A Critical Examination to the Unitarized ππ\pi\pi Scattering Chiral Amplitudes

We discuss the Pad\'e approximation to the $\pi\pi$ scattering amplitudes in 1--loop chiral perturbation theory. The approximation restores unitarity and can reproduce the correct resonance poles, but the approximation violates crossing symmetry and produce spurious poles on the complex $s$ plane and therefore plagues its predictions on physical quantities at quantitative level. However we find that one virtual state in the IJ=20 channel may have physical relevance.Comment: 13 pages + 4 eps figures submit to Commun. Theor. Phy

Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.Comment: Accepted at ICML 202

Study of the adsorption of Co(II) on the chitosan-hydroxyapatite

The adsorption of cobalt ions (Co2+) from aqueous solution onto chitosan-hydroxyapatite composite is investigated in this study. The effects of adsorption time, initial concentration, temperature, and pH are studied in details. Kinetics and thermodynamics of the adsorption of Co2+ onto the chitosan-hydroxyapatite are also investigated and the adsorption kinetics is found to follow the pseudo-second-order model with an activation energy (Ea) of 10.73 kJ/mol. Thermodynamic studies indicates that the adsorption follows the Langmuir adsorption equation. The value of entropy change (∆Sө) and enthalpy change (∆Hө) are found to be 83.50 and 18.09 kJ/mol, respectively. The Gibbs free energy change (∆Gө) is found to be negative at all fives temperatures, demonstrating that the adsorption process is spontaneous and endothermic.

Learning to Search for Job Shop Scheduling via Deep Reinforcement Learning

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modeling partial solutions at each construction step. This paper proposes a novel DRL-based method to learn improvement heuristics for JSSP, where graph representation is employed to encode complete solutions. We design a Graph Neural Network based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, we design a novel message-passing mechanism that can evaluate multiple solutions simultaneously. Extensive experiments on classic benchmarks show that the improvement policy learned by our method outperforms state-of-the-art DRL-based methods by a large margin

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.Comment: Accepted at IJCAI 2022 (short oral

Rapid mangrove expansion triggered by low river discharge episode in Nanliu river estuary, Beibu Gulf of China

Mangrove forest is a critical primary producer, biological habitat, and carbon sink in the subtropical-tropical coast zone, and the natural variation of mangrove coverage deserves study for a better understanding of the dynamics of mangrove coastal evolution. In this study, multispectral Landsat images from 1985 to 2018 are used to reconstruct the change in the coverage of mangrove (dominant species is Aegiceras corniculatum) and salt marsh (dominant species is Cyperus malaccensis) in the Nanliu River estuary. Tidal flat elevation measuring and 210Pb dating is used to study the substrate elevation when mangroves first colonize salt marsh. Historical temperature records, river discharge records, and the time series N/P concentration in sediment are analyzed. It is found that the mangrove forests have expanded rapidly in salt marsh since the mid-1980s. The change in factors such as accommodation space, cold event frequency, and nutrient supply cannot explain the origin of mangrove expansion. A low river discharge episode lasting for 8 years since 1986 is considered to have triggered the mangrove expansion in this area, as previously established salt marsh plants died due to germination restriction caused by high salinity and mangroves colonized the salt marsh habitat during this period. This case proves again that estuarine wetlands are very sensitive to salinity variation

Basic considerations for Monte Carlo calculations in soil

Abstract Monte Carlo codes are extensively used for probabilistic simulations of various physical systems. These codes are widely used in calculations of neutron and gamma ray transport in soil for radiation shielding, soil activation by neutrons, well logging industry, and in simulations of complex nuclear gauges for in soil measurements. However, these calculations are complicated by the diversity of soils in which the proportions of solid, liquid and gas vary considerably together with extensive variations in soil elemental composition, morphology, and density. Nevertheless use of these codes requires knowledge of the elemental composition and density of the soil and its physical characteristics as input information for performing these calculations. It is shown that not always all of the soil parameters are critical but depend on the objectives of the calculations. An approach for identifying soil elemental composition and some simplifying assumptions for implementing the transport codes are presented.