A Randomized Controlled Trial of the Effectiveness of Brief-CBT for Patients with Symptoms of Posttraumatic Stress Following a Motor Vehicle Crash

published_or_final_versio

Multi-Objective Optimization for Spectrum and Energy Efficiency Tradeoff in IRS-Assisted CRNs with NOMA

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the S-procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS

Spectrum and Energy Efficiency Tradeoff in IRS-Assisted CRNs with NOMA: A Multi-Objective Optimization Framework

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NO-MA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated. An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes

Accurate Spectrum Map Construction Using An Intelligent Frequency-Spatial Reasoning Approach

Spectrum map is of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, the existing spectrum map construction schemes mainly depend on spatial interpolation and cannot construct the spectrum map when the measurement data of the target frequency are not obtained. In order to overcome this challenge, an accurate spectrum map construction scheme is proposed by using an intelligent frequency-spatial reasoning approach. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct the highly accurate spectrum maps of the frequencies without spectrum data. A novel autoencoder adapting to the three-dimensional (3D) spectrum data is proposed. Simulation results demonstrate that our proposed scheme is superior to the benchmark schemes in terms of the construction accuracy. Moreover, it is shown that our proposed autoencoder network has a fast convergence speed

Myocardial fiber length mapping with MR diffusion tensor imaging

Diffusion tensor MRI is emerging as a rapid, nondestructive method to map myocardial fiber organization. A precise biological description of myocardial fiber performance requires knowledge of four variables: length, force, velocity and time. However, study of quantification of myocardial fiber length is lacking. The current study aims to show myocardial fiber length maps of formalin-fixed heats. Diffusion tensor MRI with medium diffusion resolution (15 directions) was performed in one isolated pig heart. Fiber length maps were investigated in multiple short-axis slices. The results provide supplementary information of myocardial fiber organization. To our knowledge, the present study is the first report of the myocardial fiber length mapping. © 2005 IEEE.published_or_final_versio

Learning to Construct Nested Polar Codes: An Attention-Based Set-to-Element Model

As capacity-achieving codes under successive cancellation (SC) decoding, nested polar codes have been adopted in 5G enhanced mobile broadband. To optimize the performance of the code construction under practical decoding, e.g. SC list (SCL) decoding, artificial intelligence based methods have been explored in the literature. However, the structure of nested polar codes has not been fully exploited for code construction. To address this issue, this letter transforms the original combinatorial optimization problem for the construction of nested polar codes into a policy optimization problem for sequential decision, and proposes an attention-based set-to-element model, which incorporates the nested structure into the policy design. Based on the proposed architecture for the policy, a gradient based algorithm for code construction and a divide-and-conquer strategy for parallel implementation are further developed. Simulation results demonstrate that the proposed construction outperforms the state-of-the-art nested polar codes for SCL decoding

Core outcome set for diabetes after pregnancy prevention across the life span: international Delphi study

Introduction: Mothers with gestational diabetes mellitus (GDM) are at high risk of future diabetes. An active area of research examines health behavior change strategies in women within 5 years of a GDM pregnancy to prevent diabetes after pregnancy. We aimed to develop a core outcome set (COS) to facilitate synthesis and comparison across trials. Research: design and methods Candidate outcomes were identified through systematic review and scored for importance (1–9) by healthcare professionals, researchers, and women with prior GDM through an international two-round electronic-Delphi survey. Outcomes retained required round two scores above prespecified thresholds (≥70% scoring 7–9) or expert panel endorsement when scores were indeterminate. The panel organized the COS by domain. Results: 115 stakeholders participated in the survey and 56 completed both rounds. SD of scores decreased by 0.24 (95%CI 0.21 to 0.27) by round 2, signaling convergence. The final COS includes 19 domains (50 outcomes): diabetes (n=3 outcomes), other related diseases (n=3), complications in subsequent pregnancy (n=2), offspring outcomes (n=3), adiposity (n=4), cardiometabolic measures (n=5), glycemia (n=3), physical activity (n=2), diet (n=4), breast feeding (n=2), behavior change theory (n=5), diabetes-related knowledge (n=2), health literacy (n=1), social support (n=1), sleep (n=1), quality of life (n=1), program delivery (n=4), health economic evaluation (n=2), and diabetes risk screening (n=2). The seven outcomes endorsed by ≥90% were diabetes development and GDM recurrence, attending the postpartum diabetes screening and completing oral glucose tolerance testing and/or other glycemia measures, weight and total energy intake, and health behaviors in general. Among the 15 at the 80%–90% endorsement level, approximately half were specific elements related to the top 7, while the remainder related to diabetes knowledge, personal risk perception, motivation for change, program element completion, and health service use and cost. Conclusion: Researchers should collect and report outcomes from the breadth of domains in the COS

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

The study of a single injection of intravitreal triamcinolone acetonide in silicone oil filled eye using MRI

Poster Presentations - Session 3: anstract no. PS3-4postprin

Joint Location and Channel Error Optimization for Beamforming Design for Multi-RIS Assisted MIMO System

Reconfigurable intelligent surface (RIS) has been proved to be a promising approach to enhance the performance of wireless communication because of its intelligently reconfiguring the passive reflecting elements. Previous works only consider the beamforming design for a fixed RIS location/deployment and perfect channel state information (CSI). While RIS's location and perfect CSI can be further optimized to enhance the system performance. In this paper, the robust beamforming design is investigated for multi-RIS assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization (WSM) problem is formulated. The considered WSM maximization problem includes channel estimation error, bandwidth as well as RIS placement variables, which results in a complicated nonconvex optimization problem. To handle this problem, we decouple the original problem into a series of subproblems, where the location, bandwidth, transmit beamforming and passive beamforming are optimized iteratively. Then, we develop an alternating optimization algorithm based on the penalty and gradient projection (GP) methods to alleviate the performance loss caused by the effect of imperfect CSI. Simulations validate that the proposed scheme can bring significant performance gains, especially considering its high spectral efficiency, when designing the location of RIS and imperfect CSI