Search Results (25)

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Main-chain conjugated and non-conjugated polyelectrolytes are an important class of materials that have many technological applications ranging from fire-retardant materials to carbon-nanotube composites, nonlinear optical materials, electrochromic materials for smart windows, and optical sensors for biomolecules. Here, we describe a series of poly(pyridinium salt)s-fluorene containing 9,9-bis(4-aminophenyl)fluorene moieties with various organic counterions that were synthesized using ring-transmutation polymerization and metathesis reactions, which are non-conjugated polyelectrolytes. Their chemical structures were characterized by Fourier transform infrared (FTIR), proton (¹H) and fluorine 19 (¹⁹F) nuclear magnetic resonance (NMR) spectrometers, and elemental analysis. They exhibited polyelectrolytic behavior in dimethyl sulfoxide. Their lyotropic liquid-crystalline phases were examined by polarizing optical microscopy (POM) and small angle X-ray scattering (SAXS) studies. Their emission spectra exhibited a positive solvatochromism on changing the polarity of solvents. They emitted greenish-yellow lights in polar organic solvents. They formed aggregates in polar aprotic and protic solvents with the addition of water (v/v, 0–90%), whose λ_em peaks were blue shifted. Full article

This paper presents a detailed investigation into the application of transient Fourier analysis in select electrical engineering contexts. Two novel approaches for addressing transient analysis are introduced. The first approach combines the Fourier series with the Laplace–Carson ($\mathcal{L}\text{-}C$) transform in the complex domain, utilizing complex time vectors to streamline the computation of the original function. The inverse transformation back into the time domain is achieved using the Cauchy-Heaviside ($C\text{-}\mathcal{H}$) method. The second approach applies the Fourier transform ($\mathcal{F}\text{-}{\rm T}$) for the transient analysis of a power converter circuit with both passive and active loads. The method of complex conjugate amplitudes is employed for steady-state analysis. Both contributions represent innovative approaches within this study. The process begins with Fourier series expansions and the computation of Fourier coefficients, followed by solving the system’s steady-state and transient responses. The transient states are then confirmed using the Fourier transform. To validate these findings, the analytical results are verified through simulations conducted in the Matlab/Simulink R2023b environment. Full article

Background: The outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. Methods: 0.25 s so-called [¹⁸F]FDG PET histo image series were generated in the scope of for detecting respiratory and cardiac frequency estimates applicable for performing device-less data-driven gated image reconstructions. The frequencies of the cardiac and respiratory motion were estimated for 18 patients using Short Time Fourier Transform (STFT) with 20 s and 30 s window segments, respectively. Results: The Fourier analysis provided time points usable as input to the gated reconstruction based on eight equally spaced time gates. The cardiac investigations showed estimates in accordance with the measured pulse oximeter references (p = 0.97) and a mean absolute difference of 0.4 ± 0.3 beats per minute (bpm). The respiratory frequencies were within the expected range of 10–20 respirations per minute (rpm) in 16 out of 18 patients. Using this setup, the analysis of three patients with visible lung tumors showed an increase in tumor SUV_max and a decrease in tumor volume compared to the non-gated reconstructed image. Conclusions: The method can provide signals that were applicable for gated reconstruction of both cardiac and respiratory motion, providing a potential increased diagnostic accuracy. Full article

The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures. Full article

Most commercially available polymers are synthesized from compounds derived from petroleum, a finite resource. Because of this, there is a growing interest in the synthesis of new polymeric materials using renewable monomers. Following this concept, this work reports on the use of muconic acid as a renewable source for the development of new polyamides that can be used as proton-exchange membranes. Muconic acid was used as a comonomer in polycondensation reactions with 4,4′-(hexafluoroisopropylidene)bis(p-phenyleneoxy)dianiline, 2,5-diaminobencensulfonic acid, and 4,4′-diamino-2,2′-stilbenedisulfonic acid as comonomers in the synthesis of two new series of partially renewable aromatic–aliphatic polyamides, in which the degree of sulfonation was varied. Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (¹H, ¹³C, and ¹⁹F-NMR) techniques were used to confirm the chemical structures of the new polyamides. It was also observed that the degree of sulfonation was proportional to the molar ratio of the diamines in the feed. Subsequently, membranes were prepared by casting, and a complete characterization was conducted to determine their decomposition temperature (T_d), glass transition temperature (T_g), density (ρ), and other physical properties. In addition, water uptake (W_u), ion-exchange capacity (IEC), and proton conductivity (σ_p) were determined for these membranes. Electrochemical impedance spectroscopy (EIS) was used to determine the conductivity of the membranes. MUFASA34 exhibited a σ_p value equal to 9.89 mS·cm⁻¹, being the highest conductivity of all the membranes synthesized in this study. Full article

This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating $ZephI{R}^{\mathrm{TM}}$ 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed. Full article

This work reports on the synthesis and self-assembly of a novel series of dual-responsive poly[2-(dimethylamino)ethylmethacrylate-co-laurylmethacrylate-co-(oligoethyleneglycol)methacrylate], P(DMAEMA-co-LMA-co-OEGMA)statistical terpolymers in aqueous solutions. Five P(DMAEMA-co-LMA-co-OEGMA) amphiphilic terpolymers, having different content of the three monomers, were synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization. The success of the synthesis was confirmed by the molecular characterization of the terpolymers via size exclusion chromatography (SEC) for the determination of molecular weights and the molecular weight distributions. By using nuclear magnetic resonance (¹H-NMR) and Fourier-transform infrared (FTIR) spectroscopy, it was possible to determine the exact composition of the terpolymers. Dynamic light scattering (DLS) and fluorescence spectroscopy (FS) indicated the formation of P(DMAEMA-co-LMA-co-OEGMA) unimolecular or multichain aggregates in aqueous solutions, as a response to pH, temperature and ionic strength changes, with their dimensions being largely affected. The amphiphilic terpolymers were able to encapsulate the hydrophobic drug curcumin (CUR) and demonstrate stability to fetal bovine serum (FBS) solutions. These terpolymer aggregates were studied by DLS, FS and UV-Vis, and it was found that they may have been used as potential nanocarriers for drug delivery and bio-imaging applications. Full article

This study evaluated the flexural properties of an experimental composite series functionalized with 5–40 wt% of a low-Na F-containing bioactive glass (F-series) and compared it to another experimental composite series containing the same amounts of the conventional bioactive glass 45S5 (C-series). Flexural strength and modulus were evaluated using a three-point bending test. Degree of conversion was measured using Fourier-transform infrared spectroscopy. Weibull analysis was performed to evaluate material reliability. The control material with 0 wt% of bioactive glass demonstrated flexural strength values of 105.1–126.8 MPa). In the C-series, flexural strength ranged between 17.1 and 121.5 MPa and was considerably more diminished by the increasing amounts of bioactive glass than flexural strength in the F-series (83.8–130.2 MPa). Analogously, flexural modulus in the C-series (0.56–6.66 GPa) was more reduced by the increase in bioactive glass amount than in the F-series (5.24–7.56 GPa). The ISO-recommended “minimum acceptable” flexural strength for restorative resin composites of 80 MPa was achieved for all materials in the F-series, while in the C-series, the materials with higher bioactive glass amounts (20 and 40 wt%) failed to meet the requirement of 80 MPa. The degree of conversion in the F-series was statistically similar or higher compared to that of the control composite with no bioactive glass, while the C-series showed a declining degree of conversion with increasing bioactive glass amounts. In summary, the negative effect of the addition of bioactive glass on mechanical properties was notably less pronounced for the customized bioactive glass than for the bioactive glass 45S5; additionally, mechanical properties of the composites functionalized with the customized bioactive glass were significantly less diminished by artificial aging. Hence, the customized bioactive glass investigated in the present study represents a promising candidate for functionalizing ion-releasing resin composites. Full article

The outputs of many real-world complex dynamical systems are time series characterized by power-law correlations and fractal properties. The first proposed model for such time series comprised fractional Gaussian noise (fGn), defined by an autocorrelation function $C\left(k\right)$ with asymptotic power-law behavior, and a complicated power spectrum $S\left(f\right)$ with power-law behavior in the small frequency region linked to the power-law behavior of $C\left(k\right)$. This connection suggested the use of simpler models for power-law correlated time series: time series with power spectra of the form $S\left(f\right)\sim 1/f^{\beta }$, i.e., with power-law behavior in the entire frequency range and not only near $f=0$ as fGn. This type of time series, known as $1/f^{\beta }$ noises or simply $1/f$ noises, can be simulated using the Fourier filtering method and has become a standard model for power-law correlated time series with a wide range of applications. However, despite the simplicity of the power spectrum of $1/f^{\beta }$ noises and of the known relationship between the power-law exponents of $S\left(f\right)$ and $C\left(k\right)$, to our knowledge, an explicit expression of $C\left(k\right)$ for $1/f^{\beta }$ noises has not been previously published. In this work, we provide an analytical derivation of $C\left(k\right)$ for $1/f^{\beta }$ noises, and we show the validity of our results by comparing them with the numerical results obtained from synthetically generated $1/f^{\beta }$ time series. We also present two applications of our results: First, we compare the autocorrelation functions of fGn and $1/f^{\beta }$ noises that, despite exhibiting similar power-law behavior, present some clear differences for anticorrelated cases. Secondly, we obtain the exact analytical expression of the Fluctuation Analysis algorithm when applied to $1/f^{\beta }$ noises. Full article

A solution cast technique was utilized to create a plasticized biopolymer-based electrolyte system. The system was prepared from methylcellulose (MC) polymer as the hosting material and potassium iodide (KI) salt as the ionic source. The electrolyte produced with sufficient conductivity was evaluated in an electrochemical double-layer capacitor (EDLC). Electrolyte systems’ electrical, structural, and electrochemical properties have been examined using various electrochemical and FTIR spectroscopic techniques. From the electrochemical impedance spectroscopy (EIS), a maximum ionic conductivity of 5.14 × 10⁻⁴ S cm⁻¹ for the system with 50% plasticizer was recorded. From the EEC modeling, the ion transport parameters were evaluated. The extent of interaction between the components of the prepared electrolyte was investigated using Fourier transformed infrared spectroscopy (FTIR). For the electrolyte system (MC-KI-glycerol), the t_ion and electrochemical windows were 0.964 and 2.2 V, respectively. Another electrochemical property of electrolytes is transference number measurement (TNM), in which the ion predominantly responsibility was examined in an attempt to track the transport mechanism. The non-Faradaic nature of charge storing was proved from the absence of a redox peak in the cyclic voltammetry profile (CV). Several decisive parameters have been specified, such as specific capacitance (C_s), coulombic efficiency (η), energy density (E_d), and power density (P_d) at the first cycle, which were 68 F g⁻¹, 67%, 7.88 Wh kg⁻¹, and 1360 Wh kg⁻¹, respectively. Ultimately, during the 400th cycle, the series resistance ESR varied from 70 to 310 ohms. Full article

Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures. In home security there is an obvious necessity to detect human presence inside the residence. For elderly care and healthcare, the system would like to know if the person is sleeping in the room, sitting on a sofa or conversely, is not present. This paper focuses on the problem of detecting presence using only the minute movements of breathing while at the same time estimating the breathing rate, which is the secondary aim of the paper. We extract the suspected breathing signal, and construct its Fourier series (FS) equivalent. Then we employ a generalized likelihood ratio test (GLRT) on the FS signal to determine if it is a breathing pattern or noise. We will show that calculating the GLRT also yields the maximum likelihood (ML) estimator for the breathing rate. We tested this algorithm on sleeping babies as well as conducted experiments on humans aged 12 to 44 sitting on a chair in front of the radar. The results are reported in the sequel. Full article

A series of poly(pyridinium salt)s-fluorene main-chain ionic polymers with various organic counterions were synthesized by using ring-transmutation polymerization and metathesis reactions. Their chemical structures were characterized by Fourier Transform Infrared (FTIR), proton (¹H), and fluorine 19 (¹⁹F) nuclear magnetic resonance (NMR) spectrometers. These polymers showed a number-average molecular weight (M_ns) between 96.5 and 107.8 kg/mol and polydispersity index (PDI) in the range of 1.12–1.88. They exhibited fully-grown lyotropic phases in polar protic and aprotic solvents at different critical concentrations. Small-angle X-ray scattering for one polymer example indicates lyotropic structure formation for 60–80% solvent fraction. A lyotropic smectic phase contains 10 nm polymer platelets connected by tie molecules. The structure also incorporates a square packing motif within platelets. Thermal properties of polymers were affected by the size of counterions as determined by differential scanning calorimetry and thermogravimetric analysis measurements. Their ultraviolet-visible (UV-Vis) absorption spectra in different organic solvents were essentially identical, indicating that the closely spaced π-π* transitions occurred in their conjugated polymer structures. In contrast, the emission spectra of polymers exhibited a positive solvatochromism on changing the polarity of solvents. They emitted green lights in both polar and nonpolar organic solvents and showed blue light in the film-states, but their λ_em peaks were dependent on the size of the counterions. They formed aggregates in polar aprotic and protic solvents with the addition of water (v/v, 0–90%), and their λ_em peaks were blue shifted. Full article

The future availability of synthetic polymers is compromised due to the continuous depletion of fossil reserves; thus, the quest for sustainable and eco-friendly specialty polymers is of the utmost importance to ensure our lifestyle. In this regard, this study reports on the use of oleic acid as a renewable source to develop new ionomers intended for proton exchange membranes. Firstly, the cross-metathesis of oleic acid was conducted to yield a renewable and unsaturated long-chain aliphatic dicarboxylic acid, which was further subjected to polycondensation reactions with two aromatic diamines, 4,4′-(hexafluoroisopropylidene)bis(p-phenyleneoxy)dianiline and 4,4′-diamino-2,2′-stilbenedisulfonic acid, as comonomers for the synthesis of a series of partially renewable aromatic-aliphatic polyamides with an increasing degree of sulfonation (DS). The polymer chemical structures were confirmed by Fourier transform infrared (FTIR) and nuclear magnetic resonance (¹H, ¹³C, and ¹⁹F NMR) spectroscopy, which revealed that the DS was effectively tailored by adjusting the feed molar ratio of the diamines. Next, we performed a study involving the ion exchange capacity, the water uptake, and the proton conductivity in membranes prepared from these partially renewable long-chain polyamides, along with a thorough characterization of the thermomechanical and physical properties. The highest value of the proton conductivity determined by electrochemical impedance spectroscopy (EIS) was found to be 1.55 mS cm⁻¹ at 30 °C after activation of the polymer membrane. Full article