Evolutionary Game Dynamics for Two Interacting Populations under Environmental Feedback

We study the evolutionary dynamics of games under environmental feedback using replicator equations for two interacting populations. One key feature is to consider jointly the co-evolution of the dynamic payoff matrices and the state of the environment: the payoff matrix varies with the changing environment and at the same time, the state of the environment is affected indirectly by the changing payoff matrix through the evolving population profiles. For such co-evolutionary dynamics, we investigate whether convergence will take place, and if so, how. In particular, we identify the scenarios where oscillation offers the best predictions of long-run behavior by using reversible system theory. The obtained results are useful to describe the evolution of multi-community societies in which individuals' payoffs and societal feedback interact.Comment: 7 pages, submitted to a conferenc

Determining Singularity-Free Inner Workspace through Offline Conversion of Assembly Modes for a 3-RRR PPM

The existing singularity avoidance methods have deficiencies, such as the conditionality of the online conversion of the assembly modes (AMs) and the kinematically redundant manipulator with the predicament of the prototype design and added complexity of the mechanism. To address these issues, a method to determine a singularity-free inner workspace through offline conversion of the AMs of the 3-RRR planar parallel manipulator (PPM) is presented. Based on the geometric relations among rods of the manipulator during the occurrence of singularity, and the singular points at or near the boundary of the workspace are permitted, the AMs and ranges of the orientation angle of the moving platform corresponding to the inner singularity-free workspace are determined through a three-dimensional search method. The simulation and experimental comparisons indicate that singular-free paths related to the constant or variable orientation angle of the moving platform can be planned on the singularity-free inner workspace

Median mandibular flexure—the unique physiological phenomenon of the mandible and its clinical significance in implant restoration

Mandibular flexure, characterized by unique biomechanical behaviors such as elastic bending and torsion under functional loading, has emerged as a crucial factor in oral clinical diagnosis and treatment. This paper presents a comprehensive review of the current research status on mandibular flexure, drawing insights from relevant studies retrieved from the PubMed database (www.ncbi.nlm.nih.gov/pubmed), including research conclusions, literature reviews, case reports, and authoritative reference books. This paper thoroughly explores the physiological mechanisms underlying mandibular flexure, discussing different concurrent deformation types and the essential factors influencing this process. Moreover, it explores the profound implications of mandibular flexure on clinical aspects such as bone absorption around dental implants, the precision of prosthesis fabrication, and the selection and design of superstructure materials. Based on the empirical findings, this review provides crucial clinical recommendations. Specifically, it is recommended to exert precise control over the patients mouth opening during impression-taking. Those with a high elastic modulus or bone-tissue-like properties should be prioritized when selecting superstructure materials. Moreover, this review underscores the significance of customizing framework design to accommodate individual variations in facial morphology and occlusal habits. Future research endeavors in this field have the potential to advance clinical diagnosis and treatment approaches, providing opportunities for improvement

Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.Comment: 10 page

COVID-19 and College Teaching in China and USA

The global outbreaks of the COVID-19 significantly changed higher education in China and the United States. Universities and colleges from the two countries had to move their faceto- face classes fully online, which posed many new and significant challenges to both faculty and students. From late February 2020 (the beginning of the Chinese spring semester), all colleges and universities in China unprecedentedly moved their traditional face-to face-classes fully online. From Mid-March, the American schools had to move their face-to-face classes online. Our study focuses on one MLIS program from the United States, one MLIS program, one liberal arts program and one sci-tech program from China. We collected data over the whole Spring Semesters of these American and Chinese programs to compare the teaching and learning behaviors before and during the outbreaks. Specifically, we examined impacts of emerging technologies on LIS education and other academic programs. It will benefit the global higher education from the perspectives of Information, Technology, and Communications

The Spectral Energy Distribution of the Hyperluminous, Hot Dust-obscured Galaxy W2246-0526

Hot dust-obscured galaxies (Hot DOGs) are a luminous, dust-obscured population recently discovered in the WISE All-Sky survey. Multiwavelength follow-up observations suggest that they are mainly powered by accreting supermassive black holes (SMBHs), lying in dense environments, and being in the transition phase between extreme starburst and UV-bright quasars. Therefore, they are good candidates for studying the interplay between SMBHs, star formation, and environment. W2246-0526 (hereafter, W2246), a Hot DOG at z ∼ 4.6, has been taken as the most luminous galaxy known in the universe. Revealed by the multiwavelength images, the previous Herschel SPIRE photometry of W2246 is contaminated by a foreground galaxy (W2246f), resulting in an overestimation of its total IR luminosity by a factor of about two. We perfor m the rest-frame UV/optical-to-far-IR spectral energy distribution (SED) analysis with SED3FIT and re-estimate its physical properties. The derived stellar mass M ∗ = 4.3 7 10 11 M ⊙ makes it among the most massive galaxies with spectroscopic redshift z > 4.5. Its structure is extremely compact and requires an effective mechanism to puff-up. Most of ( > 95%) its IR luminosity is from AGN torus emission, revealing the rapid growth of the central SMBH. We also predict that W2246 may have a significant molecular gas reservoir based on the dust mass estimation

Jump-seq: Genome-Wide Capture and Amplification of 5-Hydroxymethylcytosine Sites

5-Hydroxymethylcytosine (5hmC) arises from the oxidation of 5-methylcytosine (5mC) by Fe2+ and 2-oxoglutarate-dependent 10–11 translocation (TET) family proteins. Substantial levels of 5hmC accumulate in many mammalian tissues, especially in neurons and embryonic stem cells, suggesting a potential active role for 5hmC in epigenetic regulation beyond being simply an intermediate of active DNA demethylation. 5mC and 5hmC undergo dynamic changes during embryogenesis, neurogenesis, hematopoietic development, and oncogenesis. While methods have been developed to map 5hmC, more efficient approaches to detect 5hmC at base resolution are still highly desirable. Herein, we present a new method, Jump-seq, to capture and amplify 5hmC in genomic DNA. The principle of this method is to label 5hmC by the 6-N3-glucose moiety and connect a hairpin DNA oligonucleotide carrying an alkyne group to the azide-modified 5hmC via Huisgen cycloaddition (click) chemistry. Primer extension starts from the hairpin motif to the modified 5hmC site and then continues to “land” on genomic DNA. 5hmC sites are inferred from genomic DNA sequences immediately spanning the 5-prime junction. This technology was validated, and its utility in 5hmC identification was confirmed