PILOT SCALE DEMONSTRATION AND EVALUATION OF INNOVATIVE NON-DESLIMED NON-CLASSIFIED GRAVITY-FED HM CYCLONE

Coal preparation plants are required in some cases to produce a high-grade product using a low specific gravity cut-point. For these situations, a second higher gravity separation would be desirable to generate a mid-grade product that can be utilized for electricity generation thereby maximizing coal recovery. A study was conducted to evaluate the potential of achieving efficient separations at two different density cut-points in a single stage using a three-product dense medium cyclone. Variations in density cut-point and process efficiency values were quantified as a function of the feed medium density, feed medium-to-coal ratio, and feed pressure using a three-level experimental design program. Results indicate the ability to effectively treat coal over a particle size range from 6mm to 0.15mm while achieving both low- and high-density cut-points up to 1.95 relative density. Ash content decreased from 27.98% in the feed to an average of 7.77% in the clean coal product and 25.76% in the middlings product while sulfur content was reduced from 3.87 to 2.83% in the clean coal product. The overall combustible recovery was maintained above 90% while producing clean coal products with ash and total sulfur content as low as 5.85 and 2.68%, respectively. Organic efficiency values were consistently about 95% and probable error values were in the range of 0.03 to 0.05, which indicates the ability to provide a separation performance equivalent to or better than traditional coal cleaning technologies

Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT.Comment: 13 page

RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.Comment: 11 pages, 9 figures, Accepted by IEEE Transactions on Geoscience and Remote Sensing 2021, project: https://rmcong.github.io/proj_RRNet.htm

Dynamic Optimal Mean-Variance Investment with Mispricing in the Family of 4/2 Stochastic Volatility Models

This paper considers an optimal investment problem with mispricing in the family of 4/2 stochastic volatility models under mean–variance criterion. The financial market consists of a risk-free asset, a market index and a pair of mispriced stocks. By applying the linear–quadratic stochastic control theory and solving the corresponding Hamilton–Jacobi–Bellman equation, explicit expressions for the statically optimal (pre-commitment) strategy and the corresponding optimal value function are derived. Moreover, a necessary verification theorem was provided based on an assumption of the model parameters with the investment horizon. Due to the time-inconsistency under mean–variance criterion, we give a dynamic formulation of the problem and obtain the closed-form expression of the dynamically optimal (time-consistent) strategy. This strategy is shown to keep the wealth process strictly below the target (expected terminal wealth) before the terminal time. Results on the special case without mispricing are included. Finally, some numerical examples are given to illustrate the effects of model parameters on the efficient frontier and the difference between static and dynamic optimality

Studies of Structure and Reactivity in Monolayer Assemblies: 1. Ozonolysis of Alkanethiolate Self Assembled Monolayers on Au. 2. In-Plane Resistivity of Ultrathin Gold Films as a High Sensitivity, Molecularly Differentiated Probe of Chemisorption at the Liquid-Metal Interface

112 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Ultrathin gold films, with thicknesses between the onset of conductivity (d ∼ 5 nm) and the electron mean free path ( d ∼ 80 nm), display surface-sensitive resistivities, which have been exploited to follow the adsorption and desorption of molecular monolayers at the metal solution interface with high precision. For nominal Au film thicknesses (d ∼ 40 nm), strongly chemisorbed thiolate monolayers increase the resistivity of the thin Au films by ca. 4%, but weakly adsorbed species, such as pyridine or phenolate at open circuit, induce no observable change in the Au film resistance. Resistivity measurements implemented with a high stability current source and high-precision digital voltmeter sampling at 1 Hz resulted in 3 sigma uncertainties in alkanethiolate coverage of 1.4 x 10--4 monolayer. Correlating chemical manipulations with changes in surface morphology as characterized by AFM indicates a significant morphology-related contribution to resistivity which must be controlled to extract meaningful information related to the electronic interaction of adsorbate and substrate.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Design of a bionic scallop robot based on jet propulsion

In numerous propulsion forms of underwater creatures, jet propulsion is one of the major ways to swim, and so far, the main bionic objective of current jet-propelled bionic robots are cephalopods (such as octopuses and squids). However, scallop has unique advantages to survive underwater for its structure and jet propulsion style. In this paper, a scallop robot that mimics the structure and the propulsion mechanism of a scallop is designed and implemented. The scallop robot consists of two shells, a special motor to drive the shells open and close periodically, and a curtain muscle to control the water flow during swimming. The scallop robot absorbs water from the front in the direction of movement, and then sprays water to the rear, with better continuity of movement and endurance compared to other jet propulsion forms. Moreover, the rigid shell protects the carrying device well. Swimming experiments show that the scallop robot\u27s special jet propulsion mode enables it move up to 1.8 body length/second. The developed scallop robot has many potential applications for its good swimming continuity, stable swimming, strong protection, and low-cost structure