Enhancement of freeze-drying technology using electromagnetic fields and artificial intelligence: Future prospects

Background and objectiveTraumatic 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 the hypothesis 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 to assess the feasibility of attempting this 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.MethodsA 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.ResultsAI 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).ConclusionStimulation 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.

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.

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.

The development of Artificial Intelligence (AI) has brought more opportunities and challenges to the reform of new engineering disciplines education, which helps students to achieve borderless learning and realize student-centered intelligent teaching in both physics and cyberspace space according to individual differences. AI effectively improves the high-level learning goals of students from multiple dimensions and the quality of teaching. This paper takes the reform of the course Electromagnetic Field and Electromagnetic Wave as an example to explore the role of AI in new engineering disciplines education and its promotion effect on curriculum ideological and political education. It discusses the application of AI in all aspects of teaching design and the potential prospect of AI-enabled curriculum resource construction. Meanwhile, the necessity and implementation guarantee of values education in AI-enabled teaching process is analyzed. And an example of course design is used to illustrate the work to improve students' innovation ability, practical ability, and social responsibility in teaching. This paper provides a new perspective and practical path for the courses construction of new engineering disciplines and curriculum ideological and political education, which has certain theoretical and practical significance for promoting higher education reform.

Artificial intelligence (AI) has been used to determine the electromagnetic field in the complex problem of a faulty overhead transmission line above earth and a buried pipeline. A suitable AI system for scaling finite element electromagnetic field calculations has been developed. This system was trained by using finite element calculations for configurations, i.e., cases having different distances between the overhead transmission line and the buried pipeline as well as different earth resistivities. The AI system may be used to calculate the electromagnetic field in new cases differing significantly from the cases used for training.

Since the 21st century, the development of information technology and artificial intelligence has reached new heights, and the intelligent manufacture of electrical equipment has become the main development goal of industrial manufacturing in China. The core part of intelligent manufacturing of electrical equipment is the use of data to build mathematical models for real-time analysis, but for large volume and complex structure of electrical equipment, the traditional finite element analysis method will involve large-scale numerical calculations, resulting in slow calculation speed, accuracy is difficult to meet the design requirements, cannot meet the real-time requirements. This paper applies deep learning theory to the electromagnetic field analysis of electric motors, using the electromagnetic field distribution data corresponding to different motor structures to train the built deep learning model to predict the electromagnetic field distribution instead of the traditional finite element calculation, improving research efficiency, reducing time costs and meeting the requirements of intelligent manufacturing for real-time. In this paper, the predicted electromagnetic field distribution is compared with the MSE and MAE of the training set and test set respectively, then the electromagnetic field distribution data is plotted as a line graph to observe the error between the true and predicted values, and finally the predicted electromagnetic field distribution data is visualised as a magnetic line graph using simulation software, and compared with the magnetic line graph obtained from ANSYS Maxwell simulation, and the data of some random points are taken for comparison.

This article addresses one fundamental controversy in philosophy. That is whether our minds, consciousnesses, and spirits are derivatives of the biochemical events occurring in our brains. In this respect, materialists and religionists hold essentially opposite views. The discussion of this article begins with showing that some human beings' intelligent capabilities are not the derivatives of the material effects that have been found occurring in our brains. Logically, this observation implies that some natural effects other than those that have been identified by scientists to be existent in our brains must play important roles in formation of our minds, consciousnesses, and spirits. The discussion is followed by showing that the existing scientific knowledge about our universe is still very limited. In particular, it is estimated that only about 4% of the total mass-energy in the universe can be detected by modern technologies. The remaining 96% is composed of dark matter (about 22%) and dark energy (about 74%), which we still do not quite understand yet. Therefore, it is likely that there are some other forms of natural forces in the universe yet to be discovered and some of these undiscovered forces and effects, which may or may not be related to dark matter and dark energy, may be the key for unwinding the mystery of how minds, consciousnesses, and spirits take shape. Accordingly, one main theme of this article is that we should open our minds and acknowledge the religious philosophies and the phenomena that have been filed by religionists because they may provide us with valuable clues in our journey toward obtaining a comprehensive picture about our universe. Based on this logic and numerous reports of religious experiences, this article proceeds to propose one form of spirit-matter monism. The proposed model of spirit-matter monism hypothesizes that some natural forces and interactions that have not been well investigated in modern physics must play major roles in formation of minds, consciousnesses, and spirits. In this respect, the proposed model does not exclude the possibility that the well-known natural forces and interactions, e.g. the electromagnetic field and chemical interactions, also play some roles.

Artificial intelligence (AI) has been used to determine the quasi-stationary two-dimensional electromagnetic fields within rectangular boundaries. Amplitude and phase of magnetic vector potential have been calculated in an iron slot with an embedded current carrying conductor. A suitable fuzzy neural network (FNN) for scaling finite elements electromagnetic field calculations has been developed. FNN has been trained, using finite elements calculations within rectangular boundaries. Then, FNN has been used to calculate the field in a new geometry differing significantly from the geometries used for training. It was concluded that FNN may be used to scale results from one geometry to another with negligible errors.

Simulating electromagnetic (EM) fields can obtain the EM responses of geoelectric models at different times and spaces, which helps to explain the dynamic process of EM wave propagation underground. EM forward modeling is regarded as the engine of inversion. Traditional numerical methods have certain limitations in simulating the EM responses from large-scale geoelectric models. In recent years, the emerging physics-informed neural networks (PINNs) have given new solutions for geophysical EM field simulations. This paper conducts a preliminary exploration using PINN to simulate geophysical frequency domain EM fields. The proposed PINN performs self-supervised training under physical constraints without any data. Once the training is completed, the responses of EM fields at any position in the geoelectric model can be inferred instantly. Compared with the finite-difference solution, the proposed PINN performs the task of geophysical frequency domain EM field simulations well. The proposed PINN is applicable for simulating the EM response of any one-dimensional geoelectric model under any polarization mode at any frequency and any spatial position. This work provides a new scenario for the application of artificial intelligence in geophysical EM exploration.

Graphene has attracted wide attention in the field of electromagnetism and has become a front runner in fabricating electronic devices, especially flexible ones. However, currently, there’s no report on the prediction of its electric resistance, which is critical to an electric component. In this work, several machine learning (ML) and artificial neural network (ANN) models are trained to predict the electric resistance(R) of printed graphene nanosheets with only the knowledge of the concentration of graphene ink, the width, and the length of the printed nanosheet. Three different ML algorithms, i.e., Kriging, Efficient Global Optimization (EGO), and Support Vector regression (SVR) are exploited, together with ANNs with different sets of parameters. Harboring high accuracy and yielding satisfactory results, the models can be applied to predict the resistance of graphene nanosheets in real-time working conditions. It paves the way for the wide application of printable graphene nanosheets in electric devices.

Soft computing is defined as a group of computational techniques based on artificial intelligence (human like decision) and natural selection that provides quick and cost effective solution to very complex problems for which analytical (hard computing) formulations do not exist. The term soft computing was coined by Zadeh [Zadeh, 1992]. Soft computing aims at finding precise approximation, which gives a robust, computationally efficient and cost effective solution saving the computational time. Most of these techniques are basically enthused on biological inspired phenomena and societal behavioural patterns. The advent of soft computing into the computing world was marked by research in machine learning, probabilistic reasoning, artificial neural networks (ANN), fuzzy logic [Jang et al ., 1997] and genetic algorithm (GA). Today, the purview of soft computing has been extended to include swarm intelligence and foraging behaviours of biological populations in algorithms like the particle swarm optimization (PSO) and bacterial foraging algorithm (BFO) [Holland, 1975; Kennedy and Eberhart, 1995; Passino, 2002]. Soft computing methods are associated with certain distinctive advantages. These include the following: • Since Soft computing methods do not call for wide-ranging mathematical formulation pertaining to the problem, the need for explicit knowledge in a particular domain can be reduced. • These tools can handle multiple variables simultaneously. • For optimization problems, the solutions can be prevented from falling into local minima by using global optimization strategies. • These techniques are mostly cost effective. • Dependency on expensive traditional simulations packages can be reduced to some degree by efficient hybridization of soft computing methods. • These methods are generally adaptive in nature and are scalable. Of late, soft computing techniques have attracted recognition amongst researchers of various branches of engineering in order to arrive at solutions to problem statements [Patnaik and Mishra, 2000; Patnaik et al ., 2005; Samii, 2006; Choudhury et al ., 2012]. The sturdiness of the above techniques has been well tested pertaining to various problems encountered in every sphere of engineering. Indeed, the last decade has seen the implementation of soft computing in microwave applications. This chapter gives a glimpse of the various soft computing techniques that are widely used in the field of electromagnetics. Artificial Neural Networks Certain features of human brain such as the capability to recognize and perceive, have been studied for decades. The remarkable characteristics of the human brain drove researchers into attempting to emulate these characteristics in computers.

Irradiation of terahertz electromagnetic wave including its short-wave band in infrared wave shows broad and important application prospects in biological science due to its noninvasive and nonionizing nature. Cell membrane is an important biological barrier for keeping cell integrity and homeostasis, and it is also the cellular structure that electromagnetic fields act first on in the case of terahertz irradiation. The responses of cell membrane to the electromagnetic fields are the mechanisms for most of the biological effects of terahertz irradiation. First, in this paper are expatiated the application safety of terahertz irradiation and its new application prospects in life medicine, neural regulation and artificial intelligence. Then, systematically described are the researches and developments in the biological effects of cell membrane under terahertz electromagnetic irradiation from the following four aspects: the dielectric response characteristics of phospholipid membrane to terahertz electromagnetic irradiation, the transmembrane transport of ions through membrane ion channel proteins under the irradiation, the transmembrane transport of macromolecules and ions through phospholipid membrane under the irradiation, and the potential applications and role of biological effects of cell membrane under the irradiation. Meanwhile, introduced in this paper are the scientific discoveries that terahertz electromagnetic irradiation is able to activate voltage-gated calcium channels, voltage-gated potassium channels and active transport calcium channels in cell membrane and to create hydrophilic pores on the phospholipid membrane of cell membrane. Finally, the directions of future efforts to study the biological effects of cell membrane under terahertz irradiation are presented.

Irradiation of terahertz electromagnetic wave including its short-wave band in infrared wave shows broad and important application prospects in biological science due to its noninvasive and nonionizing nature. Cell membrane is an important biological barrier for keeping cell integrity and homeostasis, and it is also the cellular structure that electromagnetic fields are firstly on in the case of terahertz irradiation. The responses of cell membrane to the electromagnetic fields are the mechanisms for most of the biological effects of terahertz irradiation. This paper at first expatiates the application safety of terahertz irradiation and its new application prospects in life medicine, neural regulation and artificial intelligence. Then, it systematically expatiates on the researches and developments in the biological effects of cell membrane under terahertz electromagnetic irradiation from the following four aspects: dielectric response characteristics of phospholipid membrane to terahertz electromagnetic irradiation, transmembrane transport of ions through membrane ion channel proteins under the irradiation, transmembrane transport of macromolecules and ions through phospholipid membrane under the irradiation, and the potential applications and role of biological effects of cell membrane under the irradiation. Meanwhile, the scientific discoveries that terahertz electromagnetic irradiation is able to activate voltage-gated calcium channels, voltage-gated potassium channels and active transport calcium channels in cell membrane and to create hydrophilic pores on the phospholipid membrane of cell membrane are introduced. At last, the summary and the directions of future efforts for the researches on the biological effects of cell membrane under terahertz irradiation are discussed.

Background and Objectives: Cartilage repair remains a critical challenge in orthopaedic medicine due to the tissue's limited self-healing ability, contributing to degenerative joint conditions such as osteoarthritis (OA). In response, regenerative medicine has developed advanced therapeutic strategies, including cell-based therapies, gene editing, and bioengineered scaffolds, to promote cartilage regeneration and restore joint function. This narrative review aims to explore the latest developments in cartilage repair techniques, focusing on mesenchymal stem cell (MSC) therapy, gene-based interventions, and biomaterial innovations. It also discusses the impact of patient-specific factors, such as age, defect size, and cost efficiency, on treatment selection and outcomes. Materials and Methods: This review synthesises findings from recent clinical and preclinical studies published within the last five years, retrieved from the PubMed, Scopus, and Web of Science databases. The search targeted key terms such as "cartilage repair", "stem cell therapy", "gene editing", "biomaterials", and "tissue engineering". Results: Advances in MSC-based therapies, including autologous chondrocyte implantation (ACI) and platelet-rich plasma (PRP), have demonstrated promising regenerative potential. Gene-editing tools like CRISPR/Cas9 have facilitated targeted cellular modifications, while novel biomaterials such as hydrogels, biodegradable scaffolds, and 3D-printed constructs have improved mechanical support and tissue integration. Additionally, biophysical stimuli like low-intensity pulsed ultrasound (LIPUS) and electromagnetic fields (EMFs) have enhanced chondrogenic differentiation and matrix production. Treatment decisions are influenced by patient age, cartilage defect size, and financial considerations, highlighting the need for personalised and multimodal approaches. Conclusions: Combining regenerative techniques, including cell-based therapies, gene modifications, and advanced scaffolding, offers a promising pathway towards durable cartilage repair and joint preservation. Future research should focus on refining integrated therapeutic protocols, conducting long-term clinical evaluations, and embracing personalised treatment models driven by artificial intelligence and predictive algorithms.

The 5th/6th generation bio-sensing technology is an emerging field which connects smart technologies like Artificial Intelligence, Internet of Things and Machine Learning with efficient micro/nano-enabled sensing platform for making point-of-care (POC) devices to investigate health management strategies. Recently, the integration and interfacing between quantum measurement, signaling, and optimized bio-actives has led to investigate the minute biological events with anomalous sensitivity. Such technologies are expected to provide the possibility to measure and record changes at quantum scales with varying pressure, temperature, and electromagnetic fields. Considering current scenarios, this perspective critically highlights state-of-art quantum sensing technology along with their challenges and prospects.