Dam Health Research Articles

Real-Time Monitoring of Water Content in Sandy Soil Using Shear Mode Piezoceramic Transducers and Active Sensing-A Feasibility Study.

A quantitative understanding of soil water content or soil water status is of great importance to many applications, such as landslide monitoring, rockfill dam health monitoring, precision agriculture, etc. In this paper, a feasibility study was conducted to monitor the soil water content in real time using permanent embedded piezoceramic-based transducers called smart aggregates (SAs). An active sensing approach using a customized swept acoustic wave with a frequency range between 100 Hz and 300 kHz was used to study the wave attenuation in the soil in correlation to soil moisture levels. Two sandy soil specimens, each embedded with a pair of SAs, were made in the laboratory, and the water percentage of the soil specimens was incrementally decreased from 15% to 3% during the tests. Due to the change of the soil water status, the damping property of the soil correspondingly changes. The change of the damping property results in the variation of the acoustic wave attenuation ratios. A wavelet packet-based energy index was adopted to compute the energy of the signal captured by the SA sensor. Experimental results show a parabolic growth curve of the received signal energy vs. the water percentage of the soil. The feasibility, sensitivity, and reliability of the proposed method for in-situ monitoring of soil water status were discussed.

Ground-based synthetic aperture radar interferometry for deformation monitoring: a case study at Geheyan Dam, China

Full coverage and continuous deformation information retrieval are key aspects for dam health diagnosis. Ground-based synthetic aperture radar (GB-SAR) interferometry is used for the remote monitoring of the Geheyan Dam, China. Although the monitoring of a dam with ground-based interferometry is not an innovation, specific issues have been found out in the case study discussed due to the large dimension of the monitored structure. More than 400 images were used for interferogram generation. Radar signals reflected from the dam were carefully analyzed: a sort of tunneling effect caused by multireflection is observed, and deformations caused by water level and temperature variations were detected during a six-day monitoring campaign. Radar monitoring results were compared to the data recorded by plummets installed in the dam. The agreement between the displacements retrieved from interferometric data and the plummets demonstrates the capability of GB-SAR for deformation monitoring, with the advantage of large area coverage.

Detrimental effects of self-administered methamphetamine during pregnancy on offspring development in the rat

BackgroundMethamphetamine (METH) abuse by pregnant women is a commonly observed phenomenon. While the harmful effects of METH are well described for adults, there is only limited knowledge of the effects of METH use during pregnancy on the developing child. In the present study, we investigated how intraveneous (iv) METH self-administration throughout pregnancy affected rat dams and their offspring through weaning, compared to controls. MethodsFemale rats (n=16) were trained to self-administer METH iv; every drug infusion by a dam also resulted in a saline injection to a yoked control (n=16). When stable levels of self-administration were reached, all females were mated. Daily, 2-h self-administration sessions continued until litters were born. General health and weight was assessed daily in dams and pups. In addition, pups were evaluated for achievement of age-appropriate developmental milestones (i.e., righting reflex, negative geotaxis, pinna detachment, fur appearance, incisor eruption and eye opening). ResultsDams self-administered 2–3mg/kg/day METH throughout gestation without consequence to dam health or weight gain during pregnancy. All females produced viable litters, and litter size and composition did not differ between saline and METH dams. Similarly, maternal pup-directed behavior was not affected by prior METH self-administration. However, despite a lack of weight difference in pups, METH-exposed pups were significantly delayed in reaching all assessed developmental milestones compared to controls. ConclusionThese results indicate that in utero exposure to moderate METH doses can profoundly and adversely affect offspring development, suggesting that even recreational METH use during pregnancy has potential for harm.

Concrete dam deformation prediction model for health monitoring based on extreme learning machine

Structural health monitoring via quantities that can reflect behaviors of concrete dams, like horizontal and vertical displacements, rotations, stresses and strains, seepage, and so forth, is an important method to evaluate operational states of concrete dams correctly and predict the future structural behaviors accurately. Traditionally, statistical model is widely applied in practical engineering for structural health monitoring. In this paper, an extreme learning machine (ELM)-based health monitoring model is proposed for displacement prediction of gravity dams. ELM is one type of feedforward neural networks with a single layer of hidden nodes, where the weights connecting inputs to hidden nodes are randomly assigned. The model can produce good generalization performance and learns faster than networks trained using the back propagation algorithm. The advantages such as easy operating, high prediction accuracy, and fast training speed of the ELM health monitoring model are verified by monitoring data of a real concrete dam. Results are also compared with that of the back propagation neural networks, multiple linear regression, and stepwise regression models for dam health monitoring.

Bovine Mastitis: Prevalence, Risk Factors, Major Pathogens and Antimicrobial Susceptibility Test on the Isolates around Addis Ababa, Central Ethiopia

A total 351 lactation cows were investigated during th e study period. Out of total 351 lactation cows the overall prevalence of mastitis were 80(22.79%) (Table 1), where lower 5 (1.99%) and higher 73 (20.79%) were respectively by clinical and sub clinical type of mastitis. And the prevalence of mastitis was l ower 20.94% and higher 24.13% in Sululta and Bareh districts respectively. From examined lactating cows 7.40% was at least 1teat blind. From currently tested few isolates using selected antimicrobial agents, all susceptible for Chloramphenicol and Gentamyc in except one each isolates of S.aureus and micrococcus spp for both and C.pyogenes for Chloramphenicol and CNS for Gentamycin which shows intermediate. Similarly most of the isolates susceptible to Amoxycillin except some isolates of S. aureus, Str.uberis, CNS and C. pyogenes. However, high resistance was observed by most of isolates to Penicillin and tetracycline which are drugs currently in use for mastitis therapy in the study area. Prevalence of mastitis, particularly, the subclin ical one could bring about major economic losses in dairy cows without notice as result of reduced milk production, poor growth or mortality of suckling calves and dam health.

Time-Varying Identification Model for Crack Monitoring Data from Concrete Dams Based on Support Vector Regression and the Bayesian Framework

The modeling of cracks and identification of dam behavior changes are difficult issues in dam health monitoring research. In this paper, a time-varying identification model for crack monitoring data is built using support vector regression (SVR) and the Bayesian evidence framework (BEF). First, the SVR method is adopted for better modeling of the nonlinear relationship between the crack opening displacement (COD) and its influencing factors. Second, the BEF approach is applied to determine the optimal SVR modeling parameters, including the penalty coefficient, the loss coefficient, and the width coefficient of the radial kernel function, under the principle that the prediction errors between the monitored and the model forecasted values are as small as possible. Then, considering the predicted COD, the historical maximum COD, and the time-dependent component, forewarning criteria are proposed for identifying the time-varying behavior of cracks and the degree of abnormality of dam health. Finally, an example of modeling and forewarning analysis is presented using two monitoring subsequences from a real structural crack in the Chencun concrete arch-gravity dam. The findings indicate that the proposed time-varying model can provide predicted results that are more accurately nonlinearity fitted and is suitable for use in evaluating the behavior of cracks in dams.

Selection of Breeding Stock among Australian Purebred Dog Breeders, with Particular Emphasis on the Dam.

Simple SummaryOne of the most important factors influencing the health and welfare of puppies is the decision made by the breeder on which dam and sire they will breed from. Unfortunately, our understanding of what dog breeders consider important when selecting their dogs, particularly the dam, is limited. In order to bridge this gap, we conducted an online survey of Australian purebred dog breeders. We identified four major factors that the breeder considered important in relation to the dam: Maternal Care; Offspring Potential; Dam Temperament; and Dam Genetics and Health. Overall, the priorities and practices of dog breeders surveyed were variable across breeds. Importantly, it seemed that not all breeders understood the importance of maternal care behaviour, despite the significant role it may play on future puppy behaviour.Every year, thousands of purebred domestic dogs are bred by registered dog breeders. Yet, little is known about the rearing environment of these dogs, or the attitudes and priorities surrounding breeding practices of these dog breeders. The objective of this study was to explore some of the factors that dog breeders consider important for stock selection, with a particular emphasis on issues relating to the dam. Two-hundred and seventy-four Australian purebred dog breeders, covering 91 breeds across all Australian National Kennel Club breed groups, completed an online survey relating to breeding practices. Most breeders surveyed (76%) reported specialising in one breed of dog, the median number of dogs and bitches per breeder was two and three respectively, and most breeders bred two litters or less a year. We identified four components, relating to the dam, that were considered important to breeders. These were defined as Maternal Care, Offspring Potential, Dam Temperament, and Dam Genetics and Health. Overall, differences were observed in attitudes and beliefs across these components, showing that there is variation according to breed/breed groups. In particular, the importance of Maternal Care varied according to dog breed group. Breeders of brachycephalic breeds tended to differ the most in relation to Offspring Potential and Dam Genetics and Health. The number of breeding dogs/bitches influenced breeding priority, especially in relation to Dam Temperament, however no effect was found relating to the number of puppies bred each year. Only 24% of breeders used their own sire for breeding. The finding that some breeders did not test for diseases relevant to their breed, such as hip dysplasia in Labrador Retrievers and German Shepherds, provides important information on the need to educate some breeders, and also buyers of purebred puppies, that screening for significant diseases should occur. Further research into the selection of breeding dams and sires will inform future strategies to improve the health and behaviour of our best friend.

Research on Dam health monitoring system

Research on Dam health monitoring system

Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams

In this work, using AVT data, a health monitoring method for concrete dams based on two different blind source separation (BSS) methods, that is, second-order blind identification (SOBI) and independent component analysis (ICA), is proposed. A modal identification procedure, which integrates the SOBI algorithm and modal contribution, is first adopted to extract structural modal features using AVT data. The method to calculate the modal contribution index for SOBI-based modal identification methods is studied, and the calculated modal contribution index is used to determine the system order. The selected modes are then used to calculate modal features and are analysed using ICA to extract some independent components (ICs). The square prediction error (SPE) index and its control limits are then calculated to construct a control chart for the structural dynamic features. For new AVT data of a dam in an unknown health state, the newly calculated SPE is compared with the control limits to judge whether the dam is normal. With the simulated AVT data of the numerical model for a concrete gravity dam and the measured AVT data of a practical engineering project, the performance of the dam health monitoring method proposed in this paper is validated.

The effect of parturition induction treatment on interval to calving, calving ease, postpartum uterine health, and resumption of ovarian cyclicity in beef heifers

The aim of this study was to compare the effects of two parturition induction protocols with a nontreated control group, on interval to calving, calving ease, postpartum uterine health, and ovarian cyclicity in beef heifers. At Day 285 of gestation, 81 crossbred recipient beef heifers carrying purebred Simmental fetuses, were blocked by live-weight, body condition score, expected calving date and fetal sex, and assigned to one of three groups: (1) control (CON; no induction treatment, n = 29); (2) induction with corticosteroids (CORT; n = 27); or (3) induction with corticosteroids plus prostaglandin (CORT + PG; n = 25). Interval from induction to calving in hours and calving ease on a scale of 1 to 5 were recorded. Vaginal mucus samples were collected on Day 21 and Day 42 after calving (Day 0) by means of a Metricheck and scored on a scale of 0 to 3. Reproductive tract examinations were conducted on Day 21 and Day 42 after calving, and uterine cytology samples were obtained on Day 21. A positive cytologic sample was defined as greater than 18% neutrophils in the sample obtained via a cytobrush technique. Cows were considered to have resumed ovarian cyclicity if the presence of the CL was confirmed. Data were analyzed using the Mixed (normally distributed data) and Genmod (nonparametric data) procedures of SAS (v. 9.3). The interval from treatment to calving was longer (P < 0.0001) for CON (161.9 ± 15.12 hours) animals compared with CORT (39.7 ± 11.64 hours) or CORT + PG (32.6 ± 12.10 hours), which did not differ. Treatment did not affect calving difficulty score. There was also no difference in incidence of retained placenta between the three groups. At Day 21 postpartum, cytology score tended to be higher for both induced groups (48%) compared with the control animals (24%), but this was not the case for vaginal mucus score (CON 52%, CORT 70%, and CORT + PG 52%). A higher proportion of CON had an involuted uterus by Day 21 postpartum (69%) compared with both induced groups (CORT 48%, CORT + PG 32%). Day 21 ovarian cyclicity was higher in both CON (52%) and CORT (59%) compared with CORT + PG (29%). By Day 42, there was no difference in ovarian cyclicity or uterine involution between CON and CORT; however, a positive relationship was observed between uterine involution score on Day 21 and return to cyclicity on Day 42 in these two groups. There was a negative relationship between uterine involution score and return to cyclicity in the CORT + PG group, and these animals were slower (P < 0.05) to resume cyclicity by Day 42 with a larger proportion animals having evidence of having resumed postpartum ovarian cyclicity in both CON (P = 0.03) and CORT compared with CORT + PG on Day 42. In conclusion, the use of corticosteroid-based treatments is an effective strategy to advance parturition in full term dams and does not have a negative effect on calving progress or dam health. However, when prostaglandin is also included in the protocol, these treatments may lead to greater delay in uterine involution with increased chance of uterine infection and slower resumption of ovarian cyclicity.

Influence Analysis of Dam Stress and Displacement Caused by Stiffness Degradation and Mass Loss

The concrete dam under dead load, water pressure load and wind load is in high stress state for a long time, whose safety is in a dangerous station to a certain extent. The static calculation of the dam was done by ANSYS. In order to know the static characteristics of the dam, and to provide a theoretical basis for dam health diagnosis and routine maintenance, the variation of the dam stress and displacement caused by the stiffness degradation and mass loss was studied. The influence degree of the dam mass loss and stiffness degradation on stress and displacement were discussed. The results show that stress along the dam height (Y direction) is higher than it along the horizontal direction (X direction), the maximum stress difference is to about 4 times. The influence degree of dam stress and displacement caused by stiffness degradation is more serious than those by mass loss.

The Health Monitoring Method of Concrete Dams Based on Ambient Vibration Testing and Kernel Principle Analysis

The ambient vibration testing (AVT) measurement of concrete dams on full-scale can show the practical dynamic properties of structure in the operation state. For most current researches, the AVT data is generally analyzed to identify the structural vibration characteristics, that is, modal parameters. The identified modal parameters, which can provide the global damage information or the damage location information of structure, can be used as the basis of structure health monitoring. Therefore, in this paper, the health monitoring method of concrete dams based on the AVT is studied. The kernel principle analysis (KPCA) based method is adopted to eliminate the effect of environmental variables and monitor the health of dam under varying environments. By taking full advantage of the AVT data obtained from vibration observation system of dam, the identification capabilities and the warning capabilities of structural damage can be improved. With the simulated AVT data of the numerical model of a concrete gravity dam and the measured AVT data of a practical engineering, the performance of the dam health monitoring method proposed in this paper is verified.

The cost-effectiveness of interbody fusions versus posterolateral fusions in 137 patients with lumbar spondylolisthesis

Background contextReimbursements for interbody fusions have declined recently because of their questionable cost-effectiveness. PurposeA Markov model was adopted to compare the cost-effectiveness of posterior lumbar interbody fusion (PLIF) or transforaminal lumbar interbody fusion (/TLIF) versus noninterbody fusion and posterolateral fusion (PLF) in patients with lumbar spondylolisthesis. Study design/SettingDecision model analysis based on retrospective data from a single institutional series. Patient sampleOne hundred thirty-seven patients underwent first-time instrumented lumbar fusions for degenerative or isthmic spondylolisthesis. Outcome measuresQuality of life adjustments and expenditures were assigned to each short-term complication (durotomy, surgical site infection, and medical complication) and long-term outcome (bowel/bladder dysfunction and paraplegia, neurologic deficit, and chronic back pain). MethodsPatients were divided into a PLF cohort and a PLF plus PLIF/TLIF cohort. Anterior techniques and multilevel interbody fusions were excluded. Each short-term complication and long-term outcome was assigned a numerical quality-adjusted life-year (QALY), based on time trade-off values in the Beaver Dam Health Outcomes Study. The cost data for short-term complications were calculated from charges accrued by the institution's finance sector, and the cost data for long-term outcomes were estimated from the literature. The difference in cost of PLF plus PLIF/TLIF from the cost of PLF alone divided by the difference in QALY equals the cost-effectiveness ratio (CER). We do not report any study funding sources or any study-specific appraisal of potential conflict of interest–associated biases in this article. ResultsOf 137 first-time lumbar fusions for spondylolisthesis, 83 patients underwent PLF and 54 underwent PLIF/TLIF. The average time to reoperation was 3.5 years. The mean QALY over 3.5 years was 2.81 in the PLF cohort versus 2.66 in the PLIFo/TLIF cohort (p=.110). The mean 3.5-year costs of $54,827.05 after index interbody fusion were statistically higher than that of the $48,822.76 after PLF (p=.042). The CER of interbody fusion to PLF after the first operation was −$46,699.40 per QALY; however, of the 27 patients requiring reoperation, the incident (reoperation) rate ratio was 7.89 times higher after PLF (2.91, 26.67). The CER after the first reoperation was −$24,429.04 per QALY (relative to PLF). Two patients in the PLF cohort required a second reoperation, whereas none required a second reoperation in the PLIF/TLIF cohort. Taken collectively, the total CER for the interbody fusion is $9,883.97 per QALY. ConclusionsThe reoperation rate was statistically higher for PLF, whereas the negative CER for the initial operation and first reoperation favors PLF. However, when second reoperations were included, the CER for the interbody fusion became $9,883.97 per QALY, suggesting moderate long-term cost savings and better functional outcomes with the interbody fusion.

Response of primary vectors and related diseases to impoundment by the Three Gorges Dam

To investigate the impact of the Three Gorges Dam on the local ecological environment. We conducted a 3-year cross-sectional survey of natural focus infectious diseases in the area before and after the water level rose to 156 m to evaluate the dam's health impacts. Direct and indirect immunofluorescence methods were applied to detect rat antigen and antibody of haemorrhagic fever with renal syndrome (HFRS). Fresh rat kidneys were inoculated in Korth's culture medium to detect Leptospira. A group of captured crabs were ground to observe the metacercariae of Paragonimus. Serum samples were collected from healthy local individuals. ELISA kits were used to detect human antibody against HFRS and paragonimiasis. Human Leptospira infections were detected by a microscopic agglutination test. Upstream rodent density increased significantly with Rattus flavipectus and Apodemus agrarius as the major pathophoric genera. The infection rate of human HFRS and Leptospira in the upstream human population samples was significantly higher than in the downstream samples and correlated with the increase in rodent density. Paragonimus infection rates remained at a low level during the study. Culex pipiens fatigan and Armigeres obturbans were the dominant species of mosquito. The creation of the Three Gorges Dam changed the proliferation of intermediary agents of diseases, but not notably. However, the ecological effects on the environment may require a prolonged period of time to manifest themselves; thus, long-term and effective surveillance of vectors and related diseases needs to be established.

The identification of a dam's modal parameters under random support excitation based on the Hankel matrix joint approximate diagonalization technique

Modal analysis is an effective method for monitoring a dam's health. Modal parameters can be identified from the measured vibration response of a dam under ambient excitation, such as that from an earthquake. In this paper, we first use the state space model to analyze the vibration of a dam under ambient support excitation and conclude that the nature excitation technique (NExT) can be used to the measured absolute acceleration response of the dam under band-limited stochastic support excitation to obtain its impulse response. To overcome some of the limitations of the traditional modal identification method for a structure under ambient excitation, we propose a modal parameter identification method based on the Hankel matrix joint approximate diagonalization (HJAD) technique. In this method, the Hankel matrix is defined as the covariance matrix of a vector, which is composed of measured acceleration responses, and their time-lagged data. This modal parameter identification method can be regarded as an improvement to the traditional time domain method because it introduces the joint approximate diagonalization (JAD) technique into the original method. On the other hand, the method can be regarded as an improvement to the SOBI-based modal identification method because it uses the Hankel matrix instead of the covariance matrix of response to perform the JAD. Therefore, this method combines the advantages of the two existing modal identification methods and overcomes some of their limitations. Compared with the SOBI-based modal parameter identification method, the implementation of the method presented in this paper is very convenient because we need only to add time-lagged response data to the analysis. The numerical analysis results show that the proposed modal parameter identification method based on HJAD technology not only has the advantage of a traditional blind modal parameter identification algorithm, but can also overcome the limitation of not being able to estimate more active modes than the number of available sensors. According to the satisfactory performance of this method in the analysis of strong-motion earthquake observation data for a gravity dam, the modal parameter identification method proposed in this paper has the potential for application in water conservancy engineering.

Simplified evaluation of embankment dam health due to ground vibration using dam health index (DHI) approach

In order to execute dam safety planning, it is necessary to develop a method to monitor dam health, which considers and simulates the actual physical failure processes of an embankment dam. The objective of this study is to monitor the behaviour of the earthen dam during the impact of ground force through experimental modal analysis. The model of an earthen dam was constructed on a steel plate, which could be easily moved by input force. Accelerometers were installed on the model in many positions to measure respondent acceleration. The post-processing data were processed to show signs such as natural frequency and mode shape using the least-square complex exponential method. Natural frequency shifts were tracked throughout the testing period and the effects of the varying water levels were identified. During vibration, the effect of liquefaction observed in tests was found to be the first factor to deteriorate on the upstream side of the embankment model. The measurement position of the accelerometer was found to be very important for the health monitoring of the embankment dam. The proposed dam health index is the comparison of acceleration at the crest and the upstream side in both time and frequency domains which can identify damage to the embankment dam.

Predicting piezometric water level in dams via artificial neural networks

The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams.

Study on an Intelligent Inference Engine in Early-Warning System of Dam Health

With systems engineering and artificial intelligent methods, an early-warning system of dam health (EWSDH) is developed. This system consists of integration control module, intelligent inference engine (IIE), support base cluster, information management and input/output modules. As a central processing unit of EWSDH, IIE is a decision support system for monitoring the operation characteristics and diagnosing unexpected behaviour of dam health. With the time-frequency domain localization properties and self-learning ability of wavelet networks based on wavelet frames, IIE builds some new monitoring models of dam health. The models are used to approximate and forecast the operation characteristics of dam. The methods of attributions reduction in rough sets theory are presented to diagnose adaptively the unexpected behaviour. The proposed system has been used to monitor dam health successfully.

Predicting an SF-6D Preference-Based Score Using MCS and PCS Scores from the SF-12 or SF-36

The SF-6D preference-based scoring system was developed several years after the SF-12 and SF-36 instruments. A method to predict SF-6D scores from information in previous reports would facilitate backwards comparisons and the use of these reports in cost-effectiveness analyses. This report uses data from the 2001-2003 Medical Expenditures Panel Survey (MEPS), the Beaver Dam Health Outcomes Survey, and the National Health Measurement Study. SF-6D scores were modeled using age, sex, mental component summary (MCS) score, and physical component summary (PCS) score from the 2002 MEPS. The resulting SF-6D prediction equation was tested with the other datasets for groups of different sizes and groups stratified by age, MCS score, PCS score, sum of MCS and PCS scores, and SF-6D score. The equation can be used to predict an average SF-6D score using average age, proportion female, average MCS score, and average PCS score. Mean differences between actual and predicted average SF-6D scores in out-of-sample tests was -0.001 (SF-12 version 1), -0.013 (SF-12 version 2), -0.007 (SF-36 version 1), and -0.010 (SF-36 version 2). Ninety-five percent credible intervals around these point estimates range from +/-0.045 for groups with 10 subjects to +/-0.008 for groups with more than 300 subjects. These results were consistent for a wide range of ages, MCS scores, PCS scores, sum of MCS and PCS scores, and SF-6D scores. SF-6D scores from the SF-36 and SF-12 from the same data set were found to be substantially different. Simple equation predicts an average SF-6D preference-based score from widely published information.

Dam health diagnosis and evaluation

Based on the bionics principle in the life sciences field, we regard a dam as a vital andintelligent system. A bionics model is constructed to observe, diagnose and evaluate damhealth. The model is composed of a sensing system (nerve), central processing unit(cerebrum) and decision-making implement (organism). In addition, the model, indexsystem and engineering method on dam health assessment are presented. The proposedtheories and methods are applied to evaluate dynamically the health of one concretedam.