Numerical Simulation of Catalytic Ozone Decomposition Reaction in a Gas-solids Circulating Fluidized Bed Riser

Computational fluid dynamics (CFD) modeling of catalytic ozone decomposition reaction in a circulating fluidized bed (CFB) riser using iron impregnated FCC particles as catalyst is carried out. The catalytic reaction is defined as a one-step reaction with an empirical coefficient. Eularian-Eularian method with kinetic theory of granular flow is used to solve the gas-solids two-phase flow in the CFB riser. The simulation results are compared with experimental data, with the reaction rate modified using an empirical coefficient to provide better simulation results than the original reaction rate. Moreover, the particle size has great effects on the reaction rate. Studies on solid particle distribution show that the influence of wall boundary condition, determined by specularity coefficient and particle-wall restitution coefficient, plays a major role in the solids lateral velocity that affects the solids distribution in the riser. The generality of the CFD model is further validated under different operating conditions of the riser

FoveaBox: Beyond Anchor-based Object Detector

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox .Comment: IEEE Transactions on Image Processing, code at: https://github.com/taokong/FoveaBo

Development and application of ZM-2 drilling fluid density adjustment mixing device

AbstractHigh-pressure shallow (gas/water) flow is often hidden in the deepwater seabed, so penetrating shallow flow in drilling without BOP will be highly risky. In this case, the conventional well killing method to balance the formation pressure with back pressure generated by well head equipment is no longer suitable. Based on the analysis of structural characteristics of domestic and foreign multi-phase mixing systems, a ZM-2 drilling fluid density adjustment mixing device with independent intellectual property right was developed according to the principles of dynamic well killing. The device is mainly composed of a throttle valve, a high-precision electromagnetic flowmeter, a mixer, dumbbell-shaped nozzles, connecting pipes and other components. Fixed on the mixer are three inlets to fill heavy mud, seawater and additives. Opposed jetting is adopted to realize rapid and uniform mixing of fluids with different densities. A laboratory test was conducted to work out the relationship between throttle opening and injection flow rate and establish a linear relationship between killing fluid density and heavy mud flow. The results of field test conducted in the Nanhai No.8 drill ship showed that the mixing device was stable in operation and excellent in mixing performance. The density difference of ingredient mixture could be controlled within 0.05 g/cm3 after the mixture flowed out of the mixing chamber of the mixer of about 0.3 m long, so such high precision can meet the requirement of dynamic well killing

The Study of Returns to Private Investment in Higher Education from the Point of Employment

The quantitative methods are used to compare the difference of vocation and employment between the university graduates and high school graduates, which including the secondary school graduates. And the following four respects are involved to describe the impact of higher education to employment: the relative concentration degree, the difference of vocation, the weekly working time and the working industry. So we come to the conclution from the aspect of employment that private investment gets not only great market returns but also nonmarket returns. Key words: higher education; private investment; non-market returns; employment Résumé: Les méthodes quantitatives sont utilisées pour comparer les différences dans la vacation et dans le type de travail entre les diplômés des universités et les diplômés des collèges, y compris les écoles secondaires. Les quatre aspects suivants sont impliqués pour décrire l’impact d’une éducation supérieure sur l’emploi : le degré de concentration relatif, les différences de vocation, les heures de travail hebdomadaires, et le type de l’industrie dans lequelle ils travaillent. Du point de vue de l’emploi, nous arrivons à la conculsion que l’investissment privé peut obtenir non seulement des rendements du marché mais aussi des rendements qui ne proviennent pas du marché. Mots-Clés: enseignement supérieur; investissement supérieur; rendements qui ne proviennent pas du marché; emplo

Learning Personalized User Preference from Cold Start in Multi-turn Conversations

This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences. In particular, the TAI system is able to automatically identify and label user preference in live interactions, manage dialogue flows for interactive teaching sessions, and reuse learned preference for preference elicitation. We develop the TAI system by leveraging BERT encoder models to encode both dialogue and relevant context information, and build action prediction (AP), argument filling (AF) and named entity recognition (NER) models to understand the teaching session. We adopt a seeker-provider interaction loop mechanism to generate diverse dialogues from cold-start. TAI is capable of learning user preference, which achieves 0.9122 turn level accuracy on out-of-sample dataset, and has been successfully adopted in production.Comment: preference, personalization, cold-start, dialogue, LLM. embeddin