Ooi Kiang Tan

Sentiment Analysis (SA) is a category of data mining techniques that extract latent representations of affective states within textual corpuses. This has wide ranging applications from online reviews to capturing mental states. In this paper, we present a novel SA feature set; Emotional Variance Analysis (EVA), which captures patterns of emotional instability. Applying EVA on student journals garnered from an Experiential Learning (EL) course, we find that EVA is useful for profiling variations in sentiment polarity and intensity, which in turn can predict academic performance. As a feature set, EVA is compatible with a wide variety of Artificial Intelligence (AI) and Machine Learning (ML) applications. Although evaluated on education data, we foresee EVA to be useful in mental health profiling and consumer behaviour applications. EVA is available at https://qr.page/g/5jQ8DQmWQT4. Our results show that EVA was able to achieve an overall accuracy of 88.7% and outperform NLP (76.0%) and SentimentR (58.0%) features by 15.8% and 51.7% respectively when predicting student experiential learning grade scores through a Multi-Layer Perceptron (MLP) ML model.

The recent development of the Internet of Things (IoT) in healthcare and indoor air quality monitoring expands the market for miniaturized gas sensors. Metal oxide gas sensors based on microhotplates fabricated with micro-electro-mechanical system (MEMS) technology dominate the market due to their balance in performance and cost. Integrating sensors with signal conditioning circuits on a single chip can significantly reduce the noise and package size. However, the fabrication process of MEMS sensors must be compatible with the complementary metal oxide semiconductor (CMOS) circuits, which imposes restrictions on the materials and design. In this paper, the sensing mechanism, design and operation of these sensors are reviewed, with focuses on the approaches towards performance improvement and CMOS compatibility.

Simultaneous enhancement on photon absorption and charge separation leads to improved photo-activities of a CuO/BiVO₄ composite over pristine BiVO₄.

Mechanochemical high energy ball milling approach was used to synthesize monoclinic BiVO ₄ (m-BiVO ₄ ) nanoparticles in an attempt to simultaneously reduce the particle size and improve the throughput for practical photocatalytic applications.

Titanium dioxide (TiO₂) films are deposited on quartz substrates by atmospheric pressure (AP)CVD, and then annealed under simulated air (80% nitrogen, 20% oxygen) at temperatures from 600 to 900 °C to investigate the change in microstructure and the effect on the photocatalytic activity on the simulated pollutant stearic acid. The as‐deposited TiO₂ film is mainly composed of pure anatase phase while the rutile phase is detected only after annealing the film at 900 °C for 1 h. The photocatalytic activity of the annealed films on stearic acid under UV irradiation is found to deteriorate after the films are annealed at temperatures above 700 °C. This decrease in photocatalytic performance is observed to be the result of two possible mechanisms induced by the annealing temperature. The first mechanism is the increase in defect concentration (O⁻ and Ti vacancies) in the annealed TiO₂ films for annealing temperatures below 800 °C, and the second mechanism is the formation of large rutile grains at a higher temperature, 900 °C.