Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

With the application of new-generation information technology in the full life cycle process of a complex product, it is showing the characteristics of multi-source, real-time, heterogeneous, cross-domain transmission. Large data volume and low value density emerge in the process of complex product design manufacturing and services (DMS). This leads to “information islands” and insufficient utilization of cross-domain reliability data in the process of integration of DMS for complex product R&D design data, manufacturing data and operation and maintenance services (O&MS) data. This paper proposes and illustrates a framework of complex product DMS integration based on reliability data, including complex product design optimization based on manufacturing and service reliability data, complex product intelligent manufacturing process optimization based on real-time reliability data and complex product O&MS optimization based on multi-source heterogeneous reliability data. Additionally, it then realizes complex product design reliability and optimization, manufacturing process reliability and optimization and O&MS reliability and intelligent decision optimization based on reliability data. Finally, the DMS integration framework based on reliability-data-driven proposal is corrected through the case of engine MDS integration, which can effectively improve the cross-domain reliability data utilization and overall product reliability of complex products. The proposed framework extends the application of reliability theory in the process of complex product DMS integration and provides a reference for enterprises in the R&D, manufacturing and O&MS of complex products

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

With the application of new-generation information technology in the full life cycle process of a complex product, it is showing the characteristics of multi-source, real-time, heterogeneous, cross-domain transmission. Large data volume and low value density emerge in the process of complex product design manufacturing and services (DMS). This leads to “information islands” and insufficient utilization of cross-domain reliability data in the process of integration of DMS for complex product R&D design data, manufacturing data and operation and maintenance services (O&MS) data. This paper proposes and illustrates a framework of complex product DMS integration based on reliability data, including complex product design optimization based on manufacturing and service reliability data, complex product intelligent manufacturing process optimization based on real-time reliability data and complex product O&MS optimization based on multi-source heterogeneous reliability data. Additionally, it then realizes complex product design reliability and optimization, manufacturing process reliability and optimization and O&MS reliability and intelligent decision optimization based on reliability data. Finally, the DMS integration framework based on reliability-data-driven proposal is corrected through the case of engine MDS integration, which can effectively improve the cross-domain reliability data utilization and overall product reliability of complex products. The proposed framework extends the application of reliability theory in the process of complex product DMS integration and provides a reference for enterprises in the R&D, manufacturing and O&MS of complex products

Mineral Elements and Active Ingredients in Root of Wild Paeonia lactiflora Growing at Duolun County, Inner Mongolia

Roots of wild Paeonia lactiflora are often used as herbs in traditional Chinese medicine. In this study, the contents of potassium (K), calcium (Ca), phosphorus (P), magnesium (Mg), iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) and the concentrations of three active ingredients such as paeoniflorin (PF), catechin (CA) and benzoic acid (BA) in roots of wild P. lactiflora collected from Duolun County of Inner Mongolia in China were evaluated. The results showed that the mean contents of eight elements followed the following order: Ca > K > P > Mg > Fe > Zn > Mn > Cu, and the concentrations of three active ingredients decreased in the order: PF > CA > BA. It was found that PF concentration was positively correlated with the contents of Fe and Mn. However, the concentration of CA was linearly decreased with Mg content. Moreover, BA concentration showed positive linear dependence upon the contents of P and Mn. Results of stepwise regression analyses showed that 39.2% of the variance in PF concentration could be explained by Fe content, whereas 28.1% of the CA concentration changes could be explained by Mg content; moreover, 42.5% of the variance in BA concentration could be accounted for by the combination of Mn and P contents. In a word, the concentrations of active ingredients in roots of P. lactiflora can be changed by adjusting mineral elements levels in roots to meet the need of appropriate quality control of P. lactiflora

Self-depleted T-gate Schottky barrier tunneling FET with low average subthreshold slope and high I<inf>ON</inf>/I<inf>OFF</inf> by gate configuration and barrier modulation

In this paper, a novel silicon-based T-gate Schottky barrier tunneling FET (TSB-TFET) is proposed and experimentally demonstrated. With enhanced electric field at source side through gate configuration for steeper subthreshold slope (SS), the device with self-depleted structure can effectively suppress the leakage current and simultaneously achieve the dominant Schottky barrier tunneling current for high ON-current without area penalty, which can alleviate the problems in silicon TFET. In addition, the proposed TSB-TFET can have comparable DIBL effect and reduced gate-to-drain capacitance compared with traditional TFET. Further device optimization is experimentally achieved by extended multi-finger gate configuration of the same footprint and barrier modulation by dopant segregation Schottky technology. With compatible bulk CMOS technology, the fabricated device can achieve steep SS over almost 5 decades of current, as well as high ION/IOFF ratio (??10 7). The proposed device with high compatibility is very promising for future low power system applications. ? 2011 IEEE.EI

Learning-prolonged maintenance of stimulus information in CA1 and subiculum during trace fear conditioning

Summary: Temporal associative learning binds discontiguous conditional stimuli (CSs) and unconditional stimuli (USs), possibly by maintaining CS information in the hippocampus after its offset. Yet, how learning regulates such maintenance of CS information in hippocampal circuits remains largely unclear. Using the auditory trace fear conditioning (TFC) paradigm, we identify a projection from the CA1 to the subiculum critical for TFC. Deep-brain calcium imaging shows that the peak of trace activity in the CA1 and subiculum is extended toward the US and that the CS representation during the trace period is enhanced during learning. Interestingly, such plasticity is consolidated only in the CA1, not the subiculum, after training. Moreover, CA1 neurons, but not subiculum neurons, increasingly become active during CS-and-trace and shock periods, respectively, and correlate with CS-evoked fear retrieval afterward. These results indicate that learning dynamically enhances stimulus information maintenance in the CA1-subiculum circuit during learning while storing CS and US memories primarily in the CA1 area

Ultralow Thermal Conductivity Achieved by All Carbon Nanocomposites for Thermoelectric Applications

Abstract Carbon‐based materials are becoming a promising candidate for thermoelectricity. Among them, graphene shows limited scope due to its ultra‐high thermal conductivity (κ). To develop graphene‐based thermoelectric devices, reduction of κ is highly desired while maintaining reasonably high electrical conductivity (σ). Herein, multiwalled carbon nanotubes (MWCNTs) and carbon black (CB) fillers are added into few layered graphene (FLG) to produce all‐carbon composites yielding ultra‐low thermal conductivity (κ) desired for thermoelectric applications. The novel preparation method of pristine FLG realizes very low κ of 6.90 W m−1 K−1 at 1248 K, which further reduces to 0.57, 0.81, and 0.69 W m−1 K−1 at the same temperature for FLG + MWCNTs, FLG + CB, and FLG + MWCNTs + CB, respectively. As‐prepared FLG composites also maintain reasonably high σ, whilst the Seebeck coefficient shows over a factor of five improvement after the inclusion of carbon‐based fillers. Consequently, the power factor (PF) is significantly improved. The ultralow κ is attributed to the increased thermal boundary resistance among graphene sheet boundaries. The realization of ultralow κ with simultaneous improvement in Seebeck coefficients and relatively small drops in σ with a facile and unique synthesis technique, highlight the potential of these composites