bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consumption Trends, Trading Patterns and Economic Development in Italy Across Centuries: Data Analysis of Roman Amphorae in a Long-Term Perspective
Authors: Komar Paulina , Brughmans Tom , Borisova Ekaterina 
Published: January 2025

AbstractThis paper presents novel insights into the long-term chronological patterns related to the distribution and consumption of amphora-borne foodstuffs in Italy. The study specifically focuses on the consumption of wine, olive oil and fish sauces, which exhibit diverse provenances. Notably, it contributes significantly to our understanding of the Roman economy by utilising an open dataset and a replicable research method. The analysis reveals a pronounced growth pattern during the late Republican to early Imperial period. Importantly, quantitative evidence demonstrates that the diverse consumption pattern observed in the capital city of Rome is less exceptional than previously believed. The study draws upon a substantial dataset comprising 28,851 diagnostic amphora fragments excavated and documented from 28 different urban and rural settlements in the North Adriatic and Central Italy, spanning the period from the 4th c. BCE to the 7th c. CE. The analytical approach employs a probabilistic aoristic method, evenly distributing amphora frequencies across relevant date ranges.
Optimized workflow with hybrid (very) high-power short-duration radiofrequency ablation renders point-by-point pulmonary vein isolation as fast and effective as cryoballoon ablation
Authors: Fusaroli Marco , Hoogendijk Mark G. , Bhagwandien Rohit E. , Wijchers Sip A. , van Boven Nick , Mahmoodi Bakthawar K. , Yap Sing-Chien 
Published: January 2025

Abstract
Introduction
A hybrid approach with very high-power short-duration (vHPSD) posteriorly and ablation-index guided HPSD (50 W) anteriorly seems to be an optimal balance between efficiency and effectiveness for point-by-point pulmonary vein isolation (PVI). The aim of the current study is to compare vHPSD/HPSD ablation to cryoballoon ablation (CBA) in patients with symptomatic atrial fibrillation (AF).

Methods and results
In this retrospective single-center study, we identified 110 consecutive patients who underwent their first PVI with either vHPSD/HPSD (n = 54) or CBA (n = 56). We compared procedural efficacy, efficiency, safety, and long-term outcomes. Baseline characteristics of both groups were comparable; however, patients in the vHPSD/HPSD group had larger left atrial volume index (35, IQR 27–45 vs. 28, IQR 21–36 ml/m2, P = 0.005). Complete PVI was achieved in all patients except two CBA cases (100% vs. 96.4%, P = 0.50). First-pass isolation rate was 79.6% in the hybrid group. Procedure times were similar between groups (53, IQR 47–63 vs. 55, IQR 49–65 min, P = 0.35), but fluoroscopy time was shorter in the vHPSD/HPSD group (3.9 [2.7, 5.6] vs. 11.9 [9.3, 14.9] min, P < 0.001). There were 3 temporary phrenic nerve palsies (5.4%) in the CBA group which resolved within 1 year. The 1-year freedom from any atrial tachyarrhythmias after a single procedure was similar between groups (68.5% vs. 73.2%, P = 0.56). During repeat procedure, the durability of PVI was comparable.

Conclusions
The use of vHPSD/HPSD ablation renders point-by-point PVI as fast and effective as CBA. Furthermore, it has lower radiation exposure compared to CBA.

Graphical abstract
A Comparative Study of Rapidly-exploring Random Tree Algorithms Applied to Ship Trajectory Planning and Behavior Generation
Authors: Tengesdal Trym , Pedersen Tom Arne , Johansen Tor Arne 
Published: January 2025

AbstractRapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch’s t-test at a significance level of $$\alpha =0.05$$

α
=
0.05

, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.
LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
Authors: Yang Liudi , Mascaro Ruben , Alzugaray Ignacio , Prakhya Sai Manoj , Karrer Marco , Liu Ziyuan , Chli Margarita 
Published: January 2025

AbstractIn this paper, we propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs to perform place recognition and then use semantic registration to estimate the 6 DoF relative pose constraint. Our place recognition algorithm has two key modules, namely, a semantic graph encoder module and a graph comparison module. The semantic graph encoder employs graph attention networks to efficiently encode spatial, semantic and geometric information from the semantic graph of the input point cloud. We then use self-attention mechanism in both node-embedding and graph-embedding steps to create distinctive graph vectors. The graph vectors of the current scan and a keyframe scan are then compared in the graph comparison module to identify a possible loop closure. Specifically, employing the difference of the two graph vectors showed a significant improvement in performance, as shown in ablation studies. Lastly, we implemented a semantic registration algorithm that takes in loop closure candidate scans and estimates the relative 6 DoF pose constraint for the LiDAR SLAM system. Extensive evaluation on public datasets shows that our model is more accurate and robust, achieving 13% improvement in maximum F1 score on the SemanticKITTI dataset, when compared to the baseline semantic graph algorithm. For the benefit of the community, we open-source the complete implementation of our proposed algorithm and custom implementation of semantic registration at https://github.com/crepuscularlight/SemanticLoopClosure.
Improving rPET/PBT Bead Foam Structure via Chain Extender Modification and Blend Variance
Authors: Himmelsbach Andreas , Akdevelioglu Yavuz , Nofar Mohammadreza , Ruckdäschel Holger 
Published: January 2025

Abstract
In this study, the influence of the chain extender (CE) and the blend ratio on the bead foam extrusion of rPET/PBT is investigated. The shape and density of the bead foams were analyzed during extrusion using a camera scanner while the morphology of the foam was investigated using scanning electron microscopy (SEM). Melt strength and thermal behavior were also investigated with Rheotens and differential scanning calorimetry (DSC), respectively. Both chain extender and blend ratio had pronounced effect on the foaming behavior. Significant improvements were observed up to 0.8 wt.-% CE in rPET50PBT50, which achieved an average cell size of 107 ± 17 μm and a density of 182 kg/m³, representing a weight reduction of 86.4% compared to the bulk material. In addition, rPET40PBT60 with 0.8 wt.-% CE gave an average cell size of 108 ± 23 μm and a foam density of 170 kg/m³, with a comparable cell size distribution. After CE modification, the melt strength of rPET-dominant blends obtained higher values but a strong decrease in elongation was observed. In contrast, the CE-modified rPET40PBT60 and rPET30PBT70 blends exhibited much higher elongation with a moderate increase in melt strength which resulted in better bead and foam morphologies. DSC analysis revealed lowest crystallization temperature in rPET50PBT50 with deviations shifting towards higher temperatures. All blends except rPET70PBT30 shows double melting peak formation, with higher rPET formulations also exhibiting cold crystallization. These findings provide crucial insight for development of rPET/PBT foams by controlling the blend and CE composition, which is critical for achieving temperature-resistant bead foams with improved structural integrity.

Graphical Abstract
The Role of Aspect During Deverbal Word Processing in Greek
Authors: Tsaprouni Eleni , Manouilidou Christina 
Published: January 2025

AbstractDeverbal formations in Greek, e.g. miˈrazo ‘to distribute’ < ˈmirazma ‘distributing’ are considered morphologically complex lexical items. Previous psycholinguistic studies in Greek and English already highlighted the importance of lexical category and argument structure of the base verb in the processing of deverbal pseudowords violating constraints pertaining to these properties. A similar study in Slovenian brought into light the role of aspectual properties of the base verb during deverbal word processing. The present study revisits the role of aspect in morphological processing of deverbal word formations by looking at Greek. To this end, an offline acceptability judgement task and an online lexical decision task were conducted using different types of pseudowords, violating derivational rules. Results provide evidence that aspect affects deverbal pseudoword processing in Greek. Aspectual rules have a distinct role in relation to categorial and argument structure ones for the suffix that creates formations with unambiguous, eventive readings and which has clear event implications but not for other suffixes. The current study extends the literature to include the role of this feature in the processing of deverbal formations.
Problem-Solving Strategies in Stoichiometry Across Two Intelligent Tutoring Systems: A Cross-National Study
Authors: Borchers Conrad , Fleischer Hendrik , Yaron David J. , McLaren Bruce M. , Scheiter Katharina , Aleven Vincent , Schanze Sascha 
Published: January 2025

AbstractIntelligent tutoring system (ITS) provides learners with step-by-step problem-solving support through scaffolding. Most ITSs have been developed in the USA and incorporate American instructional strategies. How do non-American students perceive and use ITS with different native problem-solving strategies? The present study compares Stoich Tutor, an ITS with a high level of scaffolding, with ORCCA, an ITS with dynamic scaffolds that can support a range of problem-solving strategies. We conducted a think-aloud study with university students in the USA (N = 10) and Germany (N = 11), where students worked with either Stoich Tutor and ORCCA before solving stoichiometry problems on paper. Two human coders derived a coding scheme to investigate the strategies American and German students employ during problem solving on paper without instructional support. We derive a taxonomy of three stoichiometry problem-solving strategies. Next to the American factor labeling method, this taxonomy includes a strategy based on equation transformations and a previously undocumented strategy using abstract symbols to isolate a target variable and then pluck in given values and compute the solution. German students exclusively used the latter strategy, which was not explicitly supported by any of the two tutoring systems. Further, students who did not use the factor-label method for paper-based problem solving, most of whom were German, initially had difficulty setting appropriate goals and working with fractions in the Stoich Tutor. While German students preferred ORCCA based on short interviews, they more often successfully solved problems in Stoich Tutor. Therefore, Stoich Tutor, although misaligned with German instruction, could still support German students’ learning. Still, revisions to ITS based on local instructional cultures could make them potentially more effective and aligned with curricular goals.
Crop diversification for pollinator conservation
Authors: Fijen Thijs P. M. , Eeraerts Maxime , Osterman Julia , Beyer Nicole , Hass Annika , Lundin Ola , Westphal Catrin 
Published: January 2025

Abstract

Context
Intensive agriculture drives insect decline impacting insect-mediated ecosystem services that support production. Crop diversification shows promise in increasing crop productivity and enhancing ecosystem services, however, the impact on biodiversity conservation, particularly of pollinators, is unclear.

Objectives
Here, we synthesize the mechanisms and current evidence base of how increasing the spatial and temporal diversity of crops within and across agricultural fields can benefit pollinator biodiversity.

Methods
We focus on research in the highly intensified agricultural regions, in Western Europe and North America, from which we know a lot about pollinator decline, but use inspiration from tropical regions.

Results
We find that higher crop diversity, with sequentially flowering cultivars, intercropping practices, and a larger coverage of flowering crops, for example through integrating the cultivation of forgotten, novel, and woody crops increases flower resource availability throughout the active flight period of pollinators. All practices can increase landscape heterogeneity, which is further enhanced by decreasing field sizes. As a result, the functional connectivity increases, which improves the flower accessibility within the foraging ranges of pollinators.

Conclusions
Our review highlights the potential benefit of various crop diversification measures for supporting pollinating insects without taking land out of production, as well as the limitations, including that only a subset of pollinator species may benefit. Empirical evidence suggest that diversification practices could benefit pollinators, but landscape-wide studies are needed to properly evaluate the true potential of crop diversification for pollinator conservation as part of the solution for bending the curve of pollinator decline.
Lateral load response of a dry-joint masonry arch subject to vertical support displacements by rigid block analysis
Authors: Ferrero Chiara , Portioli Francesco P. A. , Calderini Chiara 
Published: January 2025

Abstract
Masonry arches play a crucial role in the seismic response of historic masonry buildings. Although masonry arches are often subjected to large support displacements that alter and deform their geometries over time, their response to seismic actions is generally analysed using idealized, perfect geometries. This paper aims to assess the effect of large support displacements on the response of masonry arches to lateral loads. To this end, a small-scale segmental dry-joint masonry arch subjected to the vertical displacement of one support was investigated. Using a rigid block modelling approach recently proposed in the literature, the arch was modelled as an assemblage of rigid voussoirs connected by no-tension frictional contact interfaces. The response of the arch to lateral loads, applied in one direction, and to vertical support displacements was assessed through nonlinear static analyses. To evaluate the effect of support displacements, lateral loads were applied to a series of deformed arch geometries resulting from the application of increasing values of the imposed vertical displacement. The results of the numerical analyses provided new insights into how support displacements affect the lateral load response of masonry arches, showing that they may significantly reduce both the horizontal action triggering collapse and the ultimate displacement capacity.
Development of natural regeneration after different regeneration cuttings in a Norway spruce peatland forest in northern Finland
Authors: Hökkä Hannu , Miettinen Janne , Karhu Jouni 
Published: January 2025

AbstractAs the clearcutting of peatland forests is known to cause harmful consequences especially for the water environment, there is increasing interest in alternative natural regeneration methods. We studied the regeneration stocking development on a southern Lapland site, where shelterwood cutting, heavy selection cutting, patch clearcutting (≤ 0.3 ha) treatments, and a non-treated control with three replicates had been established in the winter of 2013. Regeneration stocking was inventoried in the springs of 2013, 2016, 2018, and 2021 from 16 seedling survey plots (size 5 m2), which were systematically located in each of the 12 treatment plots. The number of small spruce seedlings (< 10 cm height) was largest in 2016 and then started to decrease. The number of large (> 10 cm) spruce seedlings increased two- to three-fold (4,500–9,000 ha− 1) towards the end of the study period for all cutting treatments. The number of large birch seedlings also increased especially for heavy selection and clearcutting and was 2–10 times higher than that of spruce. Birch was less abundant in shelterwood cutting. The tallest spruce and birch seedlings were found in clearcuts. Large spruce and birch seedlings were most evenly distributed for heavy selection and clearcut treatments. All cutting treatments showed high average regeneration density for a mixed seedling stand of Norway spruce and downy birch within the eight post-cutting years.
Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning
Authors: Ahmad Izhar , Farooq Rashid , Ashraf Muhammad , Waseem Muhammad , Shangguan Donghui 
Published: January 2025

AbstractFloods are natural disasters with significant economic and infrastructural impacts. Assessing flood susceptibility in mountainous urban regions is particularly challenging due to the complicated interaction in which urban structures and mountainous terrain affect flood behavior. This study employs two ensemble machine learning algorithms, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to develop flood susceptibility maps for the Hunza-Nagar region, which has been experiencing frequent flooding for the past three decades. An unsteady flow simulation is carried out in HEC-RAS utilizing a 100-year return period flood hydrograph as an input boundary condition, the output of which provided the spatial inundation extents necessary for developing the flood inventory. Ten explanatory factors, including climatic, geological, and geomorphological features namely elevation, slope, curvature, topographic wetness index (TWI), normalized difference vegetation index (NDVI), land use land cover (LULC), rainfall, lithology, distance to roads and distance to rivers are considered for the flood susceptibility mapping. For developing flood inventory, random sampling technique is adopted to create a spatial repository of flood and non-flood points, incorporating the ten geo-environmental flood conditioning factors. The models’ accuracy is assessed using the area under the curve (AUC) of receiver operating characteristics (ROC). The prediction rate AUC values are 0.912 for RF and 0.893 for XGBoost, with RF also demonstrating superior performance in accuracy, precision, recall, F1-score, and kappa evaluation metrics. Consequently, the RF model is selected to represent the flood susceptibility map for the study area. The resulting flood susceptibility maps will assist national disaster management and infrastructure development authorities in identifying high flood susceptible zones and carrying out early mitigation actions for future floods.
Photonic circuit of arbitrary non-unitary systems
Authors: Talib Hussein , Sewell Phillip D. , Vukovic Ana , Phang Sendy 
Published: January 2025

Abstract
A design framework to implement non-unitary input–output operations to a practical unitary photonic integrated circuit is described. This is achieved by utilising the cosine-sine decomposition to recover the unitarity of the original operation. The recovered unitary operation is decomposed into fundamental unitary building blocks, forming a photonic integrated circuit network based on directional couplers and waveguide phase shifters. The individual building blocks are designed and optimised by three-dimensional full-wave simulations and scaled up using a circuit approach. The paper investigates the scalability and robustness of the design approach. Our study demonstrates that the proposed approach of performing unitary matrix completion can be applied to any arbitrary matrices. This design approach allows for implementation of non-unitary operations to perform various linear functions in neuromorphic photonics for computing, sensing, signal processing and communications.
Deep learning and machine learning based highly accurate reflection prediction model for multi layers anti-reflection coatings
Authors: Oktay Semih , Duru İremnur , Bakır Halit , Tabaru Timuçin Emre 
Published: January 2025

Abstract
This study focuses on creating an accurate reflection prediction model that will guide the design of filters with multilayer Anti-Reflection Coating (ARC) to optimize the thickness parameters using Machine Learning (ML) and Deep Learning (DL) techniques. This model aims to shed light on the design process of a multilayer optical filter, making it more cost-effective by providing faster and more precise production. In creating this model, a dataset containing data obtained from 3000 (1500 Ge–Al2O3, 1500 Ge–SiO2) simulations previously performed on a computer based on the thicknesses of multilayer structural materials was used. The data are generated using Computational Electromagnetic simulation software based on the Finite-Difference Time-Domain method. To understand the mechanism of the proposed model, two different two-layer coating simulations were studied. While Ge was used as the substrate in both coatings, Al2O3 and SiO2 were used as the second layers. The data set consists of the 3–5 µm and 8–12 µm bands typical for the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands and includes reflectance values for wavelengths ranging between these spectra. In the specified 2-layer data set, the average reflectance was obtained with a minimum of 0.36 at 515 nm Ge and 910 nm SiO2 thicknesses. This value can be increased by adapting the proposed model to more than 2 layers. Six ML algorithms and a DL model, including artificial neural networks and convolutional neural networks, are evaluated to determine the most effective approach for predicting reflectance properties. Furthermore, in the proposed model, a hyperparameter tuning phase is used in the study to compare the efficiency of ML and DL methods to generate dual-band ARC and maximize the prediction accuracy of the DL algorithm. To our knowledge, this is the first time this has been implemented in this field. The results show that ML models, particularly decision tree (MSE: 0.00000069, RMSE: 0.00083), random forest (MSE: 0.00000525, RMSE: 0.00229), and bagging (MSE: 0.00000667, RMSE: 0.00258) methods, outperform the DL model (MSE: 0.0040107, RMSE: 0.06333) in predicting reflectance values and provide a valuable reference for designing and fabricating optical thin-film filters.