OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures

Deep learning-based methods have recently been established as fast and accurate surrogate simulators for optical multilayer thin film structures. However, existing methods only work for limited types of structures with different material arrangements, preventing their applications towards diverse and universal structures. Here, we propose the Opto-Layer (OL) Transformer to act as a universal surrogate simulator for enormous types of structures. Combined with the technique of structure serialization, our model can predict accurate reflection and transmission spectra for up to $10^{25}$ different multilayer structures, while still achieving a six-fold degradation in simulation time compared to physical solvers. Further investigation reveals that the general learning ability comes from the fact that our model first learns the physical embeddings and then uses the self-attention mechanism to capture the hidden relationship of light-matter interaction between each layer.Comment: 4 pages, 4 figure

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed

Study on stability of underlying room and pillar old goaf in close coal seam and mining of the upper coal seam

Possible issues during mining of the upper coal seam in old goaf of nearby coal seams, including step subsidence, gas overflow in goaf, and roadway around rock fragmentation. Using the Hanjiawa Coal Mine’s upper coal seam mining, which takes place 28 m above the working face of the lower coal seam, as the research’s focal point. The paper focuses on the self-stability of the coal pillar in the old goaf, the failure form of the upper coal seam mining floor, the roof caving rule of the old goaf in the lower coal seam mining of the upper coal seam, and the bearing capacity of the interlayer rock strata using the pillar goaf stability evaluation system, field geological borehole electrical logging and borehole peeping, finite element difference numerical calculation, and other methods. The conclusion that the old goaf’s coal pillar can be completely stable and that the interlayer rock strata can bear the stress of upper coal seam mining is reached. The results show that the failure depth of the coal pillar in the lower coal seam old goaf is 1–3 m, the maximum failure depth accounting for 15% of the width of the coal pillar, and the failure depth of the roof in the old goaf is 0–3 m; After the mining of the upper coal seam, the floor above the coal pillar of the lower coal seam is plastic failure, and the failure depth is 1–10 m, and the failure depth of the roof of the old goaf of the lower coal seam is 3–15 m, which is 4 times greater than that before the mining. The maximum failure depth of the interlayer rock strata is 22 m, accounting for 78.6% of the rock strata spacing. The interlayer rock strata can bear the mining disturbance of the upper coal seam. The plastic zone of the floor of the upper coal seam is not connected with the plastic zone of the roof of the lower coal seam

Cobalt Phosphide Single Site Electrocatalysts derived from 2D ZIF-8 for Hydrogen Evolution Reaction

Hydrogen (H2) is a promising energy carrier due to its high gravimetric energy density and environmental friendliness. Among different approaches of H2 production, hydrogen evolution reaction (HER) via electrochemical water splitting has attracted significant research interests when electricity can be supplied by sustainable energy sources, such as solar, wind, and tidal energy. Electrocatalysts play a crucial role in accelerating HER's kinetics. Platinum (Pt)-based electrocatalysts have excellent catalytic activities for HER due to their optimal adsorption/desorption energy for hydrogen and have been are widely used in industrial water electrolyzers. However, their high cost and poor stability seriously limit their broad adoption. Tremendous research efforts have been devoted to developing electrocatalysts based on earth-abundant transition metals, such as iron (Fe), cobalt (Co), nickel (Ni), and molybdenum (Mo). In this regard, emerging single-atom catalysts (SACs) with maximum atom utilization is considered as one of the most promising solutions. Here, a new cobalt phosphide single site HER electrocatalyst was synthesized from ultrathin 2D ZIF-8 metal-organic framework (MOF) nanosheets using a mixed solution containing Zn2+, Co2+, 2-methylimidazole. The 2D MOF nanosheets facilitate the phosphating of single atom Co sites rather than forming cobalt phosphide nanoparticles. Structural characterizations indicated that the as-synthesized electrocatalyst (denoted as CoPSS) has a CoN2P2 coordination structure, where Co metal centres coordinate with two P atoms and two N atoms. CoPSS demonstrates an excellent HER performance in an alkaline electrolyte with an overpotential of 117 mV at 10 mA cm-2 and a Tafel slope of 55 mV dec-1. This work provides a facile and cost-effective strategy for preparing high efficient HER electrocatalysts

Learning to Optimize: Applications in Physical Designs and Manufacturing

Engineering and physical science often involve the design and manufacturing of physical devices. Conventionally, optimizing the physical design and the manufacturing process heavily relies on domain expertise and requires an iterative trial-and-error process conducted by human experts before achieving desired performance. Though numerical optimization methods have been developed for assisting domain experts, they often rely on heuristics that could be sub-optimal for the tasks of interest. Additionally, the performance of conventional optimization methods does not improve as more tasks are solved. This dissertation frames optimization as a learning problem, i.e., learning-to-optimize, where machine learning models are trained to solve optimization problems. We propose three methods for solving practical optical inverse design and manufacturing problems. Our first proposed method OML-PPO treats optical multilayer thin films design tasks as sequence generation problems. Sequence generation networks that can discover optimal designs corresponding to user-specified optical properties are trained by reinforcement learning. The proposed method has been used to design a perfect broadband absorber with reflectance higher than 99%, an incandescent light bulb filter that can enhance the brightness by 16.3 times, and chrome replacement coatings with a close appearance to chrome films. Instead of targeting generic optical design tasks, our second method NEUTRON is a hybrid machine learning and optimization approach for efficiently designing optical multilayer thin films for structural color applications. By modeling the structural color inverse design as a bi-level optimization problem, NEUTRON applies machine learning models for fast, approximate material selection and particle swarm optimization for an exact search of the optimal thickness. We applied NEUTRON to both the chrome replacement coating and image reconstruction tasks. The results show that NEUTRON can achieve more accurate designs than machine learning or optimization alone. Thanks to the high efficiency of NEUTRON, we can reconstruct images with more than 200,000 pixels within a few hours. Our third method M2BOP addresses the costly data collection problem common in manufacturing problems by combining meta-learning and model-based offline reinforcement learning. By learning a meta environment model using offline data collected from relevant tasks, M2BOP can solve new tasks efficiently with a handful of data. On robot locomotion control tasks, M2BOP outperforms baseline approaches, especially on offline datasets that contain sub-optimal demonstrations.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/172757/1/hzwang_1.pd

Diode-Pumped Single-Longitudinal-Mode Pr³⁺:YLF Laser Based on Combined Fabry–Perot Etalons at 522.67 nm

We create a rate equation theoretical model of a continuous-wave end-pumped Pr3+:YLF SLM laser that characterizes the output properties of a single-longitudinal-mode (SLM) green laser. After inserting two Fabry–Perot (F–P) etalons with thicknesses of 0.3 mm and 0.5 mm and angles of 1.42° and 0.69° into the cavity, a single-longitudinal-mode green laser was generated. The maximum output power in single-longitudinal mode was 183 mW. The maximum absorbed pump power was 6.2 W. The corresponding linewidth is about 18 MHz. This work presents a simple method for generating a single-longitudinal-mode laser in the green spectral region, providing a practical approach for various green-laser-related applications

Study on the Mechanism of a Hanging Roof at a Difficult Caving End in a Fully-Mechanized Top Coal Caving Face

During the mining process of fully-mechanized caving faces, the roof of the roadway behind the working face easily forms an arched-shape hanging roof structure with the working face pushed forward, which results in potential hazards such as gas accumulation and large-scale roof collapse. Based on the actual situation of a hanging roof at a difficult caving end in fully-mechanized top coal caving faces, through borehole exploration, surrounding rock displacement observation, bolt stress monitoring, theoretical formula calculation, and numerical simulation methods, the structure characteristics of the hanging roof at the end of the fully-mechanized caving face are studied. The ultimate failure depth and ultimate break distance of the hanging roof structure at the end of the working face are obtained, and its formation mechanism is analyzed. It is concluded that the hanging structure is formed by the following reasons: the lithology of sandy mudstone and fine sandstone above the top coal of the roadway is strong; the hanging roof structure is less affected by working-face mining; there is a result of insufficient rotary pressure of the upper mudstone while working together with the protective coal pillar and end support the caving step distance of the curved hanging roof structure is 10~13.55 m

Coal Wall Spalling Mechanism and Grouting Reinforcement Technology of Large Mining Height Working Face

To control the problem of coal wall spalling in large mining height working faces subject to mining, considering the Duanwang Mine 150505 fully mechanized working face, the mechanism of coal wall spalling in working faces was investigated by theoretical analysis, numerical simulation and field experiment. Based on analysis of coal wall spalling in the working face, a new grouting material was developed. The stress and plastic zone changes affecting the coal wall, before and after grouting in the working face, were analyzed using numerical simulation and surrounding rock grouting reinforcement technology was proposed for application around the new grouting material. The results showed that: (1) serious spalling of the 150505 working face was caused by the large mining height, fault influence and low roof strength, and (2) the new nano-composite low temperature polymer materials used have characteristics of rapid reaction, low polymerization temperature, adjustable setting time, high strength and environmental protection. Based on analysis of the working face coal wall spalling problem, grouting reinforcement technology based on new materials was proposed. Industrial tests were carried out on the working face. Field monitoring showed that the stability of the working face coal wall was significantly enhanced and that rib spalling was significantly improved after comprehensive anti-rib-spalling grouting measures were adopted. These results provide a basis for rib spalling control of working faces under similar conditions