Net Ecosystem Production (NEP) of the Great Plains, United States

Gross primary production (GPP) and ecosystem respiration (Re) are the fundamental environmental characteristics that promote carbon exchanges with the atmosphere (Chapin and others, 2009), although other exchanges of carbon, such as direct oxidation (Lovett and others, 2006), can modify net ecosystem production (NEP). The accumulation of carbon in terrestrial ecosystems results in systems in which soil organic matter (SOM) carbon often exceeds biomass carbon (Post and Kwon, 2000). This SOM pool exists at a steady state between GPP and Re in ecosystems unless drivers change or the ecosystem endures environmental perturbations (for example, climatic). As indicated by Wilhelm and others (2011), conversion of grasslands to agriculture and cultivation can result in reduced soil carbon, with the release of carbon dioxide (CO2 ) to the atmosphere by stimulated oxidation and higher Re; therefore, land-use and land management practices have clear effects on NEP, with potential repercussions on ecosystems. The recent demand for biofuels has changed land-use and cropping patterns, especially in Midwestern United States (Wilhelm and others, 2011). It is important to ensure the sustainability of these and other land uses and to assess the effects on NE

Monte Carlo localization algorithm based on particle swarm optimization

In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node\u27s maximum speed of 70 m/s

Evaluation of Carbon Fluxes and Trends (2000e2008) in the Greater Platte River Basin: A Sustainability Study for Potential Biofuel Feedstock Development

This study evaluates the carbon fluxes and trends and examines the environmental sustainability (e.g., carbon budget, source or sink) of the potential biofuel feedstock sites identified in the Greater Platte River Basin (GPRB). A 9-year (2000e2008) time series of net ecosystem production (NEP), a measure of net carbon absorption or emission by ecosystems, was used to assess the historical trends and budgets of carbon flux for grasslands in the GPRB. The spatially averaged annual NEP (ANEP) for grassland areas that are possibly suitable for biofuel expansion (productive grasslands) was 71e169 g C m2 year1 during 2000e2008, indicating a carbon sink (more carbon is absorbed than released) in these areas. The spatially averaged ANEP for areas not suitable for biofuel feedstock development (less productive or degraded grasslands) was 47 to 69 g C m2 year1 during 2000e2008, showing a weak carbon source or a weak carbon sink (carbon emitted is nearly equal to carbon absorbed). The 9-year pre-harvest cumulative ANEP was 1166 g C m2 for the suitable areas (a strong carbon sink) and 200 g C m2 for the non-suitable areas (a weak carbon sink). Results demonstrate and confirm that our method of dynamic modeling of ecosystem performance can successfully identify areas desirable and sustainable for future biofuel feedstock development. This study provides useful information for land managers and decision makers to make optimal land use decisions regarding biofuel feedstock development and sustainability

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI

The mechanism of atopic march may be the ‘social’ event of cells and molecules (Review)

The skin, the conjunctivae, the airways and the digestive tract compose a huge vulnerable biological surface, which is exposed to the external environment. An allergen can often trigger an allergic reaction at a number of sites or result in an atopic march. However, the mechanism of atopic march remains unclear. Less attention has been paid to the connection between the primary site and the atopic site, because current knowledge is established directly against harmful factors. Allergic hypersensitivity manifests in parts of the human body far away from the allergen. Growing evidence suggests that the epithelial cells serve as the 'engine' which initiates an allergic reaction through the production of large quantities of cytokines, chemokines and growth factors. Because the epithelial cells cover the entire surface of the skin, the conjunctivae, the airways, and the digestive tract, and are positioned at the terminals of neurons and the blood supply, the connection between the primary site and the atopic site can not be easily understood by the current knowledge of anatomy and of the neuroendocrine immune network. What is the linkage between these huge vulnerable biologic surfaces? This article highlights selected frontiers in allergy research of atopic march, and focuses on recently attained insights into the cellular and molecular events of primary and atopic lesions in the allergy progress. Special attention is paid to the homogeneity of the cellular and molecular events on the huge vulnerable surface. Based on currently available data we conclude that the skin, conjunctivae, airways and digestive tract may join together to form the frontier 'commonwealth union' in order to fight the allergen. The epithelial cells are the 'engine' as well as the main target which initiates both primary and atopic inflammatory reactions. The atopic lesion may 'duplicate' the primary contacted site of cellular and molecular events. The atopic march may be due to the intrinsic 'social' involvements of the positioned epithelial cells, but may not be totally controlled by the anatomic connection or the circulating systemic factors involved in allergy pathogenesis

Automating Collision Attacks on RIPEMD-160

As an ISO/IEC standard, the hash function RIPEMD-160 has been used to generate the Bitcoin address with SHA-256. However, due to the complex doublebranch structure of RIPEMD-160, the best collision attack only reaches 36 out of 80 steps of RIPEMD-160, and the best semi-free-start (SFS) collision attack only reaches 40 steps. To improve the 36-step collision attack proposed at EUROCRYPT 2023, we explored the possibility of using different message differences to increase the number of attacked steps, and we finally identified one choice allowing a 40-step collision attack. To find the corresponding 40-step differential characteristic, we re-implement the MILP-based method to search for signed differential characteristics with SAT/SMT. As a result, we can find a colliding message pair for 40-step RIPEMD-160 in practical time, which significantly improves the best collision attack on RIPEMD-160. For the best SFS collision attack published at ToSC 2019, we observe that the bottleneck is the probability of the right-branch differential characteristics as they are fully uncontrolled in the message modification. To address this issue, we utilize our SAT/SMT-based tool to search for high-probability differential characteristics for the right branch. Consequently, we can mount successful SFS collision attacks on 41, 42 and 43 steps of RIPEMD-160, thus significantly improving the SFS collision attacks. In addition, we also searched for a 44-step differential characteristic, but the differential probability is too low to allow a meaningful SFS collision attack

Entertainment apps, limited attention and investment performance

With the advent of the “information age,” investors are now faced with the challenges of the “mobile age,” which has had a profound impact on the daily lives of people worldwide. Investors must process more information while experiencing increasing mobile phone-related distractions, particularly those generated by the fast-growing entertainment-type app industry. Attention is a limited cognitive resource that is vital for deliberate and thoughtful analysis. We analyzed data from an online peer-to-peer lending market to evaluate the impact of mobile distractions on investment performance. Our findings revealed that investors with a large number of mobile phone entertainment apps were more likely to exhibit higher default rates and lower investment returns. The results are robust, even when using exogenous internet service outage of the entertainment server and instrumental variables. We observed that the negative impact of distraction was more pronounced on Fridays and in regions with high-speed Internet access. A further examination of the mechanisms underlying this phenomenon revealed that investment decisions made while being distracted by mobile apps were influenced by information neglect and familiarity biases