The onset of the COVID-19 pandemic prompted a rapid adoption of video conferencing tools to support teaching and learning. Even though the pandemic has receded, these tools have cemented their place in the education landscape. This research aims to review the literature on the use of videoconferencing tools, specifically Zoom, in the context of higher education (HE). Articles were retrieved from two databases: Scopus and ERIC (Education Resources Information Center). The final sample included a total of 84 articles. The review uncovers six benefits of using Zoom in HE: Social presence, flexibility and accessibility, facilitating synchronous remote academic advising, conducting assessments, perceived usability, and inclusivity. It also identified seven challenges: Engagement issues, simulating hands-on experience, technical issues, privacy and security issues, familiarity issues, instructor difficulty, and Zoom fatigue. Several implications of these findings for future educational practices are discussed.
Millions are fighting to maintain mental well-being in our high-pressure world. Stress, isolation, and mental illnesses can all be relentless foes, and neglecting them can have a domino effect on physical health, leading to a decline in overall well-being. Technological advances, particularly spatial computing, offer promising solutions to this problem. Spatial computing's main feature is the contextualization of digital information within users' physical environments. Past studies have shown that this feature can support mental health by enabling remote therapy delivery and providing immersive therapeutic environments anywhere, at any time. While most studies on spatial computing in mental health highlight its benefits, the potential drawbacks and societal integration challenges remain largely unexplored. This work is a systematic literature review aimed at identifying opportunities for integrating spatial computing into mental healthcare, as well as the challenges it could face upon entering society and any potentially negative impacts it may have. The findings demonstrate that spatial computing can improve mental well-being through enhanced accessibility and immersive experiences. However, integration challenges and potentially negative impacts of spatial computing were also concerning, such as high costs and the risk of addiction. Due to the lack of extensive studies on these aspects, the findings on integration challenges and potential negative impacts require further investigation. Future research and real-world applications of spatial computing are necessary to validate this study's findings and uncover any underlying effects.
Data drift, caused by changes in environment from training, can significantly degrade the performance of neural networks in real-world computer vision tasks. Traditional drift detection methods often focus on a single type of drift, limiting their robustness in complex scenarios. In this paper, we extend an existing method that estimates the degree of a single drift for an image classification model by targeting multi-drift problem for which a set of answer keys is prepared for combination of drift levels and the answer keys are used to predict the magnitudes of drift combination applied to the images. We evaluated our extended method using the CIFAR-10 dataset with two drifts of various levels of Gaussian noise and rain effects. Experimental results confirmed that predicting multiple drifts together effectively prevents the overestimation of drift magnitudes. Additionally, the proposed drift detection model further improves accuracy of drift magnitude estimation.
This study aims to classify anxiety disorders in adolescents using three machine learning algorithms, namely naive bayes, C4.5, and K-Nearest Neighbor (K-NN). The data used was taken from the DASS-21 questionnaire, which contains 418 samples with 11 attributes, including age, gender, and seven anxiety-related questions. The algorithm was tested using the holdout technique with 80:20, 70:30, and 60:40 data splits, as well as the k-fold cross-validation technique. The results showed that the C4.5 algorithm performed best with 100% accuracy in the holdout technique, followed by naive bayes with 99% accuracy, and K-NN with 94% accuracy. In cross-validation testing, C4.5 also showed the highest accuracy of 98%, while naive bayes and K-NN achieved 89% and 92% respectively. This study concludes that the C4.5 algorithm is superior in classifying anxiety compared to naive bayes and K-NN, so it can be relied upon for machine learning-based diagnostic applications in supporting the detection of anxiety disorders efficiently.
Due to the persisting environmental concerns and resource depletion caused by traditional energy sources, people have begun to seek renewable sources of energy and develop energy harvesting tools. The application of the piezoelectric effect has been utilized by many large and small-scale energy harvesters as a way to combat the energy crisis many communities face today. Although underutilized, piezoelectric's potential for energy harvesting remains; with advancements in material science and energy-harvesting techniques, it can be more widely and effectively deployed. Compared to piezoelectric films, solar films are more utilized in many fields; nonetheless, their application in a residential setting remains uncommon. This paper focuses on developing a plant-shaped hybrid energy harvester with Platanus Acerifolia Leaf designed to cater to wind resistance. In controlled experiments, the piezoelectric leaves were shown to produce 2.268V at the wind speed of 9 m/s, while the solar films generated 4.2V at an illuminance of 2000 lux. In an uncontrolled residential test, the hybrid system produced a constant output voltage of 3.6V with a total power generation of 343.6 mW over 9 hours resulting in a total energy collection of 3,092.4 mWh. This enhanced plantshaped energy harvester is capable of providing an additional source of energy for many, especially in a residential setting. Moreover, it contributes to and promotes the field of material science and the utilization of energy-harvesting techniques.
