Induction Sensors Research Articles

Aluminum dust concentration detection based on LSTM-Kalman filter

Industrial dust emissions present serious hazards, including respiratory issues and explosion risks. Traditional dust concentration detection methods are often compromised by environmental factors. This study introduces a novel FFT-KF-LSTM-NET model to predict high-concentration aluminum powder levels, utilizing 128 sets of electrostatic induction measurement data. By applying the Fast Fourier Transform (FFT) to eliminate low-frequency interference, integrating Long Short-Term Memory (LSTM) networks for advanced time series analysis, and using the Kalman Filter (KF) for rapid model convergence, this approach significantly enhances prediction accuracy. The model achieves an 82% improvement in Mean Squared Error (MSE), reducing it to 0.1336, outperforming traditional methods. Furthermore, by modeling the voltage signal generated by charged dust particles in the electrostatic induction sensor's sensing area and using the first derivative of the voltage signal as a learning feature, the model's prediction speed is increased from 1.5 s-2 s–0.5 s, with improved anti-interference capabilities. These advancements position the FFT-KF-LSTM-NET model as a crucial tool for real-time, reliable dust concentration detection in industrial environments.

Drone‐towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

AbstractDrone‐based geophysical surveying is an emerging measuring platform. High time‐cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone‐towed electromagnetic induction and magnetic gradient sensor system used for near‐surface characterization and areal mapping.The system uses various datasets to enhance processing and interpretation. The system includes; an electromagnetic induction instrument; magnetic sensors; GNSS‐IMU system; photogrammetry; Lidar model data; and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone‐towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the data.We conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100200 m. We then implemented and applied a one‐dimensional laterally constrained non‐linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5–8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2–4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

Magnetic induction sensor based on a dual-frequency atomic magnetometer

Magnetic induction sensor based on a dual-frequency atomic magnetometer

USING THE ARDUINO PLATFORM IN THE RESEARCH OF INTERNAL COMBUSTION ENGINES

Modern measuring systems used in the study of internal combustion engines require significant investments. The universal Arduino platform offers ready-made powerful hardware modules for data collection (shields) and control, which work in a wide range of frequencies and signal amplitudes, provide their analysis and processing, allow to implement equipment management, and have a relatively low cost and free software with standard libraries. These facts testify to the high relevance of the use of the Arduino platform in the study of internal combustion engines. The purpose of this work is to determine the possibilities and features of using the Arduino hardware and software platform in the study of processes occurring in energy power plants with internal combustion engines, early prototyping of their control and diagnostic systems. The paper considers the possibility of hardware and software connection of sensors for measuring basic physical quantities to the Arduino platform: crankshaft rotation frequency (based on induction sensors and Hall sensors), pressure, air and fuel flow, temperatures (resistance sensors and thermocouples). In addition, the characteristics (dependence of the measurement parameter on the output value) of some popular sensors used in the study of internal combustion engines are given. It is important to consider that the equipment used for the study of processes in the ICE must meet the certification requirements. In the case of Arduino, this certification may not be available. However, the recommendations for the use of various sensors, shields and Arduino boards can be applied if the measured quantity is not the determining factor. It is important to consider these limitations and use Arduino with an understanding of the capabilities and limitations of the platform. The use of Arduino in the educational process in the training of specialists in the field of power engineering and in the research of energy installations of vehicles will allow students to apply the knowledge, skills and abilities acquired in the learning process at the hardware and software levels, which will create additional motivation for further study devices and principles of operation of automated and automatic vehicle control systems.

Design, Development and Application of a Modular Electromagnetic Induction (EMI) Sensor for Near-Surface Geophysical Surveys.

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study, we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows for the separations between the transmitter to be adjusted and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for one-person operation, provides 4 h of operation time on one battery charge, and provides sufficient rigidity for practical field operations.

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

The fast development in precision manufacturing and product quality inspection of ferromagnetic material desperately demands that the defect determining the performance, service life and reliability of the mechanical part should be accurately sensed, detected and evaluated. However, conventional defect detection methods and related magnetic induction sensors are usually characterized with low accuracy in small defect detection, which makes the non-destructive testing (NDT) for ferromagnetic material full of challenges. Thus, a new magnetic focusing sensor is proposed and designed for the precision defect detection of ferromagnetic steel plate here. First, the theoretical principle analysis of the magnetic focusing sensor is introduced, and the preliminary finite element modeling (FEM) is conducted to demonstrate the feasibility of the magnetic focusing sensor in tiny defect inspection. Then, both the static and coupling dynamic characterizations of the magnetic focusing sensor are investigated using the FEM method. The optimal structural parameters of the magnetic focusing (MF) sensor in static conditions are summarized. Furthermore, the effects of the dynamic movement of the MF sensor on the multi-physical fields of the magnetization field, electric field, electromagnetic attraction force field and thermal field are all revealed. Finally, experiments regarding the performance evaluation and comparison between the MF sensor and other two magnetic flux leakage (MFL) sensors are conducted, and the prominent capabilities of accurate and precise defect detection for the MF sensor is demonstrated through the defect detection with different sizes. The effectiveness and feasibility of the MF sensor in detecting tiny defects on ferromagnetic object, as well as its great potential for practical application is therefore validated. Additionally, the main conclusions, advantages of the MF sensor are summarized and the future work is discussed.

Precise 6-DOF Motion Tracking of Fine Motor Skills of Fingers Based on Wearable Magnetic Induction Sensors

Precise 6-DOF Motion Tracking of Fine Motor Skills of Fingers Based on Wearable Magnetic Induction Sensors

3-D imaging of whole-space environments with electromagnetic induction sensors based on linear approximation

Accurate mapping of the periphery of tunnel has become an essential step in underground mining and engineering. Because of high sensitivity to electric conductivity and magnetic permeability, the whole-space electromagnetic (EM) method has played an important role in geological forecast of groundwater and exploration of mineral ore. However, obtaining a three-dimensional (3-D) image of the whole-space EM method by 3-D inversion is challenging due to the limited observation space and inefficiencies in 3-D forward modeling algorithms. While 1-D inversion algorithms have long been used to map the periphery of the tunnel, various 3-D inversion algorithms have been successfully applied in other scenarios. Recently, the portable frequency domain loop-loop EM induction (EMI) measurement has been considered as an active source magnetic method in near surface surveys, because of the dominance of magnetic permeability in the in-phase (IP) response. The 3-D linear forward modeling method is developed based on the location-specific magnetic sensitivity function, and the 3-D inversion algorithm has been effectively employed for mapping magnetic anomalies in large-scale area. In this study, we intend to demonstrate that the EMI measurement can give 3-D image of the periphery of the tunnel by theory and numerical experiments. The IP response of EMI in whole-space is separated into three parts, and the 3-D linear forward modeling method is examined by analytical solution. The 1-D Fourier transform is used to accelerate the 3-D forward modeling, and the 3-D inversion show a good performance of mapping the magnetic abnormal bodies in underground space.

Temporal Stability of Management Zone Patterns: Case Study with Contact and Non-Contact Soil Electrical Conductivity Sensors in Dryland Pastures.

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization.

Three-dimensional, soft magnetic-cored solenoids via multi-material extrusion

ABSTRACT This study reports fully 3D-printed, three-dimensional, soft magnetic-cored solenoids that generate three times the largest magnetic fields previously reported from 3D-printed solenoids. The devices are fabricated on a customised, multi-material 3D printer that can extrude both filaments and pellets. Three different kinds of materials are employed to manufacture the reported soft magnetic-cored solenoids: pure PLA (dielectric portions), PLA doped with copper particles (electrically conductive structures), and nylon or PLA doped with metallic particles (soft magnetic cores). Via manufacturing optimisation, the reported devices are 33% smaller and can withstand about twice the current, generating three times more magnetic field. The 3D-printed solenoids generate Gauss-level magnetic fields while drawing tens-of-milliamps currents and can be readily used to implement fully 3D-printed induction sensors. The results of this work extend the state of the art in 3D-printed electronics, enabling the creation of more complex and capable solenoids for in-situ manufactured and in-space manufactured electromagnetic systems.

MagSign: Harnessing Dynamic Magnetism for User Authentication on IoT Devices

User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implementability</i> thanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices void of text entry interfaces. Biometrics may not scale well as they require both non-trivial sensors and cumbersome user involvement. Proximity-based methods exploiting shared ambient contexts are vulnerable to co-located malicious attacks. Therefore, a low-cost authentication scheme widely implementable on heterogeneous IoT devices is urgently demanded. To this end, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagSign</i> that leverages two fundamental capabilities owned by common IoT devices: the ubiquity of magnetic induction sensors and the power of screens to change magnetic field. Essentially, MagSign controls screen contents of an authorized device (possessed by a user) to generate specific currents in its electronic components that in turn induce a magnetic signature. This signature, sensed by a nearby device, allows the user to be authenticated and hence to unlock that device. In designing MagSign, we explore critical parameters employable to magnetic signature generation by analyzing electronic components' workflow. Moreover, we innovatively encode binary sequences into magnetic intensity transitions, so that a sequence issued from a trusted server can be converted into a magnetic signature. Different from existing proximity-based approaches relying on shared static environment information, magnetic signature is directly derived from a server-issued sequence, allowing for dynamic signature generation that effectively thwarts potential attacks. The comprehensive experiments show MagSign has a false acceptance rate (FAR) of 0.38% and a false rejection rate (FRR) of 3.13%.

Analysis of Possibility of Induction Sensor Designing for Estimating Quantitatively the Phase Composition of Samples with an Elevated Surface Temperature

Analysis of Possibility of Induction Sensor Designing for Estimating Quantitatively the Phase Composition of Samples with an Elevated Surface Temperature

Comparing quasi-3D soil moisture derived from electromagnetic induction with 1D moisture sensors and correlation to barley yield in variable duplex soil

Farms in Western Australia (WA) are highly variable in soil texture and water retention capacity; therefore, information of soil moisture spatial variability in the field is important for effective crop management. In practice, farmers often rely on point sensors to determine soil moisture in their fields for crop planning. The limitation of point measurements to account for spatial variability highlights the need to develop methods that can assess soil moisture across variable broadacre fields. This information can enable more effective site-specific crop management practices. In this study, we used a mobile non-intrusive electromagnetic induction (EMI) sensor to map soil apparent electrical conductivity (ECa) to predict soil moisture levels at three different depths (0–0.5, 0.5–0.8 and 0.8–1.6 m) across the field and compared it with barley yield production. To convert the depth specific electrical conductivity (EC) from EMI surveys into soil moisture estimates, calibrations between electrical resistivity tomography (ERT) to volumetric moisture were used. These calibrations were developed for the different soil textural classes of the field, with R2 of 0.97–0.99. There was no significant difference between the estimated soil moisture from EMI surveys and the point moisture measurements obtained from soil moisture sensors and soil sample extracts, with Pearson R values of 0.64 for 0–0.5 m depth and 0.73 for 0.5–0.8 m depth. Additionally, correcting the temperature drift relative to the measured soil temperature on EMI surveys by 10% in dry season and 31% in wet season, improved the moisture estimation results. This study successfully demonstrated the effectiveness of spatial soil moisture estimation using EMI sensor in a field characterized by horizontal and vertical soil texture variability. However, no significant correlation was found between the mapped soil moisture or soil texture with barley yield. This may be due to relatively high initial moisture levels resulting from two years of fallow rotation.

The Use of Induction Sensors and Helmet-Based Shielding Technology to Identify Differences in Electromagnetic Fields in Patients With Cranial Neurological Disease Versus Healthy Controls.

Background and objective Electromagnetic fields (EMFs) stemming from neural circuits have been evaluated in healthy human subjects by using non-invasive induction sensor technologies with adjunctive shielding constrained to a helmet constructed of Mu-metal and copper mesh. These EMF measurements have been analyzed and discerned to alterphysiological states of movement, thoughts of movement, emotional thoughts, andplanned activities. However, these technologies have not yet been investigated as a diagnostic tool in patients with cranial neurological pathology to evaluate differences in patterns in the pathologic state compared to healthy controls. In light of this, we conducted this study to address this scarcity of data. Methods An observational study was conducted in which patients at a single center with cranial neurological disease of all causes were eligible to enroll; they had real-time non-invasive continuous EMF measurements obtained using induction sensors and a shielded helmet. These measurements were obtained in the resting state and then compared to previously obtained measurements in healthy volunteers. Post-processing analysis was conducted to evaluate the derivatives of these EMFs to identify changes in patterns. Results Fourteen patients with traumatic injury, stroke, and neoplasm with ages ranging from 14 to 81 years underwent successful analysis and post-processing of their cortically generated EMF waves. Patterns of EMF waves were compared topreviously obtained data from four healthy controls. It was identified that there was less variation in the EMF measurements in patients with neurological disease compared to healthy controls. This was identified based on differences in derivatives of the EMF waves anddecreased numbers of peaks and valleys in the EMF waves. Conclusions Novel induction sensors with an engineered, layered Mu-metal and copper mesh helmet for shielding with Mu-metal EMF channels appear to be efficient in measuring neural circuit-driven EMF non-invasively, in real-time, and continuously and can discern differences in EMF patterns between healthy volunteers and patients with neural circuit pathology. The decreased variability in EMF measurements in patients with neural pathology and greater decreases in slope within low-frequency measurements may be correlated with disrupted neural signaling from dysfunctional neurons and abnormalities in spatial and temporal summation. Some EMF changes inill individuals correspond to changes in the experimentally induced lesions in the animal model. Further studies are warranted to devise models of disease and healthy states to improve these technologies as a diagnostic modality.

A Swine Model of Neural Circuit Electromagnetic Fields: Effects of Immediate Electromagnetic Field Stimulation on Cortical Injury.

Background Neurologic diseases have profound disability, mortality, and socioeconomic effects worldwide. Treatment of these disorders varies but is largely limited to unique factors associated with neural physiology. Early studies have evaluated alterations in electromagnetic fields (EMF) due to neural disorders with subsequent modulation of EMF as a potential treatment modality. Swine models have begun to be evaluated as translational models in this effect. Methods EMF measurements of a Yucatan miniswine were recorded using proprietary non-contact, non-invasive induction sensors with a dual layer Mu-metal and interlaced copper mesh helmet. The swine then underwent controlled cortical impact (CCI) to simulate traumatic brain injury (TBI). Twenty minutes post-injuryafter surgical wound closure, the swine underwent targeted EMF signal modulation using a signal generator to stimulate the swine's injured cortical circuit using a sinusoidal wave individualized at 2.5 Hz with a 500mV positive offset at 1V. After 10 days of stimulation, settings were modified to another individualized frequency of 5.5 Hz, 500mV positive offset and 1V for stimulation. Behavioral patterns in swine were evaluated, and EMF measurements were recorded daily prior to, during, and after stimulation. Artificial intelligence (AI) models evaluated patterns in EMF signals.Histology of the stimulated swine cortex was evaluated using hematoxylin and eosin staining and pentachrome staining and compared to a control swine without stimulation and a swine that had received stimulation two days post-injury in a delayed fashion. Serial serum specimens and tissue at the time of euthanasia were obtained for assessment of neuron-specific enolase (NSE) concentration. Results Pre-operative and post-stimulation measurements demonstrated differences in patterns and activity early on. There was an identified peak at 1.6Hz, not frequently seen pre-operatively. There were convergent frequencies in both data setsat 10.5 Hz and 3.9 Hz. Plateaus and decreased variability of changes in slope were identified early in the post-injury phase. AI modeling identified early similarities in pre-operative and post-stimulation measurements through the patterns of peaks with similarities on postoperative day 10 and similarities in the valleys on postoperative day 17.Histologic specimens identified increased degrees of apoptosis and cellular death in the non-stimulated control compared to the stimulated swine. Similarly, the immediately stimulated swine had less apoptosis and increased histologic viability at the site of injury compared to the two-day delayed stimulation swine. There were increased levels of NSE noted in the stimulated swine at the site of injury compared to non-injured sites and the control swine. Conclusions Cortical function was appropriately measured through induction sensors and shielding in the form of a helmet and electromagnetic field channels. Early stimulation resulted in the early and durable recovery of neuronal circuit-driven electromagnetic field patterns. Histology identified increased viability of neurons with fewer apoptotic neurons and glial cells in stimulated swine with early stimulation identifying the best effect compared to a non-stimulated subject. This recovery identifies change and recovery at the circuit, cellular, and subcellular levels that potentiatethe need for further study of EMF modulation as a treatment modality in neurological disorders.

Estimation of soil water content using electromagnetic induction sensors under different land uses

The complex nature of podzolic soils makes investigating their subsurface challenging. Near-surface geophysical techniques, like electromagnetic induction (EMI), offer significant assistance in studying podzolic soils. Multi-coil (MC-EMI) and multi-frequency (MF-EMI) sensors were selected to maximize soil water content (SWC) prediction in this study. The objectives were to (i) compare apparent electrical conductivity (ECa) measurements from the MC and MF-EMI sensors under different land use conditions, (ii) investigate the spatial variation of ECa, SWC, texture, soil organic matter (SOM), and bulk density (BD) under different land use conditions, and (iii) use statistical and geostatistical analysis to evaluate the effectiveness of ECa measurements in characterizing SWC under different land use conditions, considering the texture, SOM, and BD contents. The study found that MC-EMI had statistically significant relations (p-value < 0.05) with SWC relative to the MF-EMI. Multiple linear regression (MLR) models were also shown to be more effective in representing SWC variations (higher coefficient of determination and lower root mean square error) than simple linear regression models. MC-EMI sensor provided better SWC predictions compared to the MF-EMI sensor, possibly due to larger sampling depths differences between time domain reflectometry measured SWC (SWCTDR) and MF-EMI sensor than those between SWCTDR and MC-EMI sensor. Lastly, cokriging of measured SWC was found to offer more accurate maps than cokriging of predicted SWC obtained from MLR across different land use conditions. The study has shown that EMI may not be highly effective for shallow depths, and ECa can be affected by various soil properties, making it difficult to extrapolate other parameters. However, EMI still shows promise as a reliable method for predicting SWC in boreal podzolic soils. Research into EMI’s usefulness for this purpose has yielded promising results, as indicated in this study. Further investigation is needed to fully harness the potential of this promising technique.

A Swine Model of Traumatic Brain Injury: Effects of Neuronally Generated Electromagnetic Fields and Electromagnetic Field Stimulation on Traumatic Brain Injury-Related Changes.

Background and objective Traumatic brain injury (TBI) has been associated with aberrations in neural circuitry attributable to the pathology resulting in electromagnetic field (EMF) changes. These changes have been evaluated in a variety of settings including through novel induction sensors with an ultra-portable shielded helmet and EMF channels with differences identified by comparing pre-injury and post-injury states. Modulation of the EMF has undergone cursory evaluation in neurologic conditions but has not yet been fully evaluated for clinical effects in treatment. Target EMF stimulation using EMF-related changes preoperatively to postoperatively has not yet been attempted and has not been completed using induction sensor technology. Our objectives in this study were twofold: we wanted to test thehypothesis that targeted stimulation using an EMF signal generator and stimulator to abnormal thresholds identified by real-time measurement of EMFs may enable early resolution of EMF changes and treatment of the TBI as modeled through controlled cortical impact (CCI); we also aimed toassess the feasibility of attemptingthis using real-time measurements with an EMF shielded helmet with EMF channels and non-contact, non-invasive induction sensors with attached EMF transmitters in real-time. Methods A singular Yucatan miniswine was obtained and baseline EMF recordings were obtained. A CCI of TBI and postoperative assessment of cortical EMF in a non-invasive, non-contact fashion were completed. Alterations in EMF were evaluated and EMF stimulation using those abnormal frequencies was completed using multiple treatments involving three minutes of EMF stimulation at abnormal frequencies. Stimulation thresholds of 2.5 Hz, 3.5 Hz, and 5.5 Hz with 1 V signal intensity were evaluated using sinusoidal waves. Additionally, stimulation thresholds using differing offsets to the sine wave at -500 mV, 0 mV, and 500 mv were assessed. Daily EMF and post-stimulation EMF measurements were recorded. EMF patterns were then assessed using an artificial intelligence(AI) model. Results AI modeling appropriately identified differences in EMF signal in pre-injury, post-injury, and post-stimulation states. EMF stimulation using a positive offset of 500 mV appeared to have maximal beneficial effects in return to baseline. Similarly targeted stimulation using thresholds of 2.5 Hz and 5.5 Hz with a positive 500 mV offset at 1 V allowed for recovery of EMF patterns post-injury towards patterns seen in baseline EMF measurements on stimulation day seven (postoperative day 17). Conclusion Stimulation of neural circuits with targeted EMF in a sinusoidal pattern with targeted thresholds after measurement with induction sensors with shielding isolated to a Mu-metal and copper mesh helmet and EMF channels is efficacious in promoting neuronal circuit recovery to preoperative baselines in the TBI miniswine model. Similarly, our findings confirm the appropriateness of this translational model in the evaluation of brain neuronal circuit EMF and that preoperative and post-trauma differences can be appropriately assessed with this technology.

Soil and satellite remote sensing variables importance using machine learning to predict cotton yield

Remote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R²), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TPI, LiDAR, and RTK elevation show the best correlations to predicting cotton yield.

A Swine Model of Changes in the Neuronal Electromagnetic Field After Traumatic Brain Injury: A Pilot Study.

Background Traumatic brain injury (TBI) is a global cause of disability and mortality. Treatment depends on mitigation of secondary injury resulting in axonal injury, necrosis, brain dysfunction, and disruption of electrical and chemical signaling in neural circuits. To better understand TBI, translational models are required to study physiology, diagnostics, and treatments in homologousspecies, such as swine. Electromagnetic fields (EMFs) from altered neural circuits can be measured and historically have been reliant on expensive shielding and supercooling in magnetoencephalography. Using proprietary induction sensors, it has been found that a non-invasive, non-contact approach with an engineered Mu-metal and copper mesh-shielded helmet effectively measures EMFs. This has not yet been investigated in swine models. We wished to evaluate the efficacy of this technology to assess TBI-dependent EMF changes in swine to describe the efficacy of these sensors and this model using a gravity-dependent controlled cortical impact (CCI). Methods A Yucatan miniswine was evaluated using non-contact, non-invasive proprietary induction sensors with an engineered dual-layer Mu-metal and interlaced copper mesh helmet with sensors within EMF channels connected to a helmet. Swine EMF recordings were obtained prior to induced gravity-dependent CCI followed by post-TBI measurements. Behavioral changesand changes in EMF measurements were assessed. EMF measurements were evaluated with an artificial intelligence (AI) model. Results Differences between room "noise" EMF measurements and pre-TBI swine electromagnetic field measurements were identified. Morphological characteristics between pre-injury and post-injury measurements were noted. AI modeling differentiated pre-injury and post-injury patterns in the swine EMF. Frequently identified frequencies seen post-injury were peaks at 2.5 Hz and 6.5 Hz and a valley at 11 Hz. The AI model identified less changes in the slope and thus decreased variation of EMF measurements post-TBI between 4.5 Hz and 7 Hz. Conclusions For the first time, it was identified thatcortical function in a swine can be appropriately measured using novel induction sensors and shielding isolated to a helmet and EMF channels. The swine model can be appropriately differentiated from the external noise signal with identifiably different pre-injury and post-injury EMFs. Patterns can be recognized within the post-injury EMF due to altered neural circuits that can be measured using these sensors continuously, non-invasively, and in real time.

Comparison of single vs composite soil electrical apparent conductivity-directed samples of irrigated soils for inorganic nitrogen estimation with electromagnetic induction sensors

The management of soil can be improved by precision agriculture. A way to achieve this is by quantifying NH4+–N and NO3−–N in soil. The sampling of soil for quantifying these N species is a laborious task. Electromagnetic induction (EMI) sensors could ease the task, providing a large quantity of data. The correlation of inorganic N with soil electrical apparent conductivity (ECa) in either single or composite samples is essential to accomplish this. Using an ECa-directed sampling approach, suitable sampling points were identified at four centre pivots. Single and composite samples were collected in 300 mm increments to 600 mm at Hofmeyr and 1500 mm at Douglas, Luckhoff and Empangeni. The samples were analysed for NH4+–N and NO3−–N from which total inorganic N (TIN) was calculated. All three inorganic N fractions were correlated insignificantly to in-field readings of ECa, implicating that EMI sensors are not suitable for inorganic N estimations in these irrigated soils. A comparison between single and composite samples, however, showed a good agreement of NH4+–N concentrations which was not true for NO3−–N or TIN concentrations. Composite samples are recommended for inorganic N measurements with an EMI sensor. Single samples could be used when only NH4+–N is analysed.