Wei Sun’s research while affiliated with Institute of Software, Chinese Academy of Sciences and other places

Manganese is abundant in the Earth's crust as a mineral and commonly enters mining and metallurgical wastewater (MMW) through extraction and smelting activities. However, comprehensive reviews on manganese removal technologies for MMW remain limited. This paper begins by introducing the basic properties and hazards of manganese and then details the formation processes, characteristics, and hazards of two representative types of manganese-containing MMW, namely, acid mine drainage (AMD) and electrolytic metal manganese residue leachate (EMMRL). This study then categorizes the current manganese removal technologies into chemical, physicochemical, and biological methods, and special attention is given to the widely used techniques, such as chemical precipitation, chemical oxidation, adsorption, and emerging electrochemical methods, along with their typical cases, removal mechanisms, and advantages and disadvantages. The key factors that influence the effectiveness of these technologies are also summarized. Resource recovery from manganese-containing wastewater is currently achieved through carbonate precipitation, phosphate precipitation, and electrochemical methods. This paper also discusses some prospects for the future removal and recovery of manganese from MMW.

Introduction Digital technologies have significantly advanced the detection of emotional disorders (EmD) in clinical settings. However, their adoption for long-term monitoring remains limited due to reliance on fixed testing formats and active user participation. This study introduces a novel approach utilizing common ball game videos–table tennis–to implicitly capture eye movement trajectories and identify EmD through natural viewing behavior. Methods An eye movement data collection system was developed using VR glasses to display sports videos while recording participants' eye movements. Based on prior research and collected data, four primary eye movement behaviors were identified, along with 14 associated features. Statistical significance was assessed using t-tests and U-tests, and machine learning models were employed for classification (SVM for single-feature analysis and a decision tree for significant features) with k-fold validation. The reliability of the proposed paradigm and extracted features was evaluated using intraclass correlation coefficient (ICC) analysis. Results Significance tests revealed 11 significant features in table tennis videos, encompassing exploration, fixation, and saccade behaviors, while only 3 features in tennis videos, which served as a supplemental stimulus, were salient in the re-testing. GazeEntropy emerged as the most predictive feature, achieving an accuracy of 0.88 with a significance p-value of 0.0002. A decision tree model trained on all significant features achieved 0.92 accuracy, 0.80 precision, and an AUC of 0.94. ICC analysis further confirmed the high reliability and significance of key features, including GazeEntropy and fixation metrics (average, maximum, and standard deviation). Discussion This study highlights the potential of ball game video viewing as a natural and effective paradigm for EmD identification, particularly focusing on two key characteristics of EmD: curiosity exploration and psychomotor function. Additionally, participant preferences for video content significantly influenced diagnostic performance. We propose that future in-home, long-term monitoring of psychological conditions can leverage interactions with daily digital devices, integrating behavioral analysis seamlessly into everyday life.

Objective Characteristic ocular symptoms are expected to serve as potential biomarkers for early diagnosis of Parkinson’s disease (PD). However, possible ocular impairments in PD patients are rarely studied. The study aimed to investigate eye movement characteristics and pupil diameter changes in early-stage PD patients using virtual reality (VR)-based system and explore their contribution in the diagnosis of early-stage PD. Methods Forty-three early-stage PD patients and 25 healthy controls were included. Eye movements and pupillary response of all subjects were recorded and evaluated by wearing VR glasses. All subjects completed pro-saccade and anti-saccade tasks. Saccadic eye movement and pupillary response parameters were analyzed. Random Forests method was used for classification task, the performance of the classification model in differentiating early-stage PD patients from healthy controls were evaluated. Results PD patients exhibited reduced pro-saccade velocity and accuracy, longer average time to complete the pro-saccade, and lower anti-saccade error correction rate than healthy controls (all p < 0.05). Significant differences were found in the trajectories of changes in pupil diameter between the two groups. After extraction of frequency-amplitude features of pupil constriction from the spectra of the eye movement signals of PD patients, it can be seen that the amplitudes of movement signals of both the left and right eyes at different frequencies during pro-saccade and anti-saccade tasks were significant. The number of significant amplitude frequencies in both eyes at low (0–6 Hz), medium (7–12 Hz) and high frequencies (13–19 Hz) was 23, 9, and 16, respectively, during pro-saccade task, which was 10, 29, and 43, respectively, during anti-saccade task. The model with all features achieved an accuracy of up to 79%. Conclusion This study presents a non-invasive approach toward the diagnosis of early-stage PD with VR technology. Eye movement and pupillary response abnormalities measured using VR may be used as effective biomarkers for the diagnosis of early-stage PD.

Surface roughness refers to the micron- or nanometer-scale irregularities (bumps and grooves) on material surfaces, and it varies greatly as particles are refined, affecting their flotation behavior, wettability, and bubble–particle interactions. In this paper, the main roughening and measurement methods for surface roughness are summarized, the effects of surface roughness on flotation behavior and wettability are reviewed, and the main wettability models for rough surfaces are also introduced. Grinding is the most commonly used method, while other methods, such as acid etching, abrasion, sand-blasting, ultrasonic pretreatments, and microwave treatments, have also been explored. Most research shows that increasing the surface roughness effectively enhances the hydrophobicity of hydrophobic surfaces and the hydrophilicity of hydrophilic surfaces. This improvement leads to better flotation recovery and kinetics for hydrophobic surfaces, whereas it deteriorates that for hydrophilic surfaces. Moreover, the relationship between surface roughness and bubble–particle interactions, including bubble–particle attachment, interaction energy, and interaction force, is introduced. Most research shows that increased surface roughness effectively decreases the attachment time and energy barrier and increases the adhesion force between air bubbles and rough hydrophobic surfaces. Conversely, these effects can be detrimental to rough hydrophilic surfaces. This paper also addresses existing problems and challenges in the field and offers references and suggestions for future research efforts.