bluuebunny/crossref_metadata_2025_split huggingface dataset:
Particle identification capability of a homogeneous calorimeter composed of oriented crystals
Authors: Monti-Guarnieri Pietro , Bandiera Laura , Canale Nicola , Carsi Stefano , De Salvador Davide , Guidi Vincenzo , Haurylavets Viktar , Lezzani Giulia , Longo Francesco , Malagutti Lorenzo , Mangiacavalli Sofia , Mazzolari Andrea , Moulson Matthew , Negrello Riccardo , Paternò Gianfranco , Perna Leonardo , Petroselli Christian , Prest Michela , Romagnoni Marco , Saibene Giosuè , Selmi Alessia , Sgarbossa Francesco , Soldani Mattia , Sytov Alexei , Tikhomirov Victor , Vallazza Erik 
Published: October 2024

Abstract
Recent studies have shown that the electromagnetic shower
induced by a high-energy electron, positron or photon incident along
the axis of an oriented crystal develops in a space more compact
than the ordinary. On the other hand, the properties of the hadronic
interactions are not affected by the lattice structure. This means
that, inside an oriented crystal, the natural difference between the
hadronic and the electromagnetic shower profile is strongly
accentuated. Thus, a calorimeter composed of oriented crystals could
be intrinsically capable of identifying more accurately the nature
of the incident particles, with respect to a detector composed only
of non-aligned crystals. Since no oriented calorimeter has ever been
developed, this possibility remains largely unexplored and can be
investigated only by means of numerical simulations. In this work,
we report the first quantitative evaluation of the particle
identification capability of such a calorimeter, focusing on the
case of neutron-gamma discrimination. We demonstrate through Geant4
simulations that the use of oriented crystals significantly improves
the performance of a Random Forest classifier trained on the
detector data. This work is a proof that oriented calorimeters could
be a viable option for all the environments where particle
identification must be performed with a very high accuracy, such as
future high-intensity particle physics experiments and
satellite-based γ-ray telescopes.
Mechanical and suture-holding properties of a UV-cured atelocollagen membrane with varied crosslinked architecture
Authors: Zhang Ruya , Brooker Charles , Whitehouse Laura L E , Thomson Neil H , Wood David , Tronci Giuseppe 
Published: October 2024

Abstract
The mechanical competence and suturing ability of collagen-based membranes are paramount in guided bone regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance in vivo. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early loss of barrier function, and irreversibly compromised clinical outcomes. To address these challenges, this study investigates the effect of the crosslinked network architecture on both mechanical and suture-holding properties of a new atelocollagen (AC) membrane. UV-cured networks were obtained via either single functionalisation of AC with 4-vinylbenzyl chloride (4VBC) or sequential functionalisation of AC with both 4VBC and methacrylic anhydride. The wet-state compression modulus (E
c) and swelling ratio (SR) were significantly affected by the UV-cured network architecture, leading up to a three-fold reduction in SR and about two-fold increase in E
c in the sequentially functionalised, compared to the single-functionalised, samples. Electron microscopy, dimensional analysis and compression testing revealed the direct impact of the ethanol series dehydration process on membrane microstructure, yielding densification of the freshly synthesised porous samples and a pore-free microstructure with increased E
c. Nanoindentation tests via spherical bead-probe atomic force microscopy (AFM) confirmed an approximately two-fold increase in median (interquartile range (IQR)) elastic modulus in the sequentially functionalised (E
AFM = 40 (13) kPa), with respect to single-functionalised (E
AFM = 15 (9) kPa), variants. Noteworthy, the single-functionalised, but not the sequentially functionalised, samples displayed higher suture retention strength (SRS = 28 ± 2–35 ± 10 N∙mm−1) in both the dry state and following 1 h in phosphate buffered saline (PBS), compared to Bio-Gide® (SRS: 6 ± 1–14 ± 2 N∙mm−1), while a significant decrease was measured after 24 h in PBS (SRS= 1 ± 1 N∙mm−1). These structure-property relationships confirm the key role played by the molecular architecture of covalently crosslinked collagen, aimed towards long-lasting resorbable membranes for predictable GBR therapy.
Construction and performance evaluation of fully biomimetic hyaline cartilage matrix scaffolds for joint defect regeneration
Authors: Sun Haidi , Wu Zhonglian , Liu Lihua , Hu Xu , Zhao Yurui , Wang Chirun , Yang Jian , Gu Zehao , Wang Dong-An , Yao Hang 
Published: October 2024

Abstract
Due to the absence of nerves and blood vessels in articular cartilage, its regeneration and repair present a significant and complex challenge in osteoarthritis treatment. Developing a specialized physical and chemical microenvironment supporting cell growth has been difficult in cartilage grafting, especially when aiming for comprehensive biomimetic solutions. Based on previous research, we have designed a tissue-engineered decellularized living hyaline cartilage graft (dLhCG). The study developed a method to improve the hydrophilicity and stiffness of scaffolds by employing chemical grafting techniques and designed a decellularized hyaline cartilage phenotype matrix scaffold for tissue engineering. Here, we reported a method using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride /N-hydroxysuccinimide (EDC/NHS) to achieve the grafting of chondroitin sulfate (CS) onto dLhCG, ultimately producing a tissue-engineered hyaline cartilage graft with the CS (dLhCG/CS). Young’s modulus measurements revealed that the cross-linked scaffolds exhibited enhanced mechanical properties. We implanted the cross-linked dLhCG/CS scaffolds into the trochlear region of rat joints and evaluated their functionality through histological analysis and biomechanical tests. After 12 weeks, the dLhCG/CS scaffolds demonstrated excellent bioinductive activity comparable to dLhCG. The regenerated tissue effectively maintained a hyaline cartilage phenotype and exhibited similar mechanical properties, playing a crucial role in cartilage regeneration.
Southeast Asian biodiversity is a fifth lower in deforested versus intact forests
Authors: Botterill-James Thomas , Yates Luke A , Buettel Jessie C , Aandahl Zach , Brook Barry W 
Published: October 2024

Abstract
Southeast Asia is highly biodiverse and currently experiences among the highest rates of tropical deforestation globally, but impacts on biodiversity are not well synthesized. We use Bayesian multi-level modeling to meta-analyse 831 pairwise comparisons of biodiversity in sites subject to land-use driven deforestation (for example, plantations or logged forest) versus undisturbed sites (control sites). After controlling for hierarchical dependencies, we show that biodiversity is a fifth lower in sites with these land-use driven deforestation (95% credible interval = 16%–28%, mean = 22%). This reduction was greater when forest losses were of high-intensity (34% reduction in biodiversity) compared to low-intensity (18% reduction), and effects were consistent across biogeographic regions and taxa. Oil-palm plantations led to the greatest reduction in biodiversity (39%, CI 27%–48%), and agroforests the least (24%, CI 10%–37%). We also find that biodiversity was reduced by 26% (CI 4%–42%) in secondary forest sites compared to undisturbed control sites, but biodiversity was the same in intermediate or mature-aged secondary forest compared to control sites (although species composition was potentially altered). Overall, our study provides a new line of evidence of the substantial detrimental impacts of land-use driven deforestation and particular types of land-use on the biodiversity of Southeast Asia.
Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers
Authors: van Oorschot Fransje , Hrachowitz Markus , Viering Tom , Alessandri Andrea , van der Ent Ruud J 
Published: October 2024

Abstract
Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity (

S

r

) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified

S

r

and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale

S

r

with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of

S

r

globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with

S

r

globally, the relation between precipitation and

S

r

varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and

S

r

for precipitation of up to 3

mm

d

−
1

, above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and

S

r

. Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of

S

r

, which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate

S

r

on a global scale has the potential to be readily and easily integrated into the parameterization of

S

r

in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.
Investigation of metal-insulator-metal waveguides with elliptical-nanodisk resonator for high contrast ratio all-optical logic gates
Authors: Korkmaz Semih 
Published: October 2024

Abstract
This research focuses on the design and analysis of all-optical Exclusive OR(XOR), NOT, and OR logic gates based on metal-insulator-metal waveguides with elliptical-nanodisk resonators. The functionality of the proposed optical logic gates is determined by constructive and deconstructive signals which are applied to the input ports and control ports. To show the logic 0 (low) and logic 1 (high) output states, the limit of threshold transmission is 1.775 × 10−13 ∼0. The transmission (T) and contrast ratio (CR) are obtained to present the performance of the optical logic gates via the method of finite-difference time-domain. The maximum transmission is reached for the OR gate as 1.38 and the highest contrast ratio is 124.75 dB for the XOR and NOT logic gates. The designed logic devices are promising for improving more efficient optical signal information processing devices.
Dihedral beams
Authors: Jaimes-Nájera Alfonso 
Published: October 2024

Abstract
In this work, a group theory-based formulation that introduces new classes of dihedral-symmetric beams is presented. Our framework leverages the algebraic properties of the dihedral group of rotations and reflections to transform input beams into closed-form families of dihedral-invariant wavefields, which will be referred to as dihedral beams. Each transformation is associated with a specific dihedral group in such a way that each family of dihedral beams exhibits the symmetries of its corresponding group. Our approach is inspired by one of the outcomes of this work: elegant Hermite–Gauss beams can be described as a dihedral interference pattern of elegant traveling waves, a new set of solutions to the paraxial equation also developed in this paper. Particularly, when taking elegant traveling waves as input beams, they transform into elegant dihedral beams possessing quasi-crystalline properties and including features like phase singularities, self-healing, and pseudo-nondiffracting propagation, as well as containing elegant Hermite and Laguerre–Gauss beams as special cases. Our approach can be applied to arbitrary scalar and vector input beams and constitutes a general group-theory formulation that can be extended beyond the dihedral group.
Unveiling citizens’ perspective on citizen assemblies among participants and non-participants of a citizen assembly on energy
Authors: Perlaviciute G , Gorter W A , Muinos G 
Published: October 2024

Abstract
Governments all over Europe are commissioning citizen assemblies (CAs), which could be one possible way to assist the representative democracy in developing more socially acceptable sustainability policies. Yet, little is known about how citizens themselves think about CAs in general, l et al one the actual real-life procedures, which perceived aspects of CAs and how influence these opinions, and whether opinions differ between those who participate in CAs and the population at large. We investigated citizens’ perceptions and acceptability of CAs, both among the participants of the Dutch Citizen Assembly on Energy (DCAE) and the general Dutch population. First, we studied to what extent both groups thought that CAs, in general, could reach the broader goals of public participation: normative (better democracy), substantive (improved decisions), and instrumental (enhanced public support) goals. Second, we studied citizens’ perceptions of the DCAE in particular, focusing on the 4Ds of public participation: dialogue, diversity, decision-making power, and deliberation. Citizens were generally positive about the potential of CAs to reach broader participation goals, except for some scepticism about their capacity to bring in new knowledge and accelerate the energy transition. Compared to the general population, citizens who join CAs may be more accepting of CAs at the start and evaluate the process more positively at the end. In contrast to conventional wisdom, decision-making power was not the key driver of public acceptability of the DCAE—engaging in dialogue and deliberation might be more important to citizens than having more power per se.
The study of 3D FDTD modelling of large-scale Bragg gratings validated by experimental measurements
Authors: Rahimof Yasmin , Nechepurenko Igor A , Mahani M R , Tsarapkin Aleksei , Wicht Andreas 
Published: October 2024

Abstract
This study discusses the importance of accurately calculating the optical response of Bragg gratings and the challenges associated with the 3D finite-difference time-domain (FDTD) method for simulating large-scale structures. The Bragg grating section in monolithic extended cavity diode lasers is of substantial size, making 3D FDTD simulations computationally challenging due to their complexity. In order to assess the accuracy of 3D simulations, we compare them with experimental results. Using a precise model design, involving a systematic analysis of simulation parameters, we obtain a good agreement between 3D FDTD simulations and the experimental results.
Multifunctional shape-memory smart window based on femtosecond-laser-printed photothermal microwalls
Authors: Chen Chao , Guo Sijia , Zhang Long , Yao Hao , Liu Bingrui , Zhang Chenchu , Zhang Yachao , Lao Zhaoxin , Wu Sizhu , Wu Dong 
Published: October 2024

Abstract
Smart windows (SWs) garner significant potential in green buildings owing to their capability of on-demand tuning the solar gains. Apart from solar regulation, people always desire a type of slippery SW which can repel the surface hydrous contaminants for anti-fouling application. Unfortunately, the up-to-date slippery SWs that respond to electrical/thermal stimuli have drawbacks of inferior durability and high energy-consumption, which greatly constrain their practical usability. This article presents our current work on an ultra-robust and energy-efficient near-infrared-responsive smart window (NIR-SW) which can regulate the optical transmittance and droplet’s adhesion in synergy. Significantly, laser-printing strategy enables us to seed the shape-memory photothermal microwalls on a transparent substrate, which can promote daylighting while maintaining privacy by near-infrared (NIR) switching between being transparent and opaque. As a light manipulator, it turns transparent with NIR-activated erect microwalls like an open louver; however, it turns opaque with the pressure-fixed bent microwalls akin to a closed louver. Simultaneously, the droplets can easily slip on the surface of erect microwalls similar to a classical lotus effect; by contrast, the droplets will tightly pin on the surface of bent microwalls analogous to the prevalent rose effect. Owing to shape-memory effect, this optical/wettability regulation is thus reversible and reconfigurable in response to thealternate NIR/pressure trigger. Moreover, NIR-SW unfolds a superior longevity despite suffering from the raindrop’s impacting more than 10 000 cycles. Remarkably, such a new-type SW is competent for thermal management, anti-icing system, peep-proof screen, and programmable optics. This work renders impetus for the researchers striving for self-cleaning intelligent windows, energy-efficient greenhouse, and so forth.
Multifunctional and reprogrammable 4D pixel mechanical metamaterials
Authors: Xin Xiaozhou , Lin Cheng , Li Bingxun , Zhang Ruikang , Zeng Chengjun , Liu Liwu , Liu Yanju , Leng Jinsong 
Published: October 2024

Abstract
Metamaterials have exotic physical properties that rely on the construction of their underlying architecture. However, the physical properties of conventional mechanical metamaterials are permanently programmed into their periodic interconnect configurations, resulting in their lack of modularity, scalable fabrication, and programmability. Mechanical metamaterials typically exhibit a single extraordinary mechanical property or multiple extraordinary properties coupled together, making it difficult to realize multiple independent extraordinary mechanical properties. Here, the pixel mechanics metamaterials (PMMs) with multifunctional and reprogrammable properties are developed by arraying uncoupled constrained individual modular mechanics pixels (MPs). The MPs enable controlled conversion between two extraordinary mechanical properties (multistability and compression-torsion coupling deformation). Each MP exhibits 32 independent and reversible room temperature programming configurations. In addition, the programmability of metamaterials is further enhanced by shape memory polymer (SMP) and 4D printing, greatly enriching the design freedom. For the PMM consisting of m × n MPs, it has 32(m × n) independent room temperature programming configurations. The application prospects of metamaterials in the vibration isolation device and energy absorption device with programmable performance have been demonstrated. The vibration isolation frequencies of the MP before and after programming were [0 Hz–5.86 Hz], [0 Hz–13.67 Hz and 306.64 Hz–365.23 Hz]. The total energy absorption of the developed PMM can be adjusted controllably in the range of 1.01 J–3.91 J. Six standard digital logic gates that do not require sustained external force are designed by controlling the closure between the modules. This design paradigm will facilitate the further development of multifunctional and reprogrammable metamaterials.
An efficient deep learning approach for ultimate bond strength estimations of corroded bar and concrete
Authors: Parida Lukesh , Moharana Sumedha , Sahani Rima 
Published: October 2024

Abstract
The corrosion behavior of the reinforced concrete structure depends heavily on the interfacial bond behavior between steel and concrete. Over the years, the deterioration of integrated reinforced steel has weakened this bond and potentially led to structural problems. Conventional methods of bond strength evaluation, such as pullout and bond beam tests, is frequently intrusive and tedious. Therefore, there is a growing need for non-intrusive, effective, and reliable forecasting algorithms capable of assessing bond deterioration caused by corrosion. Traditional algorithms for predicting bond strength make it difficult to capture the complex nature of steel-concrete bonds. The present study proposes two different deep learning algorithms, i.e., convolutional neural networks (CNN) and long short-term memory (LSTM), for predicting maximum bond strength in the presence of corrosion. The predictive model is based on a comprehensive dataset comprising 218 datasets from previous studies encompassing diverse input and output variables for predicting the models. The models were trained and tested using the given data to improve early predictions of corrosion-induced bond degradations. The predictive model’s effectiveness was assessed by applying various performance metrics. From this study, the CNN model exhibits higher accuracy and efficiency with mean absolute error, root mean square error, and mean absolute percentage error of 0.25, 0.28, and 95.72, respectively, for predicting ultimate bond strength estimations. The findings of this study provide an accurate and robust prediction model to improve the reliability and safety of the concrete structure by enhancing the residual load-bearing capacity of the concrete structure that has undergone corrosion.
Vision-based displacement monitoring for the integral lifting of a large-span spatial truss structure
Authors: Bai Qiye , Qiu Fuxiang , Fu Jing , Cheng Jiaming , Zheng Zijian , Jin Hui 
Published: October 2024

Abstract
Large-span spatial truss structures are widely used for their high performance, making it crucial to ensure proper installation quality. Currently, the lifting process of the large-span spatial truss structure is guided by visual observation, which is both imprecise and time-consuming. This paper presents a vision-based monitoring system designed to guide the lifting process of large-span spatial truss structures in real-time. A deep learning-based target detector initially tracks the trajectory of welded balls during the lifting process. To precisely extract the center point of each welded ball within the detected bounding box, we propose a novel ellipse recognition algorithm that uses morphological operations and the least squares method to identify the center coordinates at the pixel level. The binocular vision system then converts these pixel coordinates into world coordinates and calculates the displacement of the welded ball during the jacking process. A field test of the lifting of a large-span structure at a high-speed railway station verifies the effectiveness of the proposed. Test results indicate that the proposed method provides relatively accurate displacement estimates with a 2.5% relative error.