Parallel navigation for 3-D autonomous vehicles

summary:In this paper, parallel navigation is proposed to track the target in three-dimensional space. Firstly, the polar kinematics models for the vehicle and the target are established. Secondly, parallel navigation is derived by using polar kinematics models. Thirdly, cell decomposition method is applied to implement obstacle avoidance. Fourthly, a brief study is given on the influence of uncertainties. Finally, simulations are conducted by MATLAB. Simulation results demonstrate the effectiveness of the parallel navigation

An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms

An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms

MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision

Previous works concerning single-view hand-held object reconstruction typically utilize supervision from 3D ground truth models, which are hard to collect in real world. In contrast, abundant videos depicting hand-object interactions can be accessed easily with low cost, although they only give partial object observations with complex occlusion. In this paper, we present MOHO to reconstruct hand-held object from a single image with multi-view supervision from hand-object videos, tackling two predominant challenges including object's self-occlusion and hand-induced occlusion. MOHO inputs semantic features indicating visible object parts and geometric embeddings provided by hand articulations as partial-to-full cues to resist object's self-occlusion, so as to recover full shape of the object. Meanwhile, a novel 2D-3D hand-occlusion-aware training scheme following the synthetic-to-real paradigm is proposed to release hand-induced occlusion. In the synthetic pre-training stage, 2D-3D hand-object correlations are constructed by supervising MOHO with rendered images to complete the hand-concealed regions of the object in both 2D and 3D space. Subsequently, MOHO is finetuned in real world by the mask-weighted volume rendering supervision adopting hand-object correlations obtained during pre-training. Extensive experiments on HO3D and DexYCB datasets demonstrate that 2D-supervised MOHO gains superior results against 3D-supervised methods by a large margin. Codes and key assets will be released soon

Prevailing PA mutation K356R in avian influenza H9N2 virus increases mammalian replication and pathogenicity

Adaptation of the viral polymerase complex comprising PB1, PB2, and PA is necessary for efficient influenza A virus replication in new host species. We found that PA mutation K356R (PA-K356R) has become predominant since 2014 in avian H9N2 viruses in China as with seasonal human H1N1 viruses. The same mutation is also found in most human isolates of emergent avian H7N9 and H10N8 viruses whose six internal gene segments are derived from the H9N2 virus. We further demonstrated the mammalian adaptive functionality of the PA-K356R mutation. Avian H9N2 virus with the PA-K356R mutation in human A549 cells showed increased nuclear accumulation of PA and increased viral polymerase activity that resulted in elevated levels of viral transcription and virus output. The same mutant virus in mice also enhanced virus replication and caused lethal infection. In addition, combined mutation of PA-K356R and PB2-E627K, a well-known mammalian adaptive marker, in the H9N2 virus showed further cooperative increases in virus production and severity of infection in vitro and in vivo. In summary, PA-K356R behaves as a novel mammalian tropism mutation, which, along with other mutations such as PB2-E627K, might render avian H9N2 viruses adapted for human infection

Highly pathogenic avian influenza H5N6 viruses exhibit enhanced affinity for human type sialic acid receptor and in-contact transmission in model ferrets

Since May 2014, highly pathogenic avian influenza H5N6 virus has been reported to cause six severe human infections three of which were fatal. The biological properties of this subtype, in particular its relative pathogenicity and transmissibility in mammals, are not known. We characterized the virus receptor-binding affinity, pathogenicity, and transmissibility in mice and ferrets of four H5N6 isolates derived from waterfowl in China from 2013-2014. All four H5N6 viruses have acquired a binding affinity for human-like SA alpha 2,6Gal-linked receptor to be able to attach to human tracheal epithelial and alveolar cells. The emergent H5N6 viruses, which share high sequence similarity with the human isolate A/Guangzhou/39715/2014 (H5N6), were fully infective and highly transmissible by direct contact in ferrets but showed less-severe pathogenicity than the parental H5N1 virus. The present results highlight the threat of emergent H5N6 viruses to poultry and human health and the need to closely track their continual adaptation in humans

Neurovirulence of avian influenza virus is dependent on the interaction of viral NP protein with host factor FMRP in the murine brain

Avian influenza viruses (AIVs) are zoonotic viruses that exhibit a range infectivity and severity in the human host. Severe human cases of AIVs infection are often accompanied by neurological symptoms, however, the factors involved in the infection of the central nervous system (CNS) are not well known. In this study, we discovered that avian-like sialic acid (SA)-α2, 3 Gal receptor is highly presented in mammalian (human and mouse) brains. In the generation of a mouse-adapted neurotropic H9N2 AIV (SD16-MA virus) in BALB/c mice, we identified key adaptive mutations in its hemagglutinin (HA) and polymerase basic protein 2 (PB2) genes that conferred viral replication ability in mice brain. The SD16-MA virus showed binding affinity for avian-like SA-α2, 3 Gal receptor, enhanced viral RNP polymerase activity, increased viral protein production and transport that culminated in elevated progeny virus production and severe pathogenicity. We further established that host Fragile X Mental Retardation Protein (FMRP), a highly expressed protein in the brain that physically associated with viral nucleocapsid protein (NP) to facilitate RNP assembly and export, was an essential host factor for the neuronal replication of neurotropic AIVs (H9N2, H5N1 and H10N7 viruses). Our study identified a mechanistic process for AIVs to acquire neurovirulence in mice

Enhanced pathogenicity and neurotropism of mouse-adapted H10N7 influenza virus are mediated by novel PB2 and NA mutations

The H10 subtype of avian influenza viruses (AIVs) circulates globally in wild birds and poultry, and this subtype has been shown to be increasingly prevalent in China. Among the various H10 viruses, H10N7 AIVs have caused repeated mammal and human infections. To investigate their genetic adaptation in mammals, we generated a mouse-adapted avian H10N7 variant (A/mallard/Beijing/27/2011-MA; BJ27-MA) which exhibited increased virulence in mice compared to wild-type virus and acquired neurotropism. Sequencing showed the absence of the widely recognized mammalian adaptation markers of E627K and D701N in PB2 in the mouse-adapted strain; instead, five amino acid mutations were identified: E158G and M631L in PB2; G218E in haemagglutinin (H3 numbering); and K110E and S453I in neuraminidase (NA). The neurovirulence of the BJ27-MA virus necessitated the combined presence of the PB2 and NA mutations. Mutations M631L and E158G of PB2 and K110E of NA were required to mediate increased virus replication and severity of infection in mice and mammalian cells. PB2-M631L was functionally the most dominant mutation in that it strongly upregulated viral polymerase activity and played a critical role in the enhancement of virus replication and disease severity in mice. K110E mutation in NA, on the other hand, significantly promoted NA enzymatic activity. These results indicate that the novel mutations in PB2 and NA genes are critical for the adaptation of H10N7 AIV in mice, and they could serve as molecular signatures of virus transmission to mammalian hosts, including humans

No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm

When no-wait constraint holds in job shops, a job has to be processed with no waiting time from the first to the last operation, and the start time of a job is greatly restricted. Using key elements of the iterated greedy algorithm, this paper proposes a population-based iterated greedy (PBIG) algorithm for finding high-quality schedules in no-wait job shops. Firstly, the Nawaz–Enscore–Ham (NEH) heuristic used for flow shop is extended in no-wait job shops, and an initialization scheme based on the NEH heuristic is developed to generate start solutions with a certain quality and diversity. Secondly, the iterated greedy procedure is introduced based on the destruction and construction perturbator and the insert-based local search. Furthermore, a population-based co-evolutionary scheme is presented by imposing the iterated greedy procedure in parallel and hybridizing both the left timetabling and inverse left timetabling methods. Computational results based on well-known benchmark instances show that the proposed algorithm outperforms two existing metaheuristics by a significant margin