Don’t Forget Your Supplier When Remanufacturing

A popular assumption in the current literature on remanufacturing is that the whole new product is produced by an integrated manufacturer, which is inconsistent with most industries. In this paper, we model a decentralised closed-loop supply chain consisting of a key component supplier and a non-integrated manufacturer, and demonstrate that the interaction between these players significantly impacts the economic and environmental implications of remanufacturing. In our model, the non-integrated manufacturer can purchase new components from the supplier to produce new products, and remanufacture used components to produce remanufactured products. Thus, the non-integrated manufacturer is not only a buyer but also a rival to the supplier. In a steady state period, we analyse the performances of an integrated manufacturer and the decentralised supply chain. We find that, although the integrated manufacturer always benefits from remanufacturing, the remanufacturing opportunity may constitute a lose-lose situation to the supplier and the non-integrated manufacturer, making their profits be lower than in an identical supply chain without remanufacturing. In addition, the non-integrated manufacturer may be worse off with a lower remanufacturing cost or a larger return rate of used products due to the interaction with the supplier. We further demonstrate that the government-subsidised remanufacturing in the non-integrated (integrated) manufacturer is detrimental (beneficial) to the environment

LSTM Learning with Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

The data generated by millions of sensors in Industrial Internet of Things (IIoT) is extremely dynamic, heterogeneous, and large scale. It poses great challenges on the real-time analysis and decision making for anomaly detection in IIoT. In this paper, we propose a LSTM-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in IIoT. In a nutshell, the LSTM-NN builds model on normal time series. It detects outliers by utilising the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of Gaussian Naive Bayes model through the predictive error. Empirical studies demonstrate our solution outperforms the best-known competitors, which is a preferable choice for detecting anomalies

Universal Information Extraction with Meta-Pretrained Self-Retrieval

Universal Information Extraction~(Universal IE) aims to solve different extraction tasks in a uniform text-to-structure generation manner. Such a generation procedure tends to struggle when there exist complex information structures to be extracted. Retrieving knowledge from external knowledge bases may help models to overcome this problem but it is impossible to construct a knowledge base suitable for various IE tasks. Inspired by the fact that large amount of knowledge are stored in the pretrained language models~(PLM) and can be retrieved explicitly, in this paper, we propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE. As different IE tasks need different knowledge, we further propose a Meta-Pretraining Algorithm which allows MetaRetriever to quicktly achieve maximum task-specific retrieval performance when fine-tuning on downstream IE tasks. Experimental results show that MetaRetriever achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.Comment: Accepted to ACL 202

Atomic Ramsey interferometry with S- and D-band in a triangular optical lattice

Ramsey interferometers have wide applications in science and engineering. Compared with the traditional interferometer based on internal states, the interferometer with external quantum states has advantages in some applications for quantum simulation and precision measurement. Here, we develop a Ramsey interferometry with Bloch states in S- and D-band of a triangular optical lattice for the first time. The key to realizing this interferometer in two-dimensionally coupled lattice is that we use the shortcut method to construct $\pi/2$ pulse. We observe clear Ramsey fringes and analyze the decoherence mechanism of fringes. Further, we design an echo $\pi$ pulse between S- and D-band, which significantly improves the coherence time. This Ramsey interferometer in the dimensionally coupled lattice has potential applications in the quantum simulations of topological physics, frustrated effects, and motional qubits manipulation

Bricks vs. Clicks: Which is better for marketing remanufactured products?

The economical and environmental benefits are the central issues for remanufacturing. Whereas extant remanufacturing research focuses primarily on such issues in remanufacturing technologies, production planning, inventory control and competitive strategies, we provide an alternative yet somewhat complementary approach to consider both issues related to different channels structures for marketing remanufactured products. Specifically, based on observations from current practice, we consider a manufacturer sells new units through an independent retailer but with two options for marketing remanufactured products: (1) marketing through its own e-channel (Model M) or (2) subcontracting the marketing activity to a third party (Model 3P). A central result we obtain is that although Model M is always greener than Model 3P, firms have less incentive to adopt it because both the manufacturer and retailer may be worse off when the manufacturer sells remanufactured products through its own e-channel rather than subcontracting to a third party. Extending both models to cases in which the manufacturer interacts with multiple retailers further reveals that the more retailers in the market, the greener Model M relative to Model 3P

Observation of Temperature-Induced Crossover to an Orbital-Selective Mott Phase in Ax_{x}Fe2−y_{2-y}Se2_2 (A=K, Rb) Superconductors

In this work, we study the A$_{x}$Fe$_{2-y}$Se$_2$ (A=K, Rb) superconductors using angle-resolved photoemission spectroscopy. In the low temperature state, we observe an orbital-dependent renormalization for the bands near the Fermi level in which the dxy bands are heavily renormliazed compared to the dxz/dyz bands. Upon increasing temperature to above 150K, the system evolves into a state in which the dxy bands have diminished spectral weight while the dxz/dyz bands remain metallic. Combined with theoretical calculations, our observations can be consistently understood as a temperature induced crossover from a metallic state at low temperature to an orbital-selective Mott phase (OSMP) at high temperatures. Furthermore, the fact that the superconducting state of A$_{x}$Fe$_{2-y}$Se$_2$ is near the boundary of such an OSMP constraints the system to have sufficiently strong on-site Coulomb interactions and Hund's coupling, and hence highlight the non-trivial role of electron correlation in this family of iron superconductors

Synergistic surface modification for high-efficiency perovskite nanocrystal light-emitting diodes: divalent metal ion doping and halide-based ligand passivation

Surface defects of metal halide perovskite nanocrystals (PNCs) substantially compromise the optoelectronic performances of the materials and devices via undesired charge recombination. However, those defects, mainly the vacancies, are structurally entangled with each other in the PNC lattice, necessitating a delicately designed strategy for effective passivation. Here, a synergistic metal ion doping and surface ligand exchange strategy is proposed to passivate the surface defects of CsPbBr3 PNCs with various divalent metal (e.g., Cd2+, Zn2+, and Hg2+) acetate salts and didodecyldimethylammonium (DDA+) via one-step post-treatment. The addition of metal acetate salts to PNCs is demonstrated to suppress the defect formation energy effectively via the ab initio calculations. The developed PNCs not only have near-unity photoluminescence quantum yield and excellent stability but also show luminance of 1175 cd m−2, current efficiency of 65.48 cd A−1, external quantum efficiency of 20.79%, wavelength of 514 nm in optimized PNC light-emitting diodes with Cd2+ passivator and DDA ligand. The “organic–inorganic” hybrid engineering approach is completely general and can be straightforwardly applied to any combination of quaternary ammonium ligands and source of metal, which will be useful in PNC-based optoelectronic devices such as solar cells, photodetectors, and transistors