Voluntary Carbon Market Participation and Unintended Consequences: An Economic Analysis

Agricultural activities account for nearly a quarter of anthropogenic greenhouse gas (GHG) emissions mainly from deforestation and livestock, soil and nutrient management. Also it is the biggest emitter of non-carbon dioxide GHGs. Meanwhile farmers typically face more than one production possibility and they typically produce varying amounts of net GHG emissions at different costs. Therefore GHG emission reductions may be achieved by providing incentives for farmers to adopt alternative production activities. Intuitively, total GHG emissions will decrease after adopting lower emitting practices. However certain incentive designs might lead to GHG net emission increases or lower than expected reductions, hence unintended consequences. Here, two major forms of carbon market program are investigated for their effects on net GHG emissions and the conditions under which the unintended consequences occur are examined analytically. This model shows for net emitters the program design can lead to increased emissions – the rebound effect. While for negative emitters (those sequestering or offsetting emissions through bioenergy), the program results in trivial emission reductions. We also find that it is desirable to alter program design to limit participation to baseline levels for those who emit and to encourage participation well beyond baseline levels for those who generate negative emissions

Network Representation Learning Guided by Partial Community Structure

Network Representation Learning (NRL) is an effective way to analyze large scale networks (graphs). In general, it maps network nodes, edges, subgraphs, etc. onto independent vectors in a low dimension space, thus facilitating network analysis tasks. As community structure is one of the most prominent mesoscopic structure properties of real networks, it is necessary to preserve community structure of networks during NRL. In this paper, the concept of k-step partial community structure is defined and two Partial Community structure Guided Network Embedding (PCGNE) methods, based on two popular NRL algorithms (DeepWalk and node2vec respectively), for node representation learning are proposed. The idea behind this is that it is easier and more cost-effective to find a higher quality 1-step partial community structure than a higher quality whole community structure for networks; the extracted partial community information is then used to guide random walks in DeepWalk or node2vec. As a result, the learned node representations could preserve community structure property of networks more effectively. The two proposed algorithms and six state-of-the-art NRL algorithms were examined through multi-label classification and (inner community) link prediction on eight synthesized networks: one where community structure property could be controlled, and one real world network. The results suggested that the two PCGNE methods could improve the performance of their own based algorithm significantly and were competitive for node representation learning. Especially, comparing against used baseline algorithms, PCGNE methods could capture overlapping community structure much better, and thus could achieve better performance for multi-label classification on networks that have more overlapping nodes and/or larger overlapping memberships

Why cuckoos remove host eggs: Biting eggs facilitates faster parasitic egg‐laying

Brood parasitism by cuckoos relies on manipulating hosts to raise their offspring and has evolved stunning adaptations to aid in their deception. The fact that cuckoos usually but not always, remove one or two host eggs while laying their eggs has been a longstanding focus of intensive research. However, the benefit of this behavior remains elusive. Moreover, the recently proposed help delivery hypothesis, predicting that egg removal by cuckoos may decrease the egg‐laying duration in the parasitism process caused by biting action, lacks experimental verification. Therefore, in this study, we examined the effects of egg removal/biting on the egg‐laying speed in the common cuckoo (Cuculus canorus) to experimentally test this hypothesis. We compared the duration of cuckoo egg‐laying in empty nests, nests with host eggs, and nests with artificial blue stick models to test whether cuckoos biting an egg/stick can significantly hasten the egg‐laying speed than no biting action. Our results showed that biting an egg or an object is associated with cuckoos laying approximately 37% faster than when they do not bite an egg or an object. This study provides the first experimental evidence for the help delivery hypothesis and demonstrates that when cuckoos bite eggs or other objects in the nest, they lay eggs more quickly and thereby avoid suffering the hosts' injurious attack

Basic properties and exploitation strategies of source rock strata

Source rock strata are filled and aggregated with large-scale continuous hydrocarbon resources, including significant volumes of in-place retained, short-distance migrated and potentially generated hydrocarbons. Source rock strata simultaneously possess the properties of reservoirs and hydrocarbon source rocks, known as source-reservoir coexisting systems. Reservoir properties refer to the physical properties concerning the storage and transmission of oil and gas, while hydrocarbon source rock properties refer to the physicochemical properties related to governing the generation, retention and expulsion of oil and gas in the source rock strata. These properties fundamentally determine the technical path for the successful exploitation of petroleum and natural gas in the source rock strata. With regard to reservoir properties, in-depth research and development of the advanced energy-storing fracturing technology can aid the construction of complex fracture networks to overcome the limitations in the connectivity properties of source rock strata. Focusing on the hydrocarbon source rock properties, an underground in-situ conversion technology should be created and developed to alleviate the shortcomings of organic matter quantity and maturity properties of the source rock strata. Furthermore, selecting the appropriate exploitation path based on the property characteristics can promote the achievement of commercial and sustainable development of oil and gas in the source rock strata.Document Type: PerspectiveCited as: Yang, Z., Zou, C., Fan, Y., Wu, S., Liu, H., Wei, Q. Basic properties and exploitation strategies of source rock strata. Advances in Geo-Energy Research, 2023, 10(2): 77-83. https://doi.org/10.46690/ager.2023.11.0

Amelioration of Experimental Acute Pancreatitis with Dachengqi Decoction via Regulation of Necrosis-Apoptosis Switch in the Pancreatic Acinar Cell

Severity of acute pancreatitis contributes to the modality of cell death. Pervious studies have demonstrated that the herb medicine formula “Dachengqi Decoction” (DCQD) could ameliorate the severity of acute pancreatitis. However, the biological mechanisms governing its action of most remain unclear. The role of apoptosis/necrosis switch within acute pancreatitis has attracted much interest, because the induction of apoptosis within injured cells might suppress inflammation and ameliorate the disease. In this study, we used cerulein (10−8 M)-stimulated AR42J cells as an in vitro model of acute pancreatitis and retrograde perfusion into the biliopancreatic duct of 3.5% sodium taurocholate as an in vivo rat model. After the treatment of DCQD, cell viability, levels of apoptosis and necrosis, reactive oxygen species positive cells, serum amylase, concentration of nitric oxide and inducible nitric oxide syntheses, pancreatic tissue pathological score and inflammatory cell infiltration were tested. Pretreatment with DCQD increased cell viability, induced apoptosis, decreased necrosis and reduced the severity of pancreatitis tissue. Moreover, treatment with DCQD reduced the generation of reactive oxygen species in AR42J cells but increased the concentration of nitric oxide of pancreatitis tissues. Therefore, the regulation of apoptosis/necrosis switch by DCQD might contribute to ameliorating the pancreatic inflammation and pathological damage. Further, the different effect on reactive oxygen species and nitric oxide may play an important role in DCQD-regulated apoptosis/necrosis switch in acute pancreatitis

A parallel self-organizing community detection algorithm based on swarm intelligence for large scale complex networks

Community detection is a critical task for complex network analysis. It helps us to understand the properties of the system that a complex network represents and has significance to a wide range of applications. Nowadays, the challenges faced by community detection algorithms include overlapping community structure detection, large scale network analysis, dynamic changing of analyzed network topology and many more. In this paper a self-organizing community detection algorithm, based on the idea of swarm intelligence, was proposed and its parallel algorithm was designed on Giraph++ which is a semi-asynchronous parallel graph computation framework running on distributed environment. In the algorithm, a network of large size is firstly divided into a number of small sub-networks. Then, each sub-network is modeled as a self-evolving swarm intelligence sub-system, while each vertex within the sub-network acts iteratively to join into or leave from communities based on a set of predefined vertex action rules. Meanwhile, the local communities of a sub-network are sent to other sub-networks to make their members have a chance to join into, therefore connecting these self-evolving swarm intelligence sub-systems together as a whole, large and evolving, system. The vertex actions during evolution of a sub-network are sent as well to keep multiple community replicas being consistent. Thus network communication efficiency has a great impact on the algorithm’s performance. While there is no vertex changing in its belonging communities anymore, an optimal community structure of the whole network will have emerged as a result. In the algorithm it is natural that a vertex can join into multiple communities simultaneously, thus can be used for overlapping community detection. The algorithm deals with vertex and edge adding or deleting in the same way as the algorithm running, therefore inherently supports dynamic network analysis. The algorithm can be used for the analysis of large scale networks with its parallel version running on distributed environment. A variety of experiments conducted on synthesized networks have shown that the proposed algorithm can effectively detect community structures and its performance is much better than certain popular community detection algorithms

Charm Quarks Are More Hydrodynamic Than Light Quarks in Final-State Elliptic Flow

We study the charm quark elliptic flow ($v_{2}$) in heavy ion as well as small system collisions by tracking the evolution history of quarks of different flavors within a multi-phase transport model. The charm quark $v_{2}$ is studied as a function of the number of collisions the charm quark suffers with other quarks and then compared to the $v_{2}$ of lighter quarks. We find that the common escape mechanism is at work for both the charm and light quark $v_{2}$. However, contrary to the naive expectation, the hydrodynamics-type flow is found to contribute more to the final state charm $v_{2}$ than light quark $v_{2}$. This could be explained by the smaller average deflection angle the heavier charm quark undergoes in each collision, so that heavy quarks need more scatterings to accumulate a significant $v_{2}$, while lighter quarks can more easily change directions with scatterings with their $v_{2}$ coming more from the escape mechanism. Our finding thus suggests that the charm $v_{2}$ is a better probe for studying the hydrodynamic properties of the quark-gluon plasma.Comment: 10 pages, 4 figure

Reinforcement learning-based mapless navigation with fail-safe localisation

Mapless navigation is the capability of a robot to navigate without knowing the map. Previous works assume the availability of accurate self-localisation, which is, however, usually unrealistic. In our work, we deploy simultaneous localisation and mapping (SLAM)-based self-localisation for mapless navigation. SLAM performance is prone to the quality of perceived features of the surroundings. This work presents a Reinforcement Learning (RL)-based mapless navigation algorithm, aiming to improve the robustness of robot localisation by encouraging the robot to learn to be aware of the quality of its surrounding features and avoid feature-poor environment, where localisation is less reliable. Particle filter (PF) is deployed for pose estimation in our work, although, in principle, any localisation algorithm should work with this framework. The aim of the work is two-fold: to train a robot to learn 1) to avoid collisions and also 2) to identify paths that optimise PF-based localisation, such that the robot will be unlikely to fail to localise itself, hence fail-safe SLAM. A simulation environment is tested in this work with different maps and randomised training conditions. The trained policy has demonstrated superior performance compared with standard mapless navigation without this optimised policy