bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detour test performance of cloned minipigs from three different clone populations
Authors: Paganelli Aurora , Felici Martina , Turini Luca , Baragli Paolo , Carlucci Lucia , Recchia Fabio Anastasio , Sgorbini Micaela 
Published: July 2023

AbstractGenetics, the uterine environment, maternal behavior, and rearing conditions can all influence animal behavioral phenotypes. Some studies on cloned pigs have found no differences between the behavioral patterns of cloned and non-cloned animals. Other studies conducted on dogs have reported similarities in the behavior of cloned subjects. This study evaluated the performance of 12 cloned minipigs from three different clone populations (A, B, C) in a detour test around symmetric and asymmetric barriers. We measured the detour time and patterns, in order to investigate the pigs’ cognitive abilities.The detour time and the detour entry/exit pattern were recorded. All the animals tended to keep a fixed entry/exit pattern instead of modifying it to accommodate changes in the working set. Significant differences in detour time were found among the populations, with animals belonging to population B being faster than the others, and also within each population.Our study is one of the few to assess the cognitive abilities of cloned minipigs. The results indicate that even animals belonging to the same cloned population may develop different cognitive, hence behavioral characteristics. Whether cloning can be utilized to obtain similar behavioral phenotypes therefore remains a matter of debate.
GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergence
Authors: Huang Xia , Chong Kai Fong Ernest 
Published: July 2023

AbstractWeb image datasets curated online inherently contain ambiguous in-distribution instances and out-of-distribution instances, which we collectively callnon-conforming(NC) instances. In many recent approaches for mitigating the negative effects of NC instances, the core implicit assumption is that the NC instances can be found via entropy maximization. For “entropy” to be well-defined, we are interpreting the output prediction vector of an instance as the parameter vector of a multinomial random variable, with respect to some trained model with a softmax output layer. Hence, entropy maximization is based on the idealized assumption that NC instances have predictions that are “almost” uniformly distributed. However, in real-world web image datasets, there are numerous NC instances whose predictions are far from being uniformly distributed. To tackle the limitation of entropy maximization, we propose$$(\alpha , \beta )$$(α,β)-generalized KL divergence,$${\mathcal {D}}_{\text {KL}}^{\alpha , \beta }(p\Vert q)$$DKLα,β(p‖q), which can be used to identify significantly more NC instances. Theoretical properties of$${\mathcal {D}}_{\text {KL}}^{\alpha , \beta }(p\Vert q)$$DKLα,β(p‖q)are proven, and we also show empirically that a simple use of$${\mathcal {D}}_{\text {KL}}^{\alpha , \beta }(p\Vert q)$$DKLα,β(p‖q)outperforms all baselines on the NC instance identification task. Building upon$$(\alpha ,\beta )$$(α,β)-generalized KL divergence, we also introduce a new iterative training framework,GenKL, that identifies and relabels NC instances. When evaluated on three web image datasets, Clothing1M, Food101/Food101N, and mini WebVision 1.0, we achieved new state-of-the-art classification accuracies:$$81.34\%$$81.34%,$$85.73\%$$85.73%and$$78.99\%$$78.99%/$$92.54\%$$92.54%(top-1/top-5), respectively.
Intrinsically motivated reinforcement learning based recommendation with counterfactual data augmentation
Authors: Chen Xiaocong , Wang Siyu , Qi Lianyong , Li Yong , Yao Lina 
Published: July 2023

AbstractDeep reinforcement learning (DRL) has shown promising results in modeling dynamic user preferences in RS in recent literature. However, training a DRL agent in the sparse RS environment poses a significant challenge. This is because the agent must balance between exploring informative user-item interaction trajectories and using existing trajectories for policy learning, a known exploration and exploitation trade-off. This trade-off greatly affects the recommendation performance when the environment is sparse. In DRL-based RS, balancing exploration and exploitation is even more challenging as the agent needs to deeply explore informative trajectories and efficiently exploit them in the context of RS. To address this issue, we propose a novel intrinsically motivated reinforcement learning (IMRL) method that enhances the agent’s capability to explore informative interaction trajectories in the sparse environment. We further enrich these trajectories via an adaptive counterfactual augmentation strategy with a customised threshold to improve their efficiency in exploitation. Our approach is evaluated on six offline datasets and three online simulation platforms, demonstrating its superiority over existing state-of-the-art methods. The extensive experiments show that our IMRL method outperforms other methods in terms of recommendation performance in the sparse RS environment.
Coupling of multilayer CAD-CAM prosthetic components: Computing the user interaction influence on the adhesion interface geometry
Authors: Graiff Lorenzo , Savio Gianpaolo , Di Fiore Adolfo , Meneghello Roberto , Uccheddu Francesca 
Published: July 2023

AbstractThe fracture resistance of multilayer zirconia crowns has recently been proven to be improved by using lithium millable disilicate glass–ceramic blocks (D’Addazio in Materials, 2020). Accordingly, the framework and the ceramic coating are designed and milled using a CAD-CAM technology and the two separated prosthetic components are then manually assembled by the dental technician and glued with the fusion of a glass–ceramic material. It is essential, during the CAD phase, to design a gap between the framework and the decorative veneer that will later be filled by the fused ceramic.Since the act of gluing the two parts is manually performed by the dental technician, we aim at investigating the operator influence on the final gap with respect to the designed gap. For this purpose, an original geometrical investigation method was developed to enable the 3D digital analysis of the whole fusion interface. During the CAD design stage, two technicians input a different setting for the gap between the two components. The framework and veneering structure were designed, the milled components were produced, and the zirconia framework was sintered, then the two CAD-on prosthetic components were scanned before and after their fusion/crystallization to analyze the physical internal gap. The results show that manual assembly cancels out any effect of the precision settings adopted during CAD-CAM design of the components, as well as any benefit expected from machining on a CNC milling machine, thus requiring, as a last step, manually retouching the prosthesis to correctly fit in the mouth.
Involvement of Proteasomal and Endoplasmic Reticulum Stress in Neurodegeneration After Global Brain Ischemia
Authors: Ziakova Katarina , Kovalska Maria , Pilchova Ivana , Dibdiakova Katarina , Brodnanova Maria , Pokusa Michal , Kalenska Dagmar , Racay Peter 
Published: July 2023

AbstractA brief period of transient global brain ischemia leads to selective ischemic neurodegeneration associated with death of hippocampal CA1 pyramidal neurons days after reperfusion. The mechanism of such selective and delayed neurodegeneration is still uncertain. Our work aimed to study the involvement of proteasomal and endoplasmic reticulum (ER) stress in ischemic neurodegeneration. We have performed laser scanning confocal microscopy analysis of brain slices from control and experimental animals that underwent global brain ischemia for 15 min and varying times of reperfusion. We have focused on ubiquitin, PUMA, a proapoptotic protein of the Bcl-2 family overexpressed in response to both proteasomal and ER stress, and p53, which controls expression of PUMA. We have also examined the expression of HRD1, an E3 ubiquitin ligase that was shown to be overexpressed after ER stress. We have also examined potential crosstalk between proteasomal and ER stress using cellular models of both proteasomal and ER stress. We demonstrate that global brain ischemia is associated with an appearance of distinct immunoreactivity of ubiquitin, PUMA and p53 in pyramidal neurons of the CA1 layer of the hippocampus 72 h after ischemic insults. Such changes correlate with a delay and selectivity of ischemic neurodegeneration. Immunoreactivity of HRD1 observed in all investigated regions of rat brain was transiently absent in both CA1 and CA3 pyramidal neurones 24 h after ischemia in the hippocampus, which does not correlate with a delay and selectivity of ischemic neurodegeneration. We do not document significant crosstalk between proteasomal and ER stress. Our results favour dysfunction of the ubiquitin proteasome system and consequent p53-induced expression of PUMA as the main mechanisms responsible for selective and delayed degeneration of pyramidal neurons of the hippocampal CA1 layer in response to global brain ischemia.
Experimental demonstration of a method to actively stabilize satellite structures against random perturbations of thermal boundary conditions using a closed-loop filter and controller approach
Authors: Möller F. , Essig T. , Holzhauer S. , Förstner R. 
Published: July 2023

AbstractModern space travel requires increasingly stable support structures for optical instruments or antennas, which can hardly be realized with the traditional approaches. Therefore, an approach for active structural stabilization of thermal-induced distortions was developed, simulated, and patented within the framework of the Infrared Astronomy Satellite Swarm Interferometry (IRASSI) project. Based on this approach, the thermomechanical transfer functions between the change in heat flux and the change in displacement of the finite-element (FE) model of a structure have already been validated in their basic functionality. In an experimental setup, the FE model was extended to include a closed-loop filter and controller approach consisting of a Kalman filter processing the temperature measurements in conjunction with a Linear Quadratic Regulator (LQR). It was shown that this setup can compensate for predefined sinusoidally oscillating disturbance heat flows, with an improvement of 4 dB compared to the uncontrolled case. In this paper, an experimental setup revised with respect to the positioning of the sensors and control heating elements, in conjunction with an extension of the filter and controller approach, will demonstrate that the system can respond to reproducible, random fluctuations in the disturbance heat flux, as well as stepped changes, and that performance has improved due to the revision.
A framework for assessing hazards related to pit lakes: application on European case studies
Authors: Al Heib M. , Varouchakis E. A. , Galetakis M. , Renaud V. , Burda J. 
Published: July 2023

AbstractThe number of abandoned mines is continuously increasing in the world. The sustainable exploitation of these abandoned mines is a top priority globally. Pit lakes created in abandoned mining sites provide benefits and present risks for the mining region. This paper presents a methodology to assess the mining and natural hazards related to pit lakes. The proposed methodology framework is based on well-established and internationally accepted risk management standards and allows the assessment of the long-term stability of pit lakes. Potential ground movements, water pollution, fire hazards are presented, and their impact evaluated based on feedback and international experience. The methodology was applied for 13 European pit lakes created in former European coal and lignite mines. A risk assessment of the Most lake was carried out, using qualitative and a weighted probabilistic approach that considers the overall and the individual effect of identified mining and natural hazards. Results showed that the main potential hazards are the ground movements, slope stability, and flooding.
Substance use disorder in young adults with stroke: clinical characteristics and outcome
Authors: Rizk Hoda Ibrahim , Magdy Rehab , Emam Khadiga , Mohammed Mona Soliman , Aboulfotooh Alshaimaa M. 
Published: July 2023

Abstract
Objective
Stroke incidence among young adults has risen in the last decade. This research attempts to determine the effect of substance use disorder (SUD) on the clinical characteristics of stroke, mortality, outcome after IV thrombolysis, and functional dependency after 1 month among young adults.

Methods
Through a retrospective study, data were extracted from the electronic medical records of stroke in young adults admitted to intensive care units in Kasr Al-Ainy Hospital (February 2018–January 2021). The National Institute of Health Stroke Scale (NIHSS) and the Modified Rankin Scale were documented at the onset and after 1 month.

Results
The study included 225 young adults with stroke (median age of 40, IQR: 34–44). Only 93 young adults (41%) met the criteria of SUD. Anabolic steroid use disorder was significantly associated with cerebral venous thrombosis (P-value = 0.02), while heroin use disorder was significantly associated with a hemorrhagic stroke (P-value = 0.01). Patients with tramadol, cannabis, and cocaine use disorders had significantly more frequent strokes in the posterior than the anterior circulation. Patients with heroin use disorders had significantly higher mortality than those without heroin use disorders (P-value = 0.01). The risk of poor outcomes was doubled by alcohol or heroin use disorder, while it was tripled by cocaine use disorder (P-value = 0.01 for each).

Conclusion
Forty-one percent of young adults diagnosed with a stroke had SUD, with a relatively higher posterior circulation involvement. Increased mortality was associated with heroin use disorder more than other substances. Poor stroke outcome was associated with alcohol, heroin, and cocaine use disorders.
Substance Abuse Amongst Refugee Youth: A Scoping Review
Authors: Aleer Elijah , Alam Khorshed , Rashid Afzalur , Mohsin Mohd , Eacersall Douglas 
Published: July 2023

Abstract
Purpose of Review
Identify and map health, economic, and social consequences of substance abuse, especially because the impacts of substance abuse can adversely and disproportionally affect refugee youth. This scoping review was conducted to exhibit gaps in the literature on the prevalence, risk factors, consequences, and interventions of substance abuse among refugee youth to provide a better understanding of the social determinants of this public health crisis.

Findings
A systematic search for studies on EBSCOhost databases, MEDLINE, and Scopus retrieved 300 citations. Following a systematic screening, 10 studies met the inclusion criteria. The findings from the studies suggest the prevalence of substance abuse amongst refugee youth is exacerbated by several risk factors such as coping with pre- and post-migration experiences, accessibility and availability of substances, and socialisation with peers. Evidence shows that refugee youth suffer negative consequences because of substance abuse. However, they also show a lack of focus on interventions to mitigate the prevalence and negative consequences.

Summary
Literature generally suggests that the presence of substance abuse amongst refugee youth leads to negative consequences. This finding is especially concerning given the lack of knowledge about mitigation strategies and the identification of several risk factors associated with substance abuse. Therefore, increased attention must be placed on substance abuse amongst refugee youth, especially noting the rising prevalence of global refugee migration.
Laser welding in e-mobility: process characterization and monitoring
Authors: Angeloni Caterina , Francioso Michele , Liverani Erica , Ascari Alessandro , Fortunato Alessandro , Tomesani Luca 
Published: July 2023

AbstractThe global automotive industry is shifting to e-mobility, where the main challenge is addressed to battery’s mass-production. To keep up with the market demand, high speed production rates and quality products must be accomplished. Since laser welding of dissimilar thins sheets has earned rising demand for battery electrodes connections, a defect-free welding process has to be performed on behalf of a closed-loop monitoring system that updates corrective and/or preventive actions in order to obtain a reliable, “zero waste, zero stop” process. However, nowadays photodiode systems do not allow real-time modification of the parameters, they only tell, at the end of the process, if any signal has gone out of threshold. The objective of this paper is to find correlations between the data collected by the monitoring system with the typical process characteristics of laser welding. Materials investigated are pure copper 300 µm and aluminum 400 µm, processed by means of different sources, length tracks, wavelengths and scanning heads. In this contribution, a Precitec system has been implemented as a possible economical and industrial-oriented solution.The experimental data was analyzed offline and the relationships between technological and signals outputs were evaluated by means of statistical analysis with MATLAB for both Al-Cu and Cu-Al configuration. Findings plotted stable signals if high speeds were set. Results further suggested the power to be the most influent variable for the closed-loop monitoring system and the dependance on the first material irradiated and the laser source used to define the threshold value for the control of the welding process.
Estimating agricultural water productivity using remote sensing derived data
Authors: Safi Celine , Pareeth Sajid , Yalew Seleshi , van der Zaag Pieter , Mul Marloes 
Published: July 2023

AbstractThe 2030 Agenda aims at ending extreme poverty, inequality, injustice and climate change. Progress is evaluated through a set of Sustainable Development Goals (SDGs), targets and indicators. However, there are various challenges affecting regular and timely reporting. Remote sensing (RS) derived data has been shown to provide a valuable complementary data source in reporting SDGs. This study focuses on how RS derived data could support SDG 6 related to water, and in particular SDG indicator 6.4.1 - change in Water Use Efficiency (WUE) over time presented in USD per m3 of water withdrawn. Although water withdrawals cannot be monitored through RS, water use in agriculture, globally withdrawing the largest amount of water, can be monitored through RS based evapotranspiration.Two approaches were modelled to compute the progress of SDG 6.4.1 in the agricultural sector. The first approach uses the standard equation of SDG 6.4.1, replacing water withdrawal with blue evapotranspiration in the irrigation sector. The second approach distributes the gross value added to the gross domestic product by irrigated agriculture according to the land productivity in irrigated agriculture as observed by RS. The results of these two approaches were compared to the standard way SDG 6.4.1 is calculated. The analyses were carried out for Lebanon, which faces critical water challenges while experiencing a difficult economic and political situation.The results for Lebanon show that the different approaches to estimate Awp show similar trends as Awe, initially showing an increasing trend followed by a sharp decline in 2019 due to the deteriorating economic situation in the country. However, the absolute values differ substantially, largely due to discrepancies between the estimated irrigated area from RS data and the static data reported in AQUASTAT. The results illustrate the spatial variability of Awp in Lebanon, with the area that contributes significantly to the agricultural production nationally (Bekaa and Baalbek) shows lower land and water productivity compared to irrigated areas in other governorates. The contribution of agriculture to the overall SDG 6.4.1 indicator was relatively small, although agriculture is a major consumer of water.
A Three-Stage Prediction Method for Track Displacement During Shield Tunneling
Authors: Tan Lei , Cao Yuan , Wang Feng , Tang Tao , Wang Xi , Li Qiang 
Published: July 2023

AbstractTrack displacement is an important factor of track irregularity. Existing researches related with track displacement prediction generally ignore the influence from underground construction engineering such as shield tunneling, resulting in inaccurate estimation of track displacement. To fill this gap, we propose a three-stage framework to predict the track displacement when the shield tunnel under crosses the existing tunnel. Firstly, by considering the curved shield tunneling, a three-dimensional model is constructed to estimate the total ground displacement during the whole tunneling process. Furthermore, the soil-tunnel interaction model is established to estimate the deformation of the existing tunnel. To tackle the issue of unknown node displacements, cubic splines are used to interpolate the unknown values of tunnel displacements. On this basis, the direct stiffness method is used to estimate the track displacement and to calculate the track irregularity. Finally, the effectiveness of the proposed method is verified and the prediction performance on the track irregularity is evaluated using a real engineering case and the finite element simulation. The main contributions of this article lie in the modeling of the curved scenario for the estimation of the ground loss, as well as the combination of cubic splines and direct stiffness method, which improve the accuracy of the track displacement estimation during shield tunneling.
Data-Driven Process Analysis for Iron Foundries with Automatic Sand Molding Process
Authors: Baitiang Chinnadit , Weiß Konrad , Krüger Mathias , Volk Wolfram , Lechner Philipp 
Published: July 2023

AbstractThis paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbon equivalent and pouring temperature were found to be the critical parameters to be stabilized. Finally, a regression model for predicting and controlling of molding sand moisture and liquidus temperature of the melt was created. The determined boundaries and the models were helpful for the foundry in assisting ongoing production control and correction of process inputs to achieve target casting quality.