MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.

In order to assess risk of mosquito-vector borne disease and to effectively target and monitor vector control efforts, accurate information about mosquito vector population densities is needed. The traditional and still most common approach to this involves the use of traps along with manual counting and classification of mosquito species, but the costly and labor-intensive nature of this approach limits its widespread use. Numerous previous studies have sought to address this problem by developing machine learning models to automatically identify species and sex of mosquitoes based on their wingbeat sounds.

High-resolution mapping of urban Aedes aegypti immature abundance through breeding site detection based on satellite and street view imagery.

Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.

Assessment of malaria risk in Southeast Asia: a systematic review.

Background: Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection.

The effect of 3D stereopsis and hand-tool alignment on learning effectiveness and skill transfer of a VR-based simulator for dental training.

Recent years have seen the proliferation of VR-based dental simulators using a wide variety of different VR configurations with varying degrees of realism. Important aspects distinguishing VR hardware configurations are 3D stereoscopic rendering and visual alignment of the user's hands with the virtual tools. New dental simulators are often evaluated without analysing the impact of these simulation aspects.

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods.

Effects of COVID-19 government travel restrictions on mobility in a rural border area of Northern Thailand: A mobile phone tracking study.

Background: Thailand is among the top five countries with effective COVID-19 transmission control. This study examines how news of presence of COVID-19 in Thailand, as well as varying levels of government restriction on movement, affected human mobility in a rural Thai population along the border with Myanmar.

Methods: This study makes use of mobility data collected using a smartphone app.

Formative feedback generation in a VR-based dental surgical skill training simulator.

Fine motor skill is indispensable for a dentist. As in many other medical fields of study, the traditional surgical master-apprentice model is widely adopted in dental education. Recently, virtual reality (VR) simulators have been employed as supplementary components to the traditional skill-training curriculum, and numerous dental VR systems have been developed academically and commercially.

Large scale detailed mapping of dengue vector breeding sites using street view images.

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots.

Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction.

Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But, choosing an appropriate resolution is a difficult problem since choice of spatial scale can have a significant impact on accuracy of predictive models.

Automated outcome scoring in a virtual reality simulator for endodontic surgery.

Background And Objective: We address the problem of automated outcome assessment in a virtual reality (VR) simulator for endodontic surgery. Outcome assessment is an essential component of any system that provides formative feedback, which requires assessing the outcome, relating it to the procedure, and communicating in a language natural to dental students. This study takes a first step toward automated generation of such comprehensive feedback.