In recent years, simulation studies have emerged as valuable tools for understanding processes. In particular, molecular dynamic simulations hold great significance when it comes to the adsorption process. However, comprehensive studies on molecular simulations of adsorption processes using different adsorbents are scarcely available for wastewater treatment covering different contaminants and pollutants. Hence, in this review, we organized the available information on various aspects of the adsorption phenomenon that were realized using molecular simulations for a broad range of potentially effective adsorbents applied in the removal of contaminants from wastewater. This review was compiled for adsorbents under five major categories: (1) carbon-based, (2) oxides and hydroxides, (3) zeolites, (4) metal–organic frameworks and (5) clay. From the review, it was found that simulation studies help us understand various parameters such as binding energy, Gibbs free energy, electrostatic field, ultrasound waves and binding ability for adsorption. Moreover, from the review of recent simulation studies, the effect of ultrasound waves and the electrostatic field was elucidated, which promoted the adsorption capacity. This review can assist in the screening of classified adsorbents for wastewater treatment using a fast and cheap approach while helping us understand the adsorption process from an atomistic perspective.

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature – deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer’s disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.

A 360° twisted helical capacitance sensor was developed for holdup measurement in horizontal two-phase stratified flow. Instead of suppressing nonlinear response, the sensor was optimized in such a way that a ‘sine-like’ function was displayed on top of the linear function. This concept of design had been implemented and verified in both software and hardware. A good agreement was achieved between the finite element model of proposed design and the approximation model (pure sinusoidal function), with a maximum difference of ±1.2%. In addition, the design parameters of the sensor were analysed and investigated. It was found that the error in symmetry of the sinusoidal function could be minimized by adjusting the pitch of helix. The experiments of air-water and oil-water stratified flows were carried out and validated the sinusoidal relationship with a maximum difference of ±1.2% and ±1.3% for the range of water holdup from 0.15 to 0.85. The proposed design concept therefore may pose a promising alternative for the optimization of capacitance sensor design.

Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes.

Living in high-expressed emotion (EE) environments, characterized by critical, hostile, or over-involved family attitudes, has been linked to increased relapse rates among individuals with schizophrenia (SZ). In our previous work (Wang et al., 2023), we conducted the first feasibility study of using functional near-infrared spectroscopy (fNIRS) with our developed EE stimuli to examine cortical hemodynamics in SZ. To better understand the neural mechanisms underlying EE environmental factors in SZ, we extended our investigation by employing functional connectivity (FC) analysis with a graph theory approach to fNIRS signals. Relative to healthy controls (N = 40), individuals with SZ (N = 37) exhibited altered connectivity across the medial prefrontal cortex (mPFC), left ventrolateral prefrontal cortex (vlPFC), and left superior temporal gyrus (STG) while exposed to EE environments. Notably, while individuals with SZ were exposed to high-EE environments, (i) reduced connectivity was observed in these brain regions and (ii) the left vlPFC-STG coupling was found to be associated with the negative symptom severity. Taken together, our FC findings suggest individuals with SZ experience a more extensive disruption in neural functioning and coordination, particularly indicating an increased susceptibility to high-EE environments. This further supports the potential utility of integrating fNIRS with the created EE stimuli for assessing EE environmental influences, paving the way for more targeted therapeutic interventions.