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Although it has often been argued that clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorder (ASD), relatively few investigations have indexed the impact of intervention and feedback approaches. This pilot study investigated the application of a novel robotic interaction system capable of administering and adjusting joint attention prompts to a small group (n = 6) of children with ASD. Across a series of four sessions, children improved in their ability to orient to prompts administered by the robotic system and continued to display strong attention toward the humanoid robot over time. The results highlight both potential benefits of robotic systems for directed intervention approaches as well as potent limitations of existing humanoid robotic platforms.

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Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

The state-of-the-art of two coal demineralization technologies, acid/alkali leaching for ultraclean coal (UCC) and solvent extraction for hypercoal (HPC), has been critically reviewed in this paper. UCC or HPC here refers to a coal-derived solid fuel with overall ash content in the order of 0.1 wt %, which has the potential to burn directly in gas turbine combined cycle (GTCC) systems with a net power generation efficiency of no less than 48% on the higher heating value (HHV) basis. The UCC or HPC can also be potentially used in direct carbon fuel cell (DCFC) systems with a net power generation efficiency larger than 60% on HHV basis. Its gasification-derived syn-gas is more energy-intensive, and can be used as a hydrogen source for high-efficiency electricity generation with zero emissions and as a feedstock for the synthesis of value-added chemicals and liquid fuels. In this paper, two typical processes for the generation of UCC and HPC have been comprehensively reviewed to address both fundamentals of the elution of metals and the practical feasibilities. In particular, direct information related to the properties of the inorganic metals remaining in HPC has been intensively addressed. Its implications to the speciation of original metals in coal, especially those embedded as organically bound metals and/or submicrometer particles in coal matrix, were summarized. Finally, the research requirement for the generation of ultraclean coal from low-rank coal was proposed.

In addition to social and behavioral deficits, individuals with Autism Spectrum Disorder (ASD) often struggle to develop the adaptive skills necessary to achieve independence. Driving intervention in individuals with ASD is a growing area of study, but it is still widely under-researched. We present the development and preliminary assessment of a gaze-contingent adaptive virtual reality driving simulator that uses real-time gaze information to adapt the driving environment with the aim of providing a more individualized method of driving intervention. We conducted a small pilot study of 20 adolescents with ASD using our system: 10 with the adaptive gaze-contingent version of the system and 10 in a purely performance-based version. Preliminary results suggest that the novel intervention system may be beneficial in teaching driving skills to individuals with ASD.