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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2009.05.049 Byline: Philip Loden (a), Qi Han (a), Lisa Porta (b), Tissa Illangasekare (b), Anura P. Jayasumana (c) Keywords: Wireless sensor systems; Contaminant monitoring; Data acquisition Abstract: In this paper, we present the use of a wireless sensor network in a lab for subsurface contaminant plume monitoring with the objective of automatic calibration of groundwater transport models. A tank configured to simulate an aquifer was used as a testbed, and a 2D model was created based on the setup. To simulate a contaminant plume, an ion tracer was injected into the tank. Sensor probes capable of detecting the plume were buried inside the tank, and wireless motes used to take readings from the sensors and relay data to a base station. More importantly, a run-time fault detection and diagnosis for abnormal sensor readings is designed and integrated into the data acquisition system. Further, an adaptive data collection technique is integrated that is able to provide evidence about the effectiveness of the groundwater transport model in use. Results from the tracer tests are presented, as well as lessons gained. Author Affiliation: (a) Department of Mathematical and Computer Sciences, Colorado School of Mines, Golden, CO 80401, United States (b) Division of Environmental Sciences and Engineering, Colorado School of Mines, Golden, CO 80401, United States (c) Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, United States Article History: Received 29 October 2008; Revised 10 February 2009; Accepted 20 May 2009 Article Note: (footnote) [star] This project is supported in part by NSF CSR Grants CNS-0720875 and CNS-0720889.

A significant increase in cases of chronic kidney disease has been observed in the dry zone of Sri Lanka. This unusual chronic kidney disease was first reported in the early 1990s among middle-aged paddy farmers. Considering epidemiologic and histopathologic findings, the disease recently was named chronic interstitial nephritis in agricultural communities (CINAC). Twenty-five years after the first report, CINAC is the most significant public health issue in the paddy farming areas with more than 70,000 estimated patients and many deaths. Histopathologically, this disease is a tubular interstitial nephritis associated with glomerular sclerosis and mild vascular changes. Morphologic and biochemical characteristics of CINAC in Sri Lanka share many similarities with Mesoamerican nephropathy. Certain natural and man-made toxins, heat stress with repeated volume and salt depletion, infections such as hantavirus and leptospirosis, and a genetic origin have been proposed and investigated as possible etiologies, and an association between CINAC and herbicides is widely discussed. Several preventive measures already have been implemented by health authorities in Sri Lanka to minimize nephrotoxin exposure and well hydrate the inhabitants in the disease-endemic region. The impact of these interventions will be watched with anticipation. [ABSTRACT FROM AUTHOR]

Organophosphate (OP) poisoning induced disruption of glucose homeostasis is well established. OP poisoning leads to accumulation of acetylcholine (ACh) due to the inhibition of acetylcholinesterases (AChE). On the other hand the incidence of type 2 diabetes mellitus (T2DM) is shown to rise along with the use of pesticides in Southeast Asia. Attenuation of the 'incretin effect' is seen in T2DM. This effect is regulated by a complex loop of mechanism involving ACh driven muscarinic receptors. We hypothesize that OP poisoning leads to disruption of glucose homeostasis by attenuation of the incretin effect. Inhibition of the Glucagon Like Peptide-1 (GLP-1) secretion is our main focus of interest. Positive finding of the hypothesis will open possibility of using incretin based treatment modalities to treat or prevent acute OP induced disruption of glucose homeostasis. [ABSTRACT FROM AUTHOR]

• Interactions in collective motion (CM) ensure their trajectory matrix is low-rank. • Low-rankness of CM is the pattern learned by our Hadamard deep autoencoder (HDA). • HDA incorporates an indicator matrix into the loss function using Hadamard product. • HDA is trained only using the observed trajectory segments. • Performance of HDA is validated against a low-rank matrix completion framework. Learning dynamics of collectively moving agents such as fish or humans is an essential task in research. Due to phenomena such as occlusion or change of illumination, the multi-object methods tracking such dynamics may lose the tracks of the agents which may result in fragmentations of trajectories. Here, we present an extended deep autoencoder (DA) that we train only on the fully observed segments of the trajectories by defining its loss function as the Hadamard product of a binary indicator matrix with the absolute difference between the outputs and the labels. The trajectory matrix of the agents practicing collective motion is low-rank due to mutual interactions and dependencies between the agents that we utilize as the underlying pattern that our Hadamard deep autoencoder (HDA) codes during its training. The performance of this HDA is compared with that of a low-rank matrix completion scheme in the context of fragmented trajectory reconstruction. [ABSTRACT FROM AUTHOR]