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Research into the effects of child-centered play therapy (CCPT) consistently highlights its positive impact on autistic children. However, its effectiveness at the neural level is underexplored. To address this research gap, the present study investigates the influence of CCPT interventions on autistic children, specifically focusing on measurable changes in brain activity through electroencephalography (EEG) alpha power analysis. Autistic children were randomly assigned to an experimental group (EXP; n = 34; M age = 7.50) and a waitlist group (WL; n = 31; M age = 7.47). The EXP group underwent weekly 45-minute individual CCPT sessions for 8 weeks. Pre- and post-intervention EEG recordings during a social activity with varied social contexts were obtained. In addition, behavioral data from the Social Responsiveness Scale-2 (SRS-2), Autism Spectrum Quotient-Child (AQ-child; social skills subscale), and Adaptive Behavior Assessment System-II (ABAS-II; social domain) were collected before and after the intervention. Linear mixed models revealed a significant increase in alpha amplitude post-intervention across all social activities and conditions in the EXP group. Notably, significant differences in SRS-2, AQ-child (social skills), and ABAS-II (social domain) scores were observed between pre- and post-CCPT intervention in the EXP group. In contrast, impacts on both neural and behavioral activity remained nonsignificant in the WL group. CCPT exhibits a significant positive effect on autistic children, as evidenced by changes in EEG alpha power and improvements in SRS-2, AQ-child (social skills), and ABAS-II (social domain) scores. These results provide data for future investigation of the intricate neural mechanisms underlying the CCPT-enhanced social behavior observed in autistic children. • Child-centered play therapy intervention was implemented with an aim to enhance children's social performance • EEG alpha amplitude showed an increase following the child-centered play therapy intervention • Positive behavioral changes were evident in the SRS-2, AQ-child (social skills), and ABAS-II (social domain) scores • Neural and behavioral effects of the intervention were absent in the control group [ABSTRACT FROM AUTHOR]

The intra-subject variability (ISV) in brain responses during cognitive processing across experimental trials has been recognised as an important facet of neural functionality reflecting an intrinsic neurophysiological characteristic of the brain. In recent decades, ISV in behaviour has been found to be significantly associated with cognitive functioning varying across individuals, development, ages, and pathological conditions. Event-related potentials (ERPs) measured in single trials are important tools for characterizing ISV at the neural level. However, due to the overlapping spectra of noise and signals, the retrieval of information from single-trial ERPs related to cognitive processing has been a challenge. We review the major problems that researchers face in the estimation of ISV in single-trial ERPs. Then, we present an extensive evaluation of several methods of single-trial latency estimation based on both simulated and real data. The relationships of ISV in ERPs and reaction times are compared between the different single-trial methods to assess their relative efficiency in predicting task performance from neural signals. The pros and cons of the methods are discussed. [ABSTRACT FROM AUTHOR]

The late components of event-related brain potentials (ERPs) pose a difficult problem in source localization. One of the reasons is the smearing of these components in conventional averaging because of trial-to-trial latency-variability. The smearing problem may be addressed by reconstructing the ERPs after latency synchronization with the Residue Iteration Decomposition (RIDE) method. Here we assessed whether the benefits of RIDE at the surface level also improve source localization of RIDE-reconstructed ERPs (RERPs) measured in a face priming paradigm. Separate source models for conventionally averaged ERPs and RERPs were derived and sources were localized for both early and late components. Jackknife averaging on the data was used to reduce the residual variance during source localization compared to conventional source model fitting on individual subject data. Distances between corresponding sources of both ERP and RERP models were measured to check consistency in both source models. Sources for activity around P100, N170, early repetition effect (ERE/N250r) and late repetition effect (LRE/N400) were reported and priming effects in these sources were evaluated for six time windows. Significant improvement in priming effect of the late sources was found from the RERP source model, especially in the Medio-Temporal Lobe, Prefrontal Cortex, and Anterior Temporal Lobe. Consistent with previous studies, we found early priming effects in the right hemisphere and late priming effects in the left hemisphere. Also, the priming effects in right hemisphere outnumbered the left hemisphere, signifying dominance of right hemisphere in face recognition. In conclusion, RIDE reconstructed ERPs promise a comprehensive understanding of the time-resolved dynamics the late sources play during face recognition. [ABSTRACT FROM AUTHOR]

Background Conventionally, event-related brain potentials (ERPs) are obtained by averaging a number of single trials. This can be problematic due to trial-to-trial latency variability. Residue iteration decomposition (RIDE) was developed to decompose ERPs into component clusters with different latency variability and to re-synchronize the separated components into a reconstructed ERP. New method RIDE has been continuously upgraded and now converges to a robust version. We describe the principles of RIDE and detailed algorithms of the functional modules of a toolbox. We give recommendations and provide caveats for using RIDE from both methodological and psychological perspectives. Results RIDE was applied to several data samples to demonstrate its ability to decompose and reconstruct latency-variable components of ERPs and to retrieve single trial variability information. Different functionalities of RIDE were shown in appropriate examples. Comparison with existing methods RIDE employs several modules to achieve a robust decomposition of ERP. As main innovations RIDE (1) is able to extract components based on the combination of known event markers and estimated latencies, (2) prevents distortions much more effectively than previous methods based on least-square algorithms, and (3) allows time window confinements to target relevant components associated with sub-processes of interest. Conclusions RIDE is a convenient method that decomposes ERPs and provides single trial analysis, yielding rich information about sub-components, and that reconstructs ERPs, more closely reflecting the combined activity of single trial ERPs. The outcomes of RIDE provide new dimensions to study brain–behavior relationships based on EEG data. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews state of the art of combined chilled ceilings and displacement ventilation (CC/DV) systems all over the world. The feasibility of the application of the combined systems in South-China area is analyzed. However, there exist three main problems such as panel surface condensation, first investment cost and enough cooling capacity to be solved. Several methods were illustrated to prevent the condensation of the combined systems in three aspects. The high investment cost problem can be solved with the development and production of new independent terminal products of CC systems. Many products can meet the cooling load requirements in most cases which are suitable for applying the combined CC/DV systems. Finally the development of combined systems in the South-China region in the future is prospected. [Copyright &y& Elsevier]

Abstract: This paper experimentally studied the frosting process of a folded–louvered-fin, parallel flow microchannel evaporator in a heat pump central air-conditioning system under three conditions, in which three open states of two capillaries were adopted. Surface temperature distribution on evaporator was measured by 16 thermocouples buried on the leeward side. Mesoscale frost formation processes on its front view surface for three different test conditions were observed using a Charge Coupled Device (CCD) camera. The results showed that the surface temperature distribution on the parallel flow evaporator was uneven and initial frost formation generally started on some partial surface areas of the louvered fins whose surface temperature was lowest after the heat pump system running 6min later under conditions B and C, while the evaporator began to frost after 8min under condition A. The non-uniform surface temperature distribution caused by the unequal distribution of the refrigerant flux in the flat tubes’ microchannels resulted in uneven distribution of frost. The ice crystals distribution and frost thickness in frost period could be obtained by the digital image processing method in which the initial pictures were converted into binary image. The results indicated that in a thin layer near fins’ surface, ice crystals had relatively high occupancy rate in the frost growing period and full growth period; the occupancy rate of ice crystals decreased almost linearly with the increasing of the frost thickness (frost height), where the decreasing rate in the frost full grown period was less than that in the frost growing period (in 0.1mm frost thickness condition, the occupancy rate of ice crystals decreased to 58% in the frost growing period, while in the frost full grown period occupancy rate of ice crystals decreased to 90%; and in 0.25mm frost thickness, they were 0%, 45% respectively). Furthermore, it can be found that the frost thickness increased as the time increasing and then finally reached maximum values of 0.3mm, 0.35mm, and 0.32mm respectively at three conditions. [Copyright &y& Elsevier]

• ReSync is a method for correcting ERP distortions due to latency jitter. • The correction is conducted by a combination of decomposition and reconstruction. • ReSync automatically identifies the dominant frequency of ERP component. • The dominant frequency is used to improve latency estimation. • ReSync was tested in both simulation and empirical data. For various reasons, the brain response activities in electroencephalography (EEG) signals are not perfectly synchronized between trials with respect to event markers—a problem commonly referred to as latency jitter. Experimental technologies have been greatly advanced to reduce technical timing errors and thereby reduce jitter. However, there remain intrinsic sources of jitter that are difficult to remove. The problem becomes more complicated when multiple sub-components possess different degrees and features of jitter. The jitter issue renders trial-averaged ERP inaccurate and even misleading. Effective methods for correcting ERP distortion due to latency jitter are needed. This study developed a simple and easy-to-use method and toolbox for correcting ERP jitter based on simple signal processing theories, named ReSync. ReSync can be used to correct multiple overlapping ERP sub-components with different degrees of jitter (including static sub-components) without their affecting each other. The theories, principles, technical details, and limitations of ReSync are presented in this paper, along with a series of simulation and real data examples used to evaluate and validate the method. ReSync was conceptually compared with previous methods in the literature that are related to tackling of the jitter issue from theoretical, methodological, and technical perspectives. Providing a novel approach to latency jitter estimation with automatic dominant frequency identification and integrated decomposition and reconstruction, the ReSync method was validated using both simulation and empirical data, and demonstrated to be an effective jitter-correction approach with implementational simplicity. [ABSTRACT FROM AUTHOR]

• Brain Signal Complexity measured by Multi-Scale Entropy decreases with increase in information input to the cognitive system. • Reliability of Multi-Scale Entropy measurements is satisfactory across ten time scales for EEG epochs of 10 s length. • Recording conditions of the EEG signals do not only influence Brain Signal Complexity on average, but they affect individual differences. • Structural Equation Modeling is a powerful tool for analyzing individual differences in Brain Signal Complexity in different experimental conditions. Multi-Scale Entropy (MSE) is a widely used marker of Brain Signal Complexity (BSC) at multiple temporal scales. There is no systematic research addressing the psychometric quality and reliability of MSE. It is unknown how recording conditions of EEG signals affect individual differences in MSE. These gaps can be addressed by means of Structural Equation Modeling (SEM). Based on a large sample of 210 young adults, we estimated measurement models for MSE derived from multiple epochs of EEG signal measured during resting state conditions with closed and open eyes, and during a visual task with multiple experimental manipulations. Factor reliability estimates, quantified by the McDonald's ω coefficient, are high at lower and acceptable at higher time scales. Above individual differences in signal entropy observed across all recording conditions, persons specifically differ with respect to their BSC in open eyes resting state condition as compared with closed eyes state, and in task processing state MSE as compared with resting state. By means of SEM, we decomposed individual differences in BSC into different factors depending on the recording condition of EEG signals. This goes beyond existing methods that aim at estimating average MSE differences across recording conditions, but do not address whether individual differences are additionally affected by the type of EEG recording condition. Eyes closed and open and task conditions strongly influence individual differences in MSE. We provide recommendations for future studies aiming to address BSC using MSE as a neural marker of cognitive abilities. [ABSTRACT FROM AUTHOR]

Handwriting is a fundamental component of school education, especially in the early phases. It is thus important to develop a scientific approach to refining the cost-efficiency of handwriting learning in young children. In this study, we proposed that an integration of behavioral and neural data tracking during the real-time process of handwriting learning can reveal the learning process and thus inform the design of handwriting training. The main rationale is that the two complementary information channels can reveal the dynamic learning curve during repetitive practices. Participants were 50 typically developing schoolchildren (aged 6–7) who had limited orthographical knowledge of Chinese characters and handwriting training. Synchronized EEG and handwriting kinematics data were collected when participants were performing a Chinese character copying task. Six unfamiliar Chinese characters at three different complexity levels were selected, and the participants copied each character repetitively for 15 times. The representative behavioral and neural features related to handwriting fluency were quantified, including writing duration, velocity, and event-related potentials (ERPs) extracted from the copying process of each character. By applying linear mixed models (LMMs), we found significant behavioral improvement and neural adaptation effect across the repetitive copying practices; and the observed behavioral and neural effects showed a systematic pattern of dependence on character complexity. These findings validated the cognitive association of the non-invasively collected neural signals, demonstrated the feasibility of combing behavioral and neural signals to track the process of children's handwriting learning, and informed the design of handwriting training programs. • A neural indicator is identified to characterize children's handwriting ability. • Both behavioral and neural indicators are associated with practice time. • Learning effects vary with character complexity at behavioral and neural levels. • Medium complexity exhibits the strongest learning effect in a single-session practice. [ABSTRACT FROM AUTHOR]

Abstract: Numerical simulations are performed to investigate the performance of an active magnetic regenerator (AMR) operating near room temperature. A two-dimensional porous model is established to analyze the impact different heat transfer fluids (HTFs) have on the performance of the AMR. The internal temperature distribution and cooling capacity of the system are analyzed and the influence of the HTF discussed. The simulation results show that when mercury is substituted in place of water as the HTF, the cooling capacity can be enhanced by nearly 600%. A fluid with high conductivity, high density, and low specific heat is most suitable for use as the HTF. Furthermore, as the environmental conditions have a great impact upon the performance of the AMR, three feasible methods of standardization testing are proposed. These involve: the evaluation index under fixed test environment conditions, a maximum exergy method, and a maximum specific exergy method around the Curie temperature. [Copyright &y& Elsevier]