Expansion des laitiers d’aciérie : à l’ouest, il y a du nouveau !

Contrairement aux laitiers de hauts fourneaux qui sont volumétriquement stables et faciles à utiliser en construction routière notamment, les laitiers d’aciérie contiennent de la chaux libre non hydratée en proportion variable qui peut occasionner leur expansion. Ainsi, afin d’envisager une exploitation maîtrisée de ces laitiers, il est indispensable d’en évaluer chaque type, selon son origine et les traitements qu’il a subi, afin de déterminer son instabilité volumétrique et son potentiel d’expansion. Or aujourd’hui, en l’absence de critères quantifiés permettant d’orienter les laitiers d’acierie vers des usages appropriés, ceux-ci demeurent peu utilisés. Les Etats-Unis ont sur ce point une longueur d’avance. Cet article présente les résultats d’études sur l’expansion volumétrique des laitiers d’acierie visant à élaborer des critères susceptibles de servir d’indicateurs pour l’utilisation de ces laitiers en tant que matériaux granulaires

A Reliable and Efficient Encounter-Based Routing Framework for Delay/Disruption Tolerant Networks

This article addresses Delay/Disruption Tolerant Networking (DTN) routing under a highly dynamic scenario, envisioned for communication in Vehicular Sensor Networks (VSNs) suffering from intermittent connection. Here, we focus on the design of a high level routing framework, rather than the dedicated encounter prediction. Based on an analyzed utility metric to predict nodal encounter, our proposed routing framework considers the following three cases: 1) Messages are efficiently replicated to a better qualified candidate node, based on the analysed utility metric related to destination. 2) Messages are conditionally replicated if the node with a better utility metric has not been met. 3) Messages are probabilistically replicated if the information in relation to destination is unavailable in the worst case. With this framework in mind, we propose two routing schemes covering two major technique branches in literature, namely Encounter-Based Replication Routing (EBRR) and Encounter-Based Spraying Routing (EBSR). Results under the scenario applicable to VSNs show that, in addition to achieving high delivery ratio for reliability, our schemes are more efficient in terms of a lower overhead ratio. Our core investigation indicates that apart from what information to use for encounter prediction, how to deliver messages based on the given utility metric is also important

Separation and Identification of HSP-Associated Protein Complexes from Pancreatic Cancer Cell Lines Using 2D CN/SDS-PAGE Coupled with Mass Spectrometry

Protein complexes are a cornerstone of many biological processes and together they form various types of molecular machinery. A broad understanding of these protein complexes is crucial for revealing and building models of protein function and regulation. Pancreatic cancer is a highly lethal disease which is difficult to diagnose at early stage and even more difficult to cure. In this study, we applied a gradient clear native gel system combined with subsequent second-dimensional SDS-PAGE to separate protein complexes from cell lysates of SW1990 and PANC-1 pancreatic cancer cell lines with different degrees of differentiation. Ten heat-shock-protein- (HSP-) associated protein complexes were separated and identified, and the differentially expressed proteins related to cancers were also found, such as HSP60, protein disulfide-isomerase A4 (ERp72), and transitional endoplasmic reticulum ATPase (TER ATPase)

Elevated CO2 and Warming Altered Grassland Microbial Communities in Soil Top-Layers.

As two central issues of global climate change, the continuous increase of both atmospheric CO2 concentrations and global temperature has profound effects on various terrestrial ecosystems. Microbial communities play pivotal roles in these ecosystems by responding to environmental changes through regulation of soil biogeochemical processes. However, little is known about the effect of elevated CO2 (eCO2) and global warming on soil microbial communities, especially in semiarid zones. We used a functional gene array (GeoChip 3.0) to measure the functional gene composition, structure, and metabolic potential of soil microbial communities under warming, eCO2, and eCO2 + warming conditions in a semiarid grassland. The results showed that the composition and structure of microbial communities was dramatically altered by multiple climate factors, including elevated CO2 and increased temperature. Key functional genes, those involved in carbon (C) degradation and fixation, methane metabolism, nitrogen (N) fixation, denitrification and N mineralization, were all stimulated under eCO2, while those genes involved in denitrification and ammonification were inhibited under warming alone. The interaction effects of eCO2 and warming on soil functional processes were similar to eCO2 alone, whereas some genes involved in recalcitrant C degradation showed no significant changes. In addition, canonical correspondence analysis and Mantel test results suggested that NO3-N and moisture significantly correlated with variations in microbial functional genes. Overall, this study revealed the possible feedback of soil microbial communities to multiple climate change factors by the suppression of N cycling under warming, and enhancement of C and N cycling processes under either eCO2 alone or in interaction with warming. These findings may enhance our understanding of semiarid grassland ecosystem responses to integrated factors of global climate change

STUDY ON VIBRATION RESPONSE OF A NON-UNIFORM BEAM WITH NONLINEAR BOUNDARY CONDITION

Forced vibration of non-uniform beam with nonlinear boundary condition is studied in this paper by proposing an iterative model combining Adomian Decomposition Method and modal analysis. An exponentially tapered beam with a hardening nonlinearity spring boundary is simulated as a case study. The model accuracy is proved by comparing iteration results and analysis solutions with linear and weakly nonlinear boundary conditions. Sin-weep nonlinear frequency spectrum is then obtained by the proposed model. The influence of boundary nonlinearity on the vibration response of non-uniform beam is analyzed. And the effect of different excitation amplitudes on nonlinearity in the vibration response is studied. The mathematical model and numerical solutions proposed in this paper can be used to solve and analysis broad vibration problems on general non-uniform beams with different nonlinear boundary conditionsunder various excitations

Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks

Recently, graph neural networks (GNNs) have shown its unprecedented success in many graph-related tasks. However, GNNs face the label scarcity issue as other neural networks do. Thus, recent efforts try to pre-train GNNs on a large-scale unlabeled graph and adapt the knowledge from the unlabeled graph to the target downstream task. The adaptation is generally achieved by fine-tuning the pre-trained GNNs with a limited number of labeled data. Despite the importance of fine-tuning, current GNNs pre-training works often ignore designing a good fine-tuning strategy to better leverage transferred knowledge and improve the performance on downstream tasks. Only few works start to investigate a better fine-tuning strategy for pre-trained GNNs. But their designs either have strong assumptions or overlook the data-aware issue for various downstream datasets. Therefore, we aim to design a better fine-tuning strategy for pre-trained GNNs to improve the model performance in this paper. Given a pre-trained GNN, we propose to search to fine-tune pre-trained graph neural networks for graph-level tasks (S2PGNN), which adaptively design a suitable fine-tuning framework for the given labeled data on the downstream task. To ensure the improvement brought by searching fine-tuning strategy, we carefully summarize a proper search space of fine-tuning framework that is suitable for GNNs. The empirical studies show that S2PGNN can be implemented on the top of 10 famous pre-trained GNNs and consistently improve their performance. Besides, S2PGNN achieves better performance than existing fine-tuning strategies within and outside the GNN area. Our code is publicly available at \url{https://anonymous.4open.science/r/code_icde2024-A9CB/}