GA-LNS Optimization for Helicopter Rescue Dispatch

Aviation emergency rescue has become one of the most effective means for natural disaster relief due to its flexible and timely characteristics. A reasonable emergency dispatch plan can guarantee the effective implementation of all the rescue measures. Most of previous studies in this area focused on the scheduling and routing but ignored the impact of the specific rescue process, for example the fuel consumption of various helicopters. In this paper, a multi-helicopter-multi-trip Aviation Rescue Routing Problem (ARRP) is analysed which covers the whole rescue process. In addition, a time-domain procedural simulation model is built which can consider different helicopters, refueling or not, various resource locations, multiple disaster sites and other operation factors. Based on that, a Genetic Algorithm (GA) hybridized Large Neighborhood Search (LNS) algorithm (GA-LNS) is proposed for optimization. In ARRP, single search algorithm may lead to the local optimum due to complexity. In contrast, the distance greedy strategy and the load ratio strategy are combined in GA-LNS which can fix the local optimum problem. More specifically, based on the helicopter-tagged-task-sequenced chromosome, the single-point crossover operator is used in GA and then, the worst removal strategy and the first/last insertion strategy are adopted in LNS. Finally, the numerical experiments are exercised to verify the effectiveness of the proposed GA-LNS algorithm which is compared with three traditional basic heuristic algorithms and a stateof-the-art memetic algorithm.</p

Improved reinforcement learning-based real-time energy scheduling for prosumer with elastic loads in smart grid

In this study, we investigate the dynamic energy-scheduling problem of a prosumer (producer/consumer) with an energy storage device (EDS) and elastic loads. The goal is to develop efficient real-time scheduling strategies for prosumers, and to minimise their total long-term costs (i.e. cost of energy purchased from the external grid and depreciation cost of the EDS). The challenges are twofold: the uncertainty of the energy output of the prosumer and the time-coupling constraints of the EDS and elastic loads. To address these challenges, we first describe the dynamic energy-scheduling problem as a Markov decision process. Then, an approximate state dual-agent Q-learning algorithm is proposed to solve the optimal dynamic scheduling problem by improving the model-free reinforcement learning(RL) method. Compared with the traditional RL method, the proposed algorithm reduces the system-state dimensions and exhibits improved performance. The proposed algorithm can only be assisted by mutual interactions between the environment with a reward feedback mechanism to dynamically respond to uncertain changes in the environments, without modelling or predicting the system environments. Finally, extensive empirical evaluations using real-world traces are conducted to study the effectiveness of the proposed algorithm. The results show that the proposed algorithm can reduce the total cost of the prosumer by up to 6.3%, 11.7% and 22.4% compared with the traditional RL method, Lyapunov optimisation and greedy algorithm, respectively

Residual complexity dose impact organic chemistry and drug discovery: The case of Rufomyazine and Rufomycin

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miR21 modulates the Hippo signaling pathway via interference with PP2A Bβ to inhibit trophoblast invasion and cause preeclampsia

Preeclampsia (PE) is a pregnancy-specific disorder attributed to deficient extravillous trophoblast (EVT) invasion into the uterus, but the mechanism of EVT invasion remains unclear. In this study, we found significantly elevated expression of microRNA 21 (miR21), which negatively regulates trophoblast invasion and migration, in preeclamptic placentae. Whole-genome RNA sequencing revealed that PPP2R2B, which encodes PP2A Bβ, and the Hippo pathway are downstream targets of miR21. The effects of miR21 on trophoblast mobility were abolished in LATS1T1079A/S909A and YAP-5SA mutants. Moreover, we found that PP2A Bβ dephosphorylates LATS1 via direct protein-protein interactions and thus modulates the phosphorylation and subcellular distribution of YAP. PPP2R2B overexpression ameliorated the miR21-induced LATS1-YAP phosphorylation and cytoplasmic sequestration of YAP, which resulted in the rescue of compromised trophoblast invasion and migration. The upregulation of placental miR21 abundance by placenta-specific nanoparticles loaded with agomir-miR21 during placentation interfered with PPP2R2B and activated the Hippo pathway in the placenta, leading to a PE-like phenotype. Thus, aberrant elevation of miR21 impairs EVT mobility by modulating the PP2A Bβ/Hippo axis, which is one of the causes of PE.</p

Strategies in Anti-Mycobacterium tuberculosis Drug Discovery based on Phenotypic Screening

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Rufomycin targets ClpC1 proteolysis in Mycobacterium tuberculosis and M. abscessus.

ClpC1 is an emerging new target for the treatment of Mycobacterium tuberculosis infections, and several cyclic peptides (ecumicin, cyclomarin A, and lassomycin) are known to act on this target. This study identified another group of peptides, the rufomycins (RUFs), as bactericidal to M. tuberculosis through the inhibition of ClpC1 and subsequent modulation of protein degradation of intracellular proteins. Rufomycin I (RUFI) was found to be a potent and selective lead compound for both M. tuberculosis (MIC, 0.02 μM) and Mycobacterium abscessus (MIC, 0.4 μM). Spontaneously generated mutants resistant to RUFI involved seven unique single nucleotide polymorphism (SNP) mutations at three distinct codons within the N-terminal domain of clpC1 (V13, H77, and F80). RUFI also significantly decreased the proteolytic capabilities of the ClpC1/P1/P2 complex to degrade casein, while having no significant effect on the ATPase activity of ClpC1. This represents a marked difference from ecumicin, which inhibits ClpC1 proteolysis but stimulates the ATPase activity, thereby providing evidence that although these peptides share ClpC1 as a macromolecular target, their downstream effects are distinct, likely due to differences in binding

Longitudinal double-spin asymmetry and cross section for inclusive jet production in polarized proton collisions at square root of s = 200 GeV

We report a measurement of the longitudinal double-spin asymmetry A(LL) and the differential cross section for inclusive midrapidity jet production in polarized proton collisions at s=200 GeV. The cross section data cover transverse momenta 5 < p(T)< 50 GeV/c and agree with next-to-leading order perturbative QCD evaluations. The A(LL) data cover 5 < p(T)< 17 GeV/c and disfavor at 98% C.L. maximal positive gluon polarization in the polarized nucleon