scholarly journals Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 626 ◽  
Author(s):  
Łukasz Bąk ◽  
Bartosz Szeląg ◽  
Aleksandra Sałata ◽  
Jan Studziński
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Sediment Quality ◽  
Drainage System ◽  
Chemical Properties ◽  
Absolute Error ◽  
Atmospheric Conditions ◽  
Drainage Systems ◽  
Catchment Characteristics

The processes that affect sediment quality in drainage systems show high dynamics and complexity. However, relatively little information is available on the influence of both catchment characteristics and meteorological conditions on sediment chemical properties, as those issues have not been widely explored in research studies. This paper reports the results of investigations into the content of selected heavy metals (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and polycyclic aromatic hydrocarbons (PAHs) in sediments from the stormwater drainage systems of four catchments located in the city of Kielce, Poland. The influence of selected physico-geographical catchment characteristics and atmospheric conditions on pollutant concentrations in the sediments was also analyzed. Based on the results obtained, statistical models for forecasting the quality of stormwater sediments were developed using artificial neural networks (multilayer perceptron neural networks). The analyses showed varied impacts of catchment characteristics and atmospheric conditions on the chemical composition of sediments. The concentration of heavy metals in sediments was far more affected by catchment characteristics (land use, length of the drainage system) than atmospheric conditions. Conversely, the content of PAHs in sediments was predominantly affected by atmospheric conditions prevailing in the catchment. The multilayer perceptron models developed for this study had satisfactory predictive abilities; the mean absolute error of the forecast (Ni, Mn, Zn, Cu, and Pb) did not exceed 21%. Hence, the models show great potential, as they could be applied to, for example, spatial planning for which environmental aspects (i.e., sediment quality in the stormwater drainage systems) are accounted.

scholarly journals Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms

2017 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Debby E. Sondakh
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Error Rates ◽  
Large Dataset ◽  
Training Time ◽  
Healthcare Data ◽  
Squared Error ◽  
Hidden Layer

Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

scholarly journals Chemical Properties of Heavy Metal-Contaminated Soils from a Korean Military Shooting Range: Evaluation of Pb Sources Using Pb Isotope Ratios

2021 ◽  
Vol 11 (15) ◽  
pp. 7099
Author(s):  
Inkyeong Moon ◽  
Honghyun Kim ◽  
Sangjo Jeong ◽  
Hyungjin Choi ◽  
Jungtae Park ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Contaminated Soils ◽  
Management Strategies ◽  
Chemical Properties ◽  
Heavy Metal Concentration ◽  
Soil Samples ◽  
Shooting Range ◽  
Geochemical Properties ◽  
Mineral Phases

In this study, the geochemical properties of heavy metal-contaminated soils from a Korean military shooting range were analyzed. The chemical behavior of heavy metals was determined by analyzing the soil pH, heavy metal concentration, mineral composition, and Pb isotopes. In total, 24 soil samples were collected from a Korean military shooting range. The soil samples consist of quartz, albite, microcline, muscovite/illite, kaolinite, chlorite, and calcite. Lead minerals, such as hydrocerussite and anglesite, which are indicative of a transformation into secondary mineral phases, were not observed. All soils were strongly contaminated with Pb with minor concentrations of Cu, Ni, Cd, and Zn. Arsenic was rarely detected. The obtained results are indicated that the soils from the shooting range are contaminated with heavy metals and have evidences of different degree of anthropogenic Pb sources. This study is crucial for the evaluation of heavy metal-contaminated soils in shooting ranges and their environmental effect as well as for the establishment of management strategies for the mitigation of environmental risks.

scholarly journals The Relevance of Grated Inlets within Surface Drainage Systems in the Field of Urban Flood Resilience. A Review of Several Experimental and Numerical Simulation Approaches

2021 ◽  
Vol 13 (13) ◽  
pp. 7189
Author(s):  
Beniamino Russo ◽  
Manuel Gómez Valentín ◽  
Jackson Tellez-Álvarez
Keyword(s):  
Overland Flow ◽  
Drainage System ◽  
Critical Conditions ◽  
Hydraulic Efficiency ◽  
Storm Events ◽  
Urban Resilience ◽  
Urban Flood ◽  
Drainage Systems ◽  
Surface Drainage ◽  
Intense Storm

Urban drainage networks should be designed and operated preferably under open channel flow conditions without flux return, backwater, or overflows. In the case of extreme storm events, urban pluvial flooding is generated by the excess of surface runoff that could not be conveyed by pressurized sewer pipes, due to its limited capacity or, many times, due to the poor efficiency of surface drainage systems to collect uncontrolled overland flow. Generally, the hydraulic design of sewer systems is addressed more for underground networks, neglecting the surface drainage system, although inadequate inlet spacings and locations can cause dangerous flooding with relevant socio-economic impacts and the interruption of critical services and urban activities. Several experimental and numerical studies carried out at the Technical University of Catalonia (UPC) and other research institutions demonstrated that the hydraulic efficiency of inlets can be very low under critical conditions (e.g., high circulating overland flow on steep areas). In these cases, the hydraulic efficiency of conventional grated inlets and continuous transverse elements can be around 10–20%. Their hydraulic capacity, expressed in terms of discharge coefficients, shows the same criticism with values quite far from those that are usually used in several project practice phases. The grate clogging phenomenon and more intense storm events produced by climate change could further reduce the inlets’ performance. In this context, in order to improve the flood urban resilience of our cities, the relevance of the hydraulic behavior of surface drainage systems is clear.

scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

scholarly journals Little Akaki River sediment enrichment with heavy metals, pollution load and potential ecological risks in downstream, Central Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Deshu Mamo Mekuria ◽  
Alemnew Berhanu Kassegne ◽  
Seyoum Leta Asfaw
Keyword(s):  
Heavy Metals ◽  
River Sediment ◽  
Addis Ababa ◽  
Biological Effects ◽  
Sediment Quality ◽  
Contamination Factor ◽  
Anthropogenic Sources ◽  
Ecological Risks ◽  
Pollution Load ◽  
Akaki River

Abstract Addis Ababa City’s river ecosystem is under extreme pressure as a result of inappropriate practices of dumping domestic and industrial wastes; thus, threatening its ability to maintain basic ecological, social and economic functions. Little Akaki River which drains through Addis Ababa City receives inorganic and organic pollutants from various anthropogenic sources. Most of inorganic pollutants such as toxic heavy metals released into the river are eventually adsorbed and settle in the sediment. The objective of this study was to evaluate the enrichment levels, pollution load and ecological risks of selected heavy metals (Zn, Cr, Cd and Pb) using various indices. The mean concentrations of heavy metals in Little Akaki River sediment were: Zn (78.96 ± 0.021–235.2 ± 0.001 mg/kg); Cr (2.19 ± 0.014–440.8 ± 0.003 mg/kg); Cd (2.09 ± 0.001–4.16 ± 0.0001 mg/kg) and Pb (30.92 ± 0.018–596.4 ± 0.066 mg/kg). Enrichment factor values indicated that sediments were moderate to significantly enriched with Zn and Cr; moderate to very highly enriched with Pb, and very highly enriched in all sampled sites with Cd. Geo-accumulation index and contamination factor values indicated that the sediments were moderate to very highly contaminated with toxic Cd and Pb. The decreasing order of pollution load index (PLI) in downstream was: (S9) > (S4) > (S8) > (S3) > (S6) > (S10) > (S5) > (S2) > (S7) > (S1). PLI and hierarchical cluster analysis revealed that the highest pollution load occurred in the lower course of the river (S9) which may be due to metals inputs from anthropogenic sources. The ecological risk (RI = 350.62) suggested that the contaminated Little Akaki River sediment can pose considerable ecological risks of pollution. The concentrations of Zn, Cr, Cd and Pb in Little Akaki River sediment surpassed eco-toxicological guideline limits of USEPA (threshold effect concentration) and CCME (Interim Sediment Quality Guidelines). Thus, the contaminated sediments can pose adverse biological effects on sediment dwelling organisms.

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