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Applying organo-silanes (OS) as water-repellent additives can enhance soil properties, crucial for use as moisture barriers in infrastructures like roads, landfills, and tunnels. This study used four soil samples and glass beads, treated with three OS products at dosages from 1:1 to 1:1000. Laboratory tests included contact angle, water drop penetration, and breakthrough pressure on 216 samples. Results showed increased hydrophobicity with higher OS dosages, with contact angles over 110° and Water Drop Penetration Test times above 3600s. However, effectiveness plateaued at certain dosages, indicated by electrical conductivity and pH changes. The primary factors (94.6% influence) were soil type, OS product, dosage, and drying condition, while reaction time, and leaching/washing had a minor impact (5.4%). Treated soils could sustain a hydrostatic head of up to 17 kPa. These insights aid in optimizing water-repellency treatments for soil performance and infrastructure durability in geotechnical applications. 

The ocean is a vast three-dimensional space that is poorly explored and understood, and harbors unobserved life and processes that are vital to ecosystem function. To fully interrogate the space, novel algorithms and robotic platforms are required to scale up observations. Locating animals of interest and extended visual observations in the water column are particularly challenging objectives. Towards that end, we present a novel Machine Learning-integrated Tracking (or ML-Tracking) algorithm for underwater vehicle control that builds on the class of algorithms known as tracking-by-detection. By coupling a multi-object detector (trained on in situ underwater image data), a 3D stereo tracker, and a supervisor module to oversee the mission, we show how ML-Tracking can create robust tracks needed for long duration observations, as well as enable fully automated acquisition of objects for targeted sampling. Using a remotely operated vehicle as a proxy for an autonomous underwater vehicle, we demonstrate continuous input from the ML-Tracking algorithm to the vehicle controller during a record, 5+ hr continuous observation of a midwater gelatinous animal known as a siphonophore. These efforts clearly demonstrate the potential that tracking-by-detection algorithms can have on exploration in unexplored environments and discovery of undiscovered life in our ocean. 

Most of the world's crops depend on pollinators, so declines in both managed and wild bees raise concerns about food security. However, the degree to which insect pollination is actually limiting current crop production is poorly understood, as is the role of wild species (as opposed to managed honeybees) in pollinating crops, particularly in intensive production areas. We established a nationwide study to assess the extent of pollinator limitation in seven crops at 131 locations situated across major crop-producing areas of the USA. We found that five out of seven crops showed evidence of pollinator limitation. Wild bees and honeybees provided comparable amounts of pollination for most crops, even in agriculturally intensive regions. We estimated the nationwide annual production value of wild pollinators to the seven crops we studied at over $1.5 billion; the value of wild bee pollination of all pollinator-dependent crops would be much greater. Our findings show that pollinator declines could translate directly into decreased yields or production for most of the crops studied, and that wild species contribute substantially to pollination of most study crops in major crop-producing regions.