ABSTRACT
Platform operations are very common in the sharing economy. Nowadays, retailers can sell the end‐of‐season product leftovers to platforms which offer product rental services to the market. ...Motivated by this observed industrial practice, we build stylized supply chain models to explore the platform supported supply chain operations. We uncover that the presence of the platform creates the “triple marginalization” problem in which supply chain coordination cannot be achieved even if the manufacturer is willing to supply at cost using the wholesale pricing contract. We show how the markdown sponsor (MS) contract can deal with the triple marginalization problem and achieve supply chain coordination. However, we illustrate that a moral hazard problem, in which the retailer has incentive to overclaim the amount of markdown sponsor, arises. We reveal that the moral hazard problem brings a loss to the manufacturer, an immoral gain for the retailer, and there is no impact on the platform and consumers. We analytically derive the impact of moral hazard (which means the loss to the manufacturer, and the gain for the retailer) and find that it relates to the markdown sponsor rate, as well as the degree of overclaiming. To overcome the moral hazard problem under MS contract, we propose measures such as the adoption of blockchain technology, and “discounted” markdown sponsor contract, to help. We also explore the implementations of other contracts to overcome the moral hazard, like virtual buyback with inventory reallocation contract, and wholesale pricing contract with side payments.
In this paper, we investigate the problem of multiuser computation offloading for cloudlet-based mobile cloud computing in a multichannel wireless contention environment. The studied system is fully ...distributed so that each mobile device user can make the offloading decisions based only on its individual information, and without information exchange. We first formulate this multiuser computation offloading decision making problem as a noncooperative game. After analyzing the structural property of the formulated game, we show that it is an exact potential game, and has at least one pure-strategy Nash equilibrium point (NEP). To achieve the NEPs in a fully distributed environment, we propose a fully distributed computation offloading (FDCO) algorithm based on machine learning technology. We then theoretically analyze the performance of the proposed FDCO algorithm in terms of the number of beneficial cloudlet computing mobile devices and the system-wide execution cost. Finally, simulation results validate the effectiveness of our proposed algorithm compared with counterparts.
An improved physics-informed neural network (IPINN) algorithm with four output functions and four physics constraints, which possesses neuron-wise locally adaptive activation function and slope ...recovery term, is appropriately proposed to obtain the data-driven vector localized waves, including vector solitons, breathers and rogue waves (RWs) for the Manakov system with initial and boundary conditions, as well as data-driven parameters discovery for Manakov system with unknown parameters. The data-driven vector RWs which also contain interaction waves of RWs and bright-dark solitons, interaction waves of RWs and breathers, as well as RWs evolved from bright-dark solitons are learned to verify the capability of the IPINN algorithm in training complex localized wave. In the process of parameter discovery, routine IPINN can not accurately train unknown parameters whether using clean data or noisy data. Thus we introduce parameter regularization strategy with adjustable weight coefficients into IPINN to effectively and accurately train prediction parameters, then find that once setting the appropriate weight coefficients, the training effect is better as using noisy data. Numerical results show that IPINN with parameter regularization shows superior noise immunity in parameters discovery problem.
•The improved PINN method with four outputs and four nonlinear equation constraints is firstly proposed.•The data-driven vector localized waves and parameters discovery for the Manakov system are considered.•The data-driven vector interaction solutions and parameters discovery are obtained.•We introduce L2 norm parameter regularization strategy with adjustable weight coefficients into improved PINN.•The training effect is better as using noisy data in improved PINN with parameter regularization.
•A holistic review of current research on the mechanical properties of AAC is provided.•The AACs reviewed include slag-based, fly ash-based, and fly ash/slag-based types.•Both static and dynamic ...mechanical properties of AAC are discussed.•The fracture, bond and high-temperature properties of AAC are also addressed.•The slag/fly ash ratio is a very influential factor to the mechanical properties of AAC.
Alkali-activated concretes (AACs) are attracting increasing attention due to their potential as alternatives to ordinary Portland cement concrete (OPCC). This paper is a holistic review of current research on the mechanical properties of AAC including research on its compressive strength, tensile strength, elastic modulus, Poisson’s ratio, stress–strain relationship under uniaxial compression, fracture properties, bond mechanism with steel reinforcement, dynamic mechanical properties, and high-temperature performance. Three types of AAC are reviewed: alkali-activated slag, alkali-activated fly ash, and alkali-activated slag-fly ash concretes. The applicability to AAC of design formulas found in codes of practice that were developed to estimate the basic mechanical performances of OPCC is also discussed. It is shown that, in general, AAC exhibits better bond performance with steel reinforcement and better strength performance after exposure to elevated temperatures than OPCC. For the other reviewed mechanical properties, the differences between AAC and OPCC largely depend on the proportions of raw materials in the concrete; specifically, the slag to fly ash ratio may be a very influential factor. As there is a trend to combine slag and fly ash in the production of AAC to achieve normal temperature curing and environmental friendliness, further research is deemed necessary to determine how the slag to fly ash ratio influences the fundamental mechanical properties of AAC and how this affects practical designs.
This paper discusses incentive mechanism design for collaborative task offloading in mobile edge computing (MEC). Different from most existing work in the literature that was based on offline ...settings, in this paper, an online truthful mechanism integrating computation and communication resource allocation is proposed. In our system model, upon the arrival of a smartphone user who requests task offloading, the base station (BS) needs to make a decision right away without knowing any future information on i) whether to accept or reject this task offloading request and ii) if accepted, who to execute the task (the BS itself or nearby smartphone users called collaborators). By considering each task's specific requirements in terms of data size, delay, and preference, we formulate a social-welfare-maximization problem, which integrates collaborator selection, communication and computation resource allocation, transmission and computation time scheduling, as well as pricing policy design. To solve this complicated problem, a novel online mechanism is proposed based on the primal-dual optimization framework. Theoretical analyses show that our mechanism can guarantee feasibility, truthfulness, and computational efficiency (competitive ratio of 3). We further use comprehensive simulations to validate our analyses and the properties of our proposed mechanism.
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named
Point Cloud Transformer
(PCT) for ...point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.
Omni-channel retailing is a popular strategy in a new retailing era when digitalization, social media, big data and other emerging technologies (e.g., Artificial Intelligence (AI), virtual reality ...(VR), augmented reality (AR), blockchain, etc.) are transforming the retail business models. Meanwhile, omni-channel related operations impose a challenge on either well-established firms or new setups that they have to make “right” decisions to fit in the new retail environment. This review paper endeavors to reveal the established knowledge behind the omni-channel retailing literature, generate managerial implications for firms, and provides a guideline for future research. We conduct this systematic review by adopting citation network analysis (CNA). The CNA helps identify seven independent and interdependent research domains, which depict (or constitute) a whole picture of “omnichannel management”. The main path analysis reveals that each identified research domain is under study. We also find that the extant literature seldom examines the roles of how new technologies play in the “omnichannel management”. Moreover, the domain of supply chain management and inventory management in the omnichannel environment is absent in this systematic literature review. Therefore, we propose a prescribed framework for “omnichannel management” (PFOM), which contributes to the literature on “omnichannel management” and provides important managerial applications to the retail firms that plan to implement the omnichannel strategy.
Quinoline and quinazoline alkaloids, two important classes of N‐based heterocyclic compounds, have attracted tremendous attention from researchers worldwide since the 19th century. Over the past 200 ...years, many compounds from these two classes were isolated from natural sources, and most of them and their modified analogs possess significant bioactivities. Quinine and camptothecin are two of the most famous and important quinoline alkaloids, and their discoveries opened new areas in antimalarial and anticancer drug development, respectively. In this review, we survey the literature on bioactive alkaloids from these two classes and highlight research achievements prior to the year 2008 (Part I). Over 200 molecules with a broad range of bioactivities, including antitumor, antimalarial, antibacterial and antifungal, antiparasitic and insecticidal, antiviral, antiplatelet, anti‐inflammatory, herbicidal, antioxidant and other activities, were reviewed. This survey should provide new clues or possibilities for the discovery of new and better drugs from the original naturally occurring quinoline and quinazoline alkaloids.
Nearly 100% triplet harvesting in conventional fluorophor‐based organic light‐emitting devices is realized through energy transfer from exciplex. The best C545T‐doped device using the exciplex host ...exhibits a maximum current efficiency of 44.0 cd A‐1, a maximum power efficiency of 46.1 lm W‐1, and a maximum external quantum efficiency of 14.5%.
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