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Communication has been a struggle for everyone since the covid outbreak and in the aftermath, people have had to get accustomed to video conferencing applications. However people with physical or mental limitations are still unable to use video conferencing apps and their interfaces. This necessitates the development of web-based video chat applications. These applications can aid those who are unable to communicate verbally and/or operate using standard mouse and keyboard inputs, but yet need to feel close to others when they are apart. The proposed application incorporates various accessibility features such as speech-to-text and text-to-speech, gaze tracking and pictorial speech interfaces. It enables individuals with disabilities to participate in virtual meetings on an equal footing with their peers. The goal is to remove barriers and promote inclusiveness in remote work and collaboration for all users, regardless of their abilities using this application.

In recent years, the emergence of sensor technologies has highlighted the key role of in situ soil moisture measurement in various hydrological, agricultural, and ecological applications. However, the widespread adoption of technology is hindered by the costs and accessibility of existing sensor devices. This study aims to bridge a significant research gap by designing an economical and user-friendly handheld device, a Low-Cost Soil Moisture (LCSM) sensor ensuring reliable in situ measurements. This study has two main objectives: developing the LCSM sensor and establishing robust calibrations to ensure accuracy. Calibration experiments were conducted to develop generalized and soil-specific calibrations for the LCSM sensor across various field sites, encompassing diverse soil types (mineral-rich and forest organic soil) and land cover conditions. A total of 408 soil samples were collected from 83 locations (70 mineral soil sites- 301 samples; 13 organic soil sites- 107 samples) for the LCSM sensor calibration. All samples were collected from the same general fields during sampling period with similar experimental conditions. At each location, three LCSM readings were taken in a triangular configuration, and a gravimetric sample was extracted from the center using a coring method. The arithmetic average of the three sensor readings was used as the representative value for calibration against the corresponding gravimetric measurement, ensuring consistency and reliability. For generalized calibration in mineral soils, we observed an overall Root Mean Square Error (RMSE) of 0.035 m 3 m −3 and a bias of <0.001 m 3 m −3 along with a strong correlation ( R = 0.90). Conversely, soil-specific calibration for mineral soils yielded a lower RMSE of 0.031 m 3 m −3 for loam soil and 0.034 m 3 m −3 for sandy loam soil. In the context of forest organic soil, the LCSM sensor exhibited a higher RMSE of 0.078 m 3 m −3 with a moderate correlation ( R = 0.80). Furthermore, the comparison of calibrated LCSM sensor soil moisture readings with commercially available handheld soil moisture sensors (HydraProbe and ThetaProbe) demonstrated a strong agreement, with a high correlation ( R > 0.90) and minimal difference in soil moisture measurements. These statistical findings highlight that the LCSM sensor measures soil moisture as accurately as commercially available sensors, strengthening its credibility and reliability for diverse conditions.

The relationship between precipitation and evapotranspiration (ET) is critical to understanding water cycle related dynamics in ecosystems, including crops. Existing studies of bioenergy crops have primarily focused on annual or seasonal ET rates, with less attention given to the immediate ET response following precipitation events. This study examines the variation in ET rates in the days subsequent to precipitation events across various bioenergy crops—corn, switchgrass, and prairies—utilizing 13 years (2010–2022) of growing season data. Meteorological and eddy covariance flux data were collected from seven eddy covariance flux towers as part of the GLBRC scale-up experiment at the Kellogg Biological Station Long Term Ecological Research sites. The analysis revealed that average ET peaked the day after precipitation and declined linearly over the following days, with a statistically significant relationship ( p -value = 0.00027, R 2 = 0.96). Neither the type of biofuel vegetation nor the historical land use significantly influenced ET post-precipitation events ( p -values = 0.53 and 0.153, respectively). Key predictors of ET following precipitation events include shortwave radiation, season, day of the year, ambient temperature, vapor pressure deficit (VPD), long-wave radiation, precipitation amount, soil moisture, and annual variability. These findings enhance our comprehension of ET responses in bioenergy crop systems, with implications for water management in sustainable agriculture.