Search Results (67)

This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article

Intracortical microelectrode arrays (MEAs) are tools for recording and stimulating neural activity, with potential applications in prosthetic control and treatment of neurological disorders. However, when chronically implanted, the long-term functionality of MEAs is hindered by the foreign body response (FBR), characterized by gliosis, neuronal loss, and the formation of a glial scar encapsulating layer. This response begins immediately after implantation and is exacerbated by factors such as brain micromotion and the mechanical mismatch between stiff electrodes and soft brain tissue, leading to signal degradation. Despite progress in mitigating these issues, the underlying mechanisms of the brain’s response to MEA implantation remain unclear, particularly regarding how cells sense and respond to the associated mechanical forces. Mechanosensitive ion channels, such as the Piezo family, are key mediators of cellular responses to mechanical stimuli. In this study, silicon-based NeuroNexus MEAs consisting of four shanks were implanted in the rat somatosensory cortex for sixteen weeks. Weekly neural recordings were conducted to assess signal quality over time, revealing a decline in active electrode yield and signal amplitude. Immunohistochemical analysis showed an increase in GFAP intensity and decreased neuronal density near the implant site. Furthermore, Piezo1—but not Piezo2—was strongly expressed in GFAP-positive astrocytes within 25 µm of the implant. Piezo2 expression appeared relatively uniform within each brain slice, both in and around the MEA implantation site across cortical layers. Our study builds on previous work by demonstrating a potential role of Piezo1 in the chronic FBR induced by MEA implantation over a 16-week period. Our findings highlight Piezo1 as the primary mechanosensitive channel driving chronic FBR, suggesting it may be a target for improving MEA design and long-term functionality. Full article

Gellan gum (GG) is a promising biomaterial due to its biocompatibility, tunable gelation, and modifiability. This study investigates the influence of triple crosslinking mechanisms—thermal gelation, UV-induced covalent crosslinking, and ionic crosslinking—on the mechanical and physicochemical properties of GG-based hydrogels, designed to function as a neuromaterial with hierarchical neuro-architecture as a potential nerve substitute for peripheral nerve injury. Initial thermal gelation forms a physical network via double-helix junctions. Methacrylation introduces vinyl groups enabling UV crosslinking, while post-treatment with Mg²⁺ ions strengthens the network through ionic bridging with carboxylate groups. Plasticizers—glycerol and triethyl citrate—were incorporated to modulate chain mobility, network hydration, swelling behavior, and mechanical flexibility. Seven-day erosion studies showed that glycerol-containing hydrogels eroded 50–60% faster than those with triethyl citrate and up to 70% more than hydrogels without plasticizers, indicating increased hydrophilicity and matrix loosening. In contrast, triethyl citrate reduced erosion, likely due to tighter polymer chain interactions and reduced network porosity. Mechanical testing of 1% v/v methacrylated GG hydrogels revealed that 1.5% v/v triethyl citrate combined with UV curing (30–45 min) produced tensile strengths of 8.76–10.84 MPa. These findings underscore the synergistic effect of sequential crosslinking and plasticizer choice in tuning hydrogel mechanical properties for neuro application. The resulting hydrogels offer potential as a neuromaterial in peripheral nerve injury where gradient mechanical properties with hydration-responsive behavior are required. Full article

Energy policies and economies of scale have promoted the expansion of renewable energy sources, leading to the displacement of conventional generation units and a consequent reduction in system inertia. Low inertia amplifies frequency deviations in response to generation–load imbalances, increasing the risk of load shedding and service interruptions. To address this issue, this paper proposes a coordinated control strategy based on neuro-fuzzy networks, applied to a hybrid energy storage system (HESS) composed of batteries and supercapacitors. The controller is designed to simultaneously emulate virtual inertia and implement virtual droop control, thereby improving frequency stability and reducing reliance on spinning reserve. Additionally, a state-of-charge (SOC) management layer is integrated to prevent battery operation in critical zones, mitigating degradation and extending battery lifespan. The neuro-fuzzy controller dynamically coordinates the power exchange both among the energy storage technologies (batteries and supercapacitors) and between the HESS and the conventional generation unit, enabling a smooth and efficient transition in response to power imbalances. The proposed strategy was validated through simulations in MATLAB R2022b using a two-area power system model with parameters sourced from the literature and validated references. System performance was evaluated using standard frequency response metrics, including performance indicators (ITSE, ISE, ITAE and IAE) and the frequency nadir, demonstrating the effectiveness of the approach in enhancing frequency regulation and ensuring the operational safety of the energy storage system. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music listening, emotion detection, and interactive devices. A prototype was created accordingly and refined through interviews with four experts and eleven users participating in a preliminary experiment. The system is grounded in a four-stage guided imagery and music framework, along with a static activity model focused on relaxation-based stress management. Emotion detection was achieved using a wearable EEG device (NeuroSky’s MindWave Mobile device) and a two-dimensional emotion model, and the emotional states were translated into visual representations using seasonal and weather metaphors. A formal experiment involving 52 users was conducted. The system was evaluated, and its effectiveness confirmed, through user interviews and questionnaire surveys, with statistical analysis conducted using SPSS 26 and AMOS 23. The findings reveal that: (1) integrating emotion sensing with music listening creates a novel and engaging interactive experience; (2) emotional states can be effectively visualized using nature-inspired metaphors, enhancing user immersion and understanding; and (3) the combination of music listening, guided imagery, and real-time emotional feedback successfully promotes emotional relaxation and increases self-awareness. Full article

Neuro-Affective Intelligence (NAI) integrates neuroscience, psychology, and artificial intelligence to support neurodivergent children through personalized Child–Machine Interaction (CMI). This paper presents an adaptive neuro-affective system designed to enhance engagement in children with neurodevelopmental disorders through serious games. The proposed framework incorporates real-time biophysical signals—including EEG-based concentration, facial expressions, and in-game performance—to compute a personalized engagement score. We introduce a novel mechanism, Bayesian Immediate Feedback Learning (BIFL), which dynamically selects visual, auditory, or textual stimuli based on real-time neuro-affective feedback. A multimodal CNN-based classifier detects mental states, while a probabilistic ensemble merges affective state classifications derived from facial expressions. A multimodal weighted engagement function continuously updates stimulus–response expectations. The system adapts in real time by selecting the most appropriate cue to support the child’s cognitive and emotional state. Experimental validation with 40 children (ages 6–10) diagnosed with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) demonstrates the system’s effectiveness in sustaining attention, improving emotional regulation, and increasing overall game engagement. The proposed framework—combining neuro-affective state recognition, multimodal engagement scoring, and BIFL—significantly improved cognitive and emotional outcomes: concentration increased by 22.4%, emotional engagement by 24.8%, and game performance by 32.1%. Statistical analysis confirmed the significance of these improvements ($p<0.001$, Cohen’s $d>1.4$). These findings demonstrate the feasibility and impact of probabilistic, multimodal, and neuro-adaptive AI systems in therapeutic and educational applications. Full article

Despite its significance, hysteresis remains underrepresented in mainstream models of plasticity. In this work, we propose a novel framework that explicitly models hysteresis in simple one- and two-neuron models. Our models capture key feedback-dependent phenomena such as bistability, multistability, periodicity, quasi-periodicity, and chaos, offering a more accurate and general representation of neural adaptation. This opens the door to new insights in computational neuroscience and neuromorphic system design. Synaptic weights change in several contexts or mechanisms including, Bienenstock–Cooper–Munro (BCM) synaptic modification, where synaptic changes depend on the level of post-synaptic activity; homeostatic plasticity, where all of a neuron synapses simultaneously scale up or down to maintain stability; metaplasticity, or plasticity of plasticity; neuromodulation, where neurotransmitters influence synaptic weights; developmental processes, where synaptic connections are actively formed, pruned and refined; disease or injury; for example, neurological conditions can induce maladaptive synaptic changes; spike-time dependent plasticity (STDP), where changes depend on the precise timing of pre- and postsynaptic spikes; and structural plasticity, where changes in dendritic spines and axonal boutons can alter synaptic strength. The ability of synapses and neurons to change in response to activity is fundamental to learning, memory formation, and cognitive adaptation. This paper presents simple continuous and discrete neuro-modules with adapting feedback synapses which in turn are subject to feedback. The dynamics of continuous periodically driven Hopfield neural networks with adapting synapses have been investigated since the 1990s in terms of periodicity and chaotic behaviors. For the first time, one- and two-neuron models are considered as parameters are varied using a feedback mechanism which more accurately represents real-world simulation, as explained earlier. It is shown that these models are history dependent. A simple discrete two-neuron model with adapting feedback synapses is analyzed in terms of stability and bifurcation diagrams are plotted as parameters are increased and decreased. This work has the potential to improve learning algorithms, increase understanding of neural memory formation, and inform neuromorphic engineering research. Full article

Introduction: Spinal and bulbar muscular atrophy (SBMA) is an X-linked neuromuscular disorder characterized by progressive muscle weakness, along with muscle cramps, tremors, and sensory neuropathy. Previous research has shown that patients with SBMA have difficulty with dynamic balance and sensory postural control during quiet stance. There have been no reports on automatic postural reactions in SBMA. Objectives: In this study, we aimed (1) to augment previous findings of sensory postural control, (2) to investigate automatic postural reactions in SBMA, and (3) to explore the relationship between strength and balance. Design: A cross-sectional design was used for the analysis. Participants: The participants were fifty male individuals with a confirmed diagnosis of SBMA. Outcome Measures: Balance testing included the NeuroCom modified Clinical Test of Sensory Interaction on Balance (mCTSIB), which measures sway velocity during quiet stance, and the NeuroCom Motor Control Test (MCT), which measures the latency and strength of postural reactions following sudden perturbations. Strength testing included maximal voluntary isometric contractions measured via fixed-frame dynamometry. Results: Forty-seven out of fifty participants were able to complete the mCTSIB test, but only thirty-eight completed the MCT test. Patients who were unable to complete the MCT were significantly weaker in all lower extremity muscles compared to those who were able to complete testing. Compared to normative data, participants showed significantly higher sway velocity during quiet stance across all conditions of the mCTSIB, except when standing on foam with eyes open. They also exhibited significantly slower postural reactions in response to sudden shifts of the force plate on the MCT. Plantarflexor weakness was significantly correlated with poor postural control on the mCTSIB and MCT. Conclusions: This study confirms previously reported abnormalities of sensory postural control in SBMA and highlights patients’ heavy reliance on visual inputs for postural control. Additionally, this study shows that automatic postural corrections are slower than normal in SBMA and provides a unique approach for measuring the combined sensory and motor components of the disease. Both the sensory and automatic balance abnormalities were found to be associated with plantarflexor weakness and may contribute to a higher risk of falls under challenging situations. Therefore, addressing this weakness may be an important step toward fall prevention in this population. Full article

Precise force tracking and overshoot suppression are critical for manipulator dynamic contact tasks, especially in unstructured environments such as complex surface cleaning that rely on dynamic feedback from force sensors. Traditional impedance control methods exhibit limitations through excessive force overshoot and steady-state error, severely impacting cleaning performance. To address this problem, this paper introduces proportional–integral–derivative (PID) control based on the traditional impedance model and verifies the stability and convergence of the controller through theoretical analysis. Meanwhile, to improve the applicability of the controller and avoid using expert experience to formulate fuzzy rules, this paper designs an adaptive neuro-fuzzy inference system (ANFIS) to dynamically adjust the update rate. To validate the effectiveness of the proposed method, simulation experiments mirroring real-world scenarios of contact cleaning tasks are constructed in Simulink. The results demonstrate that, compared to adaptive impedance control (AIC) and adaptive variable impedance control (AVIC), the proposed controller achieves a faster steady-state response and exhibits negligible overshoot and minimal force steady-state error during both constant and sinusoidal force tracking. Furthermore, the controller demonstrates superior stability under abrupt changes in stiffness and desired force. Full article

This study presents a novel integrated framework for the structural analysis and optimization of multi-floor buildings by combining validated theoretical models with machine learning and evolutionary algorithms. The proposed Process–Action–Response System (PARS-Solution) accurately computes key structural responses—such as deformations, shear forces, and bending moments—based on eleven critical design parameters ($P_1$ to $P_{11}$). The significance of this research lies in its ability to automate and accelerate complex structural analysis using Adaptive Neuro-Fuzzy Inference Systems (ANFISs), achieving an average error of less than 2% in multi-variable prediction scenarios. The results were compared against reference calculations and ETABS simulations to validate its effectiveness, demonstrating deviations of less than 3%. The methodology combines MATLAB-based coding, interpolation from verified reference diagrams, and iterative stiffness adjustment across floors, offering transparency and accuracy. Optimization is performed using Multi-Objective Particle Swarm Optimization (MOPSO), enabling efficient exploration of Pareto-optimal solutions that balance deformation and material usage. Extensive parametric studies reveal the dominant impact of core wall dimensions and floor number on structural efficiency, while the application of stiffness reduction factors (e.g., $P_{11}$) proves effective in reducing material without compromising performance. This hybrid approach enables the delegation of labor-intensive calculations to a trained ANFIS model and supports rapid pre-validation of structural configurations in early design phases. As such, the framework offers a powerful data-driven tool for engineers seeking optimal, lightweight, and high-performance solutions in high-rise building design. Full article

This study introduces a Neuro-Visual Adaptive Control (NVAC) architecture designed to enhance precision and safety in robot-assisted surgery. The proposed system enables semi-autonomous guidance of the laparoscope based on image input. To achieve this, the architecture integrates the following: (1) a computer vision system based on the YOLO11n model, which detects surgical instruments in real time; (2) a Model Reference Adaptive Control with Proportional–Derivative terms (MRAC-PD), which adjusts the robot’s behavior in response to environmental changes; and (3) Closed-Form Continuous-Time Neural Networks (CfC-mmRNNs), which efficiently model the system’s dynamics. These networks address common deep learning challenges, such as the vanishing gradient problem, and facilitate the generation of smooth control signals that minimize wear on the robot’s actuators. Performance evaluations were conducted in CoppeliaSim, utilizing real cholecystectomy images featuring surgical tools. Experimental results demonstrate that the NVAC achieves maximum tracking errors of 1.80 × 10⁻³ m, 1.08 × 10⁻⁴ m, and 1.90 × 10⁻³ m along the x, y, and z axes, respectively, under highly significant dynamic disturbances. This hybrid approach provides a scalable framework for advancing autonomy in robotic surgery. Full article

Parkinson’s disease (PD) and multiple sclerosis (MS) are two chronic neurological diseases (CNDs) that have a high demand for early and continuous rehabilitation. However, accessing professional care remains a challenge, making it a key priority to identify sustainable solutions for ensuring early rehabilitation availability. Objective: The FIT4TeleNEURO pragmatic trial proposes to investigate, in real-life care settings, the superiority in terms of the effectiveness of early rehabilitation intervention with harmonized, mix-model telerehabilitation (TR) protocols (TR single approach, task-oriented—TRsA; TR combined approach, task-oriented and impairment-oriented—TRcA) compared to conventional management (control treatment, CeT) in people with PD and MS. Design, and Methods: This multicenter, randomized, three-treatment arm pragmatic trial will involve 300 patients with CNDs (PD, N = 150; MS, N = 150). Each participant will be randomized (1:1:1) to the experimental groups (20 sessions of TRsA or TRcA according to a mix-model—3 asynchronous + 1 synchronous session/week) or the control group (20 sessions of CeT). Primary and secondary outcome measures will be obtained at the baseline (T0), post-intervention (T1, 5 weeks after baseline), and follow-up (T2, 3 months after the end of the treatment). A multidimensional evaluation (cognitive, motor, and quality of life domains) will be conducted at each time point of assessment (T0; T1; T2). The primary outcome measures will be the assessment of change (T0 vs. T1 vs. T2) in static and dynamic balance, measured using the Mini-Balance Evaluation Systems Test. Usability and acceptability assessment will be also investigated. Expected Results: Implementing TR protocols will enable a more targeted and efficient response to the growing demand for rehabilitation in the early stages of CNDs. Both the TRsA and TRcA approaches are expected to be more effective than CeT, with the combined approach likely providing greater benefits in secondary outcome measures. Finally, the acceptability of the asynchronous modality could open the door to scalable solutions, such as digital therapeutics. Full article

Considering that neurological injuries cannot typically self-recover, there is a need to develop new methods to study neuronal outgrowth in a controllable manner in vitro. In this study, a precise flow-controlled microfluidic system featuring custom-designed chambers that integrate laser-microstructured polyethylene terephthalate (PET) substrates comprising microgrooves (MGs) was developed to investigate the combined effect of shear stress and topography on Neuro-2a (N2a) cells’ behavior. The MGs were positioned parallel to the flow direction and the response of N2a cells was evaluated in terms of growth and differentiation. Our results demonstrate that flow-induced shear stress could inhibit the differentiation of N2a cells. This microfluidic system could potentially be used as a new model system to study the impact of shear stress on cell differentiation. Full article

The performance of pH controllers in industrial cooling towers is critical for maintaining optimal operational conditions and ensuring system efficiency. Industries such as the fertilizer, petrochemical, oil refinery, gas production, and power plant sectors rely on cooling towers, where poor pH regulation can lead to corrosion, scaling, and microbial growth. Traditional proportional–integral–derivative (PID) controllers are used for pH neutralization but often struggle with the cooling tower environments’ dynamic and nonlinear nature, resulting in suboptimal performance and increased operational costs. A hybrid particle swarm optimization (PSO) algorithm combined with a multiple adaptive neuro-fuzzy inference system (MANFIS) was developed to address these challenges. The MANFIS leverages fuzzy logic and neural networks to handle nonlinear pH fluctuations, while PSO improves the convergence speed and solution accuracy. This hybrid approach optimized the PID controller parameters for real-time adaptive pH control. The methodology involved collecting open-loop pH data, deriving the system transfer function, designing the PID controller, and implementing the PSO–MANFIS algorithm to fine-tune PID gains. Three tuning methods—MATLAB Tuner, MANFIS, and PSO–MANFIS—were compared. The findings proved that the PSO–MANFIS approach markedly enhanced the closed-loop efficiency by reducing overshoot and enhancing the dynamic response. These findings demonstrate that the PSO–MANFIS approach effectively maintains pH levels within desired limits, reduces energy consumption, and minimizes chemical usage and the risk of mechanical equipment damage. This study provided valuable insights into optimizing pH control strategies in industrial cooling tower systems, offering a practical solution for improving efficiency and reliability. Full article