Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension

In this paper, we develop a model to plan energy-efficient speed trajectories of electric trucks in real-time by taking into account the information of topography and traffic ahead of the vehicle. In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. We then formulate an energy minimization problem and solve it by an alternating direction method of multipliers (ADMM) method that exploits the structure of the problem. A model predictive control framework is then employed to deal with topographic and traffic uncertainties in real-time. An empirical study is conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The experimental results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life extended by as high as 35.35% compared to benchmarking solutions

Multi-microjoule GaSe-based mid-infrared optical parametric amplifier with an ultra-broad idler spectrum covering 4.2-16 {\mu}m

We report a multi-microjoule, ultra-broadband mid-infrared optical parametric amplifier based on a GaSe nonlinear crystal pumped at ~2 {\mu}m. The generated idler pulse has a flat spectrum spanning from 4.5 to 13.3 {\mu}m at -3 dB and 4.2 to 16 {\mu}m in the full spectral range, with a central wavelength of 8.8 {\mu}m. The proposed scheme supports a sub-cycle Fourier-transform-limited pulse width. A (2+1)-dimensional numerical simulation is employed to reproduce the obtained idler spectrum. To our best knowledge, this is the broadest -3 dB spectrum ever obtained by optical parametric amplifiers in this spectral region. The idler pulse energy is ~3.4 {\mu}J with a conversion efficiency of ~2% from the ~2 {\mu}m pump to the idler pulse.Comment: 5 pages, 5 figure

ROBUST OPTIMIZATION OF STOCHASTIC HYBRID JOB-SHOP SCHEDULING WITH MULTIPROCESSOR TASK

Due to the large number of uncertainties in the production workshop, the actual performance of the scheduling scheme deviated significantly from the theoretical value. In order to enhance its anti-jamming capability, this paper developed the robust optimization of stochastic hybrid job-shop scheduling with multiprocessors tasks. Firstly, predictable uncertainties were abstracted into processing time variations and described by scenario analysis in the modeling process. Secondly, based on the analysis of the advantages and disadvantages of traditional robust optimization models, a new Expected Cmax and the Worst scenario Model (ECWM) was proposed. The model improved the single-index robust optimization model and avoided the disadvantage that the Max Regret Model is computationally intensive. Finally, the effectiveness of ECWM is verified by simulation experiments. The results show that the scheduling obtained by ECWM has good average performance and anti-risk ability, which indicates that the model achieves a good balance in scheduling performance enthusiasm and risk resistance

Joint Transceiver Optimization for Two-Way MIMO Relay Systems with MSE Constraints

Transceiver design for two-way multiple-input multiple-output (MIMO) relay systems has attracted much research interest recently. However, there is little research on the impact of quality-of-service (QoS) constraints on two-way MIMO relay systems, which greatly affects the user experience. In this letter, we propose a transceiver design for two-way MIMO relay systems which minimizes the total network transmission power subjecting to QoS constraints expressed as upper-bounds on the mean-squared error (MSE) of the signal waveform estimation at both destinations. An iterative algorithm is developed to optimize the source, relay, and receive matrices. Simulation results demonstrate the fast convergence of the proposed algorithm

DXVNet-ViT-Huge (JFT) Multimode Classification Network Based on Vision Transformer

Aiming at the problem that traditional CNN network is not good at extracting global features of images, Based on DXVNet network, Conditional Random Fields (CRF) component and pre-trained ViT-Huge (Vision Transformer) are adopted in this paper Transformer model expands and builds a brand new DXVNet-ViT-Huge (JFT) network. CRF component can help the network learn the constraint conditions of each word corresponding prediction label, improve the D-GRU method based word label prediction errors, and improve the accuracy of sequence annotation. The Transformer architecture of the ViT (Huge) model can extract the global feature information of the image, while CNN is better at extracting the local features of the image. Therefore, the ViT (Huge) Huge pre-training model and CNN pre-training model adopt the multi-modal feature fusion technology. Two complementary image feature information is fused by Bi-GRU to improve the performance of network classification. The experimental results show that the newly constructed Dxvnet-Vit-Huge (JFT) model achieves good performance, and the F1 values in the two real public data sets are 6.03% and 7.11% higher than the original DXVNet model, respectively

The efficacy and acceptability of pharmacological monotherapies and e-cigarette on smoking cessation: a systemic review and network meta-analysis

Background and aimsSeveral pharmacological interventions, such as nicotine replacement therapy (NRT), varenicline, and bupropion, have been approved for clinical use of smoking cessation. E-cigarettes (EC) are increasingly explored by many RCTs for their potentiality in smoking cessation. In addition, some RCTs are attempting to explore new drugs for smoking cessation, such as cytisine. This network meta-analysis (NMA) aims to investigate how these drugs and e-cigarettes compare regarding their efficacy and acceptability.Materials and methodsThis systematic review and NMA searched all clinical studies on smoking cessation using pharmacological monotherapies or e-cigarettes published from January 2011 to May 2022 using MEDLINE, COCHRANE Library, and PsychINFO databases. NRTs were divided into transdermal (TDN) and oronasal nicotine (ONN) by administrative routes, thus 7 network nodes were set up for direct and indirect comparison. Two different indicators measured the efficacy: prevalent and continuous smoking abstinence. The drop-out rates measured the acceptability.ResultsThe final 40 clinical studies included in this study comprised 77 study cohorts and 25,889 participants. Varenicline is more effective intervention to assist in smoking cessation during 16–32 weeks follow-up, and is very likely to prompt dropout. Cytisine shows more effectiveness in continuous smoking cessation but may also lead to dropout. E-cigarettes and oronasal nicotine are more effective than no treatment in encouraging prevalent abstinence, but least likely to prompt dropout. Finally, transdermal nicotine delivery is more effective than no treatment in continuous abstinence, with neither significant effect on prevalent abstinence nor dropout rate.ConclusionThis review suggested and agreed that Varenicline, Cytisine and transdermal nicotine delivery, as smoking cessation intervention, have advantages and disadvantages. However, we had to have reservations about e-cigarettes as a way to quit smoking in adolescents

The Ras Superfamily of Small GTPases in Non-neoplastic Cerebral Diseases

The small GTPases from the Ras superfamily play crucial roles in basic cellular processes during practically the entire process of neurodevelopment, including neurogenesis, differentiation, gene expression, membrane and protein traffic, vesicular trafficking, and synaptic plasticity. Small GTPases are key signal transducing enzymes that link extracellular cues to the neuronal responses required for the construction of neuronal networks, as well as for synaptic function and plasticity. Different subfamilies of small GTPases have been linked to a number of non-neoplastic cerebral diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), intellectual disability, epilepsy, drug addiction, Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS) and a large number of idiopathic cerebral diseases. Here, we attempted to make a clearer illustration of the relationship between Ras superfamily GTPases and non-neoplastic cerebral diseases, as well as their roles in the neural system. In future studies, potential treatments for non-neoplastic cerebral diseases which are based on small GTPase related signaling pathways should be explored further. In this paper, we review all the available literature in support of this possibility

Bifunctional biomass-derived N, S dual-doped ladder-like porous carbon for supercapacitor and oxygen reduction reaction

In recent years, heteroatom-doped biomass-derived carbon has attracted intensive attention in vast fields due to their inexpensive precursors and abundant resources, especially in oxygen reduction reaction and supercapacitors. This research demonstrates a simple strategy to prepare mulberry leaves-derived nitrogen, sulfur dual-doped ladder-like porous carbon material, which possesses high content of nitrogen (8.17 at %), sulfur (1.97 at %), large surface area (1689 m g) and porous structure with a mass of micropores and mesopores. With respect to electrode material of supercapacitor, the nitrogen, sulfur dual-doped ladder-like carbon exhibits large specific capacitance of 243.4 F g at 0.1 A g and outstanding durability (94% retention after 5000 cycles at 3 A g). Moreover, in comparison to Pt/C catalyst, nitrogen, sulfur dual-doped ladder-like porous carbon presents excellent electrochemical performances of long term stability (90.2% retention after 20000 s) and resistance to methanol crossover for oxygen reduction reaction. This work successfully may provide a new case to take advantage of nature materials to fabricate heteroatom-doped carbon for energy conversion and storage