Miracle-dz/UniFAR-FTUs huggingface dataset:
Processus impliqués dans la structuration des peuplements de grands herbivores : le rôle des éléphants dans le fonctionnement d'une savane africaine. Modification des habitats et interférence dans l'accès à l'eau

Depuis le milieu des annees 1980, les populations de grands herbivores ont diminue dans le Parc National de Hwange, au Zimbabwe. Cette these explore l'un des scenarios avances pour expliquer de telles chutes, a savoir le role des elephants. En effet, les declins observes sont correles a une forte croissance de la population d'elephants qui a atteint l'une des plus fortes densites du contient africain. Si les elephants, veritables ingenieurs des ecosystemes, modifient la structure de la vegetation, leur presence n'a pas pour autant entraine de diminution des surfaces boisees a l'echelle du paysage. En revanche, les effets indirects causes par les elephants sur la visibilite dans les milieux, qui constitue un indice du risque de predation, semblent influencer la selection des habitats par les autres especes puisqu'elles selectionnent les sites modifies par les elephants. L'eau de surface etant une ressource rare en saison seche et les elephants etant de potentiels competiteurs cles, le role des elephants dans l'acces a l'eau des autres especes a egaiement ete etudie. Si certaines especes semblent etre poussees par les elephants a deplacer leur niche temporelle ou a consacrer plus de temps autour des points d'eau pour boire, nos resultats ne suggerent pas que ces reponses Comportementales a court terme puissent se traduire en reponses demographiques a long terme. La presente etude exclu donc les hypotheses basees sur l'effet de la modification des milieux par les elephants et sur les mecanismes d'interference aux points d'eau entre les elephants et les autres especes pour expliquer le declin des grands herbivores. Des hypotheses alternatives sont alors discutees. (Resume d'auteur)
The Heterogeneous Effects of Carbon Pricing: Macro and Micro Evidence

This paper investigates the economic effects of carbon pricing policies using a panel of countries that are members of the EU Emissions Trading System. Carbon pricing shocks lead, on average across countries, to a decline in economic activity, higher inflation, and tighter financial conditions. These average responses mask a large degree of heterogeneity: the effects are larger for higher carbon-emitting countries. To sharpen identification, we exploit granular firm-level data and document that firms with higher carbon emissions are the most responsive to carbon pricing shocks. We develop a theoretical model with green and brown firms that accounts for these empirical patterns and sheds light on the transmission mechanisms at play.
L-Homoarginine: An overview and its application in animal nutrition

L-Homoarginine (hArg) is a non-essential cationic amino acid that is synthesised from its arginine by transamination or by means of lysine catabolism involving the enzymes of ornithine transcarbamoylase (OTC), an enzyme from the urea cycle or the arginine: glycine amidinotransferase (AGAT), an enzyme from the creatine biosynthesis pathway. These enzymes are tissue-specific, hence they synthesised L-hArg in animals and human organs such as the liver, kidneys, brains
The Optimal Inflation Rate in New Keynesian Models : Should Central Banks Raise Their Inflation Targets in Light of the ZLB ?

We study the effects of positive steady-state inflation in New Keynesian models subject to the zero bound on interest rates. We derive the utility-based welfare loss function taking into account the effects of positive steady-state inflation and solve for the optimal level of inflation in the model. For plausible calibrations with costly but infrequent episodes at the zero-lower bound, the optimal inflation rate is low, less than two percent, even after considering a variety of extensions, including optimal stabilization policy, price indexation, endogenous and state-dependent price stickiness, capital formation, model-uncertainty, and downward nominal wage rigidities. On the normative side, price level targeting delivers large welfare gains and a very low optimal inflation rate consistent with price stability. These results suggest that raising the inflation target is too blunt an instrument to efficiently reduce the severe costs of zero-bound episodes.
A BOUNDARY CORRECTED EXPANSION OF THE MOMENTS OF NEAREST NEIGHBOR DISTRIBUTIONS

In this paper, the moments of nearest neighbor distance distributions are examined. While the asymptotic form of such moments is wellknown, the boundary effect has this far resisted a rigorous analysis. Our goal is to develop a new technique that allows a closed-form high order expansion, where the boundaries are taken into account up to the first order. The resulting theoretical predictions are tested via simulations and found to be much more accurate than the first order approximation obtained by neglecting the boundaries. While our results are of theoretical interest, they definitely also have important applications in statistics and physics. As a concrete example, we mention estimating Renyi entropies of probability distributions. Moreover, the algebraic technique developed may turn out to be useful in other, related problems including estimation of the Shannon differential entropy.
Physical Synthesis and Study of Ag@CaF2 Core@Shell Nanoparticles: Morphology and Tuning of Optical Properties

Pre‐formed Ag nanoparticles (NPs) and Ag@CaF2 core–shell NPs are physically synthesized using DC magnetron‐based NP source and deposited on Si‐SiOx wafers. The samples are prepared by co‐depositing Ag nanoparticles and CaF2 produced by an evaporation source, or by sequential deposition method, i.e., by depositing in a sequence a CaF2 buffer layer, the Ag NPs generated by the NP source and a capping CaF2 layer. The supported films are characterized by Scanning Electron Microscopy (SEM), X‐ray Photoelectron Spectroscopy (XPS), and Surface Differential Reflectivity (SDR). SEM shows that Ag NPs deposited directly on Si‐SiOx tend to diffuse and to agglomerate, affecting the size distribution of the nanostructures. The presence of a CaF2 buffer layer between Ag and Si‐SiOx limits this effect, while XPS reveals electrical charging, caused by the insulating nature of the CaF2 continuous film. The surface plasmon resonance behavior for different samples is analyzed using SDR with p‐polarized light. There is a clear evidence of a blue shift in the plasmon excitation due to the presence of CaF2 on Si, which can represent a potential advantage for the technological applications in photovoltaics and optoelectronics.
The Relationship between Attachment to God and Students' Psychological Well-Being

This study aims to determine the relationship between attachment to God and psychological well-being among 244 Muslim students. This study is quantitative in nature because it uses a Likert scale. Data were collected using the Short 18 item scale of psychological well-being developed by Ryff and the Muslim Spiritual Attachment Scale (M-SAS) by Yildiz. The hypothesis shows that there is a positive correlation between attachment to God and emotional stability. The results showed that there was a positive relationship between attachment to God and students' psychological well-being with a value of r = 0.325 and p = 0.000 (p <0.05). This shows that the increase in attachment to God helps to develop psychological well-being. Therefore, the hypothesis in this study is accepted.
Overview of Planned Coupled Chemistry-Climate Simulations to Support Upcoming Ozone and Climate Assessments

SPARC has established a new validation activity, CCMVal, for coupled chemistry-climate models (CCMs). The activity is based on the framework developed at the SPARC workshop on processoriented CCM validation held in Grainau, Germany in November 2003 (Eyring et al., 2004, 2005) and draws upon the experiences within the SPARC GCM-Reality Inter-comparison Project (GRIPS) (Pawson et al., 2000; GRIPS report in this issue). As more climate models include chemical components, the time has arrived for formal comparisons of these coupled chemistry-climate models. Within SPARC, this new activity will be one of the supporting “pillars” of the integrated themes.
Navigating the Geography of Regional Disparities: Market Access and the Core-Periphery Divide

This paper investigates the impact of market proximity on subnational development worldwide, considering the heterogeneous effects on core and peripheral regions, as well as on countries with different income levels. A gravity-based market potential index is revised to accurately assess distances for land and maritime trips to better capture geographic limitations. Estimations are performed in cross-section with country-fixed effects, by addressing endogeneity issues with instrumental variables. Robustness checks are also conducted with panel data on a smaller sample. The findings reveal that regions with better access to markets and port experience higher regional income per capita, with the effect being higher for wealthier regions. Peripheral regions consistently exhibit a 2 percentage point lower elasticity to market potential compared to core regions. The paper also highlights the potential negative impact of proximity to foreign markets on peripheral regions, especially if they are central to foreign core markets without free trade agreement in place between the respective countries. Results suggest that policies which aim at improving the connectivity of peripheral regions to core domestic markets and develop trade agreements could help mitigate the adverse effects of trade barriers and reduce regional disparities within countries.
Characterization of Lactobacillus Isolated from the Microflora of Different Fermentation Processes and Doderlein Flora

Lactobacillus contribute to the organoleptic quality of foods during the fermentation process in which they are involved. They also have the ability to inhibit the growth of undesirable germs by producing metabolites such as bacteriocins. The aim of this study was to isolate and identify the different Lactobacillus species from various fermentation processes and from the Doderlein flora. Lactobacillus were isolated from raw cow milk, curdled milk, cassava ferment (Magnan) and Doderlein flora on MRS agar. Strains were identified by mass spectrometry (MALDI-TOF). 213 Lactobacillus strains were isolated and identified from the different matrices. The main species isolated from raw cow's milk and curdled milk were Lactobacillus plantarum (31.58%), Lactobacillus casei (26.31%), Lactococcus lactis (21.05%), Lactobacillus delbrueckii spp bulgaricus (31%), Lactobacillus helveticus (30%) and Lactobacillus fermentum (25%) respectively. In the cassava ferment, Lactobacillus plantarum (37.5%), Lactobacillus rhamnosus (25%) and Lactobacillus reuteri (25%) were the most isolated species. The main species of Doderlein flora were Lactobacillus acidophilus (50%) and Lactobacillus plantarum (25%). This study showed a diversity of Lactobacillus present and involved in the different fermentation processes of foods and Doderlein flora (Commensal flora of the vagina). The qualitative and quantitative proportions of the different Lactobacillus species depend on the matrix. These Lactobacillus can potentially produce substances (bacteriocins) that inhibit the growth of pathogenic bacteria and multi-resistant bacteria.
Visualization approaches for communicating real‐time flood forecasting level and inundation information

The January 2005 flood event in the Eden catchment (UK) has focused considerable research effort towards strengthening and extending operational flood forecasting in the region. The Eden catchment has become a key study site within the remit of phase two of the Flood Risk Management Research Consortium. This paper presents a synthesis of results incorporating model uncertainty analysis, computationally efficient real‐time data assimilation/forecasting algorithms, two‐dimensional (2D) inundation modelling, and data visualization for decision support. The emphasis here is on methods of presenting information from a new generation of probabilistic flood forecasting models. Using Environment Agency rain and river‐level gauge data, a data‐based mechanistic model is identified and incorporated into a modified Kalman Filter (KF) data assimilation algorithm designed for real‐time flood forecasting applications. The KF process generates forecasts within a probabilistic framework. A simulation of the 6‐h ahead forecast for river levels at Sheepmount (Carlisle) covering the January 2005 flood event is presented together with methods of visualizing the associated uncertainty. These methods are then coupled to the 2D hydrodynamic LISFLOOD‐FP model to produce real‐time flood inundation maps. The value of incorporating probabilistic information is emphasized.
Development and Characterization of Electrospun Composites Built on Polycaprolactone and Cerium-Containing Phases

The current study reports on the fabrication of composite scaffolds based on polycaprolactone (PCL) and cerium (Ce)-containing powders, followed by their characterization from compositional, structural, morphological, optical and biological points of view. First, CeO2, Ce-doped calcium phosphates and Ce-substituted bioglass were synthesized by wet-chemistry methods (precipitation/coprecipitation and sol-gel) and subsequently loaded on PCL fibres processed by electrospinning. The powders were proven to be nanometric or micrometric, while the investigation of their phase composition showed that Ce was present as a dopant within the crystal lattice of the obtained calcium phosphates or as crystalline domains inside the glassy matrix. The best bioactivity was attained in the case of Ce-containing bioglass, while the most pronounced antibacterial effect was visible for Ce-doped calcium phosphates calcined at a lower temperature. The scaffolds were composed of either dimensionally homogeneous fibres or mixtures of fibres with a wide size distribution and beads of different shapes. In most cases, the increase in polymer concentration in the precursor solution ensured the achievement of more ordered fibre mats. The immersion in SBF for 28 days triggered an incipient degradation of PCL, evidenced mostly through cracks and gaps. In terms of biological properties, the composite scaffolds displayed a very good biocompatibility when tested with human osteoblast cells, with a superior response for the samples consisting of the polymer and Ce-doped calcium phosphates.
Antigen rapid tests, nasopharyngeal PCR and saliva PCR to detect SARS-CoV-2: A prospective comparative clinical trial

Background Nasopharyngeal antigen Rapid Diagnostic Tests (RDTs), saliva RT-PCR and nasopharyngeal (NP) RT-PCR have shown different performance characteristics to detect patients infected by SARS-CoV-2, according to the viral load (VL)—and thus transmissibility. Methods In October 2020, we conducted a prospective trial involving patients presenting at testing centres with symptoms of COVID-19. We compared detection rates and performance of RDT, saliva PCR and nasopharyngeal (NP) PCR, according to VL and symptoms duration. Results Out of 949 patients enrolled, 928 patients had all three tests performed. Detection rates were 35.2% (95%CI 32.2–38.4%) by RDT, 39.8% (36.6–43.0%) by saliva PCR, 40.1% (36.9–43.3%) by NP PCR, and 41.5% (38.3–44.7%) by any test. For those with viral loads (VL) ≥106 copies/ml, detection rates were 30.3% (27.3–33.3), 31.4% (28.4–34.5), 31.5% (28.5–34.6), and 31.6% (28.6–34.7%) respectively. Sensitivity of RDT compared to NP PCR was 87.4% (83.6–90.6%) for all positive patients, 94.5% (91.5–96.7%) for those with VL≥105 and 96.5% (93.6–98.3%) for those with VL≥106. Sensitivity of STANDARD-Q®, Panbio™ and COVID-VIRO® Ag tests were 92.9% (86.4–96.9%), 86.1% (78.6–91.7%) and 84.1% (76.9–89.7%), respectively. For those with VL≥106, sensitivity was 96.6% (90.5–99.3%), 97.8% (92.1–99.7%) and 95.3% (89.4–98.5%) respectively. No patient with VL<104 was detected by RDT. Specificity of RDT was 100% (99.3–100%) compared to any PCR. RDT sensitivity was similar <4 days (87.8%, 83.5–91.3%) and ≥4 days (85.7%, 75.9–92.6%) after symptoms onset (p = 0.6). Sensitivity of saliva and NP PCR were 95.7% (93.1–97.5%) and 96.5% (94.1–98.1%), respectively, compared to the other PCR. Conclusions RDT results allow rapid identification of COVID cases with immediate isolation of most contagious individuals. RDT can thus be a game changer both in ambulatory care and community testing aimed at stopping transmission chains, and even more so in resource-constrained settings thanks to its very low price. When PCR is performed, saliva could replace NP swabbing. Trial registration ClinicalTrial.gov Identifier: NCT04613310 (03/11/2020).
Lithium electrodeposition for energy storage: filling the gap between theory and experiment

Lithium (Li) metal has been considered a promising anode material for high-energy-density rechargeable batteries, but its utilization is impeded by the nonuniform electrodeposition during the charging process which leads to poor cycling life and safety concerns. Thus, understanding the electrodeposition mechanism of Li-metal anode is of great importance to develop practical engineering strategies for rechargeable Li-metal batteries. The electrodeposition of Li is controlled by both thermodynamic and kinetic factors, such as the solvation free energy of Li-ions, the Li nucleation, the surface diffusion of Li atom, and the strength of the interaction between Li-ion and the electrolyte anion. The scale of the whole process from the Li-ion reduction to the growth of a Li nucleus goes from sub-nanometer up to a few micrometers, which poses an outstanding challenge to both experiments and simulation. In this perspective, we discuss the top-down, the bottom-up, and the middle-way approaches to this challenge and the possible synergies between them. © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
One Step In Situ Co-Crystallization of Dapsone and Polyethylene Glycols during Fluidized Bed Granulation

Several studies have demonstrated the feasibility of in situ co-crystallization in different pharmaceutical processes such as spray drying, hot melt extrusion, and fluidized bed granulation (FBG) to produce co-crystal-in-excipient formulations. However, no previous studies have examined such a one step in situ co-crystallization process for co-crystal formulations where the coformer is a polymer. In the current study, we explored the use of FBG to produce co-crystal granules of dapsone (DAP) and different molecular weight polyethylene glycols (PEGs). Solvent evaporation (SE) was proven to generate DAP-PEGs co-crystals at a particular weight ratio of 55:45 w/w between DAP and PEG, which was subsequently used in FBG, using microcrystalline cellulose and hydroxypropyl methyl cellulose as filler excipient and binder, respectively. FBG could generate co-crystals with higher purity than SE. Granules containing DAP-PEG 400 co-crystal could be prepared without any additional binder. DAP-PEG co-crystal granules produced by FBG demonstrated superior pharmaceutical properties, including flow properties and tableting properties, compared to DAP and DAP-PEG co-crystals prepared by SE. Overall, in situ co-crystallization via FBG can effectively produce API-polymer co-crystals and enhance the pharmaceutical properties.
Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWAS) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N=52) and nonsmokers (N=171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and using a two-stage multiple testing approach with eigenMT and Bonferroni corrections. We found >2 million significant meQTLs (padj<0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects; five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTLs for 958 unique eGenes (padj<0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTIN1 and ITIH4 colocalized across all data types (GWAS + meQTL + eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.
Decision-Change Informed Rejection Improves Robustness in Pattern Recognition-Based Myoelectric Control

Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, these techniques have largely been tested individually and on well-behaved, stationary data, failing to fully evaluate their trade-offs between smoothing and latency during dynamic use. Correspondingly, in this work, we survey and compare 8 different post-processing and decision stream improvement schemes in the context of continuous and dynamic class transitions: majority vote, Bayesian fusion, onset locking, outlier detection, confidence-based rejection, confidence scaling, prior adjustment, and adaptive windowing. We then propose two new temporally aware post-processing schemes that use changes in the decision and confidence streams to better reject uncertain decisions. Our decision-change informed rejection (DCIR) approach outperforms existing schemes during both steady-state and transitions based on error rates and decision stream volatility whether using conventional or deep classifiers. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.
What does ChatGPT know about natural science and engineering?

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to have a large impact on society, research, and education. An essential step to understand ChatGPT's expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions from 198 faculty members across 5 faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are on average perceived as ``mostly correct''. Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the complexity level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude.