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Despite the promise conservation paleobiology holds for using geohistorical data and insights to solve conservation problems, training in the field typically does not equip students to be competent environmental problem solvers. The intention of this perspective piece is to start a conversation about how we might train conservation paleobiology students better, focusing on the competencies needed to promote deep engagement with “wicked” conservation problems that are difficult to solve. Ongoing conversations regarding design of academic programs in sustainability, a field allied with conservation science, can inform our discussion. The sustainability literature has defined an interrelated set of “core competencies” that go beyond general academic competencies to enable real-world sustainability problem solving: systems thinking, temporal thinking, normative thinking, strategic thinking, and interpersonal competence. Conservation paleobiology is usually taught within geology programs, where students are exposed to systems thinking and temporal thinking. However, the remaining competencies typically are absent or insufficiently developed. To infuse these competencies into conservation paleobiology curricula, we recommend: (1) enhancing connections with sustainability programs and encouraging a more cross-disciplinary approach to training; (2) developing a “menu” of concepts and methodologies for each competence from which to choose; and (3) recognizing that different skills are appropriate at different levels of education and experience. The proposed competency-based framework serves as a shared reference that can be used to develop pedagogies to better prepare conservation paleobiology students to navigate the wicked conservation challenges of our time.

Publisher Copyright: IEEE With the ever-escalating scale of urban distribution networks (UDNs), the traditional model-based reconfiguration methods are becoming inadequate for smart system control. On the contrary, the data-driven deep reinforcement learning method can facilitate the swift decision-making but the large action space would adversely affect the learning performance of its agents. Consequently, this paper presents a novel multi-agent deep reinforcement learning method for the reconfiguration of UDNs by introducing the concept of 'switch contribution'. First, a quantification method is proposed based on the mathematical UDN reconfiguration model. The contributions of controllable switches are effective quantified. By excluding the controllable switches with low contributions during network reconfiguration, the dimensionality of action space can be significantly reduced. Then, an improved QMIX algorithm is introduced to improve the policy of multiple agents by assigning the weights. Besides, a novel two-stage learning structure based on a reward-sharing mechanism is presented to further decompose tasks and enhance the learning efficiency of multiple agents. In the first stage, agents control the switches with higher contributions while switches with lower contributions will be controlled in the second stage. During the two-stage process, the proposed reward-sharing mechanism could guarantee a reliable UND reconfiguration and the convergence of our learning method. Finally, numerical results based on a practical 297-node system are performed to validate our method's effectiveness. Peer reviewed

Abstract The proper understanding and description of the spatial distributions of volatile species is important to the circulating fluidized bed (CFB) reactor design and modeling. Aiming at this issue, a mathematical model has been developed in the present work, which constitutes of two main modules that are validated by experiments, one-dimensional fluid dynamic model and single fuel particle devolatilization model. The simulation of a 135 MWe CFB boiler was conducted. Results show that all volatile species exhibit in general a vertical release profile of bimodal shape (coal inlet area and bottom dense bed) while with quantitative differences, which is mainly attributed to the wide size range of feeding fuel. Compared to the influence of bed temperature, the effect of feeding coal size on the volatile nitrogen release is more obvious. This model can be applied as the devolatilization sub-model for the integral combustion simulation of a CFB boiler.

The data is arranged into 2 folders: 1. SEM folder, which contains the raw SEM images of the surface morphology of the investigated material and the results of transmission Kikuchi diffraction (TKD). 2. TEM folder, which contains the raw TEM images acquired on the cross-section of the investigated material in as-deposited and He-irradiated conditions. One may also find the selected area diffraction (SAED) patterns and the EDS elemental maps.

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. ; © 2023 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/). ; fi=vertaisarvioitu|en=peerReviewed|

Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome-environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances.

Abstract The co-gasification characteristics of petroleum coke (PC), hydrochar (PS), and their blends with different ratios were studied by using thermogravimetric analysis. The Coats–Redfern model was employed to calculate the gasification activation energies of different samples. The results manifested that the gasification process of PS and blends could be classified into two stages: pyrolysis and char gasification, but for PC, there was only one primary char gasification stage. The activation energy of the pyrolysis stage was significantly smaller than the char gasification stage. In the latter stage, with the increase in the ratio of PS from 20% to 80%, the activation energy was reduced from 114.1 kJ/mol to 82.8 kJ/mol, which indicated that the PS had a significant promoting influence on the PC gasification. The research results can provide a theoretical guiding significance for the efficient use of PS and PC.