Search Results (48)

Low-field magnetic stimulation (LFMS) has been proposed as a non-invasive approach for modulating cortical oscillations through electromagnetic coupling. Frequency-aligned enhancement of alpha-band activity is of interest due to its association with cortical inhibitory balance and relaxed wakefulness. This study investigates whether a 10 Hz LFMS applied to the occipital area can induce measurable alpha-band modulation. Electromagnetic simulations were performed to determine magnetic flux distributions within a simplified spherical head model with magnetic susceptibility, which was approximating the brain’s parameters. The 10 Hz stimulation waveform—a positive ramp sawtooth—was analyzed in both time and frequency domains. Electroencephalographic (EEG) recordings were obtained before and after stimulation, and spectral analyses of relevant occipital channels were used to quantify the power redistributions. Simulations indicated localized magnetic field gradients in the occipital region. Post-stimulation EEG recordings showed a redistribution of spectral power toward the alpha-band, representing approximately 50% of total occipital spectral power, with relative increases exceeding 140% across the analyzed channels. These combined modeling and electrophysiological findings provide preliminary proof-of-concept evidence that frequency-aligned LFMS is associated with a redistribution of spectral power toward the alpha-band. Full article

Objective neural network-based two-phase flow regime classifiers are developed for vertical, narrow, rectangular channels and a 1 inch round pipe using Kohonen Self-Organizing Maps. In the rectangular channel, the classifier uses five geometric inputs obtained from a two-sensor droplet-capable conductivity probe (DCCP-2): the bulk gas void fraction ${\alpha }_g$, ligament void fraction ${\alpha }_{\mathrm{lig}}$, normalized ligament chord length $y_{\mathrm{lig}}$, normalized large bubble chord length $y_{\ell ,\mathrm{bb}}$, and a droplet indicator. These parameters allow for the objective identification of bubbly/distorted bubbly, cap-turbulent, churn-turbulent, annular, rolling wispy, and wispy flow regimes, and yield quantitative transition boundaries in the $(j_f,j_g)$ plane for a densely populated test matrix. In the round pipe, a four-sensor droplet-capable conductivity probe (DCCP-4) provides the mean and standard deviation of droplet, bubble, and ligament chord length distributions, which are used as inputs to a Self-Organizing Map (SOM) classifier that separates rolling annular and wispy annular regimes at high void fractions. The resulting regime maps are discussed in terms of the associated phase geometries and their impact on interfacial area, drag, and entrainment, providing regime-dependent geometric inputs that can be used to improve Two-Fluid Model closures for reactor downcomers, core channels, and other nuclear thermal–hydraulic applications. Full article

Abrasive water jet machining (AWJM) is a non-traditional machining process that is increasingly employed for shaping hard-to-machine materials, particularly titanium (Ti)-based alloys such as Ti-6Al-4V. Owing to its non-thermal nature, AWJM enables effective material removal while minimising metallurgical damage and preserving subsurface integrity. The process performance is governed by several interacting parameters, including jet pressure, abrasive type and flow rate, nozzle traverse speed, stand-off distance, jet incident angle, and nozzle design. These parameters collectively influence key output responses such as the material removal rate (MRR), surface roughness, kerf geometry, and subsurface quality. The existing studies consistently report that the jet pressure and abrasive flow rate are directly proportional to MRR, whereas the nozzle traverse speed and stand-off distance exhibit inverse relationships. Nozzle geometry plays a critical role in jet acceleration and abrasive entrainment through the Venturi effect, thereby affecting the cutting efficiency and surface finish. Optimisation studies based on the design of the experiments identify jet pressure and traverse speed as the most significant parameters controlling the surface quality in the AWJM of titanium alloys. Recent research demonstrates the effectiveness of artificial neural networks (ANNs) for process modelling and optimisation of AWJM of Ti-6Al-4V, achieving high predictive accuracy with limited experimental data. This review highlights research gaps in artificial intelligence-based fatigue behaviour prediction, computational fluid dynamics analysis of nozzle wear mechanisms and jet behaviour, and the development of hybrid AWJM systems for enhanced machining performance. Full article

Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analog electronics or high-power stimulation hardware. This study investigates a proof-of-concept digital custom-designed chip that generates a stable 6 Hz oscillation capable of imposing a stable rhythmic pattern onto digitized seizure-like EEG dynamics. Using a publicly available EEG seizure dataset, we extracted and averaged analog seizure waveforms, digitized them to emulate neural front-ends, and directly interfaced the digitized signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip’s pulse train was resampled and low-pass-reconstructed to produce an analog 6 Hz waveform, allowing direct comparison between seizure morphology, its digitized representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable neuromodulation device for future healthcare diagnostics. Full article

Background: Rhythmic sensory stimulation, including structured musical interventions, has gained renewed interest as a non-pharmacological strategy that may modulate cortical excitability and network stability in focal epilepsy. Although several small studies have reported changes in seizure frequency or epileptiform activity during rhythmic or music exposure, the underlying mechanisms and translational relevance remain insufficiently synthesized. Objective: This narrative review summarizes clinical evidence on music-based and rhythmic sensory interventions in focal epilepsy, outlines plausible neurophysiological mechanisms related to neural entrainment and large-scale network regulation, and discusses emerging opportunities for digital delivery of rhythmic protocols in everyday self-management. Methods: A structured search of recent clinical, neurophysiological, and rehabilitation literature was performed with emphasis on rhythmic auditory, tactile, and multimodal stimulation in epilepsy or related conditions. Additional theoretical and translational sources addressing oscillatory dynamics, entrainment, timing networks, and patient-centered digital tools were reviewed to establish a mechanistic framework. Results: Existing studies—although limited by small cohorts and heterogeneous methodology—suggest that certain rhythmic structures, including specific musical compositions, may transiently modulate cortical synchronization, reduce epileptiform discharges, or alleviate seizure-related symptoms in selected patients. Evidence from neurologic music therapy and rhythmic stimulation in other neurological disorders further supports the concept that externally delivered rhythms can influence timing networks, attentional control, and interhemispheric coordination. Advances in mobile health platforms enable structured rhythmic exercises to be delivered and monitored in real-world settings. Conclusions: Music-based and rhythmic sensory interventions represent a promising but underexplored adjunctive approach for focal epilepsy. Their effectiveness likely depends on individual network characteristics and on the structure of the applied rhythm. Digital integration may enhance personalization and adherence. Rigorous clinical trials and mechanistic studies are required to define optimal parameters, identify responders, and clarify the role of rhythmic stimulation within modern epilepsy care. Full article

Epilepsy is increasingly conceptualized as a disorder of large-scale network instability, involving impairments in interhemispheric connectivity, prefrontal inhibitory control, and slow-frequency temporal processing. Rhythmic sensory stimulation—auditory, vibrotactile, or multisensory—can entrain neuronal oscillations and modulate attentional and sensorimotor networks, yet its mechanistic relevance to epileptic network physiology remains insufficiently explored. This conceptual and mechanistic article integrates empirical findings from entrainment research, prefrontal timing theories, multisensory integration, and network-based models of seizure dynamics and uses them to formulate a hypothesis-driven framework for multisensory exogenous rhythmic stimulation (ERS) in focal epilepsy. Rather than presenting a tested intervention, we propose a set of speculative mechanistic pathways through which low-frequency rhythmic cues might serve as an external temporal reference, engage fronto-parietal control systems, facilitate multisensory-driven sensorimotor coupling, and potentially modulate interhemispheric frontal coherence. These putative mechanisms are illustrated by exploratory neurophysiological observations, including a small pilot study reporting frontal coherence changes during mobile ERS exposure, but they have not yet been validated in controlled experimental settings. The framework does not imply therapeutic benefit; instead, it identifies theoretical pathways through which rhythmic sensory cues may transiently interact with epileptic networks. The proposed model is intended as a conceptual foundation for future neurophysiological validation, computational simulations, and early feasibility research in the emerging field of digital neuromodulation, rather than as evidence of clinical efficacy. This Hypothesis article formulates explicitly testable predictions regarding how multisensory ERS may transiently modulate candidate physiological markers of prefrontal network stability in focal epilepsy. Full article

Music is a near-universal anthropological and sensory phenomenon that engages distributed brain networks and peripheral physiological systems to shape emotion, cognition, sociality, and bodily regulation. Evidence from electrophysiology, neuroimaging, endocrinology, randomized controlled trials, and longitudinal training studies indicates that both receptive and active musical experiences produce experience-dependent neural and systemic adaptations. These include entrainment of neural oscillations, modulation of predictive and reward signaling, autonomic and neuroendocrine changes, and long-term structural connectivity alterations that support affect regulation, cognition, social functioning, motor control, sleep, and resilience to neuropsychiatric illness. This narrative review integrates mechanistic domains with clinical outcomes across major conditions, such as depression, anxiety, schizophrenia, dementia, and selected neurodevelopmental disorders, by mapping acoustic and procedural parameters onto plausible biological pathways. We summarize how tempo, beat regularity, timbre and spectral content, predictability, active versus passive engagement, social context, dose, and timing influence neural entrainment, synaptic and network plasticity, reward and prediction-error dynamics, autonomic balance, and immune/endocrine mediators. For each condition, we synthesize randomized and observational findings and explicitly link observed improvements to mechanistic pathways. We identify methodological limitations, including heterogeneous interventions, small and biased samples, sparse longitudinal imaging and standardized physiological endpoints, and inconsistent acoustic reporting, and translate these into recommendations for translational trials: harmonized acoustic reporting, pre-specified mechanistic endpoints (neuroimaging, autonomic, neuroendocrine, immune markers), adequately powered randomized designs with active controls, and long-term follow-up. Contextual moderators including music education, socioeconomic and cultural factors, sport, sleep, and ritual practices are emphasized as critical determinants of implementation and effectiveness. Full article

Air-void characteristics are defined in the EN-480-11 test method. The primary criticism of Powers’ model comes from the fact that the spacing factor is calculated with the average chord length, without taking into account the chord length distribution. The aim of this study is to determine whether an analysis of the chord length distribution can provide a more accurate estimate of the spacing factor. A data set containing 110 air-entrained concretes with various characteristics was analyzed. The artificial neural network method was applied to develop a model that determines the relationship between the spacing factor, L2, and the parameters of the air-void structure. The input parameters for the ANN-L2 model included the following: A, d, and W—characteristics of the chord size distribution, P—cement paste content, and N5—number of large pores. The ANN model allows for a sufficiently accurate estimation of the spacing factor, L2. The most significant factors that influenced L were the peak amplitude, A; peak width, W; and cement paste content, P. There was a strong correlation between the results of the ANN model and the standard spacing factor L2, indicating that both calculation methods produced comparable results. Finally, a simple method for using the ANN model to calculate the spacing factor in Excel is demonstrated. Full article

Alzheimer’s disease (AD), the most prevalent form of dementia, is marked by progressive cognitive decline, affecting memory, language, orientation, and behavior. Pathological hallmarks include extracellular amyloid plaques and intracellular tau tangles, which disrupt synaptic function and connectivity. Neural oscillations, the rhythmic synchronization of neuronal activity across frequency bands, are integral to cognitive processes but become dysregulated in AD, contributing to network dysfunction and memory impairments. Targeting these oscillations has emerged as a promising therapeutic strategy. Preclinical studies have demonstrated that specific frequency modulations can restore oscillatory balance, improve synaptic plasticity, and reduce amyloid and tau pathology. In animal models, interventions, such as gamma entrainment using sensory stimulation and transcranial alternating current stimulation (tACS), have shown efficacy in enhancing memory function and modulating neuroinflammatory responses. Clinical trials have reported promising cognitive improvements with repetitive transcranial magnetic stimulation (rTMS) and deep brain stimulation (DBS), particularly when targeting key hubs in memory-related networks, such as the default mode network (DMN) and frontal–parietal network. Moreover, gamma-tACS has been linked to increased cholinergic activity and enhanced network connectivity, which are correlated with improved cognitive outcomes in AD patients. Despite these advancements, challenges remain in optimizing stimulation parameters, individualizing treatment protocols, and understanding long-term effects. Emerging approaches, including transcranial pulse stimulation (TPS) and closed-loop adaptive neuromodulation, hold promise for refining therapeutic strategies. Integrating neuromodulation with pharmacological and lifestyle interventions may maximize cognitive benefits. Continued interdisciplinary efforts are essential to refine these approaches and translate them into clinical practice, advancing the potential for neural oscillation-based therapies in AD. Full article

Accurate performance estimation of the entrained-flow pulverized coal gasification unit is essential for production scheduling and process optimization, but these are often hindered by inaccurate or insufficient measurements in the industrial system. This paper proposes a data reconciliation-based method to address this challenge. The thermodynamic equilibrium model is employed as constraints of the gasification and quench processes, and the Particle Swarm Optimization (PSO) algorithm is applied for parameter estimation. Measured data under stable and variable operating conditions are reconciled, detecting and eliminating a 12% error in syngas flow rate at the scrubber outlet, thereby improving gasification performance accuracy. Two characteristic models concerning carbon conversion rate and the flow rate of reacted quench water are derived from the reconciled results. By combining these models with thermodynamic equilibrium models, the modified R² of offline predicted syngas flow rate exceeds 0.92, and those of syngas compositions reach 0.72–0.85. Additionally, an Artificial Neural Network (ANN) model, trained on reconciled and predicted data, is proposed for real-time performance estimation. The ANN model calculates performance metrics within 10 s and achieves R² values above 0.95 for most parameters. This method can be integrated into control systems and serves as a valuable tool for gasification process monitoring and optimization. Full article

Complex harvesting environments and varying crop conditions often lead to threshing cylinder blockage and increased entrainment loss in maize grain harvesters. To address these issues, an electric-driven automatic control system for maize threshing concave clearance based on real-time entrainment loss monitoring was developed. The system automatically adjusts concave clearance parameters at different harvesting speeds to maintain grain entrainment loss within an optimal range. First, an adjustable concave structure based on a crank-link mechanism was designed, with a threshing clearance adjustment range of 15–47 mm and motor rotation angle of 0–48°. Subsequently, an EDEM simulation model of the mixed material discharge inside the threshing cylinder was established to determine the optimal installation position of the entrainment loss monitoring sensor based on piezoelectric ceramic-sensitive elements. The sensor was positioned at the left tail end of the concave sieve, with a minimum distance of 58 mm between the sensitive plate centerline and threshing concave sieve and an installation angle of 65° relative to the horizontal plane. A maize threshing clearance control method based on fuzzy neural network PID control algorithm was proposed, and Simulink simulation optimization verified its superior performance with fast response speed. After system integration, field trials were conducted at low, medium, and high operating speeds with preset ideal entrainment loss intervals. The results showed that control was unnecessary at low speed, the control system-maintained entrainment loss within set range at medium speed, and maximum threshing clearance was needed at high speed. Finally, comparative trials of threshing performance with and without the control system were conducted at medium harvesting speed. Results showed that the entrainment loss rate decreased by 43.75% with the control system activated, significantly reducing maize threshing entrainment losses. This study overcame the barrier of maize threshing parameter adjustment being heavily reliant on manual experience and provided theoretical support for the intelligent grain harvesting equipment. Full article

Background: The human sensorimotor system can naturally synchronize with environmental rhythms, such as light pulses or sound beats. Several studies showed that different styles and tempos of music, or other rhythmic stimuli, have an impact on physiological rhythms, including electrocortical brain activity, heart rate, and motor coordination. Such synchronization, also known as the “entrainment effect”, has been identified as a crucial mechanism impacting cognitive, motor, and affective functioning. Objectives: This review examines theoretical and empirical contributions to the literature on entrainment, with a particular focus on the physiological mechanisms underlying this phenomenon and its role in cognitive, motor, and affective functions. We also address the inconsistent terminology used in the literature and evaluate the range of measurement approaches used to assess entrainment phenomena. Finally, we propose a definition of “physiological entrainment” that emphasizes its role as a fundamental mechanism that encompasses rhythmic interactions between the body and its environment, to support information processing across bodily systems and to sustain adaptive motor responses. Methods: We reviewed the recent literature through the lens of the “embodied cognition” framework, offering a unified perspective on the phenomenon of physiological entrainment. Results: Evidence from the current literature suggests that physiological entrainment produces measurable effects, especially on neural oscillations, heart rate variability, and motor synchronization. Eventually, such physiological changes can impact cognitive processing, affective functioning, and motor coordination. Conclusions: Physiological entrainment emerges as a fundamental mechanism underlying the mind–body connection. Entrainment-based interventions may be used to promote well-being by enhancing cognitive, motor, and affective functions, suggesting potential rehabilitative approaches to enhancing mental health. Full article

Background: Humans exhibit a remarkable ability to synchronize their actions with external auditory stimuli through a process called auditory–motor or rhythmic entrainment. Positive effects of rhythmic entrainment have been demonstrated in adults with neurological movement disorders, yet the neural substrates supporting the transformation of auditory input into timed rhythmic motor outputs are not fully understood. We aimed to systematically map and synthesize the research on the neural correlates of auditory–motor entrainment and synchronization. Methods: Following the PRISMA-ScR guidelines for scoping reviews, a systematic search was conducted across four databases (MEDLINE, Embase, PsycInfo, and Scopus) for articles published between 2013 and 2023. Results: From an initial return of 1430 records, 22 studies met the inclusion criteria and were synthesized based on the neuroimaging modality. There is converging evidence that auditory–motor synchronization engages bilateral cortical and subcortical networks, including the supplementary motor area, premotor cortex, ventrolateral prefrontal cortex, basal ganglia, and cerebellum. Specifically, the supplementary motor area and the basal ganglia are essential for beat-based timing and internally guided rhythmic movements, while the cerebellum plays an important role in tracking and processing complex rhythmic patterns and synchronizing to the external beat. Self-paced tapping is associated with additional activations in the prefrontal cortex and the basal ganglia, suggesting that tapping in the absence of auditory cues requires more neural resources. Lastly, existing studies indicate that movement rate and the type of music further modulate the EEG power in the alpha and beta frequency bands. Conclusions: These findings are discussed in the context of clinical implications and rhythm-based therapies. Full article

An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about $5\%$, but individual comparisons resulted in an $R^2$ of $0.36$. Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to $R^2>0.95$. This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations. Full article