Combustible dust particles suspended in the air may lead to a dust cloud and may eventually contribute to an explosion in the presence of an ignition source. This study focuses on developing an image-based early warning system to monitor the formation of the dust cloud and prevent an explosion. Transmission of light and visibility at varying intensities through suspended dust clouds is a result of light scattering and absorption by particles and the medium. The reduction in the intensity of light passing through a dust cloud is referred to as light extinction. Mathematical models exist for the relationship between light extinction coefficient and dust clouds. With the availability of smartphones, changes in light intensity during dust dispersion can be detected using a camera’s sensor and correlated to dust concentrations. The objective of this research is to determine the light extinction coefficient of combustible dust clouds, viz, grain dust, corn starch, and sawdust, with particle sizes of 100, 300, and 500 µm. Dust at concentrations of 30, 40 and 50 gm-3 were aerosolized in a 0.3 x 0.3 x 0.45 m3 enclosed chamber at light intensities of 100, 300 and 600 lumens. Images were extracted from video streams of dust at a sampling rate of 5 frames per second. Using image analysis, the suspended dust concentration was correlated to the Fraunhofer light extinction for particles of diameters much larger than the light intensity. 

Erythritol is a natural, zero-calorie sweetener that can be used as a sugar substitute and as a humectant for different products like confectionaries, tablets, etc. Methods like extraction, chemical synthesis for erythritol synthesis are not feasible due to lower yield, higher operating cost. Hence, fermentation is widely used due to low operating cost and better yield. In the present study, submerged fermentation was carried out to obtain erythritol from molasses which is a by-product of the sugar industry using an osmophilic yeast. Erythrose reductase enzyme assay was used for quantifying erythritol yield. Plackett-Burman experimental design was used for screening the three most influential factors out of 12 contributing factors. Molasses, yeast extract, and KH2PO4 were optimized using RSM and ANN-GA to obtain erythritol yield of 0.6g/g of substrate.  Erythritol crystals of ~94% purity were obtained after partial purification and characterization. 

Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material is not homogeneous. It involves stratifying the available experimental material based on the covariates causing non-homogeneity and then randomizing the treatment within those strata (known as blocks). This eliminates the unwanted effect of the covariates on the causal effects of interest. We investigate the problem of finding a stable set of covariates to be used to form blocks, that minimizes the variance of the causal effect estimates. Using the underlying causal graph, we provide an algorithm to obtain such a set for a general semi-Markovian causal model. 

It is estimated that more than 1.6 million people in the United States and over 6.8 million people globally are affected by inflammatory bowel diseases (IBD). Although fecal calprotectin is commonly used as a non-invasive biomarker for IBD diagnosis, it is unable to differentiate the IBD types (between CD and UC). We introduce an ingestible capsule that allows simple sampling of GI fluid within the small intestine without the need for any complex procedures such as sedation, ionizing radiation, or air insufflation. The capsule is made of a 3D-printed container that holds superabsorbent hydrogel that provides the required sampling environment as well as the mechanical actuation for closing the capsule after sampling is completed. The captured calprotectin within the hydrogel is simply retrieved by removing hydrogel from the capsule and placing it in an extraction solution. The capsule’s performance in the effective sampling of calprotectin was evaluated in three different conditions; i) in vitro GI-simulated environment, and ii) ex vivo porcine small intestine. Our results demonstrate the sampling reliability of the capsule and its independence from the sampling environment composition. As a result, our sampling device accompanied by the standard fecal calprotectin assay will enable a promising way not only to diagnose the IBD and differentiate its type, but also to monitor the patient status as a cost-effective disease management tool in the future. We believe that this sampling capsule can potentially open new horizons to different applications in GI tract related disease diagnostics and treatment. 

Agricultural conservation practices can have a significant impact on water quality outcomes and while much work has been done, there remains great uncertainty about the interactions of hydroclimatic, geomorphic, and human-driven factors, particularly at the watershed scale. The Great Bend of the Wabash watershed in central Indiana is in an EPA Hypoxia Task Force priority watershed. We targeted two small watersheds (Little Pine and Little Wea) in Central Indiana to leverage a long-term monitoring effort (2009 to 2015) and cost-share conservation assistance program in Little Wea to track changes in water quality in relation to conservation and soil health practices that have been implemented over the past 10 years. Starting in October 2021, we have been collecting stream water samples at the watershed outlets twice daily using automated samplers and analyzing for ammonia, nitrate, soluble reactive phosphorus, and total suspended solids. Sensors are deployed to measure discharge, dissolved oxygen, conductivity, temperature, and turbidity at 15-minute intervals. We collected historical and current practice information through partnerships with our watershed partners to identify conservation practices throughout the watershed. Additionally, we are partnering with an agricultural data company to provide insight into farmer specific behavior – specifically those that may not be ascertained from reporting and/or remote sensing methods, such as nutrient management planning. Collection of these robust agricultural practices datasets coupled with high frequency water quality datasets provide a better picture of how farmers are playing a role in the complex interactions of water quality outcomes in these two watersheds.