Inclusive Methods in Art Classroom for Students with Autism in China

Cultivating professional knowledge and developing specific inclusive training has always been important for educators in China. Within the context of special art education in China, art educators are supposed to adjust the instruction effectively for students with a variety of disabilities. This qualitative study sought to gain insight of the strategies of modifying and adapting art instruction for children with autism. Data were collected and analyzed by comparative method through interviews with three art educators. During the interview, three participants addressed strategies they use when working with autistic students in art classroom and proved the ways to modify and adapt art instruction to enhance autistic children’s pedagogical skills. Art teachers can also identify students’ preferred art materials to engage autistic students in learning art. Beyond helping my educational practice, this study could further serve as an impetus to assist other educators in China thus expand teachers’ professional knowledge in teaching children with special needs

Steam reforming of toluene as biomass tar model compound in a gliding arc discharge reactor

Non-thermal plasma is considered a promising and attractive approach for the removal of tars from biomass gasification to deliver a clean and high quality syngas (a mixture of H2 and CO). In this study, an AC gliding arc discharge (GAD) reactor has been developed for the conversion of toluene as a tar model compound using nitrogen as a carrier gas. The presence of steam in the plasma reaction produces OH radicals which open a new reaction route for the conversion of toluene through a stepwise oxidation of toluene and intermediates, resulting in a significant enhancement in both the conversion of toluene and the energy efficiency of the plasma process. The effects of steam-to-carbon (S/C) molar ratio, toluene feed rate and specific energy input (SEI) on the performance of the plasma steam reforming of toluene have been investigated. The optimal S/C molar ratio was found to be between 2 and 3 for high toluene conversion and energy efficiency. The maximum toluene conversion of 51.8% was achieved at an optimal S/C molar ratio of 2, a toluene feed flow rate of 4.8 ml/h and a SEI of 0.3 kWh/m3, while the energy efficiency of the plasma process reached a maximum (∼46.3 g/kWh) at a toluene feed flow rate of 9.6 ml/h and a SEI of 0.19 kWh/m3. H2, CO and C2H2 were identified as the major gas products with a maximum syngas yield of 73.9% (34.9% for H2 and 39% for CO). Optical emission spectroscopy (OES) has been used to understand the role of steam on the formation of reactive species in the plasma conversion of toluene. The possible reaction pathways in the plasma conversion of toluene have also been proposed by combined means of the analysis of gas and liquid samples and OES diagnostics

LLMCad: Fast and Scalable On-device Large Language Model Inference

Generative tasks, such as text generation and question answering, hold a crucial position in the realm of mobile applications. Due to their sensitivity to privacy concerns, there is a growing demand for their execution directly on mobile devices. Currently, the execution of these generative tasks heavily depends on Large Language Models (LLMs). Nevertheless, the limited memory capacity of these devices presents a formidable challenge to the scalability of such models. In our research, we introduce LLMCad, an innovative on-device inference engine specifically designed for efficient generative Natural Language Processing (NLP) tasks. The core idea behind LLMCad revolves around model collaboration: a compact LLM, residing in memory, takes charge of generating the most straightforward tokens, while a high-precision LLM steps in to validate these tokens and rectify any identified errors. LLMCad incorporates three novel techniques: (1) Instead of generating candidate tokens in a sequential manner, LLMCad employs the smaller LLM to construct a token tree, encompassing a wider range of plausible token pathways. Subsequently, the larger LLM can efficiently validate all of these pathways simultaneously. (2) It employs a self-adjusting fallback strategy, swiftly initiating the verification process whenever the smaller LLM generates an erroneous token. (3) To ensure a continuous flow of token generation, LLMCad speculatively generates tokens during the verification process by implementing a compute-IO pipeline. Through an extensive series of experiments, LLMCad showcases an impressive token generation speed, achieving rates up to 9.3x faster than existing inference engines

Magnetic Properties of Ni-doped ZnO Nanocombs by CVD Approach

The search for above room temperature ferromagnetism in dilute magnetic semiconductors has been intense in recent year. Arrays of perpendicular ferromagnetic nanowire/rods have recently attracted considerable interest for their potential use in many areas of advanced nanotechnology. We report a simple low-temperature chemical vapor deposition (CVD) to create self-assembled comb-like Ni-/undoped ZnO nanostructure arrays. The phases, compositions, and physical properties of the studied samples were analyzed by different techniques, including high-resolution X-ray diffraction/photoelectron spectroscopy/transmission electron microscopy, photoluminescence, and MPMS. In particular, the Ni-doped ZnO nanocombs (NCs) with ferromagnetic and superparamagnetic properties have been observed whereas undoped ZnO NCs disappear. The corresponding ferromagnetic source mechanism is discussed, in which defects such as O vacancies would play an important role