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One of the most prevalent behavioral impairments in autistic people is difficulty processing sensory information. People commonly observe this phenomenon as either hypersensitivity or hyposensitivity to tactile stimuli. To rectify this irregularity, numerous researchers have suggested wearable sensor-based systems and applications within the realm of virtual environments. However, they have neglected to carry out an adequate evaluation and proof of its feasibility for autistic people. Hence, this study compares three methods to identify the most effective approach to understanding tactile sensory processing in autistic people using haptic technology. The evaluation included behavioral response analysis, which involves observing autistic people; statistical analysis on tactile sensory patterns (TSP), which analyzes data from 9-axis IMU sensors and EMG sensors; and machine learning models, such as recurrent neural networks (RNNs), trained on tactile sensory sensitivity data. The study demonstrates that behavioral response analysis is limited by subjectivity and variability in responses, despite its capacity to provide useful qualitative perspectives. Meanwhile, statistical analysis reveals limitations in its ability to predict sensory outcomes, despite its capacity to provide quantitative measurements of variations in tactile sensory processing. Comparative analysis using machine learning, on the other hand, outperforms both behavioral response analysis and statistical analysis in tactile sensory processing classification and prediction. In particular, the RNN model exhibits remarkable accuracy and correctness in detecting tactile sensory processing among autistic people. This study demonstrated that machine learning can be advantageous for autistic people to analyze tactile sensory processing, explore, and develop touch sensitivity to improve their quality of life.

Autistic people typically need methodical support as they explore and interact with their immediate surroundings and the objects associated with them, emphasising the importance of spatial knowledge and cognitive skills in improving and understanding their surroundings. The objective of this research paper is to present a conceptual and technical framework that could be of significant assistance in developing spatial ability and cognitive skills in autistic people. This framework is constructed based on aspects of autistic behaviour, spatial cognition, and technical aspects of haptic technology and virtual environments (VEs). The framework uses the Model, View, Controller (MVC) pattern as its structural foundation. This paper highlights the important elements required for scholars and designers to create and develop a haptic-based virtual environment (HBVE) for autistic people in order to improve spatial knowledge and cognitive skills. Furthermore, this research also aimed to create an HBVE application based on the proposed framework in order to assess its effectiveness. This research paper evaluates the proposed framework based on end-user perspectives. The findings indicate that the experimental study conducted with autistic people has demonstrated notable progress in enhancing spatial knowledge and cognitive abilities among this population. The experimental implementation of these findings supports the developed framework’s efficacy.

Li2MnSiO4 has attracted significant attention as cathode material for lithium ion batteries due to its structural diversity, abundance, low cost, thermal stability, and high theoretical capacity (330 mAh g−1). However, it suffers from low electronic conductivity, poor cycling performance, and limited reversible capacity. To overcome these deficiencies, as well as to further enhance its thermal stability and rate capability, we discover that surface modification utilizes an ex-situ carbon coating method using sucrose as the carbon source. Our approach enabled a uniform coating of amorphous carbon on Li2MnSiO4 with a monoclinic crystalline structure, which was confirmed by X-ray diffraction, electron microscopy, and Raman studies. The influence of sucrose on the performance of carbon coating has been analyzed in the half-cell configuration employing 1.0 M LiPF6 in ethylene carbonate/diethyl carbonate (1:1 v/v) as an electrolyte. Hence, the impact on the controlled carbon coating significantly improves the cycle characteristics of redox peaks (Mn2+/Mn3+ and Mn3+/Mn4+) originally observed in the pristine monoclinic phase of Li2MnSiO4 within a potential range of 2.5–4.4 V vs. Li/Li+. A well-tuned carbon-coated product benefits from the optimum thickness results in achieving excellent thermal stability by protective action of carbon coating and rate capability retains ~ 92% of its initial capacity of 159 mAh g−1 after 200 cycles at a higher current rate 0.5 C. [ABSTRACT FROM AUTHOR]