartment. Plasma relmapirazin concentrations were measured contemporaneously and the corresponding plasma GFR for each participant was determined. Linear regression analysis was used to compare the FCR to the indexed plasma GFR.

Results:
Participant age range was 23 to 80 years old, with 59% females. The two-compartment pharmacokinetic behavior was observed in the fluorescence intensity vs time data and a FCR was successfully deduced for every participant completing the 12-hour study. The FCR vs. the indexed plasma GFR yielded an excellent correlation over the range of GFR measured and for all skin colors with a r2 = 0.90 (95% confidence interval 0.85 to 0.94). No severe adverse events were reported.

Conclusions:
Point-of-care transdermal detection of the fluorescent GFR tracer agent relmapirazin was feasible in patients with normal to impaired kidney function and for a range of skin color types.
French as a foreign language learners’ metacognitive strategy: A comparative study between Indonesia, Thailand, and Vietnam
Authors: Pranowo Dwiyanto Djoko , Tobing Roswita Lumban , Herman Herman , Van Cong Tran 
Published: February 2025

Metacognition is central to language learning because it encompasses the processes that govern the efficient use of learning strategies. It is especially important as a way to address language learners’ issues of limited repertoire of strategies and their application. While much research on language learning strategies have been related to the learning of English, this study observes the increasingly appealing French as an additional language hence French as a Foreign Language (FFL) is gaining importance. This research was carried out by examining some similarities and differences in models of learning strategies of FLE students in three ASEAN countries. The present study aims to investigate into FFL learners’ metacognitive strategies which contribute to the development of learners’ autonomy, despite the different contexts of study in Indonesia, Thailand and Vietnam. This study is descriptive qualitative research involving 56 students from three universities in Bangkok (Thailand), Hanoi (Vietnam) and Yogyakarta (Indonesia) as an ad-hoc sample to study their French Language Education (FLE) learning strategies. Using a Metacognitive Strategy Questionnaire (MSQ), this study result in a few insights of FFL learning process. The participants showed a consistent metacognitive strategy in terms of planning, monitoring and evaluating. Even though they are located in different countries, these strategies are proved to contribute to their autonomy as an FFL learner with different specificities.
Local Seismic Tomography Reveals the Crustal Structure Beneath Arraiolos (Central Portugal): Earthquake of ML 4.9 (15 January 2018)
Authors: Hamak Ines , Wachilala Piedade , Borges José , Koulakov Ivan , Araújo Alexandre , Bezzeghoud Mourad 
Published: February 2025

Abstract
An ML 4.9 earthquake occurred in Arraiolos, central Alentejo, on the 15 January 2018. As the mainshock was recorded as the highest magnitude in the region, a thorough investigation of the cause of this seismicity was carried out. In this study, local seismic tomography was conducted for the first time to demonstrate the importance of a uniform spatial distribution of earthquakes for obtaining accurate crustal images via seismic inversion. Two different inversions were performed using local tomographic software. The main difference between both inversions is that the first integrated the total amount of aftershocks, whereas the second one only incorporates one part. In addition, in both cases, additional events spread throughout a larger part of the region were selected. This investigation confirmed that the spatial spread of earthquakes has a greater impact on the accuracy and reliability of tomographic models than the number of earthquakes present in a region. The boundaries between the distributions of the P- and S-velocity anomalies are significantly correlated with the existing outcropping faults, revealing the probable segmentation of the São Gregorio (S.G.) fault and extension of the Santana do Campo (S.C.) fault. Moreover, a source alignment oriented west-southwest/east-northeast in the seismogenic zone was highlighted at the end of the second segment of the S.G. fault, below the main rupture zone. The velocity anomalies observed in both the P and S waves were strongly correlated with the geological formations at the surface, indicating the reliability of the seismic inversion; this contributes to a deeper comprehension of the Arraiolos seismic zone and offers valuable insights into its tectonic structure.
Efficient Perfectly Matched Layer Implementation for Seismic-Wave Modeling in 3D Spherical Coordinate Sections
Authors: Zhang Wei , Li Tong , Shen Yang , Chen Xiaofei 
Published: February 2025

Abstract
Perfectly matched layer (PML) has been successfully used in various wave propagation simulations in the Cartesian coordinate. But in cylindrical or spherical coordinates, the wave equations contain trigonometric terms, which make the PML equations derived by the complex stretching of the coordinate axes hard to transform back to the time domain for numerical solving. To use PML in 3D spherical sections for regional-scale seismic-wave simulation, we propose a trivial implementation of the PML in the spherical coordinate by implementing PML in a local Cartesian coordinate. The variables and spatial derivatives in the PML equations are directly expressed by the counterparts in the spherical coordinate, thus no interpolation or coordinate rotation is required during the time marching. Numerical simulations using a layered model and a slab model show that the proposed spherical-Cartesian PML implementation provides a stable PML on 3D spherical sections and can efficiently absorb waves at all the edges of the spherical section, including the convex bottom boundary.
Foresterie rurale au Sénégal – Le projet d'aménagement et de reboisement des forêts du centre-est (P.A.R.C.E.)
Authors: Montagne Pierre 
Published: February 2025

L'objectif du projet P.A.R.C.E. était d'apporter des solutions au problème de la déforestation dans les zones sahéliennes, ici le Centre-Est du Sénégal, en incitant les paysans à développer eux-mêmes la forêt en milieu rural. Les objectifs du volet « foresterie rurale » du projet étaient la création de 3 000 ha de plantations familiales et communautaires dans les départements de Kaffrine et de Tambacounda, ainsi que la gestion effective du sol forestier par les paysans. Pour ce faire, des Agents de Vulgarisation et d'Animation Forestière sénégalais ont été formés. Leur mission était de faire comprendre aux populations rurales que la désertification n'était pas une fatalité, qu'elles devaient coopérer spontanément et librement, et qu'elles bénéficieraient de moyens et d'une organisation pour la suite des opérations après la fin du financement actuel. Après 5 ans d'action, on note un développement des pépinières villageoises, mais les pépinières individuelles représentent encore 30 % du total et 22 % de la production. Les taux de réussite sont de l'ordre de 50 % après 3-4 ans. Le taux pourrait certainement être amélioré.
Applications of Geological Features Style Mixing for Reservoir History Matching
Authors: Ahn Seongin , Choe Jonggeun 
Published: February 2025

Summary
Ensemble-based data assimilation, when combined with generative adversarial networks (GANs), has proved effective for history matching based on production data in petroleum engineering research. However, the geological realizations produced are often constrained by the characteristics of the initial ensemble and require extensive training data sets to achieve diversity. To address these limitations, we introduce a novel method that enhances history matching in reservoir simulations by integrating a geological-style-mixing approach with GAN-based optimization (StyleGAN). This method addresses the challenges of capturing complex geological features and heterogeneities that influence reservoir performance. We use the StyleGAN architecture to generate diverse geological scenarios with enhanced style diversity. By adapting the style-mixing mechanism of StyleGAN for geological modeling, we developed a framework capable of producing a variety of geological styles. Each style possesses unique characteristics that are distinct from those of the initial ensemble. This approach combines the styles from assorted geological realizations to create new realizations that exhibit a broad spectrum of geological features, thereby significantly improving the history-matching process. The effectiveness of our method is demonstrated through case studies involving a 2D binary permeability field, a 2D Gaussian permeability field, and a 3D bimodal log permeability distribution. Our optimized models displayed considerable improvement over conventional GAN-based optimizations. The correlation with the reference model increased from 0.94 to 0.98 for the binary permeability field, from 0.97 to 0.99 for the Gaussian permeability field, and from 0.97 to 0.99 for the 3D bimodal permeability field. In addition, the production rate matching error improved from 66% to 86% for the binary permeability field, from 81% to 93% for the Gaussian permeability field, and from 81% to 88% for the 3D bimodal log permeability field, with substantial reductions in the root mean squared error (RMSE) compared with the initial model. The proposed method was compared with the previously developed convolutional neural network-principal component analysis (CNN-PCA) and demonstrated similar history-matching performance. However, it qualitatively showed better preservation of the geological style of the trained reservoir ensemble. These findings demonstrate that integrating a geological-style-mixing approach with GAN-based optimization presents a promising avenue for overcoming the limitations of current ensemble-based history-matching methods, particularly in scenarios characterized by high geological complexity and data uncertainties. This research advances history-matching methodologies and signifies the potential of machine learning and artificial intelligence in enhancing reservoir simulation and management.
Conceptualizing systems thinking and complexity modelling for circular economy quantification: A systematic review and critical analysis
Authors: Boral Soumava , Black Leon , Velis Costas 
Published: February 2025

Abstract

Circular economy (CE) quantification features intrinsic complexity, mandating the application of systems thinking and associated methodologies to navigate multifaceted and dynamic intricacies; posing challenges for science-policy interfacing. Well-established approaches such as System Dynamics (SD) and emergent Agent-Based Modeling and Simulation (ABMS) are adept at interrogating such complexities within intricate systems. While SD employs a macroscopic, top-down lens, ABMS delves into a microscopic, bottom-up perspective. However, to date there are no comprehensive reviews quantifying circularity through systems thinking and its associated complexity modelling. Here, we analyse this topic through a systematic scoping review using PRISMA-ScR. Our analysis has identified core limitations in existing approaches, regarding the extent to which CE complexity has been captured holistically. Although both SD and ABMS can address circularity’s dynamic interactions and feedback loops, they are predominantly applied in isolation due to the absence of standardised platforms that can integrate both approaches, and to reduce computational costs. Exploration of the potential synergies from combining these two approaches and coupling them with traditional decision-support tools such as life-cycle and multi-criteria ones are minimal. Such a fragmented approach limits their ability to model internal dynamics; in turn restricting their utility to inform system-wide decision-support. The review also accentuates the lack of standardised metrics and the need for a more holistic evaluation framework for CE incorporating economic, environmental, social, and technical value metrics. A more unified approach to support sustainable, informed decisions in the pursuit of circularity is imperative for improving evidence-based policymaking and empowering industrial adoption of circularity.
Bayesian optimization of solution-processed thermoelectric polymers using a database of ab initio electronic structure data
Authors: Ganley Connor , Katz Howard E. , Clancy Paulette 
Published: February 2025

Abstract

Historically, organic materials design takes place wherein the components in the system are chosen based on expert knowledge of organic synthetic chemistry. Experimental synthesis and processing, often followed by computational validation, is a standard route to design materials such as polymers. The advent of data-driven machine learning approach can now shift that paradigm to one in which computational information \textit{leads} experimental validation to select a much smaller, more promising and perhaps unexpected set of new materials. This work describes one such investigation to characterize novel, hypothetical, and promising polymer candidates by their calculated molecular-scale electronic and chemical parameters. Subsequently, we optimize them for a key chemical property using a chemically-informed Bayesian surrogate model representing those properties. As a test case, over 7300 combinations of novel, hypothetical semiconducting diketopyrrolopyrrole-based (DPP) polymers and commonly used solvents, generated by density functional theory, were screened based on their free energies of solvation, G$_{solv}$. From this synthetic data set, we trained a physics-informed Gaussian process model that linked molecular-scale electronic structure properties to G$_{solv}$, and then used Bayesian optimization via the PAL 2.0 software to identify key descriptors for predicting optimal polymer solvation. Implicit and explicit solvation models of these synthetic polymers, combined with Bayesian optimization, exhibited a minimum in $\Delta$G$_{solv}$ as a function of the solvent dielectric. As a result, we predict an ''optimal'' solvent dielectric (around 10) for the entire DPP-based polymer class. The chemically-informed nature of our approach allowed us to identify a molecular electronic property that demonstrates near-linear additivity when constructing an aggregate synthetic polymer from functional group sub-units. This linearly related property was the isotropic quadrupole moment, which we found to be highly correlated to $\Delta$G$_{solv}$. As validation of this chemical insight, we showed that this minimum $\Delta$G$_{solv}$ was associated with the highest experimentally measured conductivity among a set of synthesized doped polymers. The importance of these observations is that PAL 2.0 has provided the chemical insight that can now allow us to quickly screen solvents for potential DPP-based polymers based on the polymer repeat unit's quadrupole moments and to identify preferred compatible solvents corresponding to a minimized $\Delta$G$_{solv}$. This study also highlights the effectiveness of the PAL 2.0 algorithm to find the optimal solution using just 6\% of the parameter space and in a very short execution time (on the order of minutes), a feat which cannot be duplicated experimentally.
Phage lysis facilitates the maintenance of costly antibiotic resistance in the absence of antibiotic pressure
Authors: Johnson David , Ruan Chujin , Vinod Deepthi 
Published: February 2025

Abstract

The persistence of antibiotic resistant (AR) bacteria in the absence of antibiotic pressure raises a paradox regarding the fitness costs associated with antibiotic resistance. These fitness costs should slow the growth of AR bacteria and cause them to be displaced by faster-growing antibiotic sensitive (AS) counterparts. Yet, even in the absence of antibiotic pressure, slower-growing AR bacteria can persist for prolonged periods of time. Here, we demonstrate a mechanism that can explain this apparent paradox. We hypothesize that lytic phage can modulate bacterial spatial organization to facilitate the persistence of slower-growing AR bacteria. Using surface-associated growth experiments with the bacterium Escherichia coli in conjunction with individual-based computational simulations, we show that phage disproportionately lyse the faster-growing AS counterpart cells located at the biomass periphery via a “peripheral kill-the-winner” dynamic. This enables the slower-growing AR cells to persist even when they are susceptible to the same phage. This phage-mediated selection is accompanied by enhanced bacterial diversity, further emphasizing the role of phage in shaping the assembly and evolution of bacterial systems. The mechanism is potentially relevant for any antibiotic resistance genetic determinant and has tangible implications for the management of bacterial populations via phage therapy.
Development of a Novel Hybprinter-SAM for Functionally Graded Tissue Engineering Constructs with Patterned and Localized Biochemical Signals
Authors: Yang Yunzhi Peter , Li Jiannan , Kim Carolyn , Alizadeh Hossein , Garg Shreya , Bruyas Arnaurd , Zhao Peng , Passos Isadora Dos , Pan Chi-Chun , Perez Andrea Flores , Skylar-Scott Mark , Kim Sungwoo 
Published: February 2025

Abstract

Engineering native-mimetic tissue constructs is challenging due to their intricate biological and structural gradients. To address this, Hybprinter-SAM was developed by integrating three bioprinting technologies: syringe extrusion (SE), acoustic droplet ejection (ADE) and molten material extrusion (MME). This system not only enables the creation of mechanical gradients by integrating soft and rigid materials spanning 7 order magnitude of stiffness but also facilitates precise patterning and controlled localization of biochemical signals within printed scaffolds. This capability is beneficial in replicating the complexity of native tissues to enhance functionality. Both the printing process and biomaterials were optimized to balance printability, mechanical integrity, and biocompatibility. As a proof of concept, Hybprinter-SAM was used in a bone-tendon regeneration study to engineer a multi-material construct with patterned fibroblast growth factor 2 (FGF-2), resulting in markers indicative of fibrocartilage development. These findings highlight the potential of Hybprinter-SAM as a versatile platform for diverse tissue engineering applications that require complex, functionally graded tissue constructs.
Mental health of healthcare workers in England during the first three years of the COVID-19 pandemic: the NHS CHECK study cohort
Authors: Lamb Danielle , Scott Hannah , Carr Ewan , Stevelink Sharon A.M. , Raine Rosalind , Hotopf Matthew , Greenberg Neil , Almeida-Meza Pamela , Dorrington Sarah , Hegarty Siobhan , Madan Ira , Moran Paul , Morriss Richard , Murphy Dominic , Rafferty Anne Marie , Weich Scott , Wessely Simon 
Published: February 2025

Abstract

Background
Maintaining the mental health of healthcare workers is vital to reduce staff absences and high turnover, which in turn should improve patient care. Most research in this area focusses on clinical staff, despite the important contributions non-clinical staff make to the health system, and uses data from one time point. Our objectives were to examine mental health status variations for all types of healthcare workers (HCWs) in England over a three-year period, and to examine which workers were at increased risk of poorer mental health outcomes.
Method
We undertook a prospective cohort study of 22,092 HCWs from 17 English acute and mental health NHS Trusts, collecting online survey data on common mental disorders (CMDs), depression, anxiety, alcohol misuse, PTSD, moral injury, burnout, wellbeing, resilience, and post-traumatic growth at four time points from April 2020 to March 2023. We analysed these data cross-sectionally over time by data collection point. Data were weighted to better represent individual Trust population demographics.
Results
Around 50% of participants reported probable CMDs across all time points. The most consistent predictor of any poor mental health outcome was having met cut-off on that outcome at baseline. There were no consistent differences between clinical and non-clinical staff. Younger, female, lower paid staff, those who felt poorly supported by colleagues/managers, and who experienced potentially morally injurious events were most at risk of poor mental health outcomes.
Conclusion
Our evidence suggests that all types of NHS staff are struggling, and that mental health has not improved since the end of pandemic restrictions. Longer term work to address structural inequalities that underlie some of these findings is needed, as well as targeted, flexible support for staff in the short term.