bluuebunny/crossref_metadata_2025_split huggingface dataset:
A dislocation-based finite element method for plastic collapse assessment in solid mechanics
Authors: Zona Renato , Minutolo Vincenzo 
Published: April 2024

AbstractA displacement-based dislocation map has been used to build the eigenstress stress, which is the base of the structure’s limit analysis. The limit load has been calculated as the upper bound of any equilibrated stress that respects the compatibility inequalities by means of a linear optimization program. The eigenstress stress nodal parameters were assumed as the design variables, and the compatibility inequalities have been obtained from the Mises–Schleicher criterion, assuming that the stress belongs to the corresponding plastic domain. The numerical application has considered a linear secant representation of the domain, with a penalty factor on stresses, to correct the linearization error. Examples concerning a simply supported cantilever beam, a pipe section, and a plate with a circular hole highlighted the accuracy of the procedure with respect to the established literature. Moreover, the procedure has been applied to investigate plane structure examples. A square plate with variable elliptic holes has been analyzed, and the influence of ellipticity on the collapse load has been shown. The effects of porosity and heterogeneity of the structure with respect to the collapse load are shown considering the porous cantilever and representative volume element. The evaluation of the limit load along different element directions envisaged a point-wise calculation of the compatibility domain of the porous material to be used in the macro-scale analysis of the structures made of porous micro-cells.
When the lava meets the sea: emplacement of the 2–4 ka San Bartolo lava flow field, Stromboli volcano (Italy)
Authors: Shajahan Rasia , Harris Andrew J. L. , Zanella Elena , Gurioli Lucia , Test Claudio Robustelli , Calvari Sonia , Drovanti Lodovico 
Published: April 2024

AbstractWhen a lava flow enters a body of water, either a lake, sea, river or ocean, explosive interaction may arise. However, when it is an 'a'ā lava flow entering water, a more complex interaction occurs, that is very poorly described and documented in literature. In this paper, we analysed the 2–4 ka San Bartolo lava flow field emplaced on the north flank of Stromboli volcano, Italy. The lava flow field extends from ~ 650 m a.s.l. where the eruptive fissure is located, with two lava channels being apparent on the steep down to the coast. Along the coast the lava flow field expands to form a lava delta ~ 1 km wide characterised by 16 lava ‘Flow’ units. We performed a field survey to characterise the features of lava entering the sea and the associated formation of different components and magnetic measurements to infer the flow fabrics and emplacement process of the lava flow system. We measured the density, porosity and connectivity of several specimens to analyse the effect of lava-water interaction on the content in vesicles and their connectivity and conducted a macroscopic componentry analysis (clast count) at selected sites to infer the character of the eroded offshore segment of the lava flow field and its component flow units. The collected data allowed us to define the main components of a lava delta fed by 'a'ā lava flows, with its channels, littoral units, ramps, lava tubes, and inflated pāhoehoe flows controlled by the arterial 'a'ā flow fronts. The spatial organisation of these components allowed us to build a three-step descriptive model for 'a'ā entering a water. The initial stage corresponds to the entry of channel-fed 'a'ā lava flow into the sea which fragments to form metric blocks of 'a'ā lava. Continued lava supply to the foreshore causes flow units to stall while spreading over this substrate. Subsequent 'a'ā lava flow units ramp up behind the stalled flow front barrier. Lava tubes extending through the stalled flow barrier feed the seaward extension of a bench made of several pāhoehoe flow units.
Benefits of applying hourly resolution in the assessment of the climate aptitude to manage tourist activities in arid regions
Authors: Nourmohammadi Fatemeh , Gómez-Martín María Belén 
Published: April 2024

AbstractThe availability of reliable information on local climatic-tourism conditions is a growing need due to the influence it exerts on the quality of the organizational strategy of tourist destination’s, and travel experience. Evaluations of the tourism potential of the climate have been carried out on a daily or monthly resolution, thus limiting the collection of detailed information that makes it possible to fine-tune tourism management and operational decision-making on an intraday scale. This research is the first case study to analyse the climatic suitability for nature tourism, using the weather types method at hourly resolution. The study applies to arid tourist destinations in Isfahan province (Iran). The detailed resolution has made it possible to identify the time slots favourable to the development of nature tourism in those periods of the year recognized as critical in the daily resolution analyses. In the same way, the hourly resolution has also identified critical bands in those periods indicated as favourable in the evaluations to daily resolution. The hourly resolution provides detailed information that can allow tourists and also tourism managers to establish intraday adaptation strategies that make it possible to develop the activity even in places with extreme climates.
Relation-wise transformer network and reinforcement learning for visual navigation
Authors: He Yu , Zhou Kang 
Published: April 2024

AbstractThe task of object goal navigation is to drive an embodied agent to find the location of a given target only using visual observation. The mapping from visual perception of observation determines the navigation actions. Heterogeneous relationships in the observation are the essential part of the scene graph, which can guide the agent to find the target more easily. In this work, we propose a novel Heterogeneous Zone Graph Visual Transformer formulation for graph representation and visual perception. It consists of two key ideas: (1) Heterogeneous Zone Graph (HZG) that explores the heterogeneous target-related zones graph and spatial information. It allows the agent to navigate efficiently. (2) Relation-wise Transformer Network (RTNet) that transforms the relationship between previously observed objects and navigation actions. RTNet extracts rich nodes and edges features as pays more attention to the target-related zone. We model self-attention on the node-to-node encoder and cross-attention on the edge-to-node decoder. We evaluate our methods on the AI2THOR dataset and show superior navigation performance. Code and datasets can be found inhttps://github.com/zhoukang12321/RTNet_VN_2023.
Prevalence of the protective OAS1 rs10774671-G allele against severe COVID-19 in Moroccans: implications for a North African Neanderthal connection
Authors: Yousfi Fatima Zahra El , Haroun Abbas Ermilo , Nebhani Chaimae , Belayachi Jihane , Askander Omar , Fahime Elmostafa El , Fares Hakima , Ennibi Khalid , Abouqal Redouane , Razine Rachid , Bouhouche Ahmed 
Published: April 2024

AbstractThe clinical presentation of COVID-19 shows high variability among individuals, which is partly due to genetic factors. The OAS1/2/3 cluster has been found to be strongly associated with COVID-19 severity. We examined this locus in the Moroccan population for the occurrence of the critical variant rs10774671 and its respective haplotype blocks. The frequency of single-nucleotide polymorphisms (SNPs) in the cluster of OAS immunity genes in 157 unrelated individuals of Moroccan origin was determined using an in-house exome database. OAS1 exon 6 of 71 SARS-CoV-2-positive individuals with asymptomatic/mild disease and 74 with moderate/severe disease was sequenced by the Sanger method. The genotypic, allelic, and haplotype frequencies of three SNPs were compared between these two groups. Finally, males in our COVID-19 series were genotyped for the Berber-specific marker E-M81. The prevalence of the OAS1 rs10774671-G allele in present-day Moroccans was found to be 40.4%, which is similar to that found in Europeans. However, it was found equally in both the Neanderthal GGG haplotype and the African GAC haplotype, with a frequency of 20% each. These two haplotypes, and hence the rs10774671-G allele, were significantly associated with protection against severe COVID-19 (p = 0.034, p = 0.041, and p = 0.008, respectively). Surprisingly, in men with the Berber-specific uniparental markers, the African haplotype was absent, while the prevalence of the Neanderthal haplotype was similar to that in Europeans. The protective rs10774671-G allele of OAS1 was found only in the Neanderthal haplotype in Berbers, the indigenous people of North Africa, suggesting that this region may have served as a stepping-stone for the passage of hominids to other continents.
Extrachromosomal circular DNA (eccDNA): from carcinogenesis to drug resistance
Authors: Li Zhaoxing , Qian Daohai 
Published: April 2024

AbstractExtrachromosomal circular DNA (eccDNA) is a circular form of DNA that exists outside of the chromosome. Although it has only been a few decades since its discovery, in recent years, it has been found to have a close relationship with cancer, which has attracted widespread attention from researchers. Thus far, under the persistent research of researchers from all over the world, eccDNA has been found to play an important role in a variety of tumors, including breast cancer, lung cancer, ovarian cancer, etc. Herein, we review the sources of eccDNA, classifications, and the mechanisms responsible for their biogenesis. In addition, we introduce the relationship between eccDNA and various cancers and the role of eccDNA in the generation and evolution of cancer. Finally, we summarize the research significance and importance of eccDNA in cancer, and highlight new prospects for the application of eccDNA in the future detection and treatment of cancer.
Brief report of fatal rainfall-triggered landslides from record-breaking 2023 storms in Auckland, New Zealand
Authors: Brook Martin S. , Nicoll Chris 
Published: April 2024

AbstractThe aim of this paper is to report on fatal landslides resulting from two extraordinary storm events in January and February 2023, in Auckland, New Zealand, including the characteristics of the terrain, the site geology, and the storms. The January 27 Auckland Anniversary storm was from an “atmospheric river,” dumping Summer’s worth of rain (265 mm) in one day. This was the highest 24-h total on record, estimated a 1 in 200-year event, with a peak 2-min rainfall of 4.2 mm, coinciding with Friday’s evening “rush hour.” This led to widespread landsliding throughout Auckland, including a fatal landslide in Parnell, central Auckland. Notably, this occurred only meters from a similarly destructive landslide in 1997. With January 2023 Auckland’s wettest January on record, and with the region still in a state of emergency, Tropical Cyclone Gabrielle crossed the region on February 13–14. This also caused widespread landsliding, including a fatal landslide at Muriwai on the west Auckland coast. Fatal landslides from the same escarpment had occurred in 1965, close to the February 13, 2023, fatal landslide. Taken together, both storm events caused > 140,000 landslides across the North Island, and flooding, making hundreds of people homeless, with damage estimates of > US$8.6 billion (3.4% of NZ’s GDP). For future disaster risk reduction, attention should be paid to revising legislation and local planning, which currently allows people to legally build (and re-build) houses in landslide-prone areas of New Zealand.
Predicting wetland soil properties using machine learning, geophysics, and soil measurement data
Authors: Driba Dejene L. , Emmanuel Efemena D. , Doro Kennedy O. 
Published: April 2024

Abstract
Purpose
Machine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples.

Materials and methods
A spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique.

Results and discussions
The generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance.

Conclusions
This study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.
A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
Authors: Marchetti Francesco , Guastavino Sabrina , Campi Cristina , Benvenuto Federico , Piana Michele 
Published: April 2024

Abstract
In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exists a discrepancy between the maximization of such scores and the minimization of the loss function in the training phase. In this paper, we provide a complete theoretical setting that formalizes weighted classification metrics and then allows the construction of losses that drive the model to optimize these metrics of interest. After a detailed theoretical analysis, we show that our framework includes as particular instances well-established approaches such as classical cost-sensitive learning, weighted cross entropy loss functions and value-weighted skill scores.
Convergence behavior of sovereign bond yields in the EU and COVID-19 government responses
Authors: Christou Christina , Eleftheriou Konstantinos , Patsoulis Patroklos 
Published: April 2024

Abstract
This paper aims to investigate the convergence pattern of sovereign bond yields in European Union (EU) countries during the Covid-19 pandemic. To this purpose we employ the Phillips and Sul convergence methodology. We further test whether the above pattern was affected by the intensity of government responses against the Covid-19 dispersion. Our findings indicate the existence of two convergence clubs. Both clubs are composed of countries that have previously been classified as either ‘core’ or ‘periphery’ countries, indicating that in this particular health crisis, this classification is too simplistic. Moreover, our results suggest that the intensity of Covid-19 containment policies had little to no effect in shaping the convergence pattern of sovereign bond yields in the EU. These conclusions are robust even after accounting for the size of the fiscal sector of each country.
Industrial location and urban macroform prediction model based on cellular automata and multi-criteria decision-making methods (ILAUFM)
Authors: Yüzer Mehmet Ali , Yüzer Ayşe Şebnem , Kuru Azem , Yüzer Mehmet Emre 
Published: April 2024

AbstractThe relationships between industrialization and urban regions, as well as technological advancements, innovations, and new work patterns and organizations, significantly impact the structuring of cities. Industrial sectors can become key drivers in shaping the form and macrostructure of a city over time. By examining and evaluating the existing system of structures and relations in the settlements in order to “estimate the land use differentiations” that may arise in the future and “to determine the spatial location of industrial facilities or sites,” a model has been developed. This model aims to predict potential “land use differentiations” and determine the “spatial location selection of industrial facilities or sites” in the future based on the analysis and evaluation of existing structures and relational systems in settlements. The model consists of the integration of the LUCAM, which is based on a cellular automata operating system, along with the “GIS-Based Integrated Industrial Area Location Selection Model,” which evaluates all data under the main headings of natural structure characteristics, socio-economic structure characteristics, and built environment characteristics using weighted overlay analysis. In this article, the general structure of the model developed within the scope of the research and İnegöl sampling are explained. The outputs of this model can be used for alternative separation for logistics centers, residential areas, secondary centers, reinforcement areas, and industrial sites in different settlements. In addition, in the research, a detailed model was designed by integrating decision-makers (central administrations, local administrations, registered users, NGOs) into the urban planning process. The proposed model can be used as a tool in designing transportation and other urban systems (distribution order of equipment areas, green area system, grading of centers, etc.).
Solvability and solution character of a hyperbolic cotangent-type difference equation of second-order
Authors: Tollu Durhasan Turgut , Yazlık Yasin 
Published: April 2024

AbstractIn the present study, we show that the second-order nonlinear difference equation $$\begin{aligned} x_{n}=\frac{x_{n-1}x_{n-2}-a}{x_{n-1}-x_{n-2}},\ n\in \mathbb {N}_{0}, \end{aligned}$$

x
n

=

x

n
-
1

x

n
-
2

-
a

x

n
-
1

-

x

n
-
2

,

n
∈

N
0

,

where $$a\in \left[ 0,\infty \right) $$

a
∈

0
,
∞

and the initial values $$x_{-2}$$

x

-
2

, $$x_{-1}$$

x

-
1

are real numbers, is solvable in closed form and that solutions can be analyzed in detail by the Fibonacci numbers.
On a Formula for All Sets of Constant Width in 3D
Authors: Kawohl Bernd , Sweers Guido 
Published: April 2024

AbstractIn the recent paper “On a formula for sets of constant width in 2D, Comm. Pure Appl. Anal. 18 (2019), 2117–2131”, we gave a constructive formula for all 2d sets of constant width. Based on this result we derive here a formula for the parametrization of the boundary of bodies of constant width in 3 dimensions, with the formula depending on one function defined on $${\mathbb {S}}^2$$

S

2

. Each such function gives a minimal value $$r_0$$

r
0

and for all $$r\ge r_0$$

r
≥

r
0

one finds a body of constant width 2r. Moreover, we show that all bodies of constant width in 3d have such a parametrization. The last result needs a tool that we describe as ‘shadow domain’ and which is explained in an appendix. The construction is explicit and offers a parametrization different from the one given by T. Bayen, T. Lachand-Robert and É. Oudet in “Analytic parametrization of three-dimensional bodies of constant width. Arch. Ration. Mech. Anal., 186 (2007), 225–249”.
A gastric invasive tubular adenocarcinomatous lesion arising from foveolar-type neoplasia: molecular histogenesis
Authors: Sugai Tamotsu , Uesugi Noriyuki , Hamada Koichi , Nagahashi Takayuki , Horikawa Yoshinori , Suzuki Masamichi , Sugimoto Ryo , Yanagawa Naoki 
Published: April 2024

AbstractHere, we report a rare case of a depressed lesion exhibiting both tubular differentiated adenocarcinomatous (TDA) and intraepithelial foveolar neoplasia (IFN) components (with the histological appearance of foveolar hyperplasia due to low-grade atypia). Histologically, the TDA surrounded the IFN, suggesting that the TDA may have originated from the IFN. Therefore, we examined molecular alterations in the TDA and IFN components separately. MUC5AC and MUC6 expression was observed immunohistochemically in both components. p53 expression was wild type in both components, suggesting no mutation of TP53. We investigated allelic imbalances at multiple loci (1p, 3p, 4p, 5q, 8q, 9p, 13q, TP53, 18q, and 22q), mutations (KRAS, BRAF, and GNAS), and DNA methylation and microsatellite status in both components using PCR-based analyses. Although multiple allelic imbalances were common to both components, allelic imbalances at 3p and TP53 were found only in the TDA component. No mutations were found, and DNA methylation status was low epigenotype for both components. Ultimately, this tumor was considered microsatellite stable. Considering the origin of TDA, which is frequently encountered in routine practice, IFN may develop into TDA.
A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline
Authors: Diri Samet , Yildirim Mehmet 
Published: April 2024

AbstractHighly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$
-
04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.