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In 1988, the Florida Institute of Phosphate Research (FIPR) funded project to develop an advanced hydrologic model for shallow water table systems. The FIPR hydrologic model (FHM) was developed to provide an improved predictive capability of the interactions of surface water and ground water using its component models, HSPF and MODFLOW. The Integrated Surface and Ground Water (ISGW) model was developed from an early version of FHM and the two models were developed relatively independently in the late 1990s. Hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, and base flow are explicitly accounted for in both models. Considerable review of FHM and ISGW and their applications occurred through a series of projects. One model evolved, known as the Integrated Hydrological Model IHM. This model more appropriately describes hydrologic processes, including evapotranspiration fluxes within small distributed land‐based discretization. There is a significant departure of many IHM algorithms from FHM and ISGW, especially for soil water and evapotranspiration (ET). In this paper, the ET concepts in FHM, ISGW, and IHM will be presented. The paper also identifies the advantages and data costs of the improved methods. In FHM and IHM, ground water ET algorithms of the MODFLOW ET package replace those of HSPF (ISGW used a different model for ground water ET). However, IHM builds on an improved understanding and characterization of ET partitioning between surface storages, vadose zone storage, and saturated ground water storage. The IHM considers evaporative flux from surface sources, proximity of the water table to land surface, relative moisture condition of the unsaturated zone, thickness of the capillary zone, thickness of the root zone, and relative plant cover density. The improvements provide a smooth transition to satisfy ET demand between the vadose zone and deeper saturated ground water. While the IHM approach provides a more sound representation of the actual soil profile than FHM, and has shown promise at reproducing soil moisture and water table fluctuations as well as field measured ET rates, more rigorous testing is necessary to understand the robustness and/or limitations of this methodology.

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Recently, the U.S. EPA issued the 303(d) list of impaired waters in Idaho State that contained the causes of impairment. This 303(d) list provides useful information that can be used to determine the Total Maximum Daily Loads (TMDLs). Implementation of TMDLs should result in pollutant reductions, which, in turn can lead to the restoration of these water bodies. Flow alteration is one of the potential sources of impairments in the Big Lost River in south-central Idaho, which have some negative impacts on the water quality and beneficial uses. Flow in the Big Lost River is altered, both in quantity and quality, and this reduces recreation activities, affects the fish assemblage, and changes the composition and relative abundance of aquatic species. The effect of riparian vegetation is another factor that needs to be predicted. In addition, three conservation schemes (construction of upstream reservoirs, downstream reservoirs, and canal linings) were proposed to restore flow in the downstream reaches of the river and compensate for water loss during the low flood seasons. However, there is no single predictive model that can be used to appropriately represent each of these issues as management decisions. In this paper, an expert system in the form of a Bayesian network, a graphical diagram of nodes and arcs, was implemented to examine all significant water management variables and relationships among these variables. Lining the irrigation canals was found to be the best scheme, followed by constructing an upstream reservoir. The TMDLs would benefit the water quality in the watershed but would not significantly increase the water quantity and solve the flow alteration problem. Consequently, this can be used to determine the sequence of decisions that can be taken in the future.

This system aims to provide a low-cost means of monitoring a vehicle's performance and tracking by communicating the obtained data to a mobile device via Bluetooth. Then the results can be viewed by the user to monitor fuel consumption and other vital vehicle electromechanical parameters. Data can also be sent to the vehicle's maintenance department which may be used to detect and predict faults in the vehicle. This is done by collecting live readings from the engine control unit (ECU) utilizing the vehicle's built in on-board diagnostics system (OBD). An electronic hardware unit is built to carry-out the interface between the vehicle's OBD system and a Bluetooth module, which in part communicates with an Android-based mobile device. The mobile device is capable of transmitting data to a server using cellular internet connection.

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Summary Clinical and pathological changes following neurectomy were studied experimentally in 46 male and female equids. Sixty‐three operations were performed using either the traditional or the Fackelman and Clodius methods of neurectomy. The effect of arteriovenous ligation was studied in 12 animals and 20 angiograms were performed post mortem to study the arterial pattern of the extremities of the operated limb. Neuroma formation (31 cases) and sloughing of the hoof (five cases) were the two main untoward sequelae. Neurectomy by the technique of Fackelman and Clodius proved superior to the traditional method. No essential changes were observed after ligation of the blood vessels, except in one case where collateral circulation was established.

Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real‐time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5‐year follow‐up. The c‐statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74–0.78), and the c‐statistic across 10 cross‐validation data sets was 0.75 (95% CI, 0.73–0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21–0.58]; intermediate‐high: hazard ratio, 2.98 [95% CI, 2.37–3.74]; high: hazard ratio, 8.44 [95% CI, 6.66–10.70], all P <0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real‐time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real‐time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real‐time risk calculator deployed at the point of care.