Asymptotic lower bounds for Gallai-Ramsey functions and numbers

For two graphs $G,H$ and a positive integer $k$, the \emph{Gallai-Ramsey number} ${\rm gr}_k(G,H)$ is defined as the minimum number of vertices $n$ such that any $k$-edge-coloring of $K_n$ contains either a rainbow (all different colored) copy of $G$ or a monochromatic copy of $H$. If $G$ and $H$ are both complete graphs, then we call it \emph{Gallai-Ramsey function} ${\rm GR}_k(s,t)$, which is the minimum number of vertices $n$ such that any $k$-edge-coloring of $K_n$ contains either a rainbow copy of $K_s$ or a monochromatic copy of $K_t$. In this paper, we derive some lower bounds for Gallai-Ramsey functions and numbers by Lov\'{o}sz Local Lemma.Comment: 11 page

Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping

With the progress of the urbanisation process, the urban transportation system is extremely critical to the development of cities and the quality of life of the citizens. Among them, it is one of the most important tasks to judge traffic congestion by analysing the congestion factors. Recently, various traditional and machine-learning-based models have been introduced for predicting traffic congestion. However, these models are either poorly aggregated for massive congestion factors or fail to make accurate predictions for every precise location in large-scale space. To alleviate these problems, a novel end-to-end framework based on convolutional neural networks is proposed in this paper. With learning representations, the framework proposes a novel multimodal fusion module and a novel representation mapping module to achieve traffic congestion predictions on arbitrary query locations on a large-scale map, combined with various global reference information. The proposed framework achieves significant results and efficient inference on real-world large-scale datasets

Soft Actor-Critic Learning-Based Joint Computing, Pushing, and Caching Framework in MEC Networks

To support future 6G mobile applications, the mobile edge computing (MEC) network needs to be jointly optimized for computing, pushing, and caching to reduce transmission load and computation cost. To achieve this, we propose a framework based on deep reinforcement learning that enables the dynamic orchestration of these three activities for the MEC network. The framework can implicitly predict user future requests using deep networks and push or cache the appropriate content to enhance performance. To address the curse of dimensionality resulting from considering three activities collectively, we adopt the soft actor-critic reinforcement learning in continuous space and design the action quantization and correction specifically to fit the discrete optimization problem. We conduct simulations in a single-user single-server MEC network setting and demonstrate that the proposed framework effectively decreases both transmission load and computing cost under various configurations of cache size and tolerable service delay

Experimental Research on Mechanical Properties of Apple Peels

Knowledge of the mechanical properties of apple peel, as the outermost tissue of the fruit, is crucial for the designing of apple harvesting machines. In this study, longitudinal and transverse tensile tests were conducted on peels from the shadow side and sunlit side of two apple cultivars (Starkrimson and Fuji) using an electronic universal testing machine, and tear tests and puncture tests were carried out on peels of both sides as well. The stress-strain curves and tear and puncture force-deformation curves of the peels were acquired and the tensile strength, elastic modulus, failure strain tear strength, puncture strength of the peels were measured. Also, scanning electron microscope images were made. The results showed that the maximum values of tensile strength, elastic modulus, fracture strain, tear strength, and puncture strength were 2.56 MPa, 24.00 MPa, 19.92%, 0.391 kN·m-1, and 0.289 N·mm-2, respectively. The tensile strength, elastic modulus, and puncture strength values for the Starkrimson peels were higher than those for the Fuji peels from the same side. Apple peel is an anisotropic heterogeneous material. The bearing capacity of the peel depends on the number and distribution of microcracks on the surface, and the size and shape of the epidermal cells. The organization and connections between the cells determine the strength of the connections between cells

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

Background: Long-distance ocean voyages may have substantial impacts on seamen’s health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. Methods: We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. Results: Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen’s health. Conclusions: The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen’s health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships

Abnormal Default Mode Network Homogeneity in Treatment-Naive Patients With First-Episode Depression

Background and Objective: The default mode network (DMN) may be an important component involved in the broad-scale cognitive problems seen in patients with first-episode treatment-naive depression. Nevertheless, information is scarce regarding the changes in network homogeneity (NH) found in the DMN of these patients. Therefore, in this study, we explored the NH of the DMN in patients with first-episode treatment-naive depression.Methods: The study included 66 patients and 74 control participants matched by age, gender, educational level and health status who underwent resting-state functional magnetic resonance imaging (rs-fMRI) and the attentional network test (ANT). To assess data, the study utilizes NH and independent component analysis (ICA). Additionally, Spearman's rank correlation analysis is performed among significantly abnormal NH in depression patients and clinical measurements and executive control reaction time (ECRT).Results: In comparison with the control group, patients with first-episode treatment-naive depression showed lower NH in the bilateral angular gyrus (AG), as well as increased NH in the bilateral precuneus (PCu) and posterior cingulate cortex (PCC). Likewise, patients with first-episode treatment-naive depression had longer ECRT. No significant relation was found between abnormal NH values and the measured clinical variables.Conclusions: Our results suggest patients with first-episode treatment-naive depression have abnormal NH values in the DMN. This highlights the significance of DMN in the pathophysiology of cognitive problems in depression. Our study also found alterations in executive functions in patients with first-episode treatment-naive depression

A reversible single-molecule switch based on activated antiaromaticity

Single-molecule electronic devices provide researchers with an unprecedented ability to relate novel physical phenomena to molecular chemical structures. Typically, conjugated aromatic molecular backbones are relied upon to create electronic devices, where the aromaticity of the building blocks is used to enhance conductivity. We capitalize on the classical physical organic chemistry concept of Hückel antiaromaticity by demonstrating a single-molecule switch that exhibits low conductance in the neutral state and, upon electrochemical oxidation, reversibly switches to an antiaromatic high-conducting structure. We form single-molecule devices using the scanning tunneling microscope–based break-junction technique and observe an on/off ratio of ~70 for a thiophenylidene derivative that switches to an antiaromatic state with 6-4-6-π electrons. Through supporting nuclear magnetic resonance measurements, we show that the doubly oxidized core has antiaromatic character and we use density functional theory calculations to rationalize the origin of the high-conductance state for the oxidized single-molecule junction. Together, our work demonstrates how the concept of antiaromaticity can be exploited to create single-molecule devices that are highly conducting

Observation of room-temperature ferroelectricity in elemental Te nanowires

Ferroelectrics are essential in low-dimensional memory devices for multi-bit storage and high-density integration. A polar structure is a necessary premise for ferroelectricity, mainly existing in compounds. However, it is usually rare in elemental materials, causing a lack of spontaneous electric polarization. Here, we report an unexpected room-temperature ferroelectricity in few-chain Te nanowires. Out-of-plane ferroelectric loops and domain reversal are observed by piezoresponse force microscopy. Through density functional theory, we attribute the ferroelectricity to the ion-displacement created by the interlayer interaction between lone pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, supporting a self-gated field-effect transistor. It enables a nonvolatile memory with high in-plane mobility, zero supply voltage, multilevel resistive states, and a high on/off ratio. Our work provides new opportunities for elemental ferroelectrics with polar structures and paves a way towards applications such as low-power dissipation electronics and computing-in-memory devices

Two-Dimensional Platinum Telluride with Ordered Te Vacancy Superlattice for Efficient and Robust Hydrogen Evolution

Defect engineering to activate the basal planes of transition metal dichalcogenides (TMDs) is critical for the development of TMD-based electrocatalysts as the chemical inertness of basal planes restrict their potential applications in hydrogen evolution reaction (HER). Here, we report the synthesis and evaluation of few-layer (7x7)-PtTe2-x with an ordered, well-defined and high-density Te vacancy superlattice. Compared with pristine PtTe2, (2x2)-PtTe2-x and Pt(111), (7x7)-PtTe2-x exhibits superior HER activities in both acidic and alkaline electrolytes due to its rich structures of undercoordinated Pt sites. Furthermore, the (7x7)-PtTe2-x sample features outstanding catalytic stability even compared to the state-of-the-art Pt/C catalyst. Theoretical calculations reveal that the interactions between various undercoordinated Pt sites due to proximity effect can provide superior undercoordinated Pt sites for hydrogen adsorption and water dissociation. This work will enrich the understanding of the relationship between defect structures and electrocatalytic activities and provide a promising route to develop efficient Pt-based TMD electrocatalysts