A UAV-GPR Fusion Approach for the Characterization of a Quarry Excavation Area in Falconara Albanese, Southern Italy

The characterization of a quarry site which is suitable for railway ballast aggregate production represents a big challenge for the mining industry. The knowledge of structural discontinuities within local geological materials is fundamental to guide mining operations, optimize investments, and guarantee quarry security. This research work presents an innovative methodology for the subsurface investigation of a quarry excavation area down to a depth of about 50 m in Falconara Albanese, Calabria, Italy. The proposed methodological approach incorporates photogrammetry, drone technology, and GPR data acquisition and processing. Photogrammetry represents the first step for obtaining a 3D topographical model reconstruction of the whole quarry, helping to detail the acquisition approach and properly plan the subsequent drone survey. In particular, two 120 MHz antennas have been mounted on the drone and two profiles have been acquired above and across the quarry. Results show the presence of fractured material and demonstrate the applicability of the method for identification of areas that are more suitable for railway ballast production. The presented method is therefore capable of detecting subsurficial fractures at a quarry site by means of a relatively fast and cost-effective procedure. Results are achieved within the framework of an industry project.

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Fabrication and characterization of piezoelectric nanogenerator based on Al/ZnO/Au structure

Microelectronics International ◽

10.1108/mi-11-2015-0092 ◽

2017 ◽

Vol 34 (1) ◽

pp. 35-39 ◽

Cited By ~ 2

Author(s):

Mubeen Zafar ◽

Muhammad Naeem Awais ◽

Muhammad Asif ◽

Amir Razaq ◽

Gul Amin

Keyword(s):

Mechanical Energy ◽

Short Circuit ◽

Research Work ◽

Cost Effective ◽

Open Circuit ◽

Thin Layers ◽

Full Potential ◽

Content Type ◽

Fabrication And Characterization

Purpose The purpose of this research work is to harvest energy using the piezoelectric properties of ZnO nanowires (NW). Fabrication and characterization of the piezoelectric nanogenerator (NG), based on Al/ZnO/Au structure without using hosting layer, were done to harvest energy. The proposed method has full potential to harvest the cost-effective energy. Design/methodology/approach ZnO NW were fabricated between the thin layers of Al- and Au-coated substrates for the development of piezoelectric NG. To grow ZnO NW, ZnO seed layer was prepared on the Al-coated substrate, and then ZnO NW were grown by aqueous chemical growth method. Finally, Au top electrode was used to conclude the Al/ZnO/Au NG structure. The Al and Au electrodes were used to establish the ohmic and Schottky contacts with ZnO NW, respectively. Findings Surface morphology of the fabricated device was done by using scanning electron microscopy, and electrical characterization of the sample was performed with digital oscilloscope, picoammeter and voltmeter. The energy harvesting experiment was performed to excite the presented device. The fabricated piezoelectric-sensitive device revealed the maximum open circuit voltage up to 5 V and maximum short circuit current up to 30 nA, with a maximum power of 150 nW. Consequently, it was also shown that the output of the fabricated device was increased by applying the stress. The presented work will help for the openings to capture the mechanical energy from the surroundings to power up the nano/micro-devices. This research work shows that NGs have the competency to build the self-powered nanosystems. It has potential applications in biosensing and personal electronics. Originality/value The fabrication of simple and cost-effective piezoelectric NG is done with a structure of Al/ZnO/Au without using hosting layer. The presented method elucidates an efficient and cost-effective approach to harvest the mechanical energy from the native environment.

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Towards a Comprehensive Framework for Climate Change Multi-Risk Assessment in the Mining Industry

Infrastructures ◽

10.3390/infrastructures4030038 ◽

2019 ◽

Vol 4 (3) ◽

pp. 38 ◽

Cited By ~ 1

Author(s):

Mavrommatis ◽

Damigos ◽

Mirasgedis

Keyword(s):

Climate Change ◽

Risk Assessment ◽

Local Level ◽

Mining Industry ◽

Cost Effective ◽

Extreme Weather Events ◽

Quantitative Methodology ◽

Mining Operations ◽

Climate Conditions ◽

Comprehensive Framework

Changing climate conditions affect mining operations all over the world, but so far, the mining sector has focused primarily on mitigation actions. Nowadays, there exists increasing recognition of the need for planned adaptation actions. To this end, the development of a practical tool for the assessment of climate change-related risks to support the mining community is deemed necessary. In this study, a comprehensive framework is proposed for climate change multi-risk assessment at the local level customized for the needs of the mining industry. The framework estimates the climate change risks in economic terms by modeling the main activities that a mining company performs, in a probabilistic model, using Bayes’ theorem. The model permits incorporating inherent uncertainty via fuzzy logic and is implemented in two versatile ways: as a discrete Bayesian network or as a conditional linear Gaussian network. This innovative quantitative methodology produces probabilistic outcomes in monetary values estimated either as percentage of annual loss revenue or net loss/gains value. Finally, the proposed framework is the first multi-risk methodology in the mining context that considers all the relevant hazards caused by climate change extreme weather events, which offers a tool for selecting the most cost-effective action among various adaptation strategies.

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Complexity of Respirable Dust Found in Mining Operations as Characterized by X-ray Diffraction and FTIR Analysis

Minerals ◽

10.3390/min11040383 ◽

2021 ◽

Vol 11 (4) ◽

pp. 383

Author(s):

Rachel Walker ◽

Emanuele Cauda ◽

Lauren Chubb ◽

Patrick Krebs ◽

Robert Stach ◽

...

Keyword(s):

Mining Industry ◽

Respirable Dust ◽

Monitoring Methods ◽

X Ray Diffraction ◽

Mining Operations ◽

Mine Dust ◽

Exposure Monitoring ◽

Site Monitoring ◽

Components Analysis

The mineralogical complexity of mine dust complicates exposure monitoring methods for occupational, respirable hazards. Improved understanding of the variability in respirable dust characteristics, e.g., mineral phase occurrence and composition, is required to advance on-site monitoring techniques that can be applied across diverse mining sectors. Principal components analysis (PCA) models were applied separately to XRD and FTIR datasets collected on 130 respirable dust samples from seven mining commodities to explore similarities and differences among the samples. Findings from both PCA models classified limestone, iron, and granite mine samples via their analytical responses. However, the results also cautioned that respirable samples from these commodities may not always fit patterns observed within the model. For example, one unique sample collected in a limestone mine contained no carbonate minerals. Future predictive quantification models should account for unique samples. Differences between gold and copper mine dust samples were difficult to observe. Further investigation suggested that the key to their differentiation by FTIR may lie in the characterization of clays. The results presented in this study provide foundational information for guiding the development of quantification models for respirable mineral hazards in the mining industry.

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DNA Metabarcoding for the Characterization of Terrestrial Microbiota—Pitfalls and Solutions

Microorganisms ◽

10.3390/microorganisms9020361 ◽

2021 ◽

Vol 9 (2) ◽

pp. 361

Author(s):

Davide Francioli ◽

Guillaume Lentendu ◽

Simon Lewin ◽

Steffen Kolb

Keyword(s):

High Throughput Sequencing ◽

Methodological Approach ◽

Soil Microbial Communities ◽

Cost Effective ◽

Trophic Levels ◽

Gene Markers ◽

Dna Metabarcoding ◽

Terrestrial Environments ◽

Sequencing Platforms

Soil-borne microbes are major ecological players in terrestrial environments since they cycle organic matter, channel nutrients across trophic levels and influence plant growth and health. Therefore, the identification, taxonomic characterization and determination of the ecological role of members of soil microbial communities have become major topics of interest. The development and continuous improvement of high-throughput sequencing platforms have further stimulated the study of complex microbiota in soils and plants. The most frequently used approach to study microbiota composition, diversity and dynamics is polymerase chain reaction (PCR), amplifying specific taxonomically informative gene markers with the subsequent sequencing of the amplicons. This methodological approach is called DNA metabarcoding. Over the last decade, DNA metabarcoding has rapidly emerged as a powerful and cost-effective method for the description of microbiota in environmental samples. However, this approach involves several processing steps, each of which might introduce significant biases that can considerably compromise the reliability of the metabarcoding output. The aim of this review is to provide state-of-the-art background knowledge needed to make appropriate decisions at each step of a DNA metabarcoding workflow, highlighting crucial steps that, if considered, ensures an accurate and standardized characterization of microbiota in environmental studies.

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Monitoring of Petroleum-Related Environmental Contamination Using Fluorescence Fingerprinting

Water Practice & Technology ◽

10.2166/wpt.2006.029 ◽

2006 ◽

Vol 1 (2) ◽

Cited By ~ 1

Author(s):

P. Literathy ◽

M. Quinn

Keyword(s):

Environmental Contamination ◽

Statistical Evaluation ◽

Oil Pollution ◽

Emission Spectra ◽

Cost Effective ◽

Petroleum Products ◽

Danube River ◽

Aromatic Components ◽

Analytical Approaches

Petroleum and its refined products are considered the most complex contaminants frequently impacting the environment in significant quantities. They have heterogeneous chemical composition and alterations occur during environmental weathering. No single analytical method exists to characterize the petroleum-related environmental contamination. For monitoring, the analytical approaches include gravimetric, spectrometric and chromatographic methods having significant differences in their selectivity, sensitivity and cost-effectiveness. Recording fluorescence fingerprints of the cyclohexane extracts of the water, suspended solids, sediment or soil samples and applying appropriate statistical evaluation (e.g. by correlating the concatenated emission spectra of the fingerprints of the samples with arbitrary standards (e.g. petroleum products)), provides a powerful, cost-effective analytical tool for characterization of the type of oil pollution and detecting the most harmful aromatic components of the petroleum contaminated matrix. For monitoring purposes, the level of the contamination can be expressed as the equivalent concentration of an appropriate characteristic standard, based on the fluorescence intensities at the relevant characteristic wavelengths. These procedures are demonstrated in the monitoring of petroleum-related pollution in the water and suspended sediment in the Danube river basin

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Green Synthesis and Spectroscopic Studies of Ag-rGO Nanocomposites for Highly Selective Mercury (II) Sensing

Nanoscience &Nanotechnology-Asia ◽

10.2174/2210681207666170705143629 ◽

2018 ◽

Vol 9 (1) ◽

pp. 101-108 ◽

Cited By ~ 1

Author(s):

Shubhangi J. Mane-Gavade ◽

Sandip R. Sabale ◽

Xiao-Ying Yu ◽

Gurunath H. Nikam ◽

Bhaskar V. Tamhankar

Keyword(s):

Green Synthesis ◽

Color Change ◽

Limit Of Detection ◽

Aqueous Media ◽

Suitable Condition ◽

Cost Effective ◽

Spectroscopic Studies ◽

Calibration Plot ◽

First Time

Introduction: Herein we report the green synthesis and characterization of silverreduced graphene oxide nanocomposites (Ag-rGO) using Acacia nilotica gum for the first time. Experimental: We demonstrate the Hg2+ ions sensing ability of the Ag-rGO nanocomposites form aqueous medium. The developed colorimetric sensor method is simple, fast and selective for the detection of Hg2+ ions in aqueous media in presence of other associated ions. A significant color change was noticed with naked eye upon Hg2+ addition. The color change was not observed for cations including Sr2+, Ni2+, Cd2+, Pb2+, Mg2+, Ca2+, Fe2+, Ba2+ and Mn2+indicating that only Hg2+ shows a strong interaction with Ag-rGO nanocomposites. Under the most suitable condition, the calibration plot (A0-A) against concentration of Hg2+ was linear in the range of 0.1-1.0 ppm with a correlation coefficient (R2) value 0.9998. Results & Conclusion The concentration of Hg2+ was quantitatively determined with the Limit of Detection (LOD) of 0.85 ppm. Also, this method shows excellent selectivity towards Hg2+ over nine other cations tested. Moreover, the method offers a new cost effective, rapid and simple approach for the detection of Hg2+ in water samples.

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Knee Muscle Force Estimating Model Using Machine Learning Approach

The Computer Journal ◽

10.1093/comjnl/bxaa160 ◽

2020 ◽

Author(s):

Anurag Sohane ◽

Ravinder Agarwal

Keyword(s):

Machine Learning ◽

Random Forest ◽

Muscle Force ◽

Vastus Lateralis ◽

Input Parameter ◽

Research Work ◽

Cost Effective ◽

Coefficient Of Determination ◽

Muscle Forces ◽

Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

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A GIS Software Module for Environmental Impact Assessment of the Open Pit Mining Projects for Small Mining Operators in Kazakhstan

Sustainability ◽

10.3390/su13126971 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6971

Author(s):

Mikhail Zarubin ◽

Larissa Statsenko ◽

Pavel Spiridonov ◽

Venera Zarubina ◽

Noune Melkoumian ◽

...

Keyword(s):

Environmental Impact ◽

Impact Assessment ◽

Environmental Impact Assessment ◽

Mining Industry ◽

Open Pit ◽

Open Pit Mining ◽

Software Module ◽

Mining Operations ◽

Open Pit Mines ◽

Gis Software

This research article presents a software module for the environmental impact assessment (EIA) of open pit mines. The EIA software module has been developed based on the comprehensive examination of both country-specific (namely, Kazakhstan) and current international regulatory frameworks, legislation and EIA methodologies. EIA frameworks and methods have been critically evaluated, and mathematical models have been developed and implemented in the GIS software module ‘3D Quarry’. The proposed methodology and software module allows for optimised EIA calculations of open pit mines, aiming to minimise the negative impacts on the environment. The study presents an original methodology laid out as a basis for a software module for environmental impact assessment on atmosphere, water basins, soil and subsoil, tailored to the context of mining operations in Kazakhstan. The proposed software module offers an alternative to commercial off-the-shelf software packages currently used in the mining industry and is suitable for small mining operators in post-Soviet countries. It is anticipated that applications of the proposed software module will enable the transition to sustainable development in the Kazakh mining industry.

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Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽

10.1007/s10346-021-01654-0 ◽

2021 ◽

Author(s):

Chiara Crippa ◽

Elena Valbuzzi ◽

Paolo Frattini ◽

Giovanni B. Crosta ◽

Margherita C. Spreafico ◽

...

Keyword(s):

Rock Slope ◽

Progressive Failure ◽

Principal Component ◽

Cost Effective ◽

Displacement Rate ◽

Machine Learning Classification ◽

Management Perspective ◽

Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

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Variable Temperature Synthesis of Tunable Flame-Generated Carbon Nanoparticles

C – Journal of Carbon Research ◽

10.3390/c7020044 ◽

2021 ◽

Vol 7 (2) ◽

pp. 44

Author(s):

Francesca Picca ◽

Angela Di Pietro ◽

Mario Commodo ◽

Patrizia Minutolo ◽

Andrea D’Anna

Keyword(s):

Carbon Nanoparticles ◽

Variable Temperature ◽

Flame Temperature ◽

Cost Effective ◽

Synthesis Process ◽

Electrical Mobility ◽

Material Synthesis ◽

Online Measurements ◽

Degree Of Graphitization

In this study, flame-formed carbon nanoparticles of different nanostructures have been produced by changing the flame temperature. Raman spectroscopy has been used for the characterization of the carbon nanoparticles, while the particle size has been obtained by online measurements made by electrical mobility analysis. The results show that, in agreement with recent literature data, a large variety of carbon nanoparticles, with a different degree of graphitization, can be produced by changing the flame temperature. This methodology allows for the synthesis of very small carbon nanoparticles with a size of about 3-4 nm and with different graphitic orders. Under the perspective of the material synthesis process, the variable-temperature flame-synthesis of carbon nanoparticles appears as an attractive procedure for a cost-effective and easily scalable production of highly tunable carbon nanoparticles.

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