The vibration performance of a tram vehicle running on the curved track is influenced by the speed of the running vehicle, the geometry of the track, the roughness of the wheel and rail, and ...environmental characteristics. This paper investigates the random vibration analysis of tram-track interaction on a curved track due to the polygonal wheel and track irregularity by the pseudo excitation method (PEM). The dynamic behaviour of tram-track interaction on a curve is modelled using the finite element method (FEM) and multi-body system (MBS). The field tests were carried out to acquire the rail vibration and deformation response, and also measuring the polygonal wheel wear profile to analyse and validate the dynamic behaviour tram-curved track model. The influence of the polygonal wheel on the vibration of tram-curved track coupled system and the wheel-rail contact responses are investigated by considering the wheelset flexibility. The results are illustrated through comparison between with and without the polygonal wheel in terms of time and frequency domain. The numerical results suggest that the wheelset flexibility under the polygonal wheel wear excitation would aggravate the vibration mode of the rail, car-body and axle-box, and also increase the dynamic wheel-rail contact force and creepage.
Rail joints are the weakest link in the railway track structure due to the presence of discontinuities in the track stiffness and wheel-rail contact geometry. This paper presents the dynamic response ...of vehicle-track interaction at the bolted rail joint (BRJ) in the presence of polygonal wheel wear. A three-dimensional coupled vehicle-track dynamic model was established using the finite element method (FEM) and the multi-body system (MBS) approach, in which the flexibilities of the wheelset and track are taken into account. The proposed dynamic model is validated by the comparisons of field measurement and calculated vertical axle-box acceleration in time and frequency domains. The effects of the flexible wheelset in the presence of polygonal wear on the vehicle-track dynamic interaction at the BRJs are illustrated through comparisons with those obtained using without polygonal wheel model. The results show that the polygonal wheel wear and the rail joint irregularities can induce high-magnitude vertical wheel-rail contact force, and exacerbate the vibration responses of the axle box, the wheelset, and the rails. The simulation results also indicate that the wheelset bending modes, the P2 resonance due to rail joint excitation, and the track vibration modes have significant effects on the vertical vehicle-track dynamic interaction at the supported and suspended BRJs.
There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing ...conditions. Machine learning methods have not been widely adopted for analysis of lake optical properties such as water clarity, despite their successful use in many other applications of environmental remote sensing. This study compares model performance for a random forest (RF) machine learning algorithm and a simple 4-band linear model with 13 previously published empirical non-machine learning algorithms. We use Landsat surface reflectance product data aligned with spatially and temporally co-located in situ Secchi depth observations from northeastern USA lakes over a 34-year period in this analysis. To evaluate the transferability of models across space and time, we compare model fit using the complete dataset (all images and samples) to a single-date approach, in which separate models are developed for each date of Landsat imagery with more than 75 field samples. On average, the single-date models for all algorithms had lower mean absolute errors (MAE) and root mean squared errors (RMSE) than the models fit to the complete dataset. The RF model had the highest pseudo-R2 for the single-date approach as well as the complete dataset, suggesting that an RF approach outperforms traditional linear regression-based algorithms when modeling lake water clarity using satellite imagery.
Globally, phytoplankton abundance is increasing in lakes as a result of climate change and land‐use change. The relative importance of climate and land‐use drivers has been examined primarily for ...mesotrophic and eutrophic lakes. However, oligotrophic lakes show different sensitivity to climate and land‐use drivers than mesotrophic and eutrophic lakes, necessitating further exploration of the relative contribution of the two drivers of change to increased phytoplankton abundance. Here, we investigated how air temperature (a driver related to climate change) and nutrient load (a driver related to land‐use and climate change) interact to alter water quality in oligotrophic Lake Sunapee, New Hampshire, USA. We used long‐term data and the one‐dimensional hydrodynamic General Lake Model (GLM) coupled with Aquatic EcoDyanmics (AED) modules to simulate water quality. Over the 31‐year simulation, summer median chlorophyll‐a concentration was positively associated with summer air temperature, whereas annual maximum chlorophyll‐a concentration was positively associated with the previous 3 years of external phosphorus load. Scenario testing demonstrated a 2°C increase in air temperature significantly increased summer median chlorophyll‐a concentration, but not annual maximum chlorophyll‐a concentration. For both maximum and median chlorophyll‐a concentration, doubling external nutrient loads of total nitrogen and total phosphorus at the same time, or doubling phosphorus alone, resulted in a significant increase. This study highlights the importance of aligning lake measurements with the ecosystem metrics of interest, as maximum chlorophyll‐a concentration may be more uniquely sensitive to nutrient load and that typical summer chlorophyll‐a concentration may increase due to warming alone.
Plain Language Summary
Clear water lakes are experiencing more frequent water quality problems due to land development and climate change. However, it is challenging to identify how land development and climate change interact to alter water quality because their effects are complex and occurring at the same time. We used three decades of observational data combined with a lake ecosystem simulation model to explore the role of land development and climate change on water quality. Our water quality indicator of focus was phytoplankton, which are small photosynthesizing organisms in the water, often referred to as “algae.” We found that the effects of land use and climate depend on if we look at yearly maximum or average phytoplankton concentrations. Average phytoplankton concentrations during the summer (representing typical summer conditions) increase with either warmer air temperatures or higher nutrient pollution. However, annual maximum phytoplankton concentration (representing phytoplankton “blooms”) only increases with higher nutrient pollution. Typical summer phytoplankton concentrations will likely increase with warmer air temperatures due to climate change alone and increase even further when combined with higher nutrient pollution. To maintain clear water lakes, nutrient pollution should be reduced even more than previously thought to compensate for increasing phytoplankton in a warmer climate.
Key Points
Simulated annual maximum and summer median lake chlorophyll‐a are positively associated with external phosphorus load and air temperature, respectively
A 2°C rise in simulated air temperature significantly increased summer median, but not annual maximum, chlorophyll‐a
Annual maximum chlorophyll‐a may be more informative than median summer chlorophyll‐a to assess nutrient load effects on water quality
Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal ...pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%.
Biomass burning plays a critical role not only in atmospheric emissions, but also in the deposition and redistribution of biologically important nutrients within tropical landscapes. We quantified ...the influence of fire on biogeochemical fluxes of nitrogen (N), phosphorus (P), and sulfur (S) in a 12 ha forested peatland in West Kalimantan, Indonesia. Total (inorganic + organic) N, NO 3 − -N, NH 4 + -N, total P, PO 4 3 − -P, and SO 4 2 − -S fluxes were measured in throughfall and bulk rainfall weekly from July 2013 to September 2014. To identify fire events, we used concentrations of particulate matter (PM10) and MODIS Active Fire Product counts within 20 and 100 km radius buffers surrounding the site. Dominant sources of throughfall nutrient deposition were explored using cluster and back-trajectory analysis. Our findings show that this Bornean peatland receives some of the highest P (7.9 kg PO 4 3 − -P ha−1yr−1) and S (42 kg SO 4 2 − -S ha−1yr−1) deposition reported globally, and that N deposition (8.7 kg inorganic N ha−1yr−1) exceeds critical load limits suggested for tropical forests. Six major dry periods and associated fire events occurred during the study. Seventy-eight percent of fires within 20 km and 40% within 100 km of the site were detected within oil palm plantation leases (industrial agriculture) on peatlands. These fires had a disproportionate impact on below-canopy nutrient fluxes. Post-fire throughfall events contributed >30% of the total inorganic N ( NO 3 − -N + NH 4 + -N) and PO 4 3 − -P flux to peatland soils during the study period. Our results indicate that biomass burning associated with agricultural peat fires is a major source of N, P, and S in throughfall and could rival industrial pollution as an input to these systems during major fire years. Given the sheer magnitude of fluxes reported here, fire-related redistribution of nutrients may have significant fertilizing or acidifying effects on a diversity of nutrient-limited ecosystems.
This survey paper is used to discuss about the detection of breast cancer tissues using different machine learning algorithms. Identification of cancers using scanned images are very important for ...correct diagnosis. Many algorithms are present for detection of cancer using image processing techniques, all these algorithms have the main goal of detecting those cell tissues. Each algorithm has their own assumptions and advantages, here is a review of some of those algorithms for breast cancer detection. This paper highlights the algorithms and their assumptions of the prior published papers.
Recent cyanobacterial blooms in otherwise unproductive lakes may be warning signs of impending eutrophication in lakes important for recreation and drinking water, but little is known of their ...historical precedence or mechanisms of regulation. Here, we examined long‐term sedimentary records of both general and taxon‐specific trophic proxies from seven lakes of varying productivity in the northeastern United States to investigate their relationship to historical in‐lake, watershed, and climatic drivers of trophic status. Analysis of fossil pigments (carotenoids and chlorophylls) revealed variable patterns of past primary production across lakes over two centuries despite broadly similar changes in regional climate and land use. Sediment abundance of the cyanobacterium Gloeotrichia, a large, toxic, nitrogen‐fixing taxon common in recent blooms in this region, revealed that this was not a new taxon in the phytoplankton communities but rather had been present for centuries. Histories of Gloeotrichia abundance differed strikingly across lakes and were not consistently associated with most other sediment proxies of trophic status. Changes in ice cover most often coincided with changes in fossil pigments, and changes in watershed land use were often related to changes in Gloeotrichia abundance, although no single climatic or land‐use factor was associated with proxy changes across all seven lakes. The degree to which changes in lake sediment records co‐occurred with changes in the timing of ice‐out or agricultural land use was negatively correlated with the ratio of watershed area to lake area. Thus, both climate and land management appeared to play key roles in regulation of primary production in these lakes, although the manner in which these factors influenced lakes was mediated by catchment morphometry. Improved understanding of the past interactions between climate change, land use, landscape setting, and water quality underscores the complexity of mechanisms regulating lake and cyanobacterial production and highlights the necessity of considering these interactions—rather than searching for a singular mechanism—when evaluating the causes of ongoing changes in low‐nutrient lakes.
Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts ...can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
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