The advantages of Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) technology include long transmission distance, high efficiency, and high power. Therefore, it shows great potential in the field of smart home. This study aims to explore the specific impacts on the cognitive functions and neuronal excitation of mice exposed to the electromagnetic fields (EMF) emitted by the MCR-WPT platform, thereby providing biological solid experimental evidence for developing Wireless Power Transfer (WPT) technology. The research employed a frequency of 90 kHz, which is suitable for wireless charging of household appliances. Mice were exposed to EMF emitted by the WPT biosafety experimental platform for various durations. And they were divided into four groups (control group, 2-week exposure group, 4-week exposure group, and 8-week exposure group). Upon completion of the exposure period, the study employed the Novel Object Recognition (NOR) test to evaluate the learning and memory capabilities of the animals. Following this, whole-cell patch-clamp experiments were conducted to record the action potentials (AP) and potassium currents. It was revealed by our observations that, in comparison to mice without electromagnetic exposure, long-term exposure to WPT-emitted EMF resulted in accelerated release of action potentials, inhibited the activation of Voltage-Gated Potassium Channels (VGKCs) current, accelerated the deactivation of K channel current, and thus significantly improved the excitability of neurons in the dentate gyrus (DG) of the hippocampus of mice, but did not significantly affect cognitive function.

Biomagnetic fluid dynamics (BFD) is an emerging and promising field within fluid mechanics, focusing on the dynamics of bio-fluids like blood in the presence of magnetic fields. This research is crucial in the medical arena for applications such as medication delivery, diagnostic and therapeutic procedures, prevention of excessive bleeding, and treatment of malignant tumors using magnetic particles. This study delves into the intricacies of blood flow induced by cilia, carrying trihybrid nanoparticles (gold, copper, and titania), within a catheterized arterial annulus under a robust magnetic field. The model incorporates factors like Hall and ion-slip currents (electromagnetic effects on charged particles), metachronal propulsion (movement of cilia for propulsion), viscous dissipation, and entropy. The physical equations in the model are transformed from the laboratory frame to a wave frame and then simplified using conditions like low Reynolds number and long wavelength. Optimal series solutions are obtained through the homotopy perturbation method (HPM). The research explores how various physical parameters shape the bloodstream's features, presenting and analyzing these visually. A notable finding is that an intensification in Hall and ion-slip parameters results in higher blood velocity within the catheterized annulus. Blood cooling is observed with a higher loading of suspended nanoparticles. Entropy generation increases with growing values of Hall and ion-slip parameters, while the reverse trend is noted for the Bejan number. The wall shearing stress (WSS) reduces by 2.84% for 1% increase in Hall parameter. The study also provides a brief overview of how blood boluses (or clumps of blood) are structured under the influence of operating parameters. The modified hybrid nano-blood (MHNB) forms smaller and fewer boluses compared to pure blood (PB). Additionally, longer cilia length results in enhanced trapping of boluses due to stronger recovery motions of the cilia. This research holds potential benefits for practitioners and researchers in diagnosing and assessing conditions such as coronary artery disease, valvular heart disease, and congenital heart abnormalities, as well as for understanding traumatic brain injury and neurological surgeries.

Due to the increase in data rate in mobile communication and the widespread use of mobile internet, electromagnetic communication systems are increasing daily. This situation causes increases in the use of more mobile communication devices and environmental non-ionizing Electromagnetic Field (EMF) levels. The rise of bee deaths and colony losses in beekeeping parallel to the increase of the EMF sources cause the concept of "electromagnetic pollution" to be considered among the reasons. Therefore, studying the effects of non-ionizing Electromagnetic Radiation (EMR) on the health of living things is one of the most significant issues today. The bees determine their direction with the Earth's magnetic field. Electromagnetic signals emitted by GSM base stations, etc. may affect the direction-finding capabilities of honey bees and constitute a stress factor. In this study, the aim was to determine the effect of EMF on honey bees and honey yield. Honey bee colonies were used, obtained from the same farm in the Trabzon region, and equalized in all respects. Moreover, these colonies were divided into five groups randomly as experiments and control groups. The experiment hives were exposed to the EMF in the frequency band of the Wi-Fi signals (2.4 GHz) and the high-voltage line (50 hz). The control hives are located far away from the EMR sources. The study was repeated in the second year to confirm the results. During the investigation, some physiological and behavioural effects of bees, such as aggressiveness, brood area, etc. were determined based on EMR exposure.

This study examined the impact of 6 GHz (0.054 W/kg SAR) Radiofrequency-Electromagnetic Field (RF-EMF) on prenatal bone development. In this study, 20 female and 20 male Wistar Albino rats divided into four groups. The Control group received no treatment, while in Group-I, only male rats were exposed to RF-EMF, female rats had no exposure. Group-II, both male and female rats received RF-EMF treatment. While in Group-III, only female rats were exposed to RF-EMF, male rats had no exposure. The exposure lasted 4 hours per day for 6 weeks. The rats were then allowed to mate within the group. After pregnancy, pregnant rats (Group-II and III) were exposed 4 hours per day for 18 days. On the 18th day of gestation, fetuses were removed and their weight and various lengths were measured. The skeletal system development of fetuses was examined with double skeletal staining method and assessed ossification in the extremities. In the study, fetal weights, head-tail length, occipital-frontal and parietal-parietal lengths significantly increased in all exposure groups when compared to the control group ( < 0.001). Although occipital-frontal length was smallest in Group-I, Group-II and Group-III were more higher than the control group ( < 0.001). The bones of the anterior and posterior extremities showed significant increases in length, ossification zone length, and ossification percentage in all experimental groups compared to the control group ( < 0.001). Our study showed that rats exposed to 6 GHz (0.054 W/kg) RF-EMF during the prenatal period had significant increases in bone development.

The aim of this study was to systematically review the preclinical studies that have applied the static magnetic field to wound healing.

The current investigation explores tri-hybrid mediated blood flow through a ciliary annular model, designed to emulate an endoscopic environment. The human circulatory system, driven by the metachronal ciliary waves, is examined in this study to understand how ternary nanoparticles influence wave-like flow dynamics in the presence of interfacial nanolayers. We also analyze the effect of an induced magnetic field on Ag-Cu-/blood flow within the annulus, focusing on thermal radiation, heat sources, buoyancy forces and ciliary motion. The Casson fluid model characterizes the non-Newtonian viscous properties of the biofluid. To describe the steady fluid flow mathematically, we use coupled partial differential equations and apply the homotopy perturbation method to derive rapidly convergent series solutions for the non-linear flow equations. The obtained hemodynamic consequences are graphically represented with the variations of emerging parameters. These are significantly influenced by the rheological factors of the nanofluid flow, improving flow velocity with changes in shear viscosity, while a decrease in flow is observed for intensified Lorentz forces. Ciliary motion accelerates the expansion of the induced magnetic field on nanolayers, while a higher Magnetic Reynolds number decreases the current density distribution. Increased radiative heat generation lowers the temperature, indicating that thermal radiation enhances heat transfer and improves cooling efficiency. In contrast, an increased ciliary length along the wall raises the temperature due to wave-like motion, which strengthens the thermal boundary layer in the fluid flow. Additionally, a higher nanoparticle concentration increases wall shear stress due to frictional forces, while enhanced magnetic forces decrease the shear stress along the ciliary wall. Furthermore, a higher Strommer's number may regulate the formation of blood boluses in the wavy flow. The key findings play an important role in the development of analytical benchmarks to validate computational methods, ensuring accuracy in clinical research tools and supporting reliable medical applications.

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

In cardiovascular research, electromagnetic fields generated by Riga plates are utilized to study or manipulate blood flow dynamics, which is particularly crucial in developing treatments for conditions such as arterial plaque deposition and understanding blood behavior under varied flow conditions. This research predicts the flow patterns of blood enhanced with gold and maghemite nanoparticles (gold-maghemite/blood) in an electromagnetic microchannel influenced by Riga plates with a temperature gradient that decays exponentially, under sudden changes in pressure gradient. The flow modeling includes key physical influences like radiation heat emission and Darcy drag forces in porous media, with the flow mathematically represented through unsteady partial differential equations solved using the Laplace transform (LT) method. Results, including shear stress (SS) and rate of heat transfer (RHT), are graphically detailed, demonstrating changes in blood velocity profile with modifications in the Hartmann number and the width of electrodes, and differences in temperature and RHT between hybrid nano-blood (HNB) and nano-blood (NB). The key results indicate an increase in blood velocity distribution with higher modified Hartmann number, and a decrease with wider electrodes. Temperature is elevated in both hybrid nano-blood (HNB) and nano-blood (NB). Notably, HNB with gold and maghemite enhances heat transmission in the flow. Furthermore, an artificial intelligence-driven methodology employing an artificial neural network (ANN) has been incorporated to facilitate rapid and precise evaluations of SS and RHT, demonstrating remarkable predictive accuracy. The proposed algorithm exhibits outstanding accuracy, achieving 99.998% on the testing dataset and 96.843% during cross-validation for predicting SS, and 100% on the testing dataset, and 95.008% during cross-validation for predicting RHT. The implementation of nanotechnology with artificial intelligence promises new tools for doctors and surgeons, potentially transforming patient care in fields such as oncology, cardiology, and radiology. This model also facilitates the generation of precise electromagnetic fields to guide drug-loaded magnetic nanoparticles for applications in targeted drug delivery, hyperthermia treatment, MRI contrast enhancement, blood flow monitoring, cancer treatment, and controlled drug release.

In this study, we investigated the inhibitory effects of radiofrequency exposure on RANKL-induced osteoclast differentiation in RAW264.7 cells, along with the underlying mechanisms. RAW264.7 cells were subjected to radiofrequency exposure at three distinct power densities: 50 µW/cm, 150 µW/cm, and 450 µW/cm. The results showed that, among the three dosage levels, exposure to 150 µW/cm of radiofrequency radiation significantly reduced the proliferation capacity of RAW264.7 cells. RF exposure at three power densities resulted in significant increases in the level of osteoclast apoptosis and notable decreases in osteoclast differentiation. Notably, the most pronounced effects on apoptosis, differentiation in RAW 264.7 cells were observed at the 150 µW/cm power density. These effects were accompanied by concurrent decreases in mRNA and protein levels of osteoclast-specific genes, including RANK, NFATc1, and TRACP. Furthermore, radiofrequency exposure at power density of 150 µW/cm induced a significant decrease in cytoplasmic NF-κB protein levels while increasing its nuclear fraction, thereby counteracting the effects of RANKL-induced NF-κB activation. These data suggest that radiofrequency exerts inhibitory properties on RANKL-induced NF-κB transcriptional activity, subsequently indirectly suppressing the expression of downstream NF-κB target genes, such as NFATc1 and TRACP. In conclusion, our study demonstrates that radiofrequency radiation effectively inhibits osteoclast differentiation by modulating the NF-κB signaling pathway. These findings have important implications for potential therapeutic interventions in osteoporosis.

The electromagnetic waves of 2.45 GHz microwave frequency have become abundant in environments worldwide. This study assessed the short-term impact of low-intensity 2.45 GHz exposure on young plants. The plants underwent a 48-hour exposure to continuous wave 2.45 GHz microwaves at a power density of 1.0 ± 0.1 W m. Experiments were conducted inside anechoic chambers. After the microwave exposure samples were subjected to morphological, genotoxicity, pigmentation, and physiochemical analysis. Microwave exposure elevated the levels of photosynthetic pigments, oxidative stress, guaiacol peroxidase activity, and ascorbic peroxidase activity in plants. Conversely, catalase activity decreased. Photosystem efficiency remained unchanged, while non-photochemical quenching increased. Leaf morphological parameters exhibited no significant alterations during this brief exposure period. Notably, despite shifts in physiological parameters and pigmentations, genomic template stability remained unaffected. The findings suggest that the non-thermal effects of microwave exposure influence the photosystem and plant physiology. Research confirmed the existence of non-thermal effects of microwave exposure; however, these effects are within tolerable limits for plants.

Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.

Results from clinical trials show that serotonergic psychedelics have efficacy in treating psychiatric disorders, where currently approved pharmacotherapies are inadequate. Developing psychedelic medicines, however, comes with unique challenges, such as tempering heightened anxiety associated with the psychedelic experience. We conceived a new strategy to potentially mitigate psychedelic effects with defined electromagnetic signals (ES). We recorded the electromagnetic fields emitted by the serotonin 2 receptor (5-HTR) agonist (±)-2,5-dimethoxy-4-iodoamphetamine (DOI) and converted them to a playable WAV file. We then exposed the DOI WAV ES to mice to assess its effects on the DOI-elicited, 5-HTR dependent head-twitch response (HTR). The DOI WAV signal significantly attenuated the HTR in mice elicited by 0.1 and 0.3 mg/kg subcutaneous DOI ( < 0.05 and  < 0.01, respectively). A scrambled WAV signal did not affect the DOI-elicited HTR, suggesting specificity of the DOI WAV signal. These results provide evidence that defined ES could modulate the psychoactive effects of serotonergic psychedelics. We discuss putative explanations for the distinct effects of the DOI WAV signal in the context of previous studies that demonstrate ES's efficacy for treating other conditions, including pain and cancer.

Breast cancer has been recognized as the most common cancer affecting women. Extremely low-frequency electromagnetic field (ELF-EMF) exposure can influence cellular activities such as cell-cell junctions and metastasis. However, more research is required to determine these fields' underlying mechanisms of action. Since cadherin switching is an important process during EMT (epithelial-mesenchymal transition), in this study, cadherin switching was regarded as one of the probable mechanisms of the effect of ELF-EMFs on metastasis suppression. For five days, breast cells received a 1 Hz, 100mT ELF-EMF (2 h/day). Cell invasion and migration were assessed in vitro by the Scratch wound healing assay and Transwell culture chambers. The expression of E- and N-cadherin was assessed using real-time PCR, western blotting, and Immunocytochemistry. ELF-EMF dramatically reduced the migration and invasion of MDA-MB 231 malignant cells compared to sham exposure, according to the results of the scratch test and the Transwell invasion test. The mRNA and protein expression levels of E-cadherin showed an increase, while the N-cadherin expression was found with a decrease, in MDA-MB231 cells receiving 1 Hz EMF compared to sham exposure. E-cadherin's mRNA and protein expression levels were enhanced in MCF10A cells receiving 1 Hz EMF compared to sham exposure. ELF-EMF can be used as a method for the multifaceted treatments of invasive breast cancer.

Anxiety is an adaptive condition characterized by heightened uneasiness, which in the long term can cause complications such as reducing the quality of life and problems related to the mental and physical health. Concerns have been raised regarding the potential dangers of extremely low frequency electromagnetic fields (ELF-EMF) ranging from 3 to 3000 Hz, which are omnipresent in our daily lives and there have been studies about the anxiogenic effects of these fields. Studies conducted in this specific area has revealed that ELF-EMF can have an impact on various brain regions, such as the hippocampus. In conclusion, studies have shown that ELF-EMF can interfere with hippocampus-prefrontal cortex pathway, inducing anxiety behavior. Also, ELF-EMF may initiate anxiety behavior by generating oxidative stress in hypothalamus and hippocampus. Moreover, ELF-EMF may induce anxiety behavior by reducing hippocampus neuroplasticity and increasing the NMDA2 receptor expression in the hippocampus. Furthermore, supplementation with antioxidants could serve as an effective protective measure against the adverse effects of FLF-FMF in relation to anxiety behavior.

Microwave applicators reported for intracavitary hyperthermia (HT) operate at single frequency and deliver fixed treatment coverage at the tumor target. In this work, we report multifrequency operation of a water-cooled monopole antenna with a sliding broadband ferrite choke for delivering intracavitary HT to the cervix with variable spatial coverage. Spatially varying treatment coverage is achieved by varying the choke position with respect to the monopole using a mechanical sliding arrangement and exciting the antenna at the modified resonant frequency. Multifrequency operation of the antenna prototype is demonstrated over 700-1000 MHz using a straight intrauterine cervix applicator. Numerical simulations confirm the ability to deliver targeted HT with axial extent varying between 35.4 and 62.0 mm by controlling the sliding choke and coupling water temperature. Applicator prototype measurements in tissue mimicking phantoms confirm multifrequency operation of the antenna and its ability to induce axially varying intracavitary HT coverage to match the tumor size using a single applicator.

Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.Here, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range - Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, Coati Optimization Algorithm (COA) optimizes MCDS-GNN's weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively.

Oncological systemic treatments such as cytotoxic chemotherapy, radiation therapy or treatment with biological response modifiers can alter the quality of life (QoL) of cancer patients.The aim of this study is to assess the effects of cardiologic magnetic and optical stimulation (CMOS) on QoL in patients with advanced cancer receiving systemic treatment. For this purpose, we designed a non-invasive device that can reproduce and dynamically modulate stimulations of the same nature as the biological electromagnetic emissions specific to the body (cardiac). These crafted emissions were sent back to the body in perfect synchronization with the Electrocardiogram (ECG) in order to foster resonance mechanisms.

The effect of non-ionizing millimeter range electromagnetic waves (MM EMW) (30-300 GHz) on the bovine serum albumin (BSA) interaction peculiarities with acridine orange (AO) has been studied in vitro. The frequencies 41.8 and 50.3 GHz were chosen, since the first one is nonresonant frequency for the water, while the second one is resonant for water. The binding constant and number of binding sites were calculated at both irradiation presence and absence. AO was revealed to bind to BSA, while after the protein irradiation the interaction force strengthens. However, it was also shown that there are differences of the interaction parameters while irradiating by 41.8 or 50.3 GHz. AO binds to BSA, irradiated by MM EMW with the frequency 41.8 GHz much more weaker, than to that, irradiated by MM EMW with the frequency 50.3 GHz.

The brain is a crucial organ that controls the body's neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies. However, timely diagnosis and accurate classification were difficult in the conventional models. Thus, the Taylor Fire Hawk optimization (TFHO) is implemented here for effective segmentation and classification. The TFHO is the merging of the Taylor series and Fire Hawk Optimizer (FHO). The de-noising is accomplished by the adaptive median filter, and the segmentation is carried out using M-Net, which has been trained by TFHO. Subsequently, image augmentation is performed to increase the image dimension, followed by the extraction of effective features. Finally, DenseNet is used for the classification, and the training is done by TFHO. The introduced method obtained 94.86% accuracy, 92.83% Negative Predictive Values, 89.33% Positive Predictive Values (PPV), 95.91% True Positive Rate (TPR), 4.37% False Negative Rate (FNR), and 90.98% F1-score.

Brain tumors present a formidable diagnostic challenge due to their aberrant cell growth. Accurate determination of tumor location and size is paramount for effective diagnosis. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are pivotal tools in clinical diagnosis, yet tumor segmentation within their images remains challenging, particularly at boundary pixels, owing to limited sensitivity. Recent endeavors have introduced fusion-based strategies to refine segmentation accuracy, yet these methods often prove inadequate. In response, we introduce the Parallel-Way framework to surmount these obstacles. Our approach integrates MRI and PET data for a holistic analysis. Initially, we enhance image quality by employing noise reduction, bias field correction, and adaptive thresholding, leveraging Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib), respectively. Subsequently, we conduct multi-modality image fusion through the Dual-Tree Complex Wavelet Transform (DTWCT) to amalgamate data from both modalities. Following fusion, we extract pertinent features using the Advanced Capsule Network (ACN) and reduce feature dimensionality via Multi-objective Diverse Evolution-based selection. Tumor segmentation is then executed utilizing the Twin Vision Transformer with dual attention mechanism. Implemented our Parallel-Way framework which exhibits heightened model performance. Evaluation across multiple metrics, including accuracy, sensitivity, specificity, F1-Score, and AUC, underscores its superiority over existing methodologies.

To enhance our understanding of electroporation and optimize the pulses used within the frequency range of 1 kHz to 100 MHz, with the aim of minimizing side effects such as muscle contraction, we introduce a novel electrical model, structured as a 2D representation employing exclusively lumped elements. This model adeptly encapsulates the intricate dynamics of living cells' impedance variation. A distinguishing attribute of the proposed model lies in its capacity to decipher the distribution of transmembrane potential across various orientations within living cells. This aspect bears critical importance, particularly in contexts such as electroporation and cellular stimulation, where precise knowledge of potential gradients is pivotal. Furthermore, the augmentation of the proposed electrical model with the Hodgkin-Huxley (HH) model introduces an additional dimension. This integration augments the model's capabilities, specifically enabling the exploration of muscle cell stimulation and the generation of action potentials. This broader scope enhances the model's utility, facilitating comprehensive investigations into intricate cellular behaviors under the influence of external electric fields.