Design of Helical Capacitance Sensor for Holdup Measurement in Two-Phase Stratified Flow: A Sinusoidal Function Approach.

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.

A review on recent advances of cellulose acetate membranes for gas separation.

This review thoroughly investigates the wide-ranging applications of cellulose-based materials, with a particular focus on their utility in gas separation processes. By focusing on cellulose acetate (CA), the review underscores its cost-effectiveness, robust mechanical attributes, and noteworthy CO2 solubility, positioning it as a frontrunner among polymeric gas separation membranes. The synthesis techniques for CA membranes are meticulously examined, and the discourse extends to polymeric blend membranes, underscoring their distinct advantages in gas separation applications. The exploration of advancements in CA-based mixed matrix membranes, particularly the incorporation of nanomaterials, sheds light on the significant versatility and potential improvements offered by composite materials. Fabrication techniques demonstrate exceptional gas separation performance, with selectivity values reaching up to 70.9 for CO2/CH4 and 84.1 for CO2/N2. CA/PEG (polyethylene glycol) and CA/MOF (metal-organic frameworks) demonstrated exceptional selectivity in composite membranes with favorable permeability, surpassing other composite CA membranes. Their selectivity with good permeability lies well above all the synthesised cellulose. As challenges in experimental scale separation emerge, the review seamlessly transitions to molecular simulations, emphasizing their crucial role in understanding molecular interactions and overcoming scalability issues. The significance of the review lies in addressing environmental concerns, optimizing membrane compositions, understanding molecular interactions, and bridging knowledge gaps, offering guidance for the sustainable evolution of CA-based materials in gas separation technologies.

Altered functional connectivity subserving expressed emotion environments in schizophrenia: An fNIRS study.

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.

Rethinking of the future sustainable paradigm roadmap for plastic waste management: A multi-nation scale outlook compendium.

The myriad consumption of plastic regularly, environmental impact and health disquietude of humans are at high risk. Along the line, international cooperation on a global scale is epitomized to mitigate the environmental threats from plastic usage, not limited to implementing international cooperation strategies and policies. Here, this study aims to provide explicit insight into possible cooperation strategies between countries on the post-treatment and management of plastic. First, a thorough cradle-to-grave assessment in terms of economic, environmental, and energy requirements is conducted on the entire life cycle across different types of plastic polymers in 6 main countries, namely the United States of America, China, Germany, Japan, South Korea, and Malaysia. Subsequently, P-graph is introduced to identify the integrative plastic waste treatment scheme that minimizes the economic, environmental, and energy criteria (1000 sets of solutions are found). Furthermore, TOPSIS analysis is also being adapted to search for a propitious solution with optimal balance between the dominant configuration of economic, environmental, and energy nexus. The most sustainable configuration (i.e., integrated downcycle and reuse routes in a closed loop system except in South Korea, which proposed another alternative to treat the plastic waste using landfill given the cheaper cost) is reported with 4.08 × 108 USD/yr, 1.76× 108 kg CO2/yr, and 2.73 × 109 MJ/yr respectively. To attain a high precision result, Monte-Carlo simulation is introduced (10,000 attempts) to search for possible uncertainties, and lastly, a potential global plastic waste management scheme is proposed via the PESTLE approach.

An fNIRS investigation of novel expressed emotion stimulations in schizophrenia.

Living in high expressed emotion (EE) environments tends to increase the relapse rate in schizophrenia (SZ). At present, the neural substrates responsible for high EE in SZ remain poorly understood. Functional near-infrared spectroscopy (fNIRS) may be of great use to quantitatively assess cortical hemodynamics and elucidate the pathophysiology of psychiatric disorders. In this study, we designed novel low- (positivity and warmth) and high-EE (criticism, negative emotion, and hostility) stimulations, in the form of audio, to investigate cortical hemodynamics. We used fNIRS to measure hemodynamic signals while participants listened to the recorded audio. Healthy controls (HCs, [Formula: see text]) showed increased hemodynamic activation in the major language centers across EE stimulations, with stronger activation in Wernicke's area during the processing of negative emotional language. Compared to HCs, people with SZ ([Formula: see text]) exhibited smaller hemodynamic activation in the major language centers across EE stimulations. In addition, people with SZ showed weaker or insignificant hemodynamic deactivation in the medial prefrontal cortex. Notably, hemodynamic activation in SZ was found to be negatively correlated with the negative syndrome scale score at high EE. Our findings suggest that the neural mechanisms in SZ are altered and disrupted, especially during negative emotional language processing. This supports the feasibility of using the designed EE stimulations to assess people who are vulnerable to high-EE environments, such as SZ. Furthermore, our findings provide preliminary evidence for future research on functional neuroimaging biomarkers for people with psychiatric disorders.

A systematic review of the molecular simulation of hybrid membranes for performance enhancements and contaminant removals.

Number of research on molecular simulation and design has emerged recently but there is currently a lack of review to present these studies in an organized manner to highlight the advances and feasibility. This paper aims to review the development, structural, physical properties and separation performance of hybrid membranes using molecular simulation approach. The hybrid membranes under review include ionic liquid membrane, mixed matrix membrane, and functionalized hybrid membrane for understanding of the transport mechanism of molecules through the different structures. The understanding of molecular interactions, and alteration of pore sizes and transport channels at atomistic level post incorporation of different components in hybrid membranes posing impact to the selective transport of desired molecules are also covered. Incorporation of molecular simulation of hybrid membrane in related fields such as carbon dioxide (CO2) removal, wastewater treatment, and desalination are also reviewed. Despite the limitations of current molecular simulation methodologies, i.e., not being able to simulate the membrane operation at the actual macroscale in processing plants, it is still able to demonstrate promising results in capturing molecule behaviours of penetrants and membranes at full atomic details with acceptable separation performance accuracy. From the review, it was found that the best performing ionic liquid membrane, mixed matrix membrane and functionalized hybrid membrane can enhance the performance of pristine membrane by 4 folds, 2.9 folds and 3.3 folds, respectively. The future prospects of molecular simulation in hybrid membranes are also presented. This review could provide understanding to the current advancement of molecular simulation approach in hybrid membranes separation. This could also provide a guideline to apply molecular simulation in the related sectors.

Optimizing Mental Workload Estimation by Detecting Baseline State Using Vector Phase Analysis Approach.

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.

Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease.

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 Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex.

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.