Grid-based Method Research Articles

Chemical Reaction Simulator on Quantum Computers by First Quantization (II)─Basic Treatment: Implementation.

Chemical simulation is a key application area that can leverage the power of quantum computers. A chemical simulator that implements a grid-based first quantization method has promising characteristics, but an implementation fully in quantum circuits seems to have not been published. Here, we present "crsQ" (chemical reaction simulator Q), which is a quantum circuit generator that generates such a chemical simulator. The generated simulator is capable of antisymmetrization of the initial wave function and time-evolution of the wave function based on the Suzuki-Trotter decomposition. The potential energy term of the Hamiltonian is implemented using arithmetic gates, such as adders, subtractors, multipliers, dividers, and square roots. Circuit diagrams and output samples are shown. The number of qubits in the circuits scales on the order of O(η log η), where η is the number of electrons. Each component of the generated circuit was verified in unit tests. Along with this development, we designed frameworks to ease the development of large-scale circuits, namely, a temporary qubit allocation framework and an abstract syntax tree framework for arithmetic formulas. These frameworks are expected to be useful in large-scale quantum circuit generators.

Reconstructing Tibetan Plateau lake bathymetry using ICESat-2 photon-counting laser altimetry

Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry reconstruction by combining ICESat-2/ATLAS (Advanced Topography Laser Altimetry System) data with a numerical model. An improved grid-based photon noise removal method is used to address the photon signal buried in the background noise during the local daytime. The developed model was validated for seven lakes on the Tibetan Plateau and showed good agreement between simulated and measured lake volumes, with an average absolute percentage error of 8.0 % for maximum water depth and 19.7 % for lake volume simulations. The model was then utilized to estimate the water volume of other lakes by combining it with the self-affine theory. The lake depths obtained from ICESat-2/ATLAS show good agreement (RMSE = 0.69 m; rRMSE = 10.3 %) with available in-situ measurements for lakes with depths <16.5 m, demonstrating the potential of ICESat-2/ATLAS for improved reconstruction of the bathymetry of clear water inland lakes. Our study reveals for the first time, that the Tibetan Plateau has an estimated total lake water volume of 1043.69 ± 341.31 km3 for 33,477 lakes (>0.01 km2) in 2022. Over 70 % (∼734.8 km3) of the lake water storage is concentrated in the Inner Plateau, with the Yellow River basin accounting for 10.9 % (∼113.9 km3), followed by the Indus River basin with 7.2 % (∼75.1 km3). Our study provides a robust method for estimating total lake volumes where in-situ measurements are scarce and can be extended to other clear water lakes, thus contributing to more accurate global assessments and towards comprehensive quantification of Earth's surface water resources distribution.

A Deep Learning Framework for Satellite-Derived Surface PM2.5 Estimation: Enhancing Spatial Analysis in the United States

Abstract Monitoring fine particulate matter (PM2.5) is crucial for evaluating air quality and its effects on public health. However, the limited distribution of monitoring stations presents a challenge in accurately assessing air pollution, especially in areas distant from these stations. To address this challenge, our study introduces a two-step deep learning approach for estimating daily gap-free surface PM2.5 concentrations across the contiguous United States (CONUS) from 2018 to 2022, with a spatial resolution of 4 km. In the first phase, we employ a Depthwise-Partial Convolutional Neural Network (DW-PCNN) to fill gaps between surface PM2.5 stations, utilizing Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the second phase, we integrate the PM2.5 grids imputed by the DW-PCNN with meteorological and anthropogenic variables into a Deep Convolutional Neural Network (Deep-CNN) to further enhance the accuracy of our estimation. This enables us to estimate gap-free surface PM2.5 concentrations accurately, evidenced by a Pearson’s correlation coefficient (R) of 0.92 and an Index of Agreement (IOA) of 0.96 in ten-fold cross-validation. We also introduce a grid-based method for calculating PM2.5 Design Values (DV), providing a continuous spatial representation of PM2.5 DV that enhances the traditional station-based approach. Our grid-based DV representations offer a comprehensive perspective on air quality, facilitating more detailed analysis. Furthermore, our model's ability to provide spatiotemporally consistent, gap-free PM2.5 data addresses the issue of missing values, supporting health impact research, policy formulation, and the accuracy of environmental assessments.

Localization of neurons from extracellular footprints

Background:High density microelectrode arrays (HD-MEAs) are now widely used for both in-vitro and in-vivo recordings, as they allow spikes from hundreds of neurons to be recorded simultaneously. Since extracellular recordings do not allow visualization of the recorded neurons, algorithms are needed to estimate their physical positions, especially to track their movements when the are drifting away from recording devices. New Method:The objective of this study was to evaluate the performance of multiple algorithms for neuron localization solely from extracellular traces (MEA recordings), either artificial or obtained from mouse retina. The algorithms compared included center-of-mass, monopolar, and grid-based algorithms. The first method is a barycenter calculation. The second algorithm infers the position of the cell using triangulation with the assumption that the neuron behaves as a monopole. Finally, grid-based methods rely on comparing the recorded spike with a projection of spikes of hypothetical neurons with different positions. Results:The Grid-Based algorithm yielded the most satisfactory outcomes. The center-of-mass exhibited a minimal computational cost, yet its average localization was suboptimal. Monopolar algorithms gave cell localizations with an average error of less than 10μm, but they had considerable variability and a high computational cost. For the grid-based method, the variability was smaller, with satisfactory performance and low computational cost. Comparison with Existing Method(s):The accuracy of the different localization methods benchmarked in this article had not been properly tested with ground-truth recordings before. Conclusion:The objective of this article is to provide guidance to researchers on the selection of optimal methods for localizing neurons based on MEA recordings.

Spatio-temporal grid-based storage method for aerospace operation and control data

Abstract The current aerospace operation and control data system has problems such as difficult integration organization and difficult data retrieval in terms of mission planning efficiency, which limits the development of satellite operation and control systems. This paper is oriented to the organization and management of aerospace operation and control data, according to the nature of the current big data indexing model. Based on the principle of earth dissection, we aim to design a grid that can be indexed. First of all, the Earth is dissected to form a hierarchical and uniformly distributed global geographic grid, and then the formed grid is encoded to store satellite attribute information and orbit data for mission planning. Finally, through the data retrieval experiment, the retrieval time is within 100 milliseconds under ten million pieces of data, which proves that the spatial-temporal grid model has the characteristics of efficient and fast retrieval. It can provide the basic information for satellite mission planning to further improve the efficiency of satellite mission planning and management and the efficiency and quality of satellite mission planning.

Optimization of Sparse Camera Array Arrangement using Grid-based Method

—This study explores the application of multi-camera array technology in super-resolution (SR) imaging system. We propose an innovative method based on grid representation to optimize camera arrangements using sparse Gaussian processes and variational inference. This approach significantly enhances the image reconstruction quality of sparse camera arrays. Unlike conventional techniques that provide only qualitative conclusions, our grid-based method precisely optimizes camera positions and effectively simulates the impact of assembly errors. The optimized camera arrangement substantially improves the SR reconstruction quality of sparse array imaging systems, reducing contour jaggedness and high-frequency aliasing. Extensive simulations and experimental validations confirm the robustness and practical applicability of our method. The results demonstrate that the optimized camera arrangements can achieve high-resolution imaging with enhanced robustness against assembly errors, indicating their potential for various applications such as biological microscopy, remote sensing, and smartphone photography. This work presents a new and effective strategy for designing sparse camera array imaging systems, offering significant improvements in both image quality and system reliability.

Low CD8+ Density Variation and R1 Surgical Margin as Independent Predictors of Early Post-Resection Recurrence in HCC Patients Meeting Milan Criteria.

Our study included 41 patients fulfilling the Milan criteria preoperatively and aimed to identify individuals at high risk of post-resection HCC relapse, which occurred in 18 out of 41 patients (43.9%), retrospectively. We analyzed whole slide images of CD8 immunohistochemistry with automated segmentation of tissue classes and detection of CD8+ lymphocytes. The image analysis outputs were subsampled using a hexagonal grid-based method to assess spatial distribution of CD8+ lymphocytes with regards to the epithelial edges. The CD8+ lymphocyte density indicators, along with clinical, radiological, post-surgical and pathological variables, were tested to predict HCC relapse. Low standard deviation of CD8+ density along the tumor edge and R1 resection emerged as independent predictors of shorter recurrence-free survival (RFS). In particular, patients presenting with both adverse predictors exhibited 100% risk of relapse within 200 days. Our results highlight the potential utility of integrating CD8+ density variability and surgical margin to identify a high relapse-risk group among Milan criteria-fulfilling HCC patients. Validation in cohorts with core biopsy could provide CD8+ distribution data preoperatively and guide preoperative decisions, potentially prioritizing liver transplantation for patients at risk of incomplete resection (R1) and thereby improving overall treatment outcomes significantly.

A Grid-Based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts.

Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets. Despite their popularity, such scatterplots suffer from occlusion, especially when informative glyphs are used to represent data instances, potentially obfuscating critical information for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts that lack the powerful capabilities of contemporary DR techniques in uncovering interesting data patterns or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, most of the best methods typically expand or distort the scatterplot area, thus reducing glyphs' size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This article presents Distance Grid (DGrid), a novel post-processing strategy to remove overlaps from DR layouts that faithfully preserves the original layout's characteristics and bounds the minimum glyph sizes. We show that DGrid surpasses the state-of-the-art in overlap removal (through an extensive comparative evaluation considering multiple different metrics) while also being one of the fastest techniques, especially for large datasets. A user study with 51 participants also shows that DGrid is consistently ranked among the top techniques for preserving the original scatterplots' visual characteristics and the aesthetics of the final results.

Estimating Urban Traffic Safety and Analyzing Spatial Patterns through the Integration of City-Wide Near-Miss Data: A New York City Case Study

City-wide near-miss data can be beneficial for traffic safety estimation. In this study, we evaluate urban traffic safety and examine spatial patterns by incorporating city-wide near-miss data (59,277 near-misses). Our methodology employs a grid-based method, the Empirical Bayes (EB) approach, and spatial analysis tools including global Moran’s I and local Moran’s I. The study findings reveal that near-misses have the strongest correlation with observed crash frequency among all the variables studied. Interestingly, the ratio of near-misses to crashes is roughly estimated to be 1957:1, providing a potentially useful benchmark for urban areas. For other variables, an increased number of intersections and bus stops, along with a greater road length, contribute to a higher crash frequency. Conversely, residential and open-space land use rates show a negative correlation with crash frequency. Through spatial analysis, potential risk hotspots including roads linking bridges and tunnels, and avenues bustling with pedestrian activity, are highlighted. The study also identified negative local spatial correlations in crash frequencies, suggesting significant safety risk variations within relatively short distances. By mapping the differences between observed and predicted crash frequencies, we identified specific grid areas with unexpectedly high or low crash frequencies. These findings highlight the crucial role of near-miss data in urban traffic safety policy and planning, particularly relevant with the imminent rise of autonomous and connected vehicles. By integrating near-miss data into safety estimations, we can develop a more comprehensive understanding of traffic safety and, thus, more effectively address urban traffic risks.

A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called “grid–coordinate” based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process.

Hybrid meshless-FEM method for 3-D magnetotelluric modelling using non-conformal discretization

SUMMARY We propose a novel method for 3-D magnetotelluric (MT) forward modelling based on hybrid meshless and finite-element (FE) methods. This method divides the earth model into a central computational region and an expansion one. For the central region, we adopt scatter points to discretize the model, which can flexibly and accurately characterize the complex structures without generating unstructured mesh. The meshless method using compact support radial basis function is applied to simulate this area's electromagnetic field. While in the expansion region, to avoid the heavy time consumption and numerical error of the meshless method caused by non-uniform nodes, we adopt a node-based finite-element method with regular hexahedral mesh for stability. Finally, the two discretized systems are coupled at the interface nodes according to the continuity conditions of vector and scalar potentials. Considering that the normal electric field is discontinuous at the interface with resistivity discontinuity, while the shape functions for the meshless method are continuous, we further adopt the visibility criterion in constructing the support region. Numerical experiments on typical models show that using the same degree of freedom, the hybrid meshless-finite element method (FEM) algorithm has higher accuracy than the node-based FEM and meshless method. In addition, the electric field discontinuity at interfaces is well preserved, which proves the effectiveness of the visibility criterion method. In general, compared to the conventional grid-based method, this new approach does not need the complex mesh generation for complex structures and can achieve high accuracy, thus it has the potential to become a powerful 3-D MT forward modelling technique.

Molecular Docking Studies and ADME-Tox Prediction of Phytocompounds from Punica granatum Peel as a Potential Anti-Alopecia Treatment

Background: Alopecia is a condition in which some or all of the hair from the scalp is lost. One recent preventative measure is the inhibition of the enzyme 5-α-reductase. Inhibition of the enzyme 5-α-reductase converts circulating testosterone to its more potent metabolite, dihydrotestosterone. Pomegranate (Punica granatum L.) peels are widely employed in industry and to treat a variety of illnesses. However, accurate identification is necessary to improve efficacy, repeatability, and quality. Pomegranate, or Punica granatum L., is a member of the Punicaceae family. It is known as "anar" or "dadima" on the Indian subcontinent. For hundreds of years, P. granatum has been used to treat conditions like peptic ulcer, dental disorders, diabetes, hypertension, hyperlipidemia, and numerous types of cancer. Method: Further scientific validation of the current investigation was done by computational based molecular docking study of lead molecules of P.granatum Peel against 5α-reducatase(SRD5As) enzyme. The binding was determined by the Auto Dock software utilizing a grid-based docking method. Compounds' 2D structures were constructed using the chem sketch, converted to 3D, and then energetically reduced up to an arms gradient of 0.01. (MMFF). Result: Punica found to be effective anti-alopecic agent and their lead molecules effectively binds to be target protein 5α-reducatase (SRD5As) with binding energy -7.15, -7.66 &amp; -7.07 kcalmol-1 for Apigenin, luteolin and taxifolin respectively. Conclusion: The results demonstrated that every lead compound that was chosen for further study exhibited strong 5'-reductase (SRD5 As) inhibitory activity, hence eliciting the anti-alopecic potential.

An efficient unstructured quadrilateral-dominated surface mesh generation method for arbitrary geometry with tiny features or crack defects in DiBFM

An efficient unstructured quadrilateral-dominated surface mesh generation algorithm is presented to generate high-quality surface elements for arbitrary geometry with tiny features or crack defects. For complex entities with proximity features, a novel type of meshing template has been proposed to solve the problem of entity boundary fitting of the outside-in grid-based method. The algorithm also works for regions with proximity features, as well as for arbitrary geometry with one or multiple cracks. The procedure incorporates aspects of well-known meshing techniques, but includes some original and efficient processes. The refinement field is constructed according to mesh size, boundary curvature, surface curvature and other geometry features. In order to effectively generate the core mesh, a fast intersection algorithm between topological elements and the solid boundary is established, which improves the efficiency of intersection calculation. The geometry-based techniques are employed for boundary matching of the core mesh. The method has been successfully employed in conjunction with the dual interpolation boundary face method (DiBFM). Several realistic geometries with cracks or proximity features are applied to demonstrate the accuracy and validity of the proposed approach.

Rapid evaluation method for compressive performance degradation of welded hollow spherical joints based on random corrosion distribution

The use of welded hollow spherical joints (WHSJs) is widespread in spatial structures. However, steel is susceptible to corrosion during operation, which can lead to a decrease in the mechanical performance of the joints and ultimately affect the overall safety of the structure. This study focuses on assessing the compressive performance degradation of WHSJs after corrosion. A sophisticated finite element model of corroded joints was developed using the life and death element method. System parameter analysis and sensitivity analysis were conducted, utilizing parameters such as the diameter-to-thickness ratio, axial corrosion ratio, circular corrosion ratio, corrosion depth, envelope ratio, and sphere diameter ratio. Considering the factors mentioned above, a grid-based method was proposed to rapidly evaluate the compressive bearing capacity degradation of corroded joints: When the corrosion area has an irregular and randomly distributed shape, this method can quickly assess the performance degradation of joints by combining "regular corrosion areas that just envelop random corrosion areas" with a "correction factor that considers the degree of random corrosion distribution". This method provides a methodological foundation and technical guidance for evaluating the safety performance of WHSJs and similar joints after corrosion.

DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots

The unmanned swarm robot system, which enables multiple robots to collaborate and perform a variety of tasks, is extensively researched for its potential applications. Accurate determination of the location of swarm robots during operation is of paramount importance, and various positioning algorithms are employed to achieve this. Specifically, in situations where global positioning system (GPS) signals are unavailable, fixed anchor nodes with known location information can be utilized for localization. However, in scenarios where fixed anchor nodes are not present, and the robots operate in a swarm, applying this technology poses challenges, necessitating a localization technique that relies solely on distance information between the robots. This paper proposes a deep neural network (DNN) technique that utilizes only the distance information between moving nodes to predict the real-time relative coordinates of each node. It is assumed that the distances between nodes are updated sequentially and periodically according to a predetermined measurement cycle. A grid-based localization technique is used as the existing method for performance comparison. Computer simulation results demonstrate that the proposed DNN-based relative Localization technique exhibits superior localization performance compared to the existing Grid-based method. Furthermore, the proposed technique shows similar performance regardless of the distance measurement cycle, indicating that it is not significantly affected by the cycle. Therefore, applying the proposed relative Localization algorithm to swarm robots could enable real-time and accurate relative positioning, facilitating precise location tracking of the swarm.

Generally Applicable Q-Table Compression Method and Its Application for Constrained Stochastic Graph Traversal Optimization Problems

We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line balancing problems belong to the class of constrained shortest pathfinding problems, which can be represented as mixed-integer optimization problems. Reinforcement learning (RL) methods have proven their efficiency in multiple complex problems. However, researchers concluded that the learning time increases radically by growing the state- and action spaces. In continuous cases, approximation techniques are used, but these methods have several limitations in mixed-integer searching spaces. We present the Q-table compression method as a multistep method with dimension reduction, state fusion, and space compression techniques that project a mixed-integer optimization problem into a discrete one. The RL agent is then trained using an extended Q-value-based method to deliver a human-interpretable model for optimal action selection. Our approach was tested in selected constrained stochastic graph traversal use cases, and comparative results are shown to the simple grid-based discretization method.

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems.

With the growing maritime economy, ensuring the quality of communication for maritime users has become imperative. The maritime communication system based on nearshore base stations enhances the communication rate of maritime users through dynamic resource allocation. A virtual queue-based deep reinforcement learning beam allocation scheme is proposed in this paper, aiming to maximize the communication rate. More particularly, to reduce the complexity of resource management, we employ a grid-based method to discretize the maritime environment. For the combinatorial optimization problem of grid and beam allocation under unknown channel state information, we model it as a sequential decision process of resource allocation. The nearshore base station is modeled as a learning agent, continuously interacting with the environment to optimize beam allocation schemes using deep reinforcement learning techniques. Furthermore, we guarantee that grids with poor channel state information can be serviced through the virtual queue method. Finally, the simulation results provided show that our proposed beam allocation scheme is beneficial in terms of increasing the communication rate.

Effects of multi-dimensionality and energy exchange on electrostatic current-driven plasma instabilities and turbulence

Large-amplitude current-driven plasma instabilities, which can transition to the Buneman instability, were observed in one-dimensional simulations to generate high-energy back-streaming ions. We investigate the saturation of multi-dimensional plasma instabilities and its effects on energetic ion formation. Such ions directly impact spacecraft thruster lifetimes and are associated with magnetic reconnection and cosmic ray inception. An Eulerian Vlasov–Poisson solver employing the grid-based direct kinetic method is used to study the growth and saturation of 2D2V collisionless, electrostatic current-driven instabilities spanning two dimensions each in the configuration (D) and velocity (V) spaces supporting ion and electron phase-space transport. Four stages characterise the electric potential evolution in such instabilities: linear modal growth, harmonic growth, accelerated growth via quasi-linear mechanisms alongside nonlinear fill-in and saturated turbulence. Its transition and isotropisation process bears considerable similarities to the development of hydrodynamic turbulence. While a tendency to isotropy is observed in the plasma waves, followed by electron and then ion phase spaces after several ion-acoustic periods, the formation of energetic back-streaming ions is more limited in the 2D2V than in the 1D1V simulations. Plasma waves formed by two-dimensional electrostatic kinetic instabilities can propagate in the direction perpendicular to the net electron drift. Thus, large-amplitude multi-dimensional waves generate high-energy transverse-streaming ions and eventually limit energetic backward-streaming ions along the longitudinal direction. The multi-dimensional study sheds light on interactions between longitudinal and transverse electrostatic plasma instabilities, as well as fundamental characteristics of the inception and sustenance of unmagnetised plasma turbulence.

Flexible multi-objective particle swarm optimization clustering with game theory to address human activity discovery fully unsupervised

Human activity recognition is a crucial field of study, but current approaches often require ground truth labels, which are not always available. We propose a new method called the Flexible Multi-Objective Particle swarm optimization clustering method based on Game theory (FMOPG), which can identify human activities without any supervision. Unlike traditional clustering methods that require an estimate of the number of clusters and are often inaccurate, FMOPG handles varying cluster numbers with an incremental technique, selecting clusters with good connectivity and separation. We enhance Particle Swarm Optimization (PSO) with mean-shift vectors for faster convergence and better handling of non-spherical clusters. Employing multi-objective optimization and Gaussian mutation, FMOPG provides a range of optimal solutions. We map the optimization problem to game theory to select the best solution based on different criteria. A smart grid-based method is proposed for population initialization, reducing variance and improving reliability. FMOPG outperforms state-of-the-art methods, improving clustering accuracy by 3.65%. Moreover, the incremental technique has improved clustering time by 71.18%.

Geochemical study of a Neolithic metallurgical site, Le Planet (France): Detection of paleopollution recorded in archaeological soils

A geochemical study was carried out at the metallurgical site of Le Planet (Fayet, Aveyron), situated in the south of France. The site dates from the 3rd millennium BC, and is one the oldest copper smelting metallurgical sites known in France. The site reveals numerous phases of occupation, all dated from the Late Neolithic. The geochemical study of the site aimed at identifying the signature of the metallurgical activities in the archaeological soils and their spatial distribution. The analyses were carried out by X-ray fluorescence (p-XRF). Two main sampling methods were used. The first, grid-based method, allowed the sampling of the totality of the archaeological excavation surface. The second sampling method aimed to study the vertical distribution of the trace elements in the stratigraphic sequence. Four vertical profiles, positioned throughout the site, allowed a high-resolution sampling of the totality of the stratigraphy, which permitted to identify the stratigraphic distribution of polluted archaeological horizons. The geochemical analyses reveal the importance of the pollution due to the prehistoric metallurgical activities, mainly of lead, copper and arsenic. For instance, the measured values of lead vary between 26 and 9831 ppm. These values for copper are between 53 and 1154 ppm, whereas for arsenic they vary between 35 and 258 ppm. The metallurgical furnaces seem to constitute the main hotspots for the spatial distribution of the pollution. The geochemical study of the profiles identifies two main polluted archaeological horizons, and thus allows having a better understanding of the stratigraphic extension of horizons containing geochemical traces of metallurgical activities.