Five-layer Model Research Articles

Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments

Background/Objectives: Parkinson’s Disease is a prevalent neurodegenerative disorder affecting millions worldwide, primarily marked by motor and non-motor symptoms due to the degeneration of dopamine-producing neurons. Despite the absence of a cure, current treatments focus on symptom management, often relying on pharmacotherapy and surgical interventions. Early diagnosis remains a critical challenge, particularly in underserved areas, as existing diagnostic protocols lack standardization and accessibility. This paper proposes a novel framework for the diagnosis and severity classification of PD using video data captured in uncontrolled environments. Methods: Leveraging deep learning techniques, our approach synthesizes Skeleton Energy Images (SEIs) from gait sequences and employs three advanced models—a Convolutional Neural Network (CNN), a Residual Network (ResNet), and a Vision Transformer (ViT)—to analyze these images. Our methodology allows for the accurate detection of PD and differentiation of its severity without requiring specialized equipment or professional oversight. The dataset used consists of labeled videos capturing the early stages of the disease, facilitating the potential for timely intervention. Results: The four models performed very accurately during the training phase. In fact, an accuracy higher than 99% was achieved by the ViT and ResNet models. Moreover, a lesser accuracy of 90% was achieved by the CNN five-layer model. During the test phase, only the best-performing models from the training experiments were tested. The ResNet-18 model has achieved a 100% accuracy. However, the ViT and the CNN five-layer models have achieved, respectively, 99.96% and 96.40% test accuracy. Conclusions: The results demonstrate high accuracy, highlighting the framework’s capabilities, and in particular the effectiveness of the workflow used for generating the SEI images. Given the nature of the dataset used, the proposed framework stands to function as a cost-effective and accessible tool for early PD detection in various healthcare settings. This study contributes to the advancement of mobile health technologies, aiming to enhance early diagnosis and monitoring of Parkinson’s Disease.

Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios

Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.

Performance of a Five-Layer ANN Model for Earthquake Magnitude Prediction and Spatial Risk Mapping in Turkey

Turkey faces significant seismic risks, necessitating accurate earthquake forecasting for effective disaster preparedness. This study employs advanced Artificial Neural Networks (ANN) to predict earthquake magnitudes and assess risks specific to Turkey. Focusing on a distinct segment of the USGS earthquake catalogue from January 2014 to August 2023, the research tailors ANN algorithms with five layers to Turkey's seismic challenges. Rigorous dataset cleaning and processing ensure accuracy, with the ANN model demonstrating exceptional alignment with earthquake data ( RMSE: 0.078, R2: 0.89). Comparative evaluations highlight the effectiveness of ANN models in forecasting earthquake magnitudes in Turkey. The study explores the spatial distribution of earthquake risk across Turkey through an ANN-based map, emphasizing the critical window for preventive measures in this seismically active region. The analysis ensures further enhancement of model accuracy in seismic-prone areas globally. This study advances earthquake prediction by showcasing the high accuracy of our five-layer ANN model in forecasting magnitudes and spatial risk, significantly improving disaster preparedness and risk management in regions such as Turkey.

Analysis of tissue-substrate adhesion by hyperspectral surface plasmon resonance microscopy.

The preparation of histology slides is a critical step in histopathology, and poor-quality histology slides with weak adhesion of tissue sections to the substrate often affect diagnostic accuracy and sometimes lead to diagnostic failure due to tissue section detachment. This issue has been ofconcern and some methods have been proposed to enhance tissue-substrate adhesion. Unfortunately, quantitative analysis of the adhesion between tissue sections and glass slides is still challenging. In this work, the adhesion of mouse brain tissue sections on gold-coated glass slides was analyzed using a laboratory-fabricated hyperspectral surface plasmon resonance microscopy (HSPRM) system that enabled single-pixel spectral SPR sensing and provided two-dimensional (2D) distribution of resonance wavelengths (RWs). The existence of the nanoscale water gap between the tissue section and the substrate was verified by fitting the RW measured in each pixel using the five-layer Fresnel reflection model. In addition, a 2D image of the tissue-substrate adhesion distance (AD) was obtained from the measured 2D distribution of RWs. The results showed that tissue-substrate AD was 20-35 nm in deionized water and 4-24 nm in saline solution. The HSPRM system used in this work has a wide wavelength range of 400-1000 nm and can perform highly sensitive and label-free detection over a large dynamic detection range with high spectral and spatial resolutions, showing significant potential applications in stain-free tissue imaging, quantitative analysis of tissue-substrate adhesion, accurate identification of tumor cells, and rapid histopathological diagnosis.

Experimental free vibration and tensile test results of a five-layer sandwich plate by comparing various carbon nanostructure reinforcements with SMA

Considering to need for the sensitive and destructive industries for high stiffness and low weight, an experimental free vibration and tensile test results of a five-layer sandwich plate by comparing various carbon nano structures reinforcements with SMA is discussed. The effect of various reinforcements, including carbon nanostructures (carbon nanotubes (CNT), carbon nanorods (CNRs), Graphene platelets (GPLs)) and nitinol shape memory alloy (SMA) wire on the vibration behavior of a five-layer sandwich plate with a foam core is investigated. The purpose and novelty of this work are to compare the effect of different reinforcements on the vibrations of a five-layer plate so that by using this comparison, one can choose the best reinforcements according to the needs of the desired industry and more suitable economic conditions. Considering a 1 % weight fraction of epoxy resin, GPLs, CNTs, and CNRs increase Young's modulus of face sheets by 30 %, 25 %, and 5.8 %, respectively. In this work, a five-layer sandwich structure model can be suggested, and numerically analyzed. The special advantage novelty of this research compared to previous research is the construction of this five-layer model. This is a simple modeling structure for the construction and preliminary examination of structures with five layers experimentally. Also, the composite structure is made by the vacuum pump method, which does not have the disadvantages of the manual method, and the method is completely optimal. At first, the Young's and shear moduli of unidirectional glass fiber (UGF) are calculated by performing a tensile test. To examine all aspects of the construction of five-layer structures during construction, in this article first, the face sheets are made separately using resin, UGF, and various reinforcements, and then the layers of face sheets and the cores are connected using glue. In the following, the equations of motion for the sandwich plate are derived using refined first-order shear deformation theory (RFSDT) by employing Hamilton's principle. Using the Halpin-Tsai equation and the extended rule of mixture, the mechanical properties of the reinforced composite face sheets are obtained. According to the Brinson model, the constitutive equations of the SMA are presented. The influence of various parameters such as side ratio, thickness ratio, weight fraction of CNRs, CNTs, GPLs and SMA wire, fiber angle, and temperature changes on dimensionless fundamental frequency is also represented. Also, the natural frequency of orthotropic plates by considering reinforcements is higher than that without considering reinforcements. By increasing the aspect ratio (a/h), the dimensionless natural frequency increases, while the natural frequency decreases. For two-layer square angular asymmetric laminated sheets (-θ/θ), the minimum dimensionless natural frequency occurred at the fiber placement angle approximately equal to 26 and 64°. The highest frequency is related to the sandwich plate in that its face sheets have higher strength and stiffness because it is placed further from the middle plane. Also, the highest frequency is related to the sandwich plate, whose face sheets are reinforced with GPLs.

Accurate internal cavities and kissing bond sizing in metal plates by using the time-of-flight of laser-induced ultrasound waves

This paper presents a nondestructive method for accurately identifying internal flaws in metal plates, which is crucial for ensuring structural integrity in safety-critical applications. The technique relies on analyzing laser-induced ultrasound (LIU) longitudinal wave time-of-flight, as demonstrated through a theoretical five-layer model. Experimental validation was conducted using a piezo-sensor in contact with a slab containing millimetric artificial cavities immersed in air, resulting in a discrepancy of 5.05%. In contrast, experiments performed in a water medium exhibited a lower discrepancy of 2.5%. (Discrepancy refers to differences between measurements obtained through an experimental time-of-flight analysis and caliper measurements.) The results obtained in water-based experiments affirm the accuracy of the proposed model. B-scan measurements and the five-layer model were utilized to generate 2D reconstructed images, enabling precise localization and sizing of cavities and kissing bonds between plates, finding an average size of kissing bond of 30 µm. In conclusion, the proposed five-layer model, based on a longitudinal wave time-of-flight analysis, provides a straightforward framework for an easy cavity and kissing bond measurements in metal plates.

Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge.

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

Comparative analysis of different model depths on convolutional neural network for handwritten digit recognition

In the rapidly advancing field of artificial intelligence, Convolutional Neural Networks (CNNs), as a representative method, have risen to prominence as a pivotal instrument for handling visual data. However, despite their widespread use, the impact of CNN depth on performance remains under-explored. This study delves into this aspect, evaluating the performance of CNN architectures with different depths - two-layer, four-layer, and five-layer - on the MNIST dataset, a version from the National Institute of Standards and Technology, a well-known benchmark dataset for handwritten digit recognition. Experimental results reveal that the four-layer model achieved the highest average accuracy of 99.76%, while the five-layer model, despite its additional complexity, only slightly trailed behind with a 99.73% accuracy rate. However, the five-layer model required a significantly longer training time. In conclusion, while deeper networks can increase accuracy, they can also introduce computational inefficiencies without significant gains in performance. This research provides a better understanding of CNN depth, guiding optimal model selection for image classification tasks.

Understanding the Machine Economy: Combining Findings from Science and Practice

The machine economy (ME) is an emergent phenomenon that combines multiple emerging digital technologies, such as Internet of Things (IoT), artificial intelligence (AI), or blockchain, enabling economically autonomous acting machines. We investigate this phenomenon by combining a literature review and a qualitative interview study across different industries to derive propositions about the ME. Finally, we develop a five-layer model of the ME from our propositions that helps academia and practice explore this emerging phenomenon further. Our results indicate that the ME builds on several emerging digital technologies and becomes increasingly relevant for practice and academia.

Development of Ventral Hernia Repair Curriculum Using the AWSSOM-a Synthetic Abdominal Wall Surgical Skills Operational Model.

Ventral hernia repair cost the U.S. healthcare system nearly 3 billion dollars annually. Surgical repair is a critical competency for residents yet hernia recurrence rates following mesh-based repair range from 0.8% to 24%. Improving surgical techniques using cadavers is often cost-prohibited for many education programs and limited research exists using simulation models with a corresponding hernia repair curriculum in the graduate medical education setting. This pilot project aimed to develop a low cost, easily reproducible novel abdominal wall reconstruction model and pilot-test the ventral hernia repair curriculum to inform further refinement prior to formal evaluation. This descriptive study pilot-tested the newly refined Abdominal Wall Surgical Skills Operative Model (AWSSOM) simulator for ventral hernia repair with mesh and its corresponding 2-h training curriculum for use at all levels of general surgery graduate medical education. The AWSSOM is a 3D printed synthetic anatomically realistic abdominal wall model consisting of silicone cured layers of skin, fat, rectus abdominis and a posterior rectus sheath fascia, and silicone tubules to simulate lateral neurovascular bundles. The curriculum incorporated didactic content reflecting surgical practice guidelines, hands-on practice, and faculty guidance promoting interactive critical thinking development during task performance. A pre-/post-assessment included a 10-item knowledge test, a 19-item psychomotor assessment, and 4-items confidence survey to examine changes in performance, knowledge, and confidence in competently completing the ventral hernia repair technique. Descriptive statistics were used to report the limited results of six military surgical resident participants and inform further model and curriculum refinement prior to formal evaluation. The five-layer AWSSOM model was manufactured in 65 h at a material cost of $87 per model frame, is reusable model, and secure base. Six surgical residents were recruited; only four completed both pre- and post-tests due to resident schedule conflicts. The average increase in knowledge was 25%, although variable changes in confidence were observed over the four program year participants. A larger sample size and a control group are needed to demonstrate curriculum effectiveness at improving knowledge, performance, and confidence in ventral hernia repair with mesh and better delineate if high scores translate to better operative skills. A key improvement requested by residents was a more secure model base for dissection and performance of the hernia repair. The novel abdominal wall surgical skills operative model fills an important proof of concept gap in simulation training. It is low cost with the potential to improve cognitive and psychomotor skills, as well as confidence to competently complete ventral hernia repair with mesh in the graduate medical education setting. Prior to formal effectiveness testing, our lessons learned should be addressed in both the model and curriculum. Future studies must include an adequately powered statistical evaluation with a larger sample across all levels of training.

A scalable blockchain-enabled federated learning architecture for edge computing.

Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services.

Finite element skin models as additional data for dynamic infrared thermography on skin lesions

ABSTRACT Skin cancer is a significant global health concern, with increasing incidence rates and a high number of deaths each year. Early detection plays a crucial role in improving survival rates, but current screening methods, such as total body skin examination, often lead to unnecessary invasive excisions. This research aims to explore the use of dynamic infrared thermography (DIRT) in combination with other technologies to potentially eliminate the need for biopsies in the future and gather information about the stage or depth of malignant skin lesions. The study involves data acquisition using a thermal camera and a finite element skin model. The FEM skin model employed in this research follows the commonly used five-layer model and is constructed in Siemens Simcenter 3D to be able to simulate the cryogenic cooling on the skin. It is possible to improve the thermal images by choosing an appropriate cooling method, cooling sequence and optimised measurement setup. While the FEM skin model shares certain similarities with the measurement data, there is room for further enhancements to optimise its performance. The acquired data is analysed to assess the effectiveness of the combined technique compared to existing clinical and diagnostic methods.

Oxygen profiles and oxygen consumption in the isolated mouse retina

The retina has a large demand for oxygen, but there is only limited information on differences between oxygen utilization (QO2) in the inner and outer retina, and limited data on mouse, which has become a prevalent animal model. This study utilized the isolated mouse retina, which allowed more detailed spatial analysis of QO2 than other methods. Oxygen sensitive microelectrodes were used to obtain profiles of oxygen tension across the isolated mouse retina, and mathematical models of retinal oxygen diffusion with four and five layers were fitted to the data to obtain values for QO2 of the outer retina (QOR) and inner retina (QIR). The boundaries between layers were free parameters in these models. The five-layer model resulted in lower error between the model and data, and agreed better with known anatomy. The three layers for the outer retina occupied half of the retina, as in prior work on rat, cat, and monkey, and the inner half of the retina could be divided into two layers, in which the one closer to the vitreous (layer 5) had much lower QO2 than the more distal inner retina (layer 4). QIR in darkness was 3.9 ml O2-100 g−1-min−1, similar to the value for intact cat retina, and did not change during light. QOR in darkness was 2.4 ml O2-100 g−1-min−1, lower than previous values in cat and rat, possibly because of damage to photoreceptors during isolation. There was a tendency for QOR to be lower in light, but it was not significant in this preparation.

Research on Intelligent Design Method and Technology of Grenade Driven by Digital Twin

In order to change the design method of grenade based on hand drawing and traditional CAD numerical calculation, solve the problem of long design cycle caused by repeated design because the result of grenade design does not meet the tactical index, and promote the transformation of grenade design to the direction of intelligence, based on the concept and architecture of digital twin, a five-layer structure model of the intelligent design system of grenade driven by digital twin is established, and the relationship and connotation of each layer are analyzed: the preliminary realization scheme of applying digital twin technology and intelligent design technology to grenade design is discussed, which provides theoretical reference for subsequent practical application.

2D Q-compensated multi-component elastic Gaussian beam migration

Elastic waves are affected by viscoelasticity during the propagation through the Earth, resulting in energy attenuation and phase distortion, in turn resulting in low seismic imaging accuracy. Therefore, viscoelasticity should be considered in seismic migration imaging. We propose a Q compensated multi-component elastic Gaussian beam migration (Q-EGBM) method to (1) separate the elastic-wave data into longitudinal (P) and transverse (S) waves to perform PP-wave and PS-wave imaging; (2) recover the amplitude loss caused by attenuation; (3) correct phase distortions caused by dispersion; (4) improve the resolution of migration imaging. In this paper, to accomplish (2), (3), and (4), we derive complex-valued traveltimes in viscoelastic media. The results of numerical experiments using a simple five-layer model and a sophisticated BP gas model show that the method presented here has significant advantages in recovering energy decay and correcting phase distortion, as well as significantly improving imaging resolution.

Capacitive-Type Pressure Sensor for Classification of the Activities of Daily Living

In order to operate a gait rehabilitation device, it is necessary to accurately classify the states appearing in activities of daily living (ADLs). In the case of force sensing resistors (FSRs), which are often used as pressure sensors in gait analysis, it is desirable to replace them with other sensors because of their low durability. In the present study, capacitive-type pressure sensors, as an alternative to FSRs, were developed, and their performance was evaluated. In addition, the timed up and go test was performed to measure the ground reaction force in healthy individuals, and a machine learning technique was applied to the calculated biosignal parameters for the classification of five types of ADLs. The performance evaluation results showed that a sensor with thermoplastic polyurethane (substrate and dielectric layer material) and multiwall carbon nanotubes (conductive layer) has sufficient sensitivity and durability for use as a gait analysis pressure sensor. Moreover, when an overlapping filter was applied to the four-layer long short-term memory (LSTM) or the five-layer LSTM model developed for motion classification, the precision was greater or equal to 95%, and unstable errors did not occur. Therefore, when the pressure sensor and ADLs classification algorithm developed in this study are applied, it is expected that motion classification can be completed within a time range that does not affect the control of the gait rehabilitation device.

Propagation Characteristics of Broadband Terahertz Waves in an Inhomogeneous Plasma With Sheath

Using terahertz (THz) waves as the communication carrier is an effective way to overcome the blackout when a spacecraft reenters the atmosphere. A blackout plasma typically has a sheath, but the physical mechanism of broadband THz wave propagation and attenuation in sheath plasma has not been clarified. This article presented experimental and theoretical studies of the propagation characteristics of broadband THz waves in a neon plasma with a sheath for plasma densities ranging from 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">17</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The experimental results showed that a broadband THz wave at a frequency of 1.9 THz had a high attenuation of about 22 dB in the plasma. We simulated the propagation characteristics of the THz wave in the plasma, based on a five-layer plasma model by using the scattering-matrix method. When the sheath plasma density, simulation parameter, was 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> , the experimental attenuation curve of THz wave in plasma was consistent with the simulation curve. This work clarified the determinants of THz wave attenuation in sheath plasma, which lays a foundation for overcoming the black barrier problem.

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

Smart Grid – Grid 2.0, is a promising grid scenario due to its low cost, high reliability, and high security. However, the high-intensity complex task interaction and the differentiated quality of service (QoS) requirements pose challenges to the energy orchestration of the dynamic and time-varying smart grid. In addition, Network Virtualization (NV) technology is widely utilized to decouple physical network resources. In this work, based on Virtual Network Embedding (VNE), we propose an energy orchestration strategy to better support the operation and maintenance of Grid 2.0. Specifically, a series of constraint indicators for energy orchestration is first formulated. Second, based on the advantages of deep reinforcement learning (DRL), a five-layer architecture energy orchestration model is proposed. It frequently extracts the grid energy matrix as the state input, adopts two-stage actions to reasonably orchestrate the grid energy, and combines the reward mechanism of DRL to effectively interact with the environment. Finally, we conduct experiments in a simulation environment and demonstrate the advantages of the proposed strategy through analysis and comparison.

Deformation Behavior of Crystalline Cr–Ni Multilayer Coatings by Using Molecular Dynamics Simulation

This work shows the atomic scale deformation behavior of crystalline multilayer coating comprising up to five Cr and Ni layers. A molecular dynamics simulation was conducted to visualize the atomic scale behavior of the multilayer during indentation/unloading and scratch. Normal and shear directional forces were recorded to compare repulsion and friction forces between the multilayer models during the indentation/unloading and scratch simulations. Dislocation lines within the layers were quantified to understand the deformation behavior of each model. Atomic scale deformation and dislocation lines after the indentation and scratch were visualized. Generation and movement of a single dislocation line during the indentation simulation were also visualized within a few picoseconds. The repulsion and friction forces of the five-layer model showed the lowest values among the models. The unloading stiffness of the five-layer model was calculated to be the lowest among the models. The amount of plastic deformation and the wear volume of the one-layer model after the indentation and scratch was calculated to be the highest among the models. The number of dislocation lines of the five-layer model showed an increasing trend during the indentation and scratch. The highest dislocation density of the five-layer model might aid in an enhancement of resistance to the plastic deformation to reduce the wear volume when scratched.

Geoelectrical Sounding to Identify Sub-surface and Groundwater State at Village Banauli, Singrauli District, Madhya Pradesh, India

Electrical resistivity (Geoelectrical) methods are well-known and common techniques for investigating the groundwater potential zone. These methods are economically viable and have the highest resolving power compared with other geophysical methods. A total of fifteen Vertical electrical soundings were conducted in the village of Banauli, located in Singrauli district in Madhya Pradesh, India. Vertical electrical sounding was carried out using Schlumberger electrode configuration with the maximum current electrode (AB) spacing of 200 m and potential electrode (MN) spacing of 10 m. For interpretation of measured resistivity, the Partial curve matching technique was used to calculate the layer parameters (resistivity and thickness) and further depict the depth section of the profile. In this study, the maximum five-layer model is obtained, and most curves are of HAK types. The first layer has a mean resistivity value of 12.41 Ωm and a mean thickness of 0.94 m. The second layer has mean resistivity of 7.93 Ωm and a mean thickness of 4.79 m. The third layer has a mean thickness value of 10.55 m and a mean resistivity value of 16.54 Ωm. The fourth layer has a mean resistivity value of 20.17 Ωm and a mean thickness of 9.20 m, and finally, the fifth layer, the bedrock, has a higher mean resistivity value of 59.92 Ωm. Thus, the obtained results may be used for identifying the drilling site for the groundwater potential zone.