Search Results (3,231)

Using Photoplethysmogram (PPG) signals for identity recognition has been proven effective in biometric authentication. However, in real-world applications, PPG signals are prone to interference from noise, physical activity, diseases, and other factors, making it challenging to ensure accurate user recognition and verification in complex environments. To address these issues, this paper proposes an improved MSF-SE ResNet50 (Multi-Scale Feature Squeeze-and-Excitation ResNet50) model based on 2D PPG signals. Unlike most existing methods that directly process one-dimensional PPG signals, this paper adopts a novel approach based on two-dimensional PPG signal processing. By applying Continuous Wavelet Transform (CWT), the preprocessed one-dimensional PPG signal is transformed into a two-dimensional time-frequency map, which not only preserves the time-frequency characteristics of the signal but also provides richer spatial information. During the feature extraction process, the SENet module is first introduced to enhance the ability to extract distinctive features. Next, a novel Lightweight Multi-Scale Feature Fusion (LMSFF) module is proposed, which addresses the limitation of single-scale feature extraction in existing methods by employing parallel multi-scale convolutional operations. Finally, cross-stage feature fusion is implemented, overcoming the limitations of traditional feature fusion methods. These techniques work synergistically to improve the model’s performance. On the BIDMC dataset, the MSF-SE ResNet50 model achieved accuracy, precision, recall, and F1 scores of 98.41%, 98.19%, 98.27%, and 98.23%, respectively. Compared to existing state-of-the-art methods, the proposed model demonstrates significant improvements across all evaluation metrics, highlighting its significance in terms of network architecture and performance. Full article

Improving charge transport in the aggregated state of nanocomposites is challenging due to the large number of defects present at grain boundaries. To enhance the charge transfer and photogenerated carrier extraction of MnO_x nanofibers, a MnO_x/GO (graphene oxide) nanocomposite was prepared. The effects of GO content and bias on the optoelectronic properties were studied. Representative light sources at 405, 650, 780, 808, 980, and 1064 nm were used to examine the photoelectric signals. The results indicate that the MnO_x/GO nanocomposites have photocurrent switching behaviours from the visible region to the NIR (near-infrared) when the amount of GO added is optimised. It was also found that even with zero bias and storage of the nanocomposite sample at room temperature for over 8 years, a good photoelectric signal could still be extracted. This demonstrates that the MnO_x/GO nanocomposites present a strong built-in electric field that drives the directional motion of photogenerated carriers, avoids the photogenerated carrier recombination, and reflect a good photophysical stability. The strength of the built-in electric field is strongly affected by the component ratios of the resulting nanocomposite. The formation of the built-in electric field results from interfacial charge transfer in the nanocomposite. Modulating the charge behaviour of nanocomposites can significantly improve the physicochemical properties of materials when excited by light with different wavelengths and can be used in multidisciplinary applications. Since the recombination of photogenerated electron–hole pairs is the key bottleneck in multidisciplinary fields, this study provides a simple, low-cost method of tailoring defects at grain boundaries in the aggregated state of nanocomposites. These results can be used as a reference for multidisciplinary fields with low energy consumption. Full article

This study proposes an integrated framework that combines nonlinear stochastic vibration analysis with reliability assessment to address the safety issues of cable systems under damage conditions. First of all, a mathematical model of the damaged cable is established by introducing damage parameters, and its static configuration is determined. Using the Pearl River Huangpu Bridge as a case study, the accuracy of the analytical solution for the cable’s sag displacement is validated through the finite difference method (FDM). Furthermore, a quantitative relationship between the damage parameters and structural response under stochastic excitation is developed, and the nonlinear stochastic dynamic equations governing the in-plane and out-of-plane motions of the damaged cable are derived. Subsequently, a Gaussian Radial Basis Function Neural Network (GRBFNN) method is employed to solve for the steady-state probability density function of the system response, enabling a detailed analysis of how various damage parameters affect structural behavior. Finally, the First-Order and Second-Order Reliability Method (FORM/SORM) are used to compute the reliability index and failure probability, which are further validated using Monte Carlo simulation (MCS). Results show that the severity parameter η shows the highest sensitivity in influencing the failure probability among the damage parameters. For the system of the Pearl River Huangpu bridge, an increase in the damage extent δ from 0.1 to 0.4 can reduce the reliability-based service life of by approximately 40% under fixed values of the damage severity and location, and failure risk is highest when the damage is located at the midspan of the cable. This study provides a theoretical framework from the point of stochastic vibration for evaluating the response and associated reliability of mechanical systems; the results can be applied in practice with guidance for the engineering design and avoid potential damages of suspended cables. Full article

Taconis oscillations represent spontaneous excitation of acoustic modes in tubes with large temperature gradients in cryogenic systems. In this study, Taconis oscillations in hydrogen and helium systems are enhanced with a porous material resulting in a standing-wave thermoacoustic engine. A theoretical model is developed using the thermoacoustic software DeltaEC, version v6.4b2.7, to predict system performance, and an experimental apparatus is constructed for engine characterization. The low-amplitude thermoacoustic model predicts the pressure amplitude, frequency, and temperature gradient required for excitation of the standing-wave system. Experimental measurements, including the onset temperature ratio, acoustic pressure amplitudes, and frequencies, are recorded for different stack materials and geometries. The findings indicate that, independent of stack, hydrogen systems excite at smaller temperature differentials than helium (because of different properties such as lower viscosity for hydrogen), and the stack geometry and material affect the onset temperature ratio. However, pressure amplitude in the excited states varies minimally. Initial measurements are also conducted in a cooling setup with an added regenerator. The configuration with stainless-steel mesh screens produces a small cryogenic refrigeration effect with a decrease in temperature of about 1 K. The reported characterization of a Taconis-based thermoacoustic engine can be useful for the development of novel thermal management systems for cryogenic storage vessels, including refrigeration and pressurization. Full article

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

Reactive oxygen species (ROS) play a dual role as both essential signaling molecules and harmful mediators of damage. Imbalances in the redox state of the liver can overwhelm antioxidant defenses and promote mitochondrial dysfunction, oxidative damage, and inflammation. Complex feedback loops between ROS and immune signaling pathways are a hallmark of pathological liver conditions, such as hepatic ischemia–reperfusion injury (IRI). This is a major cause of liver transplant failure and is of increasing significance due to the increased use of marginally discarded livers for transplantation. This review outlines the major enzymatic and metabolic sources of ROS in hepatic IRI, including mitochondrial reverse electron transport, NADPH oxidases, cytochrome P450 enzymes, and endoplasmic reticulum stress. Hepatocyte injury activates redox feedback loops that initiate immune cascades through DAMP release, toll-like receptor signaling, and cytokine production. Emerging regulatory mechanisms, such as succinate accumulation and cytosolic calcium–CAMKII signaling, further shape oxidative dynamics. Pharmacological therapies and the use of antioxidant and immunomodulatory approaches, including nanoparticles and redox-sensitive therapeutics, are discussed as protective strategies. A deeper understanding of how redox and immune feedback loops interact is an exciting and active area of research that warrants further clinical investigation. Full article

This study details how 3-(naphthalen-2-yl)-1-phenylprop-2-en-1-one (3NPEO) behaves in terms of photophysics when exposed to different solvents. The solvatochromic effect study reveals significant polarity shifts in the excited states of the 3NPEO compound, likely due to an intramolecular proton transfer mechanism. Measurements of dipole moments provide insight into their resonance structures in both ground and excited states. Electrochemical analysis revealed a reversible redox process, indicating a favorable charge transport potential. HOMO and LUMO energies of the compound were computed via oxidation and reduction potential standards. 3NPEO exhibits optimal one-photon and two-photon absorption characteristics, validating its suitability for visible wavelength laser applications in photonic devices. Furthermore, molecular docking and dynamics simulations demonstrated strong interactions between 3NPEO and the progesterone receptor enzyme, supported by structure–activity relationship (SAR) analyses. In vitro cytotoxicity assays on the MDAMB-231 breast cancer cell line showed moderate tumor cell inhibitory activity. Apoptosis studies confirmed the induction of both early and late apoptosis. These findings suggest that 3NPEO holds promise as a potential anticancer agent targeting the progesterone receptor in breast cancer cells. Overall, the findings highlight the substantial influence of solvent polarity on the photophysical properties and the design of more effective and stable therapeutic agents. Full article

The solvatochromic properties of 48 solvents of three benzo-[f]-quinolinium methylids (BfQs) were analyzed within the theories of the variational model and Abe’s model of the liquid. The electro-optical properties of BfQs in the first excited state were determined based on the charge transfer process that occurs from the ylid carbon to the nitrogen atom. The dipole moments and the polarizabilities in the first excited state were calculated according to the two models. The quantum chemical calculations helped in understanding the relationship between the molecular structure and absorption properties of the ground state. It is concluded that several key parameters modulate the strength of the charge transfer and they work in synergy, and the most important are as follows: (i) isomerism around the single polar bond, and (ii) the properties of the solvent. The link between geometrical conformation and the zwitterionic character make the studied BfQs very sensitive chromophores for sensors and optical switching devices. Full article

In the present scenario, coherent states of a quantum harmonic oscillator are generalized with positive Fox H auxiliary functions. The novel generalized coherent states provide canonical coherent states and Mittag-Leffler or Wright generalized coherent states, as particular cases, and resolve the identity operator, over the Fock space, with a weight function that is the product of a Fox H function and a Wright generalized hypergeometric function. The novel generalized coherent states, or the corresponding truncated generalized coherent states, are characterized by anomalous statistics for large values of the number of excitations: the corresponding decay laws exhibit, for determined values of the involved parameters, various behaviors that depart from exponential and inverse-power-law decays, or their product. The analysis of the Mandel Q factor shows that, for small values of the label, the statistics of the number of excitations becomes super-Poissonian, or sub-Poissonian, by simply choosing sufficiently large values of one of the involved parameters. The time evolution of a generalized coherent state interacting with a thermal reservoir and the purity are analyzed. Full article

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation. Full article

The valence-shell electronic state spectroscopy of β-butyrolactone (CH₃CHCH₂CO₂) is comprehensively investigated by employing experimental and theoretical methods. We report a novel vacuum ultraviolet (VUV) absorption spectrum in the photon wavelength range from 115 to 320 nm (3.9–10.8 eV), together with ab initio quantum chemical calculations at the time-dependent density functional (TD-DFT) level of theory. The dominant electronic excitations are assigned to mixed valence-Rydberg and Rydberg transitions. The fine structure in the CH₃CHCH₂CO₂ photoabsorption spectrum has been assigned to C=O stretching, $v_7^{\prime }\left(a\right)$, CH₂ wagging, $v_{14}^{\prime }\left(a\right)$, C–O stretching, $v_{22}^{\prime }\left(a\right)$, and C=O bending, $v_{26}^{\prime }\left(a\right)$ modes. Photolysis lifetimes in the Earth’s atmosphere from 0 km up to 50 km altitude have been estimated, showing to be a non-relevant sink mechanism compared to reactions with the ^•OH radical. The nuclear dynamics along the C=O and C–C–C coordinates have been investigated at the TD-DFT level of theory, where, upon electronic excitation, the potential energy curves show important carbonyl bond breaking and ring opening, respectively. Within such an intricate molecular landscape, the higher-lying excited electronic states may keep their original Rydberg character or may undergo Rydberg-to-valence conversion, with vibronic coupling as an important mechanism contributing to the spectrum. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

In this study, three alternating donor–acceptor (D–A) type [12]cycloparaphenylene ([12]CPP) derivatives ([12]CPP 1a, 2a, and 3a) were designed through precise molecular engineering, and their multidimensional photophysical responses and chiroptical properties were systematically investigated. The effects of the alternating D–A architecture on electronic structure, excited-state dynamics, and optical behavior were elucidated through density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations. The results show that the alternating D–A design significantly reduced the HOMO–LUMO energy gap (e.g., 3.11 eV for [12]CPP 2a), enhanced charge transfer characteristics, and induced pronounced red-shifted absorption. The introduction of an imide-based acceptor ([12]CPP 2a) further strengthened the electron push-pull interaction, exhibiting superior performance in two-photon absorption, while the symmetrically multifunctionalized structure ([12]CPP 3a) predominantly exhibited localized excitation with the highest absorption intensity but lacked charge transfer features. Chiral analysis reveals that the alternating D–A architecture modulated the distribution of chiral signals, with [12]CPP 1a displaying a strong Cotton effect in the low-wavelength region. These findings not only provide a theoretical basis for the molecular design of functionalized CPP derivatives, but also lay a solid theoretical foundation for expanding their application potential in optoelectronic devices and chiral functional materials. Full article