Search Results (807)

Background/Objectives: In dental implant surgeries, automatic detection and classification of maxillary sinus membrane thickening from Cone-Beam Computed Tomography (CBCT) images used for diagnosis and treatment planning, using an artificial intelligence-based system, will both reduce specialists’ error rates and enable faster processing. Therefore, in this study, a unique model was developed to automatically detect maxillary sinus membrane thickening from CBCT images. Methods: The first step of the developed model is data preprocessing. Then, feature extraction is performed using the backbone Convolutional Neural Network (CNN). The obtained features are processed in parallel with Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and Efficient Channel Attention (ECA) attention mechanisms. In this stage, channel, spatial, and local relationships are better represented. The combined features are passed to a Multilayer Perceptron (MLP) with dropout to mitigate overlearning, and the final class estimation is performed by a softmax layer. Results: Thanks to the attention mechanisms employed in the developed hybrid model, both the model’s representational power and its classification performance have improved. The model developed to automatically detect and classify maxillary sinus membrane thickening from CBCT images achieved a test accuracy rate of 99.43%. Conclusions: The developed model was compared with pre-trained models accepted in the literature and demonstrated superior performance. The current results demonstrate that the proposed model exhibits high performance in classifying maxillary sinus membrane thickness in a single-center CBCT dataset. However, independent external validation and prospective clinical evaluation are necessary to determine its potential for clinical use. Full article

Multimodal luminescent materials integrating spectral and temporal information are highly desirable for dynamic optical information encoding. However, constructing such systems often requires complicated molecular design or multiple synthetic steps. Herein, we report a simple Sb-introduction strategy to regulate excited-state dynamics in the zero-dimensional (0D) organic–inorganic hybrid metal halide (AP)₂ZnCl₄ (AP = 2-aminoacetophenone). The pristine host intrinsically combines prompt AP⁺ fluorescence with long-lived AP⁺-derived room-temperature phosphorescence (RTP). Upon Sb introduction, an additional broad Sb-related localized/self-trapped excitonic emission appears and the excited-state relaxation kinetics are redistributed while the native RTP pathway remains operative. These composition-dependent responses enable a proof-of-concept sequential time-gated optical encoding/decoding scheme with “WWW”, “SUV”, and “RTP” outputs. The results highlight dopant-mediated excited-state regulation in 0D hybrid metal halides for dynamic optical information encoding. Full article

Direct-drive wave power generation systems based on permanent magnet linear generators (PMLGs) produce fluctuating electromagnetic power under irregular wave excitation, which may affect DC-bus voltage stability and load-side power quality. To smooth the fluctuating output power, this paper develops an empirical mode decomposition (EMD)-based power allocation strategy for a battery–supercapacitor hybrid energy storage system (HESS). In the proposed strategy, EMD is used to decompose the fluctuating electromagnetic power into low-frequency and high-frequency components according to their time-scale characteristics. The low-frequency component is assigned to the battery for energy buffering, while the high-frequency component is assigned to the supercapacitor for transient power compensation. Finite-control-set model predictive current control (FCS-MPCC) is adopted on the generator side to improve the current response of the PMLG, and an MPC-based HESS controller is designed to track the assigned power commands and regulate the DC-bus voltage. Simulation results show a battery power-tracking error of 3.93 W and a DC-bus voltage standard deviation of 0.108 V; compared with LPF, EMD reduced the load-step voltage deviation by 11.94%. Experiments confirm that the PMLG back-EMF follows the translator velocity, the storage currents track their references, and the DC-bus voltage remains within ±2 V of its reference. Full article

This paper proposes a novel axially parallel salient pole hybrid excitation synchronous generator (PSPHESG) for small and medium hydropower (SMHP). To prevent irreversible demagnetization of the permanent magnets (PMs), while improving the power density and reducing the volume compared with those of a conventional electrically excited synchronous generator equipped with an AC exciter, a salient pole series hybrid excitation machine is axially integrated with an electrically excited machine, with the latter serving as the power compensation section. The basic structure and operating principles of the proposed PSPHESG are introduced. Finite-element analysis (FEA) is used to investigate the magnetic field distribution and no-load characteristics. Moreover, the phase angle deviation characteristics and output performances under load are analyzed, showing favorable voltage output capability over a wide load range during steady-state operation. Finally, the anti-demagnetization capability of the PM is studied under field forcing (FF) and de-excitation (DE). The results confirm that the PSPHESG not only provides good constant-voltage capability but also effectively avoids irreversible PM demagnetization during FF and DE, indicating its promising applicability to SMHP systems. Full article

In applications with a wide constant power speed range, the use of conventional permanent magnet machines is complicated by their uncontrolled magnetic flux, increased losses at high speeds, increased inverter current, and dangerous open circuit back EMF. For this reason, synchronous machines without magnets and with a field winding on the rotor are increasingly being used in traction applications. However, due to high electrical losses in the field winding, rotor cooling becomes a critical issue. An alternative is to use hybrid excited machines, which retain the advantages of electrically excited machines while significantly reducing rotor losses. This paper presents a comparison between a conventional electrically excited machine and a novel hybrid excited machine for traction applications with a wide constant power speed range of 9.3:1 (4200 to 450 rpm, mechanical power 23.6 kW). Both machines have the same external dimensions and were optimized using the same optimization algorithm. It is shown that the hybrid excited machine provides a reduction in rotor losses by 2.0–3.25 times depending on load conditions. Its total loss is also reduced, although its cost of active materials increased by a factor of 1.6 due to the use of permanent magnets. Full article

The vertical excitation energies of 13 porphyrins and metalloporphyrins were benchmarked using 28 double-hybrid (DH) density functionals within the full TD-DFT framework, in conjunction with the def2-TZVP basis set and CPCM solvation model. The assessed functionals comprise global double hybrids (GDHs), range-separated double hybrids (RSDHs), and their spin-component-/spin-opposite-scaled variants (SCS/SOS-GDHs and SCS/SOS-RSDHs). Performance was evaluated for the B, Q1, and Q3 bands by comparison with experimental absorption maxima. B2PLYP provides one of the most balanced overall agreements, with mean absolute errors of approximately 0.03, 0.06, and 0.07 eV for the B, Q1, and Q3 bands, respectively. B2GPPLYP, $\omega$B88PP86, and $\omega$PBEPP86 also yield MAEs below 0.1 eV for all three bands. In contrast, several SCS/SOS-RSDH functionals perform very well for the B band but show pronounced systematic underestimation for the Q bands, revealing substantial band dependence. Comparison with published multireference vertical excitation energies further shows that close agreement with experimental absorption maxima does not necessarily imply equally close agreement with theoretical vertical excitation energies, particularly for the Q states. Overall, the results highlight the importance of state-specific assessment when benchmarking DH functionals for porphyrinic excitations. Full article

This study establishes a field-measurement-based wideband modeling and station-level simulation framework for conducted high-frequency (HF) disturbance studies in modular multilevel converter-based high-voltage direct-current (MMC-HVDC) stations. Full-scale engineering-site frequency-response measurements are used to identify kHz-to-MHz terminal models of the arm reactor and the valve-side winding of the converter transformer. The arm reactor is fitted in the admittance domain by vector fitting and synthesized as a passive parallel network containing the main inductive path and multiple damped resistor–inductor–capacitor (RLC) branches. The transformer valve-side winding is represented by a Foster I/II hybrid $\pi$-type terminal network reconstructed from two single-phase port-impedance measurements. The validated equipment models are integrated into a representative Power Systems Computer-Aided Design (PSCAD) station model. A 2 ms valve-side source sequence, constructed from nearest-level-control switching instants and a parameterized switching-transient template, is applied in paired injection and zero-injection simulations. For the representative event, the source peak is 605.6 V. Over the first 4 $\mathsf{\mu }$s, the arm-reactor terminal reaches 972.6 V, while the direct-current (DC)-side, valve-side alternating-current (AC), and point-of-common-coupling (PCC) responses reach 534.2, 438.4, and 151.9 V, respectively. The corresponding peak changes relative to the source are $+4.11$, $-1.09$, $-2.81$, and $-12.01$ dB. The DC-side response contains a dominant damped oscillation near 0.61 MHz, and the AC/PCC transfer varies markedly across 0.2–2.0 MHz. In a separate control-identical comparison over the first 2.5 $\mathsf{\mu }$s, the field-identified and lumped models give DC-side peaks of 171.9 and 1.23 V and PCC peaks of 121.4 and 3.93 V under the same excitation. The framework connects field-identified equipment terminal behavior with station-level time-domain propagation analysis and provides a modeling basis for broadband resonance screening and conducted electromagnetic-interference (EMI) assessment. Full article

Hybrid manganese bromides have emerged as promising lead-free narrow-band green emitters for wide-gamut displays, but their practical application is still limited by insufficient operational stability. Herein, Zn²⁺ substitution is employed to enhance the stability of tetraethylammonium manganese bromide (TEA₂MnBr₄) microcrystals (MCs) synthesized via an ultrafast self-assembly method. With increasing Zn²⁺ contents, the photoluminescence intensity of TEA₂Mn_1−xZn_xBr₄ MCs gradually decreases, while the x = 0.10 MCs still exhibit efficient narrow-band green emission at 518 nm and a high photoluminescence efficiency of about 85% under blue excitation. More importantly, Zn²⁺ incorporation substantially enhances the storage and thermal stability, as well as operational stability of the corresponding green LED devices (retaining above 99% of initial luminous efficacy after 160 h continuous operation at 20 mA). Finally, a white LED fabricated using the x = 0.10 MCs achieves a high luminous efficiency of about 136 lm/W and covers about 112% of the National Television System Committee 1931 color gamut. These results demonstrate that Zn²⁺ substitution is an effective strategy for stabilizing hybrid manganese bromide emitters and highlight their potential for lead-free wide-gamut display applications. Full article

Light-responsive materials enabling external modulation of bioactivity and spatial control are highly requested for photopharmacology—a booming research area of modern medicine. We present organo-inorganic hybrids of photoswitchable, bioactive symmetric diamine-phosphine oxides conjugated with luminescent carbon quantum dots (CQDs). The phosphonate compound was found to undergo Z-E isomerization upon 266 nm laser irradiation and exhibit butyrylcholinesterase (BChE) inhibition that increases twofold (15–30%) after photoconversion. Hybrids were fabricated via physical adsorption and chemisorption using different CQD surface groups, and characterized by UV-Vis, luminescence, and FTIR spectroscopy, confirming hybrid formation and retention of functional properties. In both binding modes, the molecules retained photoswitching capability despite steric constraints. All hybrids displayed orthogonal functions: luminescence (excitation at 350 nm) and photomodulation of BChE inhibition (at 266 nm). Remarkably, the binding mode dictated the bioactivity window—chemisorbed hybrids showed narrow 1.5-fold modulation, whereas physisorbed hybrids exhibited ultra-wide >10-fold modulation. This tunable responsiveness, achieved simply by altering the binding mode, demonstrates the exceptional potential of this hybrid design strategy for developing photoswitchable materials with tailored photopharmacological performance. Full article

Robotic Aerial Work Platforms (RAWPs) are subjected to multi-source excitations including wind gusts and inertial loads, which result in strongly nonlinear and time-varying coupled triaxial attitude vibrations. Conventional recurrent architectures such as LSTM and GRU suffer from gradient vanishing when processing long sequences, while Transformer utilize self-attention mechanisms to learn simple periodic correlations; however, the vibrations in RAWPs exhibit a complex time-series pattern composed of low-frequency oscillations superimposed with high-frequency impacts and accumulates errors through autoregressive decoding. To address these limitations, this paper proposes an improved Transformer model featuring dual-channel periodic positional encoding and global–local hybrid multi-head attention for one-shot multi-step long-sequence prediction of RAWPs attitude vibrations. The proposed method designs a dual-channel independent sine–cosine positional encoding with a tunable periodic modulation factor to explicitly embed the multi-scale periodicity priors of vibration signals and introduces a global–local hybrid attention mechanism that parallelly extracts transient amplitude impact features in the time domain and periodic fluctuation features in the frequency domain. A full-scale aerial experimental platform is established to collect triaxial vibration data under two operating conditions at a sampling frequency of 20 Hz. The results determine the optimal periodic modulation factor and input window length, and ablation studies validate the synergistic gains of the two proposed modules. Comparative results demonstrate that the proposed model achieves substantially reduced prediction errors. In terms of pitch angle, the proposed model achieves a performance improvement of 53.89% over Transformer, 52.11% over LSTM, and 57.67% over GRU. The proposed model effectively provides a reliable data-driven prediction framework for attitude monitoring and active vibration suppression of aerial work platforms. Full article

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning models across multiple soil and groundwater prediction tasks. This study systematically evaluated the interactions between 10 metaheuristic feature-selection algorithms and 13 predictive models, including random forest (RF), convolutional neural network (CNN), recurrent architectures, CNN–recurrent neural network (RNN) hybrids, squeeze-and-excitation (SE)-enhanced hybrids, and iTransformer-based hybrids, across four prediction tasks involving soil organic carbon (SOC), soil–water extract electrical conductivity (ECe), apparent electrical conductivity (ECa), and groundwater level (GWL) in Xinjiang, China. A total of 149 candidate environmental covariates were considered for ECe, SOC, and ECa, whereas 122 candidate covariates were considered for GWL. The results showed that no single feature-selection method consistently performed best across all four targets; instead, predictive performance depended on the interaction among the feature-selection strategy, predictive architecture, and target variable. CNN–RNN hybrid architectures generally achieved higher predictive performance than standalone models, although their benefits varied among prediction targets. The best-performing combinations yielded coefficient of determination (R²) values of 0.9826, 0.6981, 0.8429, and 0.8085 for GWL, SOC, ECe, and ECa, respectively. These findings indicate that target-specific compatibility, rather than aggressive dimensionality reduction or a universally superior algorithm, is a key determinant of predictive performance in high-dimensional DSM. By demonstrating that feature-selection effectiveness is jointly influenced by model architecture and target characteristics, this study provides a methodological reference for developing target-specific digital soil mapping models in arid regions. Full article

This study investigates the hybrid squeeze–flow damping characteristics of a previously developed magnetorheological (MR) mount, which integrates two vertically symmetric squeeze gaps and four flow channels. Based on the magnetic-circuit configuration, a damping-force prediction model was established specifically for the proposed hybrid structure. Magnetostatic finite element analysis (FEA) was conducted to compare the magnetic field characteristics under co-directional and opposite-direction coil excitation, and the influence of magnetic isolation components on the magnetic field distribution was additionally investigated. The results indicate that co-directional current excitation generates higher magnetic flux density in both the squeeze gaps and flow channels, enabling the magnetorheological fluid (MRF) to approach magnetic saturation at an excitation current of 2 A. The magnetic isolation components further improve the magnetic flux distribution and enhance the magnetic flux density in the squeeze gaps and flow channels. A one-way coupled numerical method combining magnetostatic FEA and computational fluid dynamics (CFD) was employed. The rheological properties of the MRF were derived from the magnetic flux density and incorporated into the CFD model via a user-defined function (UDF) to calculate the pressure losses and predict the damping force of the MR mount. The proposed model was experimentally validated over an excitation frequency range of 5–30 Hz at an amplitude of 0.15 mm, showing good agreement with the experimental results under most operating conditions. Beyond the experimentally validated range, the model was further employed to investigate the predicted damping characteristics under extended excitation conditions. The extrapolated numerical results indicate that the total damping force can reach 958.2512 N at an excitation amplitude of 0.3 mm and a frequency of 200 Hz. This result should be regarded as a model-based prediction rather than experimentally validated high-frequency performance. The squeeze mode provides the dominant damping contribution, while the contribution of the flow mode becomes increasingly significant with increasing excitation frequency. The results provide a basis for evaluating the potential of the hybrid squeeze–flow MR mount for vehicle engine vibration isolation. Full article

This paper presents the Post-Stimulus Time-Dependent Event Descriptor (P-TED), a novel “pure event” feature descriptor designed for neuromorphic vision data. Unlike conventional frame-based approaches or hybrid methods that transform event data into intermediate representations, P-TED operates directly on asynchronous event streams, thereby preserving the intrinsic low-latency and high-temporal-resolution advantages of event-based sensors. The descriptor integrates two complementary feature sets: a motion feature vector, which aggregates spatial relationships within a Moore neighbourhood to quantify stimulus direction, and a pattern feature vector, which employs rate encoding to capture temporal excitation signatures. The efficacy of the P-TED framework is validated through three distinct experiments: object and character recognition (MNIST-DVS and CIFAR10-DVS), mobile robot movement analysis, and complex non-rigid robotic hand gesture recognition (RoShamBo). Experimental results demonstrate that the P-TED achieves a significant reduction in classification latency, requiring only 2.7 ms compared to the 10.3 ms recorded by the state-of-the-art Distribution-Aware Retinal Transform (DART) framework. Additionally, P-TED exhibits superior robustness in disambiguating symmetric and mirrored motions, as well as in maintaining stability under non-linear fluctuations in event density caused by changing scale. This work establishes P-TED as a high-speed, computationally efficient, and explainable solution for real-time neuromorphic robotic vision systems. Full article

Conventional flux-reversal permanent magnet motors have problems such as excessive torque ripple and rich harmonic content in direct drive applications such as oil exploration, which restrict their application in high-precision scenarios. To address this issue, this paper presents a hybrid excitation topology that integrates double-armature windings, stator Halbach hybrid permanent magnet arrays, rotor-staggered unequal-tooth and rotor-hybrid permanent magnets. Two-dimensional finite element analysis was conducted using ANSYS Maxwell 2023 R1 to evaluate electromagnetic performance under rated steady-state conditions, rated power 300 kW, rated speed 83 rpm, rated voltage 660 V, rated phase current 307 A, and axial core length 200 mm. The simulation results show that the proposed topology has an average output torque of 34.5 kN·m at rated conditions compared with the traditional flux-to-reverse permanent magnet motor of the same size, and the torque ripple rate is reduced from 27.5% to 17.4%, a relative reduction of 36.8%. The results are based only on numerical simulation and have not been verified by physical prototype experiments. Dynamic control strategies, multi-load transient responses and experimental verification will be carried out in subsequent work. Full article

This paper presents an iterative hybrid finite element boundary integral and physical optics (FEBI-PO) method for efficient electromagnetic scattering analysis of electrically large targets that contain locally complex structures. The key idea is to assign as much of the perfectly conducting exterior surface as possible to the physical optics (PO) region while retaining the FEBI formulation in the geometrically and materially complex region. This decomposition reduces the number of boundary integral unknowns associated with the finite element boundary integral (FEBI) subproblem. The disturbed excitation produced by PO currents is further accelerated through an auxiliary matrix-vector product combined with the multilevel fast multipole algorithm (MLFMA). Numerical examples involving an inhomogeneous cavity on a large platform and a ship model with an inhomogeneous cabin show that the proposed method preserves the acceptable accuracy compared with full-wave reference solutions while reducing the computational cost. The method is therefore suitable for scattering from targets with locally complex structures. Full article