Supply chain process integration: A conceptual and empirical examination.

The third paper takes a holistic approach to examining the role of supply chain process integration in the customer orientation-innovation-performance framework. Results of an empirical study indicate that supply chain process integration is the missing link between customer orientation and service innovative capability and that service innovative capability plays a critical role between supply chain process integration and firm performance. This study also empirically confirms the sequential link between internal process integration and external process integration.The second paper focuses on defining and operationalizing the construct of supply chain process integration. An extensive literature review revealed no consensus in its conceptualization. With empirical support, it is proposed that internal and external supply chain process integration should be treated as two separate constructs, each comprised of two dimensions: connectivity and simplification.The dissertation follows a three paper format. In the first conceptual paper, it is suggested that supply chain integration should be understood from an internal-external perspective and a process view. A conceptual model is proposed based on the strategy-structure-performance (SSP) framework and the resource based view (RBV) of firms. Firms' strategic orientations and decision-making structures are examined as key factors of supply chain process integration. Furthermore, it is argued that superior performance is likely to be achieved when necessary supply chain capabilities are developed through supply chain process integration.Through examining supply chain process integration conceptually and empirically, this study makes significant contributions to future research on integration which have important implications for managers

Practically Solving LPN in High Noise Regimes Faster Using Neural Networks

We conduct a systematic study of solving the learning parity with noise problem (LPN) using neural networks. Our main contribution is designing families of two-layer neural networks that practically outperform classical algorithms in high-noise, low-dimension regimes. We consider three settings where the numbers of LPN samples are abundant, very limited, and in between. In each setting we provide neural network models that solve LPN as fast as possible. For some settings we are also able to provide theories that explain the rationale of the design of our models. Comparing with the previous experiments of Esser, Kubler, and May (CRYPTO 2017), for dimension $n = 26$, noise rate $\tau = 0.498$, the ''Guess-then-Gaussian-elimination'' algorithm takes 3.12 days on 64 CPU cores, whereas our neural network algorithm takes 66 minutes on 8 GPUs. Our algorithm can also be plugged into the hybrid algorithms for solving middle or large dimension LPN instances.Comment: 37 page

HiQR: An efficient algorithm for high dimensional quadratic regression with penalties

This paper investigates the efficient solution of penalized quadratic regressions in high-dimensional settings. We propose a novel and efficient algorithm for ridge-penalized quadratic regression that leverages the matrix structures of the regression with interactions. Building on this formulation, we develop an alternating direction method of multipliers (ADMM) framework for penalized quadratic regression with general penalties, including both single and hybrid penalty functions. Our approach greatly simplifies the calculations to basic matrix-based operations, making it appealing in terms of both memory storage and computational complexity.Comment: 18 page

A new PEDOT synthesis method and its application in energy storage

Conducting polymer is a common electrode material in supercapacitors, people usually create nano-structure on electrode surface to enhance the performance of supercapacitors. Synthetic nanostructured polymers have been implemented in many ways, most of them are template-based synthesis, which is time consuming and expensive, people are pursuing an easier way to synthesize nanostructured conducting polymers. Combining ferric chloride solution hydrolysis and EDOT polymerization to get PEDOT nano fiber in one step has been achieved. We develop a new method to synthesis PEDOT nano fiber from solid-state iron in one step. Our process takes place in a single step inside a sealed hydrothermal reactor when monomer vapor contacts a rust coating undergoing dissolution – this approach is scalable requiring only a rusted steel surface, acid vapor and monomer vapor. Freestanding nanofibrillar PEDOT films delaminate from a steel substrate characterized by an electronic conductivity of 323 S cm-1 and high electrochemical stability

The role of returns management orientation, internal collaboration, and information support in reverse logistics

While reverse logistics has gained significant interest in recent years, the research on its antecedents is still far from comprehensive. The current study utilizes data collected from China to empirically test a conceptual model that is developed based on the resource based view of the firm. It is proposed that returns management orientation, internal collaboration, and information support are important predictors of reverse logistics performance. The structural equation modeling analysis supports these proposed relationships. Furthermore, the current study also confirms the positive relationship between a firm’s reverse logistics performance and market performance

CityDreamer: Compositional Generative Model of Unbounded 3D Cities

In recent years, extensive research has focused on 3D natural scene generation, but the domain of 3D city generation has not received as much exploration. This is due to the greater challenges posed by 3D city generation, mainly because humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects of the same class, exhibit a wider range of appearances compared to the relatively consistent appearance of objects like trees in natural scenes. To address these challenges, we propose CityDreamer, a compositional generative model designed specifically for unbounded 3D cities, which separates the generation of building instances from other background objects, such as roads, green lands, and water areas, into distinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth, containing a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. Through extensive experiments, CityDreamer has proven its superiority over state-of-the-art methods in generating a wide range of lifelike 3D cities.Comment: Project page: https://haozhexie.com/project/city-dreame

The Digital Supply Chain of the Future: Technologies, Applications and Business Models

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Autonomous trucks: A supply chain adoption perspective

Autonomous trucks can potentially have a huge impact on supply chain networks. Though gaining a lot of attention in the industry, the topic has gained sparse interest from academia. This paper sets out to answer the question: What factors could potentially predict autonomous truck adoption? Though it is inherently difficult to make predictions for the future, we have conducted scenario analysis based on input from key experts in the field. Our findings suggest that technological maturity and regulation will be the two most important factors to observe, while also being very uncertain