Search Results (1,353)

The aim of this study is to analyze the signal generated in the brain for a specific motor task and to identify the region where it occurs. For this purpose, electroencephalography (EEG) signals were divided into delta, theta, alpha, and beta frequency sub-bands, and feature extraction was performed by looking at the time-frequency characteristics of the signals belonging to the obtained sub-bands. The epoch corresponding to motor imagery or action and the signal source in the brain were determined by power spectral density features. This study focused on a hand open–close motor task as an example. A machine learning structure was used for signal recognition and classification. The highest accuracy of 92.9% was obtained with the neural network in relation to signal recognition and action realization. In addition to the classification framework, this study also incorporated advanced preprocessing and energy analysis techniques. Eye blink artifacts were automatically detected and removed using independent component analysis (ICA), enabling more reliable spectral estimation. Furthermore, a detailed channel-based and sub-band energy analysis was performed using fast Fourier transform (FFT) and power spectral density (PSD) estimation. The results revealed that frontal electrodes, particularly Fp1 and AF7, exhibited dominant energy patterns during both real and imagined motor tasks. Delta band activity was found to be most pronounced during rest with T1 and T2, while higher-frequency bands, especially beta, showed increased activity during motor imagery, indicating cognitive and motor planning processes. Although 30 s epochs were initially used, event-based selection was applied within each epoch to mark short task-related intervals, ensuring methodological consistency with the 2–4 s windows commonly emphasized in the literature. After artifact removal, motor activity typically associated with the C3 region was also observed with greater intensity over the frontal electrode sites Fp1, Fp2, AF7, and AF8, demonstrating hemispheric symmetry. The delta band power was found to be higher than that of other frequency bands across T0, T1, and T2 conditions. However, a marked decrease in delta power was observed from T0 to T1 and T2. In contrast, beta band power increased by approximately 20% from T0 to T2, with a similar pattern also evident in gamma band activity. These changes indicate cognitive and motor planning processes. The novelty of this study lies in identifying the electrode that exhibits the strongest signal characteristics for a specific motor activity among 64-channel EEG recordings and subsequently achieving high-performance classification of the corresponding motor activity. Full article

In this paper, a high-precision sensor for angle measurement with angle amplification based on the double-layer regular prisms structure was designed. The angle amplification was achieved by multiple reflections of the measurement laser between the inner and outer double-layer regular prism structure. The trajectory of the measurement laser within the double-layer regular prism structure was investigated, and a corresponding mathematical model was developed. A position-sensitive detector (PSD) measures displacement variations in the measurement laser and ultimately enables angle measurement by applying the displacement-to-angle conversion relationship derived from analysis of the reflection trajectory model. The sensor prototype achieved a measurement precision of ±0.5″. Additionally, the feasibility of the alternative measurement method using multiple measurement units was experimentally verified, while its measurement accuracy remained comparable to that of a single unit. The 360° angle measurement through proper arrangement of multiple PSDs can be achieved as well, and its feasibility has been discussed. Full article

Bio-based polymeric composites were prepared by dispersing different amounts of olive pit (OP) powder within an acrylate epoxidized soybean oil (AESO) photocurable resin using tetrahydrofurfuryl acrylate (THFA) as diluent and (2,4,6-trimethylbenzoyl), phosphine oxide (BAPO) as photo-initiator, and they were photocured by Vat Photopolymerization (VP) using a Liquid Crystal Display (LCD) 3D printer. Formulation viscosity was studied because of its important role in a VP process able to influence the printability of the final parts. Different 3D printed architectures were successfully realized with good resolution and accuracy, high level of detail, and flexibility. The effect of OP addition was investigated by thermal (TGA and DSC), morphological (SEM and PSD), viscoelastic (DMA), and mechanical (tensile testing) characterization. The filler led to an increase in the T_g, storage modulus, and tensile properties, underlining the stiffening effect induced by the OP particles onto the polymeric starting resin. This underlines the possibility to apply these bio-based composites in many application fields by valorizing agro-wastes, developing more sustainable materials, and taking advantages of VP 3D printing, such as low costs, minimal wastage, and customized geometry. Biocompatibility tests were also successfully carried out. The results clearly indicate that the AESO-based composites promote cell adhesion and viability. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

Visual field (VF) testing is crucial for the management of glaucoma. However, the process is often hindered by technician shortages and reliability issues. In this study, we leveraged machine learning to predict pattern standard deviation (PSD) using clinical inputs. This machine learning retrospective study used publicly accessible data from 743 eyes (541 glaucoma and 202 non-glaucoma controls). An automated neural network (ANN) model was trained using seven clinical input features: mean retinal nerve fiber layer (RNFL), IOP, patient age, CCT, glaucoma diagnosis, study protocol, and laterality. The ANN demonstrated efficient training across 1000 epochs, with consistent error reduction in training and test sets. Mean RMSEs were 1.67 ± 0.05 for training, and 2.27 ± 0.27 for testing. The r was 0.89 ± 0.01 for training, and 0.81 ± 0.04 for testing, indicating strong predictive accuracy with minimal overfitting. The LOFO analysis revealed that the primary contributors to PSD prediction were RNFL, CCT, IOP, glaucoma status, study protocol, and age, listed in order of significance. Our neural network successfully predicted PSD from RNFL and clinical data with strong performance metrics, in addition to demonstrating construct validity. This work demonstrates that neural networks hold the potential to predict or even generate VF estimations based solely on RNFL and clinical inputs. Full article

In this paper, we propose a deep learning-based surrogate model for Multivariate Empirical Mode Decomposition (MEMD) using Long Short-Term Memory (LSTM) networks, aimed at efficiently extracting Intrinsic Mode Functions (IMFs) from electroencephalographic (EEG) signals. Unlike traditional data-driven methods, our approach leverages temporal sequence modeling to learn the decomposition process in an end-to-end fashion. We further enhance the decomposition targets by employing Noise-Assisted MEMD (NA-MEMD), which stabilizes mode separation and mitigates mode mixing effects, leading to better supervised learning signals. Extensive experiments on synthetic and real EEG data demonstrate the superior performance of the proposed LSTM surrogate over conventional feedforward neural networks and standard MEMD-based targets. Specifically, the LSTM trained on NA-MEMD outputs achieved the lowest mean squared error (MSE) and the highest signal-to-noise ratio (SNR), significantly outperforming the feedforward baseline, even when compared using the Power Spectral Density (PSD). These results confirm the effectiveness of combining LSTM architectures with noise-assisted decomposition strategies to approximate nonlinear signal analysis tasks such as MEMD. The proposed surrogate model offers a fast and accurate alternative to classical empirical methods, enabling real-time and scalable EEG analysis. Full article

This study examines the particle size distribution (PSD) of calcareous soils under cultivated and natural conditions in Chenggong District of Kunming, Yunnan Province, China, using single-fractal and multifractal analyses. Soil samples were collected from the profiles of both land use types, and the PSD parameters, organic matter, and total nitrogen were determined. Single-fractal analysis showed that the single-fractal dimension (D) was mainly influenced by the clay content, with higher clay fractions corresponding to larger D values. The generalized dimension spectrum revealed clear differences between natural and cultivated soils: natural soils exhibited greater sensitivity to probability density weight index(q) changes and a more compact particle distribution, whereas cultivation led to broader PSD ranges and higher heterogeneity. The ratio D₁/D₀ was negatively correlated with the clay content, and multifractal spectrum asymmetry (Δf) indicated that fine particles dominate the variability in deeper layers. Compared with natural soils, cultivated soils had higher organic matter and total nitrogen, reflecting the influence of fertilization and tillage on the soil aggregation and PSD. These findings demonstrate that fractal theory provides a sensitive tool for characterizing soil structural complexity and land use impacts, offering a theoretical basis for soil quality assessment and the sustainable management of calcareous soils. Full article

In order to solve the problem of low thermal efficiency of a 130 t/h industrial circulating fluidized bed boiler, a computational particle fluid dynamic approach was used in this work to study two-phase gas–solid flow, heat transfer, and combustion. The factors influencing coal particle size distributions, air distribution strategies, and operational loads are addressed. The results showed that particle distribution exhibits “core–annulus” flow with a dense-phase bottom region and dilute-phase upper zone. A higher primary air ratio (0.8–1.5) enhances axial gas velocity and bed temperature but reduces secondary air zone (2.5–5.8 m) temperature. A higher primary air ratio also decreases outlet O₂ mole fraction and increases fly ash carbon content, with optimal thermal efficiency at a ratio of 1.0. In addition, as the coal PSD decreases and the load increases, the overall temperature of the furnace increases and the outlet O₂ mole fraction decreases. Full article

Wnt/β-catenin signaling is hyper-activated in several cancer cells and cancer stem cells. Dishevelled/Dvl is a key adapter protein that acts as a bridge between the Wnt receptor Frizzled (Fzd) and other cytosolic factors. In detail, the C-terminal cytosolic region is the ligand of the PSD-95, disks large, and zonula occludens-1 (PDZ) domain of Dvl. Therefore, the PDZ domain (Dvl-PDZ) is thought to be a potential drug target. In this paper, we determined the first crystal structure of the PDZ domain of human Dvl1 (hDvl1-PDZ) at a 2.4 Å resolution. The domain was adapted into a unique trimeric form in which all the canonical ligand-binding clefts were occupied by the β2-β3 loop of the neighbor molecule, like an auto-inhibiting trimer. We used solution nuclear magnetic resonance (NMR) experiments to assess the presence of the self-associated oligomer of hDvl1-PDZ in the solution. Introducing the Ala substitution at Asp 272, the key residue of the β2-β3 loop, partly abolished the concentration-dependent chemical shift change, which suggests that this residue is one of the key residues for formation. Based on these observations, we propose an auto-inhibiting trimer formation of Dvl-PDZ in a Dvl-Axin hetero-oligomerization model of Wnt/β-catenin signal transduction. Full article

As a key parameter that defines the spectral characteristics of lasers, the precise measurement of laser linewidth is crucial for a wide range of advanced applications. This review systematically summarizes recent advances in laser linewidth measurement techniques, covering methods applicable from GHz-level broad linewidths to sub-Hz ultranarrow regimes. We begin by presenting representative applications of lasers with varying linewidth requirements, followed by the physical definition of linewidth and a discussion of the fundamental principles underlying its measurement. For broader linewidth regimes, we review two established techniques: direct spectral measurement using high-resolution spectrometers and Fabry–Pérot interferometer-based analysis. In the context of narrow-linewidth lasers, particular emphasis is placed on the optical beating method. A detailed comparison is provided between two dominant approaches: power spectral density (PSD) analysis of the beat signal and phase-noise-based linewidth evaluation. For each technique, we discuss the working principles, experimental configurations, achievable resolution, and limitations, along with comparative assessments of their advantages and drawbacks. Additionally, we critically examine recent innovations in ultra-high-precision linewidth metrology. This review aims to serve as a comprehensive technical reference for the development, characterization, and application of lasers across diverse spectral regimes. Full article

Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to delays that reduce the effectiveness of early interventions. Given the lack of a definitive cure, accelerating and improving diagnosis is critical to slowing disease progression. Electroencephalography (EEG), a widely used non-invasive technique, captures AD-related brain activity alterations, yet extracting meaningful features from EEG signals remains a significant challenge. This study introduces a machine learning (ML)-driven approach to enhance AD diagnosis using EEG data. Methods: EEG recordings from 36 AD patients, 23 Frontotemporal Dementia (FTD) patients, and 29 healthy individuals (HC) were analyzed. EEG signals were processed within the 0.5–45 Hz frequency range using the Welch method to compute the Power Spectral Density (PSD). From both the time-domain signals and the corresponding PSD, a total of 342 statistical and spectral features were extracted. The resulting feature set was then partitioned into training and test datasets while preserving the distribution of class labels. Feature selection was performed on the training set using Spearman and Pearson correlation analyses to identify the most informative features. To enhance classification performance, hyperparameter tuning was conducted using Bayesian optimization. Subsequently, classification was carried out using Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) the optimized hyperparameters. Results: The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods. Conclusions: These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes. Full article

Multiple differentiation protocols have emerged in recent years, producing neurons with diverse morphologies, gene and protein expression profiles, and functionality. Many of these differentiation techniques require months of culture and the use of expensive growth factors. Most importantly, the derived neurons usually do not exhibit any electrical activity. This limits the value of the protocol as a tool for engineering and investigating neural networks. Here, we describe an efficacious method for differentiating mouse embryonic stem cells into functional neurons. CGR8 cells were neurally induced via the simultaneous application of retinoic acid and purmorphamine. The derived cells expressed neuronal (TUJ1 and NeuN) and synaptic (GAD2, PSD-95, Synaptophysin, and VGLUT1) markers. During whole-cell recordings, neurons exhibited inward and outward currents, likely caused by fast-inactivating voltage-gated potassium channels. Upon current injection, miniature action potentials were also recorded. The efficient generation of diverse subtypes of functional neurons can be a useful tool in fundamental investigations of neural network activity and translational studies. Full article

We report a method of improving the precision and resolution of sensing systems based on position sensitive detectors (PSDs). In the method, we improved the precision and resolution by reducing the gain of the condition circuit and conducting spatial filtering on the measured spot position. To demonstrate the method, we experimentally built a PSD-based displacement sensing system. With the system, a precision of 0.3 μm and a resolution of 0.5 μm were obtained. The precision is two orders of magnitude better than that obtained with the use of a commercial condition circuit (SPC02, SiTek, Partille, Sweden) and without using any filter. Moreover, we tested the radiation-resistance performance of the system using a ⁶⁰Co radiation source. The system kept the precision and resolution after exposure to radiation with a dose set to 100 krad. Our study is very useful to realize high-precision PSD-based sensing in space. Full article

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid approach combining interquartile range filtering and Isolation Forest algorithm. Interpolation methods are adaptively selected based on time intervals. For short-term gaps, cubic spline interpolation is applied, otherwise, a method that combines dominant periodicity estimation with physical constraints based on power spectral density (PSD) is proposed. An adaptive unscented Kalman filter (AUKF), integrated with the Singer motion model, are applied for denoising, dynamically adjusting to local noise statistics and capturing acceleration dynamics. Afterwards, a set of time-frequency features are extracted, including centrality, directional dispersion, and wavelet transform-based features. Taking the lower Yangtze River as a case study, representative buoys are selected based on dynamic time warping similarity. The features analysis result show that the movement of buoys is closely related to the dynamics dominated by the semi-diurnal tide, and is also affected by runoff and accidents. The method improves the quality and interpretability of buoy motion data, facilitating more robust monitoring and hydrodynamic analysis. Full article

The present study explores the sustainable potential of volcanic ash sourced from the active Sakurajima volcano (Japan) as an eco-friendly alternative to Portland cement—a binder known for its high carbon emissions—in concrete and mortar production. The abundant pyroclastic material, currently a waste burden for the residents of Sakurajima and the Kagoshima Bay region, presents a unique opportunity for valorization in line with circular economy principles. Rather than treating this ash as a disposal problem, the research investigates its transformation into a valuable supplementary cementitious material (SCM), contributing to more sustainable construction practices. The investigation focused on the material characterization of the ash (including chemical composition, morphology, and PSD) and its pozzolanic activity index, which is a key indicator of its suitability as a cement replacement. Mortars were prepared with 25% of the commercial binder replaced by volcanic ash—both in its raw form and after mechanical activation—and tested for compressive strength after 28 and 90 days of water curing. Additional assessments included workability of the fresh mix (flow table test), apparent density, and flexural strength of the hardened composites. Tests results showed that the applied volcanic ash did not influence the workability of the mix and showed negligible effect on the apparent density (changes of up to 3.3%), although the mechanical strength was deteriorated (decrease by 15–33% after 7 days, and by 25–26% after 28 days). However, further investigation revealed that the simple mechanical grinding significantly enhances the pozzolanic reactivity of Sakurajima ash. The ground ash achieved a 28-day activity index of 81%, surpassing the 75% threshold set by EN 197-1 and EN 450-1 standards for type II mineral additives. These findings underscore the potential for producing low-carbon mortars and concretes using locally sourced volcanic ash, supporting both emissions reduction and sustainable resource management in construction. Full article