Search Results (413)

This study aimed to classify Escherichia coli phages using bioinformatics analysis systematically and to establish corresponding PCR and qPCR detection methods for rapid molecular typing and identification. Based on 419 complete E. coli phage genomes available in NCBI, phylogenetic and pan-genomic analyses were conducted to classify the phages at the family, subfamily, and genus levels and to identify highly conserved core genes. Specific primers targeting these core genes were designed, and their specificity, sensitivity, and reproducibility were verified using conventional PCR and dye-based qPCR. A total of 357 phages were successfully classified, encompassing 10 families, 20 subfamilies, and 67 genera. Pan-genomic analysis identified type-specific core genes within 16 taxa, including Ackermannviridae and Demerecviridae, for which 16 pairs of primers were designed. Validation using bacteriophages isolated from Xinjiang cattle farms showed distinct single PCR bands with high specificity, and the qPCR assay achieved a sensitivity of up to 10⁻⁵ µg/µL. This study established an efficient and broad-spectrum molecular typing and detection method for E. coli phages, providing a powerful preliminary screening tool for phage selection. Full article

Wrapped unsupervised band selection (WUBS) is a powerful means of reducing the dimensions of hyperspectral images (HSIs) and has drawn much focus recently. Nevertheless, numerous WUBS approaches struggle to strike a balance between computational complexity and performance and typically disregard high-level information between bands. This paper presents a new reconstruction error-based algorithm called distance density (DD) and Wasserstein generative adversarial network (WGAN)-driven WUBS (DW-WUBS), which is intended to overcome these problems. Minutely, DW-WUBS employs DD to weigh the spectral fluctuation in different band groups and thus determine the detailed expression of the importance of each group. At the same time, it uses a sequential search method on the important band group instead of the original HSIs, thereby reducing the computational complexity of band retrieval. Afterwards, DW-WUBS trains a WGAN and applies its critical network to test the representativeness of the searched bands by considering their contribution to HSI reconstruction. This automatically derives underlying and higher-level structure information of the spectrum. The superiority of DW-WUBS is certified by comprehensive experiments on three benchmark data sets. For instance, on the Pavia Center scene, the peaked mean accuracy (MA) using the twelve bands chosen via DW-WUBS with the CART classifier exceeds the baseline (i.e., all bands) by 0.91% in the classification task. Full article

Mid-infrared (MIR) supercontinuum (SC) sources are critical for spectroscopy, biomedical imaging, and environmental monitoring. However, conventional generation methods based on free-space experiments using optical parametric amplifiers (OPAs) and difference frequency generation (DFG) lasers suffer from narrow bandwidth and low power distribution in the MIR region. This paper presents a cascaded pumping technique using two soft-glass fibers. A picosecond thulium-doped fiber amplifier (TDFA) pumps a Germania-doped fiber (GDF) to generate an intermediate broadband spectrum, which then pumps a fluorotellurite fiber (TBY) with higher nonlinearity and a wider transmission window. Using this configuration, we achieved an Octave-Spanning SC generation covering 1–4 μm with 7.20 W output power. Notably, 32.8% of total power lies above 3.0 μm, with 11.2% beyond 3.5 μm, demonstrating excellent long-wavelength performance. In addition, we applied numerical simulation methods to investigate SC generation in GDF and TBY by solving the nonlinear Schrödinger equation. The close match between simulated and experimental results facilitates theoretical examination of how SC broadening occurs. This cascaded approach offers a feasible solution in terms of spectral band matching, material compatibility, and system integration potential. Full article

In this paper, laser-assisted machining (LAM) of SiC ceramics was taken as the research object, and the different spectrum and energy spectrum characteristics and their changing trends of acoustic emission (AE) signals under processing states of brittleness, plasticity and thermal damage were analyzed. The numerical characterization of ceramic softening degree was indirectly realized by the energy spectrum characteristics of low-frequency band energy ratio, marking a methodological breakthrough in transitioning from qualitative analysis to quantitative detection for identifying plastic processing state. First, the surface morphology of the machined surface based on the single-factor experiment of laser power was analyzed, and three different processing states and ranges of laser power were determined, namely brittle state (0–185 W), plastic state (185–225 W) and thermal damage state (>225 W). Then, the wavelet packet denoising and spectrum analysis of AE signals under different processing states were carried out to obtain the corresponding frequency of the maximum amplitude and the amplitude change trend of the characteristic frequency (515 kHz) in the high-frequency domain. Finally, the energy spectrum analysis of acoustic emission signals was carried out, and the method of indirect characterization of ceramic softening degree by low-frequency band energy ratio was proposed. This paper provides a numerical characterization method and theoretical guidance for the detection and identification of the plastic processing state of ceramic laser-assisted cutting. Full article

In this work, a layout-level design methodology is presented for Low-Noise Amplifiers (LNAs), targeting a wide frequency spectrum from RF to millimeter-wave (mmWave) bands, and implemented using a 22 nmFDSOI CMOS process. A nested inductor structure is introduced at RF frequencies to reduce silicon footprint, with magnetic crosstalk effects characterized through electromagnetic (EM) simulations using Ansys^® RaptorX, Release 2024 R2, ANSYS, Inc. and integrated into the design process. Single-ended LNA architectures are employed for RF bands, while at mmWave frequencies, a differential topology is adopted to enhance linearity and enable simultaneous input and output impedance matching. An EM-based verification flow is applied across all designs to ensure RF/mmWave design flow compatibility, simulation accuracy, and enhanced performance. The proposed designs are evaluated using key metrics including input/output matching, reverse isolation, forward gain, noise figure, linearity ($I{P}_1,I{P}_3$), stability factor, power consumption, and total chip area to quantify the efficiency of the proposed methodology. The simulation results demonstrate that nested inductors are highly effective for area reduction in RF LNAs, while differential topologies are more suitable for mmWave designs, providing a unified framework for area-efficient and high performance LNA implementation. Full article

With the rapid advancement of offshore wind power, structural vibration induced by multi-source excitations in complex marine environments is a critical concern. This study developed a multi-degree-of-freedom (MDOF) dynamic model of an offshore wind turbine using a lumped mass approach, which was then reduced to a first-order linear system to improve frequency-domain analysis and optimization efficiency. Given the non-stationary, broadband nature of wind and wave loads, a band-pass filtering technique was applied to extract dominant frequency components, enabling linear modeling of excitations within primary modal ranges. The displacement response spectrum, derived via system transfer functions, served as the objective function for optimizing tuned mass damper (TMD) parameters. Both single TMD and multiple TMD (MTMD) strategies were designed and compared. A hierarchical particle swarm optimization (H-PSO) algorithm was proposed for MTMD tuning, using the optimized single TMD as an initial guess to enhance convergence and stability in high-dimensional spaces. The results showed that the frequency-domain optimization framework achieved a balance between accuracy and computational efficiency, significantly reducing structural responses in dominant modes and demonstrating strong potential for practical engineering applications. Full article

Civilian Global Navigation Satellite Systems (GNSS) play a crucial role in critical infrastructure and safety-critical applications, where their low signal power and open descriptions make them vulnerable to threats such as jamming and spoofing. To address these major challenges and growing concerns, NovAtel’s OEM7 receivers are equipped with an advanced GNSS Resilience and Integrity Technology (GRIT) to identify and respond to GNSS threats effectively. This includes Interference Toolkit (ITK), Spoofing Detection Toolkit (SK) and Robust Dual-Antenna Receiver (RoDAR), which employ a range of countermeasures, from jamming detection and characterization to spoofing detection and mitigation, ensuring solution integrity and reliability. The newly developed Galileo Open Service Navigation Message Authentication (OSNMA) module also offers an additional layer of protection by checking for the authenticity of the navigation message for Galileo E1 signals. This paper evaluates the performance of NovAtel’s OEM7 receivers in detecting and mitigating jamming and spoofing using real event data. Effective jamming detection was achieved through spectrum monitoring across all GNSS bands. The effectiveness of GRIT’s anti-jamming and anti-spoofing technologies was demonstrated in advanced test cases. OSNMA results are discussed, highlighting its role as a complementary protection layer for enhanced GNSS security. Full article

Emerging services such as artificial intelligence (AI), 5G, the Internet of Things (IoT), cloud data services and teleworking are growing exponentially, pushing bandwidth needs to the limit. Space Division Multiplexing (SDM) in the spatial domain, along with Ultra-Wide Band (UWB) transmission in the spectrum domain, represent two degrees of freedom that will play a crucial role in the evolution of backbone optical networks. SDM and UWB technologies necessitate the replacement of conventional Wavelength-Selective-Switch (WSS)-based architectures with innovative optical switching elements capable of handling both higher port counts and flexible switching across various granularities. In this work, we introduce a novel Photonic Integrated Circuit (PIC)-based switching element called flex-Waveband Selective Switch (WBSS), designed to provide flexible band switching across the UWB spectrum (~21 THz). The proposed flex-WBSS supports a hierarchical three-layered Multi-Granular Optical Node (MG-ON) architecture incorporating optical switching across various granularities ranging from entire fibers and flexibly defined bands down to individual wavelengths. To evaluate its performance, we develop a custom network simulator, enabling a thorough performance analysis on the critical performance metrics of the node. Simulations are conducted over an existing network topology evaluating three traffic-oriented switching policies: Full Fiber Switching (FFS), Waveband Switching (WBS) and Wavelength Switching (WS). Simulation results reveal high Optical-to-Signal Ratio (OSNR) and low Bit Error Rate (BER) values, particularly under the FFS policy. In contrast, the integration of the WBS policy bridges the gap between existing WSS- and future FFS-based architectures and manages to mitigate capacity bottlenecks, enabling rapid scalable network upgrades in existing infrastructures. Additionally, we propose a probabilistic framework to evaluate the node’s bandwidth utilization and scaling behavior, exploring trade-offs among scalability, component numbers and complexity. The proposed framework can be easily adapted for the design of future transport optical networks. Finally, we perform a SWaP-C (Size, Weight, Power and Cost) analysis. Results show that our novel MG-ON achieves strong performance, reaching a throughput exceeding 10 Pb/s with high OSNR values ≈14–20 dB and BER ≈10⁻⁹ especially under the FFS policy. Moreover, it delivers up to 7.5× cost reduction compared to alternative architectures, significantly reducing deployment/upgrade costs while maintaining low power consumption. Full article

The global low-carbon transition is driving the use of renewable energy for ecological monitoring. Traditional power supply for forest monitoring sensor equipment is constrained by high wired costs, frequent battery replacement, and the limitations of low light levels and special spectra under forest canopies on photovoltaic (PV) compatibility. Existing research lacks exploration of the correlation between under-forest spectra and PV performance. This study measured the summer understory light spectra of five tree species in Beijing, evaluated the performance of three types of PV cells—monocrystalline silicon, polycrystalline silicon, and amorphous silicon—and designed a low-light energy harvesting circuit. Results indicate that spectral differences under tree canopies are concentrated from 380–680 nm, exhibiting a distinctive forest-specific spectral feature of “high-band enrichment” above 680 nm. Under low-light conditions, polycrystalline silicon photovoltaics demonstrates optimal performance when adapted to this high-band spectrum. The designed circuit can activate at 5 W/m² irradiance and stably output 4.16 V voltage. This study fills a spectral gap in northern summer tree canopies, providing a comprehensive solution of “material adaptation + circuit customization” for the practical deployment of shaded forest PV systems. Full article

Background: Resting-state electroencephalography (EEG) abnormalities have been widely studied in mild cognitive impairment (MCI) and are linked to cognition. Traditionally, research has focused on the absolute power spectrum, which includes both aperiodic (1/f) and periodic components. However, fewer studies have examined aperiodic (1/f) and periodic components separately and their relationship to cognition in cognitively healthy older adults and individuals with MCI. Objectives: This study examined (i) group differences in resting-state absolute power, 1/f-adjusted power, and 1/f slope in individuals with MCI and cognitively healthy controls, and (ii) associations between cognition and 1/f-adjusted power and slope within each group. Methods: Nineteen individuals were included in each group. All participants completed eyes-open resting-state EEG and a cognitive battery assessing global functioning, cognitive control, verbal fluency, naming, and episodic memory. Absolute power and 1/f-adjusted power in theta (4–7 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands and 1/f slope were extracted. Results: No group differences emerged in the resting-state measures. In the controls, a flatter 1/f slope was linked to worse verbal fluency, but no significant associations were observed in the MCI group. Conclusions: Although there were no group differences, the link between 1/f slope and cognition in the controls highlights the value of separately examining periodic and aperiodic brain activity to better understand cognition in individuals with MCI and healthy aging. Full article

For modern axial fans optimized for low self-noise, additional noise emission from guard grilles mounted downstream of the fan can become one of the dominant sources of sound. In the present case, the overall sound power level increases by up to 6 dB. Based on narrow-band acoustic measurements and numerical Lattice-Boltzmann simulations of wind tunnel setups using round wires, it is observed that periodic flow separations behind the wires (von Kármán vortex street) lead to a pronounced hump in the noise spectrum. This occurs in a frequency range that corresponds to the grille-induced noise increase observed with an axial fan under comparable flow conditions. By examining various wire geometries, it was found that disrupting the von Kármán vortex street along the longitudinal direction of the wire and reducing the homogeneity of flow separation can significantly decrease sound generation. As a result, a guard grille prototype incorporating the most promising structures was manufactured for a modern low-noise axial fan. Comparative experimental results for the fan are presented. Full article

The orange mullein is a biennial plant whose tall yellow flower spikes contain mucilage, saponins, and other medicinal compounds that have a beneficial effect on respiratory problems. As light quality is known to influence plant morphology and physiology, with effects often depending on the species, understanding these responses in mullein is of particular interest. Therefore, this study aimed to investigate the combined effects of different light-emitting diodes (white, red and blue) and their corresponding photon flux densities (PPFD) on the morphology, pigment composition, antioxidant activity, fluorescence parameters and OJIP transient curves in mullein (Verbascum phlomoides L.) seedlings. Seedlings grown under blue light, which had relatively higher PPFD, showed the greatest root length, leaf number, leaf and root fresh and dry biomass. Red light, with lower PPFD, resulted in the lowest values for these parameters. Compared to white light, pigment analysis showed that blue light increased chlorophyll a, total chlorophyll, carotenoid content, and the Chl a/b ratio. Also, blue light enhanced antioxidant activity, as well as the accumulation of phenolic compounds and flavonoids, indicating that it appeared to enhance the synthesis of secondary metabolites under this spectrum. In contrast, seedlings under red light exhibited the lowest ferric reducing antioxidant power values and tended to reduce levels of phenols and flavonoids, indicating a weaker antioxidative response. It was found that white light appeared to enhance the photochemical activity of photosystem II (PSII) and energy dissipation. Blue light improved linear electron transport, photosystem I (PSI) activity and overall photosynthetic performance. Red light preferentially increased electron flow towards the final acceptors of PSI, affecting the terminal part of the electron transport chain. Analysis of OJIP curves revealed spectrum and intensity-specific changes in the L, K, H, and G bands, demonstrating that light treatments with differing PPFDs selectively modulate PSII and PSI function. Full article

The Laser Doppler Vibrometer (LDV) is widely used in precision vibration measurement due to its non-contact nature and high accuracy. However, when measuring non-cooperative targets, the internal stray light in the LDV interferes with the target’s return light, creating competition with the reference light, a phenomenon known as interference competition. This issue is particularly prominent in integrated transceiver LDV systems, where the backscattered light from the lens can be comparable in intensity to the target’s return light, significantly degrading phase extraction accuracy and limiting the LDV’s applicability. To address this challenge, this paper proposes a noise suppression algorithm based on the In-phase and Quadrature (IQ) demodulation. The algorithm uses the power spectrum within each frame’s relevant frequency band as an evaluation metric and employs the Three-point Probe Extremum Localization (3P-PEL) method to estimate the amplitude and phase of the stray light interference with the reference light in real time. This enables the accurate extraction of the interference signal between the measurement light and the reference light. Both simulations and experiments validate the effectiveness of the proposed method. The simulation results demonstrate that when the stray-to-measurement power ratio is below 0.25, the proposed algorithm can suppress spurious signals induced by multi-beam interference by more than 25 dB, while experimental results show it can reduce such signals below the LDV’s noise floor in various motion scenarios. The proposed algorithm holds potential applications in laser interferometry and effectively enhances LDV measurement accuracy. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

This study introduces a novel approach for analyzing theoretical Raman spectra, designed to facilitate spectral interpretation, particularly for complex systems such as functional mesoporous silica-based thin films. The proposed methodology relies on spectral decomposition supported by theoretical calculations, representing a step toward the development of autonomous research laboratories. The method assigns vibrational shifts to individual atoms within a molecular model and uses this information to generate partial spectra corresponding to specific atomic groupings. Unlike separate calculations for isolated components, this approach preserves the mutual interactions within the entire molecular structure, providing a more accurate representation of the vibrational environment. Decomposing the theoretical spectrum into contributions from atomic groups significantly simplifies the assignment of Raman bands to specific structural units, thereby enhancing the interpretative power of theoretical spectra and their correlation with experimental data. The method was demonstrated using real Raman spectroscopic data obtained from mesoporous SBA-15 silica thin films containing copper phosphonate groups. This work also highlights the critical role of molecular modeling and DFT calculations in Raman spectral analysis and outlines future perspectives for the use of artificial intelligence to automate and optimize the spectral interpretation process. Full article