Search Results (8,799)

It has long been desired to achieve mechanical enhancement and structural health monitoring by introducing carbon nanotubes (CNTs) into traditional carbon fiber (CF) composites. Herein, the initiation of micro-damage and crack propagation has been investigated by utilizing in situ electrical resistance changes in interlaminar hybrid CNT network/CF composites during the shear loading process. The results show a clear relationship between the crack propagation and the electrical resistance response particularly when approaching the failure of the single-layer CNT network hybrid composites. Furthermore, the chemically modified CNT network exhibits evident enhancement on main mechanical properties of the CF composites, superior to the thermoplastic toughening method. The characterizations manifest that the multiscale interlayered CNT/CF structure can simultaneously resist the crack propagation along both the in-plane direction and the cross-plane direction, which consequently enhances the flexural and compressive strengths of the composite material. This discovery provides a novel idea for the potential application of CNT network/CF hybrid composites in the integration of mechanical reinforcement and structural health monitoring, namely, that the CNT network acts not only as a reinforcing phase but also as a sensor for the structural health monitoring of the composites. Full article

Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory–inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging—including functional MRI, diffusion MRI, and EEG/MEG—demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies. Full article

In this paper, an anisotropic object electromagnetic image reconstruction system based on a two-stage multi-frequency extended network is developed by deep learning techniques. We obtain the scattered field information by irradiating the TM different polarization waves to uniaxial objects located in free space. We input the measured single-frequency scattered field into the Deep Residual Convolutional Neural Network (DRCNN) for training and to be further extended to multi-frequency data by the trained model. In the second stage, we feed the multi-frequency data into the Deep Convolutional Encoder–Decoder (DCED) architecture to reconstruct an accurate distribution of the dielectric constants. We focus on EMIS applications using Transverse Magnetic (TM) and Transverse Electric (TE) waves in 2D scenes. Numerical findings confirm that our method can effectively reconstruct high-contrast uniaxial objects under limited information. In addition, the TM/TE scattering from uniaxial anisotropic objects is governed by polarization-dependent Lippmann–Schwinger integral equations, yielding a nonlinear and severely ill-posed inverse operator that couples the dielectric tensor components with multi-frequency field responses. Within this mathematical framework, the proposed two-stage DRCNN–DCED architecture serves as a data-driven approximation to the anisotropic inverse scattering operator, providing improved stability and representational fidelity under limited-aperture measurement constraints. Full article

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation. Full article

Critical mineral supply chains underpin electric mobility, power electronics, clean hydrogen, and advanced manufacturing. Drawing on the resource-based view (RBV), the relational view, and dynamic capabilities, we conceptualize advantage not as ownership of ore bodies but as orchestration of multi-tier resource systems: upstream access, midstream processing know-how, standards and permits, and durable inter-organizational ties. In a world of high concentration at key stages (refining, separation, engineered materials), full “decoupling” is economically costly and technologically constraining. We argue for structured cooperation among the United States, European Union, China, and other producers and consumers, combined with selective domestic capability building for bona fide security needs. Methodologically, we conduct a structured conceptual synthesis integrating RBV, relational view, dynamic capabilities, and network-of-network research, combined with a structured comparative policy analysis of U.S./EU/Chinese instruments anchored in official documents. We operationalize the argument via technology–material dependency maps that identify midstream bottlenecks and the policy/standard levers most likely to expand qualified, compliant capacity. Full article

Perioperative pain remains a major clinical challenge, with many surgical patients experiencing inadequate analgesia and progression to chronic postsurgical pain. Conventional opioid-centered strategies are limited by narrow therapeutic windows, systemic toxicity, tolerance, opioid-induced hyperalgesia, and poor efficacy in neuroimmune-driven pain states. Advances in molecular neuroscience and biomedical engineering have catalyzed the development of nonpharmacologic analgesic technologies that modulate pain pathways through biophysical rather than receptor–ligand mechanisms. This narrative review synthesizes emerging nonpharmacologic analgesic platforms relevant to anesthesiology, integrating molecular, cellular, and systems-level mechanisms with clinical evidence. It examines how peripheral sensitization, spinal dorsal horn plasticity, glial and neuroimmune activation, and supraspinal network dysfunction create ideal targets for device-based interventions. Electrical neuromodulation strategies, including peripheral and central techniques, are discussed alongside temperature-based, photonic, and focused-energy modalities. These include cryoneurolysis, radiofrequency techniques, photobiomodulation, and low-intensity focused ultrasound. Clinical integration within enhanced recovery pathways, patient selection, workflow considerations, and limitations of the current human evidence base are reviewed. While many of these technologies are established in chronic pain management, this review emphasizes available human perioperative data and discusses how chronic pain evidence informs perioperative translation within opioid-sparing multimodal anesthesia care. Collectively, these technologies support a mechanism-based, systems-level approach to pain modulation, with perioperative relevance varying by modality and strength of available human evidence. Full article

In electrical networks, the coordination and selectivity of protective devices are key to improving reliability and ensuring operational safety. Protections play a fundamental role in maintaining system stability and detecting faults within the power system. This study presents an optimized genetic algorithm (OGA) as a method to optimize the configurations of overcurrent protections in high voltage distribution systems. The OGA obtained the best results in all tested systems, demonstrating its effectiveness in coordinating protections according to IEC 60255-151:2009. In addition, simulations performed with the integration of Python and PowerFactory DigSILENT software validated the correct coordination of the protections, showing that the OGA not only optimizes response times, but also guarantees greater selectivity and reliability in the protection of the electrical system in an efficient way. Full article

Despite the increased interest in neuromorphic materials—a physical implementation of neural networks that could overcome the so-called von Neumann architecture’s limitations—most studies have been performed on the basis of systems specially constructed for this purpose. It has previously been shown that analogues of neural networks can spontaneously arise in solutions of hydrophilic polymers, but these systems involved molecules of different natures or required direct interaction between macromolecular clusters. The present paper proposes a theory that indicates the possibility of an analogue of neural network formation even in a single-component solution of a relatively weak polyacid. A model is suggested based on the account of heterogeneous distribution of polymer ionogenic groups within the volume leading to the fluctuations of electric fields and, as a result, to the local changes in the degree of ionisation of functional groups. Theoretical description of the system shows how it was reduced to a solution of the analogue based on the Poisson–Boltzmann equation. The results obtained showed that it is just fluctuations in the distribution of charges that provide the collective response of the system to external influences and serve as an argument in favour of analogy of such a solution within a neural network. The results are discussed in the context of a potential simple hydrophilic polymer system as a prototypical neuromorphic and evolving material that is relevant for organic electronics, metamaterials, and studies on prebiological evolution. Full article

Switching to electric vehicles (EVs) could help reduce air pollution in cities. This is especially important for cities in Russia that have grown quickly because of industry, like those in Siberia, where environmental problems are particularly acute. However, several factors continue to hinder the rapid expansion of EVs on the market, such as an additional strain on the energy infrastructure, which threatens to cause power outages. This study proposes a model for estimating the electricity consumption by EVs in the largest Russian cities, taking into account the technical characteristics of the EV fleet and climatic conditions. The calculations indicate that if 15% of the current car fleet are replaced by EVs, electricity consumption in the 16 largest cities in Russia would increase by 2.2 TWh per year in total. The estimated additional demand in particular cities varies between 33 mln and 769 mln kWh per year, depending on the number of vehicles and the local climate. Furthermore, we conducted an intra-day simulation of electricity consumption from EVs in a conditional Russian city with a population of over one million people. Three scenarios for the power grid load have been developed: (A) the maximum scenario, in which all EVs have a battery level of 0%; (B) the medium scenario, where EVs’ state of charge is distributed between 0% and 100%, and (C) the minimum scenario, involving charging scheduling that allows only EVs with a battery level of 20% or less to charge. The findings show that replacing just 15% of the car fleet with electric vehicles will trigger an increase in current daily household urban consumption of 28.4% in scenario (C), 75.6% in scenario (B) and 141.8% in scenario (A). Consequently, even in Russia’s largest cities, the further proliferation of EVs requires large-scale investments in power infrastructure. An additional 1 mln kWh used by EVs per day may require $160.7 mln investments in energy facilities and urban distribution networks. These findings highlight the necessity of a more thorough cost–benefit analysis of widespread electric vehicle adoption in densely populated urban areas. Full article

This study focuses on classifying electromyographic (EMG) signals to identify seven specific hand movements, including complete hand closure, individual finger closures, and a pincer grip. Accurately distinguishing these movements is challenging due to overlapping muscle activation patterns. To address this, a methodology structured in five stages was developed: placement of electrodes on specific forearm muscles to capture electrical activity during movements; acquisition of EMG signals from twelve participants performing the seven types of movements; preprocessing of the signals through filtering and rectification to enhance quality, followed by the extraction of features from three distinct types of preprocessed signals—filtered, rectified, and envelope signals—to facilitate analysis in the temporal, frequency, and time–frequency domains; extraction of relevant features such as amplitude, shape, symmetry, and frequency variance; and classification of the signals using eight machine learning algorithms: support vector machine (SVM), multiclass logistic regression, k-nearest neighbors (k-NN), Bayesian classifier, artificial neural network (ANN), random forest, XGBoost, and LightGBM. The performance of each algorithm was evaluated using different sets of features derived from the preprocessed signals to identify the most effective approach for classifying hand movements. Additionally, the impact of various signal representations on classification accuracy was examined. Experimental results indicated that some algorithms, especially when an expanded set of features was utilized, achieved improved accuracy in classifying hand movements. These findings contribute to the development of more efficient control systems for myoelectric prostheses and offer insights for future research in EMG signal processing and pattern recognition. Full article

Objective: This study compares the brain protective effects of L-borneolum and its main components (a combined application of L-borneol and L-camphor) on the rat model of middle cerebral artery occlusion/reperfusion (MCAO/R). It also makes clear the intrinsic regulatory mechanisms that link the neuroprotective effects of these compounds on IS to the blood-brain barrier (BBB), based on network pharmacology predictions. Furthermore, the study investigates the relationship between these compounds and the Major Facilitator Superfamily Domain-containing Protein 2A (MFSD2A)/Caveolin-1 (Cav-1) signaling axis. Methods: The MCAO/R model in rats was established to evaluate the therapeutic effect of L-borneolum (200 mg/kg) and its main components combination of L-borneol and L-camphor (6:4 ratio, 200 mg/kg). Neurological scores, 2,3,5-triphenyl tetrazolium chloride (TTC) staining, hematoxylin-eosin (HE) staining, and Nissl staining were performed to evaluate the neurological damage in the rats. Cerebral blood flow Doppler was applied to monitor the cerebral blood flow changes. Immunofluorescence analysis of albumin leakage and transmission electron microscopy (TEM) were conducted to evaluate blood-brain barrier (BBB) integrity. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to determine the optimal drug concentration. Trans-epithelial electrical resistance (TEER) and horseradish peroxidase (HRP) assays were employed to confirm the successful establishment of an in vitro BBB co-culture model. Network pharmacology was utilized to predict the biological processes, molecular functions, and cellular components involved in the treatment of ischemic stroke (IS) by the main components of L-borneolum (L-borneol and L-camphor). Finally, immunofluorescence, real-time fluorescent quantitative PCR (RT-qPCR) and western blot analyses were performed to detect the expression of Major Facilitator Superfamily Domain Containing 2A (MFSD2A), caveolin-1 (CAV-1), sterol regulatory element-binding protein 1 (SREBP1) in brain tissue and hCMEC/D3 cells. Results: Network pharmacology prediction indicated that L-borneolum and its main components (L-borneol and L-camphor) in the treatment of IS are likely associated with vesicle transport and neuroprotection. Treatment of IS with L-borneolum and its main components significantly decreased neurological function scores and cerebral infarction area, while alleviating pathological morphological changes and increasing the number of Nissl bodies in the hippocampus. Additionally, it improved cerebral blood flow, reduced albumin leakage, and decreased vesicle counts in the brain. The trans-epithelial electrical resistance (TEER) of the co-culture model stabilized on the fifth day after co-culture, and the permeability to horseradish peroxidase (HRP) in the co-culture model was significantly lower than that of the blank chamber at this time. RT-qPCR and Western blot results demonstrated that, compared to the model group, the expression of SREBP1 and MFSD2A significantly increased, while the expression of Cav-1 decreased. Conclusions: L-borneolum and its main components combination (L-borneol/L-camphor, 6:4 ratio) may exert a protective effect in rats with IS by improving BBB transport function through modulation of the MFSD2A/Cav-1 signaling pathway. Full article

In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, nozzle numbers, heights of nozzle combinations, and oil flow rates. In addition, an artificial neural network (ANN) model based on two algorithms is developed for predicting thermal performance under various operating conditions. The flat jet nozzle shows the lowest maximum winding temperature of 120.3 °C and a superior heat transfer coefficient of 3028.6 W/m²-K compared to both full cone nozzles. The power consumption for the flat jet nozzle is higher at 123.9 W compared to other nozzle types. The combination of four flat jet nozzles shows improved oil spray distribution and enhanced cooling compared to combinations of two and six flat jet nozzles. Further, the thermal performance of oil spray cooling with four flat jet nozzles improves when height and oil flow rate are increased. Oil spray cooling with the best configuration shows a winding temperature, heat transfer coefficient, and injection pressure of 98.9 °C, 3408.6 W/m²-K and 4.86 bar, respectively, at a flow rate of 20 LPM. The proposed neural network model with a Levenberg–Marquardt (LM) training variant and logarithmic–sigmoidal (Log) transfer function shows the lowest prediction error within ±2%. Full article

The exploitation of hydrokinetic resources represents a sustainable and efficient alternative for renewable energy generation. This study presents the design and real-world implementation of a compact hydrokinetic system capable of converting rainwater runoff into electricity within smart homes. Unlike conventional large-scale hydrokinetic technologies, this system was specifically engineered for intermittent, low-flow conditions typical of residential rainwater collection networks. The turbine was manufactured using 3D-printed biodegradable materials to promote environmental sustainability and facilitate rapid prototyping. Through CFD simulations and laboratory testing, the system’s hydraulic behaviour and energy conversion efficiency were validated across different flow scenarios. The complete system, consisting of four turbines rated at 120 W each, was integrated into a real smart home without structural modifications. From an academic perspective, this study contributes a quantitatively validated hybrid hydrokinetic–low-head framework for residential rainwater energy recovery, addressing intermittent and low-flow urban conditions insufficiently explored in existing literature. Field tests demonstrated that the hydrokinetic system provides complementary energy during rainfall events, generating up to 6000 Wh per day and enhancing household energy resilience, particularly during periods of low solar availability. The results confirm the technical feasibility, sustainability, and practical viability of decentralized hydrokinetic energy generation for residential applications. Full article

Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and vehicle-to-vehicle (V2V) charging networks using magnetic resonance and laser-based power transfer. Existing solutions often struggle with misalignment sensitivity, unpredictable arrivals, and disconnected ground–aerial scheduling. This work introduces a three-layer architecture that integrates a handshake protocol for coordinated charging and billing, a misalignment correction algorithm for magnetic resonance and laser-based systems, and three scheduling strategies: Static Heuristic Charging Scheduling and Planning (SH-CSP), Dynamic Heuristic Charging Scheduling and Planning (DH-CSP), and the Safety, Scheduling, and Sustainability-Aware Feasibility-Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG). SAFE-DDPG extends vanilla DDPG with feasibility-aware action filtering, prioritized replay, and adaptive exploration to enable real-time scheduling in heterogeneous and congested charging networks. Results show that SAFE-DDPG significantly improves scheduling efficiency, reducing average wait times by over 70% compared to DH-CSP and over 85% compared to SH-CSP, demonstrating its potential to support scalable and coordinated ground–aerial charging ecosystems. Full article

We apply a new operator neural network to solve the Electrical Impedance Tomography (EIT) inverse problem. The EIT inverse problem involves reconstructing the conductivity inside a specific body or domain, given the electric potential along the boundary of said body. Mathematically speaking, the inverse problem is known to be severely ill-posed, that is, hard to reliably solve. However, we demonstrate the efficacy of our proposed algorithm utilizing the aforementioned neural network, dubbed the Radial Basis Operator Network (RBON) in its seminal work, when applied to the EIT inverse problem. Full article