The increasing demand for sustainable energy has driven significant research into efficient water splitting, particularly the development of electrocatalysts for the oxygen evolution reaction (OER) which is limited by sluggish kinetics.

Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics.

E-reading devices are becoming more and more common in our daily life, and they are used under a wide range of ambient light levels, from completely dark to extremely bright conditions. In this study, a psychophysical experiment is carried out to investigate how ambient light level affects the visual comfort of an e-reading device. Human observers compare the visual comfort of pairs of different text-background lightness combinations on a tablet device under three ambient light levels (i.e., 150, 1500, and 15,000 lx). With our previous work, the experimental results show that the trend of visual comfort interval scales below 1500 lx (i.e., Dark, 150, 300, and 1500 lx) are similar to each other but not for those under illuminance above the 1500 lx (i.e., 3000 and 15,000 lx). For the same lightness difference between text and background, the observers tend to read the text with a white background compared to a black background, especially for 3000 and 15,000 lx. Moreover, a black text on a light-gray background is the most comfortable combination under these two illuminance levels. An evaluation model is proposed based on ambient illuminance, screen parameters, and visual estimation to design an optimal viewing condition when reading on the tablet display.

In recent years, the general public and the technology industry have favored stereoscopic vision, immersive experience, and real-time visual information reception of virtual reality (VR) and augmented reality (AR). The device carrier, the Head-Mounted Display (HMD), is recognized as one of the next generation’s most promising computing and communication platforms. HMD is a virtual image optical display device that combines optical lens modules and binocular displays. The visual impact it brings is much more complicated than the traditional display and also influences the performance of image quality. This research investigated the visual threshold of stray light for three kinds of VR HMD devices, and proposes a qualitative model, derived from psychophysical experiments and the measurement of images on VR devices. The recorded threshold data of the psychophysical stray light perception experiment was used as the target when training. VR display image captured by a wide-angle camera was processed, through a series of image processing procedures, to extract variables in the range of interest. The machine learning algorithm established an evaluation method for human eye-perceived stray light in the study. Four supervised learning algorithms, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), were compared. The established model’s accuracy was about 90% in all four algorithms. It also proved that different percentages of thresholds could be used to label data according to demand to predict the feasibility of various subdivision inspection specifications in the future. This research aimed to provide a fast and effective stray light qualitative evaluation method to be used as a basis for future HMD optical system design and quality control. Thus, stray light evaluation will become one of the critical indicators of image quality and will be applicable to VR or AR content design.

Appropriate colour combinations improved aesthetic design quality and provided a comfortable and pleasant visual experience. However, applying current colour harmony models to clothing colour matching raised doubts about whether the existing theory needed to be refined, requiring further clarification with modern aesthetic perspectives. The study conducted a psychophysical experiment to investigate modern people's perceptions of colour harmony in clothing colour combinations. The results indicated that modern perceptions of colour harmony in clothing differed significantly from previous theories. For applications, two colour harmony models were established with unified fashion datasets for evaluating clothes matching based on colour harmony rules and observers' perceptions. The result showed that the rule-based model could accurately predict all items following nine colour harmony theories, and the perception-based colour harmony evaluation models aligned with contemporary aesthetic preferences were developed with a semi-supervised learning approach. The models, including support vector machines and custom convolutional neural networks, could predict colour harmony perception for the input images with high performance, and the model based on the generative adversarial network could provide colour recommendations for colour matching. The machine learning and deep learning model proposed in the study could be used for aesthetic judgment to generate clothing colour recommendations and provide valid design suggestions for clothing colour matching in the fashion and clothing industry.

The study aims to propose an approach of white LED spectral optimization based on mesopic luminance and color gamut volume for dim lighting conditions. Three optimal white LED spectra with relatively higher mesopic luminance and color gamut volume, the highest mesopic luminance, and the largest gamut volume are recommended for reducing energy consumption and enhancing color perception and recognition of human eyes. The theoretical simulation shows that the spectra with higher correlated color temperatures (CCT) and S/P-ratio increase the mesopic luminance and also extend the range of color gamut with the decreasing of lighting level. An evaluation model is developed to faster predict mesopic luminance, color gamut volume, and S/P ratio for lighting applications.