Efficient D2D Discovery Scheme for Channel Measurement of Interference Alignment

Device-to-device (D2D) communications have the potential to improve spectrum efficiency and link capacity by allowing nearby devices to communicate directly with each other on the licensed frequency bands. Hence, co-channel interference (CCI) among D2D users are major issues to be solved when they are utilizing the same frequency bands. Consequently, interference alignment (IA) as an effective interference management approach has been implemented to the D2D communications for the frequency sharing. However, the measurement of channel state information (CSI) between transmitter and receiver of a D2D pair and cross-channel state information (C-CSI) among D2D pairs are the major issues that need to be resolved for the implementation of IA from theory to practical. Therefore, in this paper, we propose an effective D2D discovery scheme to overcome this problem, which can measure the CSI and C-CSI based on the transmission of discovery messages. Simulation results show that, under perfect conditions, even though the proposed D2D discovery scheme increases the needed time slots to establish D2D communications compared with the conventional D2D discovery without considering IA by 6.2%, it increases the throughput up to 50% than the conventional D2D communications without considering IA, i.e., it improves the spectrum efficiency.

Experimental study on vibration behavior of rotating manipulator in the process of scramming

In order to study the vibration behavior of rotating manipulator in the process of scramming, an experimental test is conducted on the vibration behavior during the process on different initial and measuring conditions. Through the experimental test, the vibration behavior exists two impact phenomena, which are found in the scramming process, and the feature extraction of the two impact phenomena is investigated. The dynamics parameters of the rotating manipulator are identified and hammer experiment is done to verify the dynamics parameters. With these parameters, an envelope model of the second impact response is established, and the applicability of the model is validated by experiments. The method of pasting damping layer on the surface of manipulator is employed to inhibit the vibration which is caused by the first impact. Via the experimental verification, the damping layer takes a certain effect on the vibration elimination

The Cognitive Load of Observation Tasks in 3D Video is Lower Than That in 2D Video

We are exposed to more and more 3D videos, some for entertainment and some for scientific research. Some experiments using 3D video as a stimulus focus only on its visual effect. We studied the cognitive difference between 3D and 2D videos by analyzing EEG. This research adopts a 2 x 4 experimental design, including 2D and 3D versions of 4 video scenes. These four video scenes can be classified into two simple task scenes and two complex task scenes. The simple task scenario and the complex task scenario each contain a video with violent content changes and a calm video. Subjects need to watch eight videos. We recorded the EEG information of the subjects and analyzed the power of alpha and theta oscillations. On this basis, we calculated the cognitive load index (CLI), which can be used as an indicator of cognitive load. The results showed that 3D videos that required subjects to perform simple tasks brought higher cognitive load to most subjects. When the video contains complex tasks, the cognitive load of subjects does not show similar regularity. Specifically, only half of the people had higher cognitive load when watching the 3D version of the video than when watching the 2D version. In addition, the cognitive load level of subjects showed significant individual differencesComment: 7 pages, 18 figure

A Link Transmission Model with Variable Speed Limits and Turn-Level Queue Transmission at Signalized Intersections

The link transmission model (LTM) is an efficient and widely used macro-level approach for simulating traffic flow. However, the state-of-the-art LTMs usually focused on segment-level modelling, in which the traffic operation differences among multiple turning directions are neglected. Such models are incapable of differentiating the turn-level queue transmission, resulting in underrepresented queue spillbacks and misidentification of bottlenecks. Moreover, a constant free-flow speed is usually assumed to formulate LTMs, restricting their applications to model dynamic traffic management strategies involving variable speed limits (VSL) and connected and automated vehicles. This study proposed an extended LTM with VSL and turn-level queue transmission to capture the traffic flow propagation at signalized intersections. First, each road segment (RS) with multiple turning directions is divided into many free-flow and queueing parts characterized by the triangular fundamental diagrams. Then, the vehicle propagation within the link is described by the turn-level link inflow, queue inflow, and outflow, which depends on the free-flow speed changes. A node model involving an iterative procedure is further defined to capture the potential queue spillback, which estimates the actual flow propagation between the adjacent RSs. Simulated and field data were used to verify the proposed model performance. Results reveal that the proposed LTM predict traffic operations of complex intersections with multiple turning movements, VSL and signal control schemes, and enables an accurate yet computationally tractable representation of flow propagation

Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit

In recent years, medical information technology has made it possible for electronic health record (EHR) to store fairly complete clinical data. This has brought health care into the era of "big data". However, medical data are often sparse and strongly correlated, which means that medical problems cannot be solved effectively. With the rapid development of deep learning in recent years, it has provided opportunities for the use of big data in healthcare. In this paper, we propose a temporal-saptial correlation attention network (TSCAN) to handle some clinical characteristic prediction problems, such as predicting death, predicting length of stay, detecting physiologic decline, and classifying phenotypes. Based on the design of the attention mechanism model, our approach can effectively remove irrelevant items in clinical data and irrelevant nodes in time according to different tasks, so as to obtain more accurate prediction results. Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options. Our experiments use information from the Medical Information Mart for Intensive Care (MIMIC-IV) database, which is open to the public. Finally, we have achieved significant performance benefits of 2.0\% (metric) compared to other SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate, 45.1\% on length of stay. The source code can be find: \url{https://github.com/yuyuheintju/TSCAN}

Diversity-Oriented Synthesis for Novel, Selective and Drug-like Inhibitors for a Phosphatase from Mycobacterium Tuberculosis

Mycobacterium protein tyrosine phosphatase B (mPTPB) is a potential drug target of Tuberculosis (TB). Small molecule inhibitors of mPTPB could be a treatment to overcome emerging TB drug resistance. Using a Diversity-Oriented Synthesis (DOS) strategy, we successfully developed a salicylic acid based and drug-like mPTPB inhibitor with an IC50 of 2 μM and >20-fold specificity over many human PTPs, making it an excellent lead molecule for anti-TB drug discovery. In addition, DOS generated bicyclic salicylic acids are also promising starting points for acquiring inhibitors targeting other PTPs

Novel anticancer agents based on targeting the trimer interface of the PRL phosphatase

PRL oncoproteins are phosphatases overexpressed in numerous types of human cancer. Elevated levels of PRL associate with metastasis and poor clinical outcomes. In principle, PRL phosphatases offer appealing therapeutic targets, but they remain underexplored due to the lack of specific chemical probes. In this study, we address this issue by exploiting a unique property of PRL phosphatases, namely, that they may function as homotrimers. Starting from a sequential structure-based virtual screening and medicinal chemistry strategy, we identified Cmpd-43 and several analogs which disrupt PRL1 trimerization. Biochemical and structural analyses demonstrate that Cmpd-43 and its close analogs directly bind the PRL1 trimer interface and obstruct PRL1 trimerization. Cmpd-43 also specifically blocks the PRL1-induced cell proliferation and migration through attenuation of both ERK1/2 and Akt activity. Importantly, Cmpd-43 exerted potent anticancer activity both in vitro and in vivo in a murine xenograft model of melanoma. Our results validate a trimerization-dependent signaling mechanism for PRL and offer proof-of-concept for trimerization inhibitors as candidate therapeutics to treat PRL-driven cancer