Decoding trust: A reinforcement learning perspective

Behavioral experiments on the trust game have shown that trust and trustworthiness are universal among human beings, contradicting the prediction by assuming \emph{Homo economicus} in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations however need to resort to some factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals update their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q-learning algorithm, where each participant is associated with two evolving Q-tables that guide one's decision making as trustor and trustee respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q-tables shows a crossover that resembles human's psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness without external factors involved. More importantly, the proposed paradigm shows the potential in deciphering many puzzles in human behaviors.Comment: 12 pages, 11 figures. Comments are appreciate

Hyperspectral and Multispectral Image Fusion using Cluster-based Multi-branch BP Neural Networks

Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains

Discussion on Advanced Seepage Reduction Characteristics of Working Face under Seepage-Damage Coupling

The water burst of roof on working face has been one of the significant geotechnical engineering problems that needs to be urgently resolved. The coupling effects of seepage and damage on the amount and intensity of water inrush from the roof are critically important. In this paper, the seepage-damage coupling mathematical model of the aquifer in the working face is studied, and the seepage-damage coupling mechanics model at different stages of the aquifer is established. Under the coupling of permeability and damage, the water-soil characteristics of the aquifer in the 101163 working face of Mindong were numerically simulated by establishing the constitutive relation between vertical stress and permeability coefficient. The numerical results show that the stress concentration factor of the mining stress field gradually increases with the coal seam mining. The water-flowing fractured zone of the overburden is close to the communication of the quaternary aquifer. When the coal seam is excavated 250–300 m. Three free surfaces appear in the groundwater pressure field, and a large falling funnel is formed to establish a deep flow S-well well flow model. The research on the mining stress field and seepage field is carried out in combination with the Jakob formula. It is found that two sectors with reduced permeability of the fan surface are formed in front of the work. The variation law of the apocalyptic permeability infiltration under different mining distances, different coal seam thicknesses, different water pressures, and different roof management modes is studied systematically. The research indicates that the seepage flow under the condition of seepage infiltration of the lower aquifer should be between 50% and 100% of the traditional calculation method. The research results can help to deepen the understanding of the process of water inrush under the coupling of stress and seepage

Simultaneous Denitrification and Carbon Removal in Microbial Fuel Cells

In this article, microbial fuel cell (MFC) was used for simultaneous denitrification and carbon removal to ascertain their electricity generation performance. The results showed that strengthening domestication and enrichment of electrogenic bacteria had the best start-up effect. An increase in volumetric loading reduced the rate of pollutant removal but promoted the output voltage. The changes of working conditions such as influent concentration, sludge concentration and temperature had a great influence on the electricity generation performance of MFC, and their optimum values were 500 mg/L, 2 000 mg/L and 35℃, respectively

Comparison study of plasma oxynitrocarburising and QPQ for 45 steel

QPQ (quench-polish-quench) is recognized as an effective surface modification technology which can improve the corrosion and wear resistance of metal materials. However, it is environmentally constrained in real application. In order to explore an environmental friendly and efficient surface modification technology, PNCO (plasma oxynitrocarburising) was developed and compared with QPQ technology for 45 steel. The cross-sectional microstructure, phase composition, microhardness profile, wear and corrosion resistance were tested and analyzed by optical microscopy, SEM, XRD, microhardness tester and wear tester etc. The results show that a compound layer thickness of 20.14 μm and effective hardened layer thickness of 59 μm, surface hardness of 760HV0.05, wear rate of 1.39×10-3 g·N-1·m-1 and corrosion mass loss rate of 0.39% were obtained by PNCO. The XRD results demonstrate that PNCO treated samples mainly contain FexN compound and Fe3O4 oxide. A comparative study between PNCO and QPQ shows that the sectional microhardness, wear and corrosion resistance of the treating layers are the same level. The study provides a feasible research area for environmentally efficient surface modification technology

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs

Effects and Mechanisms of Chinese Herbal Medicine in Ameliorating Myocardial Ischemia-Reperfusion Injury

Myocardial ischemia-reperfusion (MIR) injury is a major contributor to the morbidity and mortality associated with coronary artery disease, which accounts for approximately 450,000 deaths a year in the United States alone. Chinese herbal medicine, especially combined herbal formulations, has been widely used in traditional Chinese medicine for the treatment of myocardial infarction for hundreds of years. While the efficacy of Chinese herbal medicine is well documented, the underlying molecular mechanisms remain elusive. In this review, we highlight recent studies which are focused on elucidating the cellular and molecular mechanisms using extracted compounds, single herbs, or herbal formulations in experimental settings. These studies represent recent efforts to bridge the gap between the enigma of ancient Chinese herbal medicine and the concepts of modern cell and molecular biology in the treatment of myocardial infarction

A New Method for Predicting the Permeability of Sandstone in Deep Reservoirs

Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value