Search Results (267)

This paper studies discontinuous quasilinear sub-elliptic systems associated with Hörmander’s vector fields under controllable and natural growth conditions. By a new $\mathcal{A}$-harmonic approximation reformulation for bilinear forms $\mathcal{A}\in \mathrm{Bil}({\mathbb{R}}^{kN},{\mathbb{R}}^{kN})$, we obtain optimal partial Hölder continuity with exact exponents for weak solutions with vanishing mean oscillation coefficients. Full article

Zinc (Zn) deficiency affects over 30% of the global population, with the highest burdens in developing countries reliant on cereal-based diets. As a major dietary staple in regions such as Sub-Saharan Africa and Latin America, common bean (Phaseolus vulgaris L.) represents a promising vehicle for addressing hidden hunger. This review critically evaluates the efficacy of various strategies to enhance Zn concentration in common bean, ranging from agronomic to genetic manipulation, and proposes promising strategies for biofortifying common bean in developing countries that are resource- and technology-limited. Biofortification strategies include agronomic practices, conventional breeding, and genetic engineering, each with distinct strengths and limitations. Agronomic methods such as soil and foliar fertilization can rapidly increase micronutrient content, but they require recurrent costs and may not be sustainable for smallholders without subsidies. Genetic engineering, particularly transgenic approaches, can significantly boost Zn levels; however, regulatory hurdles, cost of production, and public acceptance remain significant obstacles to widespread adoption. Conventional breeding is secure and widely adopted, but is time-consuming and limited by genetic diversity, making it less precise and slower than genetic engineering. We argue for a context-specific and integrated biofortification framework that prioritizes agronomic interventions such as biofertilizer, seed priming, soil Zn application, and foliar Zn application as approaches for quick results. Moderate- to long-term progress towards a biofortified common bean can be achieved using conventional breeding methods by selecting for local germplasm that accumulates higher Zn amounts in grain. On the other hand, genetic engineering is best for rapid, targeted nutrient enhancement where genetic diversity is lacking, but faces regulatory and acceptance challenges. We recommend that policymakers prioritize frameworks that harmonize these approaches, improve communication and education regarding the benefits of biofortified crop produce, subsidize and strengthen biofortified seed systems, and promote soil health initiatives. Full article

Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a non-invasive, real-time diagnostic medium—offers transformative potential for early disease identification and personalized health monitoring. This review synthesizes advancements in electrochemical sensor technologies tailored for Trp and Tryp quantification, emphasizing their clinical relevance in diagnosing conditions like oral squamous cell carcinoma (OSCC), Alzheimer’s disease (AD), and breast cancer, where dysregulated Trp metabolism reflects immune dysfunction or tumor progression. Electrochemical platforms have overcome the limitations of conventional techniques (e.g., enzyme-linked immunosorbent assays (ELISA) and mass spectrometry) by integrating innovative nanomaterials and smart engineering strategies. Carbon-based architectures, such as graphene (Gr) and carbon nanotubes (CNTs) functionalized with metal nanoparticles (Ni and Co) or nitrogen dopants, amplify electron transfer kinetics and catalytic activity, achieving sub-nanomolar detection limits. Synergies between doping and advanced functionalization—via aptamers (Apt), molecularly imprinted polymers (MIPs), or metal-oxide hybrids—impart exceptional selectivity, enabling the precise discrimination of Trp and Tryp in complex matrices like saliva. Mechanistically, redox reactions at the indole ring are optimized through tailored electrode interfaces, which enhance reaction kinetics and stability over repeated cycles. Translational strides include 3D-printed microfluidics and wearable sensors for continuous intraoral health surveillance, demonstrating clinical utility in detecting elevated Trp levels in OSCC and breast cancer. These platforms align with point-of-care (POC) needs through rapid response times, minimal fouling, and compatibility with scalable fabrication. However, challenges persist in standardizing saliva collection, mitigating matrix interference, and validating biomarkers across diverse populations. Emerging solutions, such as AI-driven analytics and antifouling coatings, coupled with interdisciplinary efforts to refine device integration and manufacturing, are critical to bridging these gaps. By harmonizing material innovation with clinical insights, electrochemical sensors promise to revolutionize precision medicine, offering cost-effective, real-time diagnostics for both localized oral pathologies and systemic diseases. As the field advances, addressing stability and scalability barriers will unlock the full potential of these technologies, transforming them into indispensable tools for early intervention and tailored therapeutic monitoring in global healthcare. Full article

Clinical application of AI/DL-aided acquisitions for quantitative bi-parametric (q-bp)MRI requires validation and harmonization across vendor platforms. An AI/DL-accelerated q-bpMRI, including 5-echo T₂ and 4-b-value apparent diffusion coefficient (ADC) mapping, was implemented on two 3T clinical scanners by two vendors alongside the qualitative standard-of-care (SOC) MRI protocols for six patients with biopsy-confirmed prostate cancer (PCa). AI/DL versus SOC bpMRI image quality was compared for MR-visible PCa lesions on a 4-point Likert-like scale. Quantitative validation and protocol bias assessment were performed using a multiparametric phantom with reference T₂ and diffusion kurtosis values mimicking prostate tissue ranges. Six-minute q-bpMRI achieved acceptable diagnostic quality comparable to the SOC. Better SNR was observed for DL/AI versus SOC ADC with method-dependent distortion susceptibility and resolution enhancement. The measured biases were unaffected by AI/DL reconstruction and related to acquisition protocol parameters: constant for spin-echo T₂ (−7 ms to +5 ms) and ADC (4b-fit: −0.37 µm²/ms and 2b-fit: −0.19 µm²/ms), while nonlinear for echo-planar T₂ (−37 ms to +14 ms). Measured phantom ADC bias dependence on b-value range was consistent with that observed for PCa lesions. Bias correction harmonized lesion T₂ and ADC values across different AI/DL-aided q-bpMRI acquisitions. The developed workflow enables harmonization of AI/DL-accelerated quantitative T₂ and ADC mapping in multi-vendor clinical settings. Full article

Loading of the Earth’s crust due to variations in global atmospheric pressure can displace the position of geodetic stations. However, the station displacements induced by the diurnal and semidiurnal atmospheric tides (S₁-S₂) are often neglected during Global Positioning System (GPS) processing. We first studied the magnitudes of S₁-S₂ deformation in the Earth’s center of mass (CM) frame and compared the global S₁-S₂ grid models provided by the Global Geophysical Fluid Center (GGFC) and the Vienna Mapping Function (VMF) data server. The magnitude of S₁-S₂ tidal displacement can reach 1.5 mm in the Up component at low latitudes, approximately three times that of the horizontal components. The most significant difference between the GGFC and VMF grid models lies in the phase of S₂ in the horizontal components, with phase discrepancies of up to 180° observed at some stations. To investigate the effects of S₁-S₂ corrections on GPS coordinates, we then processed GPS data from 108 International GNSS Service (IGS) stations using the precise point positioning (PPP) method in two processing strategies, with and without the S₁-S₂ correction. We observed that the effects of S₁-S₂ on daily GPS coordinates are generally at the sub-millimeter level, with maximum root mean square (RMS) coordinate differences of 0.18, 0.08, and 0.51 mm in the East, North, and Up components, respectively. We confirmed that part of the GPS draconitic periodic signals was induced by unmodeled S₁-S₂ loading deformation, with the amplitudes of the first two draconitic harmonics induced by atmospheric tide loading reaching 0.2 mm in the Up component. Moreover, we recommend using the GGFC grid model for S₁-S₂ corrections in GPS data processing, as it reduced the weighted RMS of coordinate residuals for 45.37%, 46.30%, and 53.70% of stations in the East, North, and Up components, respectively, compared with 39.81%, 44.44%, and 50.00% for the VMF grid model. The effects of S₁-S₂ on linear velocities are very limited and remain within the Global Geodetic Observing System (GGOS) requirements for the future terrestrial reference frame at millimeter level. Full article

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. 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

Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological sound coexistence. Deep learning-based BSS methods have gained wide attention for their superior nonlinear modeling capabilities. However, existing approaches in underwater acoustic scenarios still face two key challenges: limited feature discrimination and inadequate robustness against non-stationary noise. To overcome these limitations, we propose a novel Robust Cross-Band Network (RCBNet) for the BSS of underwater acoustic mixed signals. To address insufficient feature discrimination, we decompose mixed signals into sub-bands aligned with ship noise harmonics. For intra-band modeling, we apply a parallel gating mechanism that strengthens long-range dependency learning so as to enhance robustness against non-stationary noise. For inter-band modeling, we design a bidirectional-frequency RNN to capture the global dependency relationships of the same signal across sub-bands. Our experiment demonstrates that RCBNet achieves a 0.779 dB improvement in the SDR compared to the advanced model. Additionally, the anti-noise experiment demonstrates that RCBNet exhibits satisfactory robustness across varying noise environments. Full article

High-current electron beams can significantly enhance the productivity of variety of applications including medical radioisotope (RI) production and wastewater purification. High-power superconducting radio frequency (SRF) linacs are capable of producing such high-current electron beams due to the key advantage to operate in continuous wave (CW) mode. However, this requires an injector capable of generating electron bunches with high repetition rate and in CW mode, while minimizing beam losses to avoid damage to SRF cavities due to quenching. RF gating to the grid of a thermionic electron gun is a promising solution, as it ensures CW bunch generation at the repetition rate same as the fundamental or sub-harmonics of the accelerating RF frequency, with minimal beam loss. This paper presents detailed beam dynamics simulations demonstrating that an RF-gated gun operating at 1.3 GHz can generate bunches with 148 ps full width with 8.96 pC charge. Full article

A discrete time crystal (DTC) is a remarkable non-equilibrium phase of matter characterized by the persistent sub-harmonic oscillations of physical observables in periodically driven many-body systems. Motivated by the question of whether such a temporal periodic order can persist when the drive becomes aperiodic, we investigate the dynamics of a Lipkin–Meshkov–Glick model under quasi-periodic Thue–Morse (TM) driving. Intriguingly, this infinite-range-interacting spin system can host “quasi-discrete time crystal” (quasi-DTC) phases characterized by periodic oscillations of the magnetization. We demonstrate that our model can host the quasi-DTC analog of both period-doubling DTCs as well as higher-order DTCs. These quasi-DTCs are robust to various perturbations, and they originate from the interplay of “all-to-all” interactions and the recursive structure of the TM sequence. Our results suggest that quasi-periodic driving protocols can provide a promising route for realizing novel non-equilibrium phases of matter in long-range interacting systems. Full article

Fine-grained sedimentary rocks are ideal carriers for astronomical cycle analysis as they can record and preserve significant astronomical cycle signals. Spectral analysis using the Multi-taper Method (MTM) and Evolutionary Harmonic Analysis (EHA) using the Fast Fourier Transform (FFT) were conducted on natural gamma data from key wells in the Es₃l sub-member in the Bonan Sag, Bohai Bay Basin, China. Gaussian bandpass filtering was applied using a short eccentricity cycle of 100 ka, and a “floating” astronomical time scale for the Es₃l sub-member (Lower 3rd sub-member of Shahejie Formation in Eocene) was established using magnetic stratigraphic ages as boundaries. Stratigraphic divisions were made for single wells in the Es₃l of the Bonan Sag, and a stratigraphic framework was established based on correlations between key wells. The research results indicate the following: Firstly, the Es₃l of the Bonan Sag records significant astronomical cycle signals, with an optimal sedimentation rate of 8.39 cm/ka identified. Secondly, the cyclical thicknesses corresponding to long eccentricity, short eccentricity, obliquity, and precession cycles are 38.9 m, 9.7 m, 4.6–3.4 m, and 1.96–1.66 m, respectively. Thirdly, the Es₃l sub-member stably records 6 long eccentricity cycles and 26 short eccentricity cycles, and the short eccentricity curve is used as a basis for stratigraphic division for high-precision stratigraphic correlations. Fourthly, the quality of sandstone-interbedded mudrock is jointly controlled by the short eccentricity and precession. Eccentricity maximum values result in thicker sandstone interlayers, while minimum precession values promote the thickness of sandstone interlayers. Through astronomical cycle analysis, the depositional evolution mechanism of sandstone-interbedded mudrock is revealed. Combined with the results of high-precision stratigraphic division, this can provide a basis for fine evaluation and “sweet spot” prediction of lacustrine shale oil reservoirs. Full article

Geometric image transformations are fundamental to image processing, computer vision and graphics, with critical applications to pattern recognition and facial identification. The splitting-integrating method (SIM) is well suited to the inverse transformation $T^{-1}$ of digital images and patterns, but it encounters difficulties in nonlinear solutions for the forward transformation T. We propose improved techniques that entirely bypass nonlinear solutions for T, simplify numerical algorithms and reduce computational costs. Another significant advantage is the greater flexibility for general and complicated transformations T. In this paper, we apply the improved techniques to the harmonic, Poisson and blending models, which transform the original shapes of images and patterns into arbitrary target shapes. These models are, essentially, the Dirichlet boundary value problems of elliptic equations. In this paper, we choose the simple finite difference method (FDM) to seek their approximate transformations. We focus significantly on analyzing errors of image greyness. Under the improved techniques, we derive the greyness errors of images under T. We obtain the optimal convergence rates $O\left(H^2\right)+O(H/N^2)$ for the piecewise bilinear interpolations ($\mu =1$) and smooth images, where $H(\ll 1)$ denotes the mesh resolution of an optical scanner, and N is the division number of a pixel split into $N^2$ sub-pixels. Beyond smooth images, we address practical challenges posed by discontinuous images. We also derive the error bounds $O\left(H^{\beta }\right)+O(H^{\beta }/N^2)$, $\beta \in (0,1)$ as $\mu =1$. For piecewise continuous images with interior and exterior greyness jumps, we have $O\left(\sqrt{H}\right)+O(\sqrt{H}/N^2)$. Compared with the error analysis in our previous study, where the image greyness is often assumed to be smooth enough, this error analysis is significant for geometric image transformations. Hence, the improved algorithms supported by rigorous error analysis of image greyness may enhance their wide applications in pattern recognition, facial identification and artificial intelligence (AI). Full article

This paper proposes a vibration reduction method for fractional slot concentrated winding (FSCW) permanent magnet synchronous motors (PMSMs) by applying a four-layer winding configuration. The radial electromagnetic force (REF), particularly its low space-harmonics, causes significant vibration in PMSMs. These low-order REF components are influenced by sub-harmonics in the airgap magnetic flux density (MFD), which occur at frequencies lower than the fundamental component generated by the armature magnetomotive force (MMF) in FSCW PMSMs. To mitigate these sub-harmonics in the MFD, the four-layer winding is applied to the FSCW PMSM. As a result, the overall vibration of the motor is reduced. To verify the effectiveness of the four-layer winding, both electrical and mechanical characteristics are compared among motors with conventional one-, two-, and, proposed, four-layer windings. Finally, the three motors are fabricated and tested, and their vibration levels are experimentally evaluated. Full article

Gallium Arsenide (GaAs) Schottky technology stands out for its superior performance in terms of conversion loss for terahertz mixers at room temperatures, which establishes it as a dominant solution in receivers for high-data-rate wireless communications. However, Indium Gallium Arsenide (InGaAs) Schottky mixers offer a notable advantage in terms of reduced power requirements due to their lower barrier height, enabling optical pumping with the incorporation of photodiodes acting as photonic local oscillators (LOs). In this study, we present the first comparative analysis of GaAs and InGaAs diode technologies under both electrical and optical pumping, which are also being compared for the first time, particularly in the context of a wireless communication system, transmitting up to 80 Gbps at 0.3 THz using 16-quadrature amplitude modulation (QAM). The terahertz transmitter and the optical receiver’s LO are based on modified uni-traveling-carrier photodiodes (MUTC-PDs) driven by free-running lasers. The investigation covers a total of two mixers, including narrow-band GaAs and InGaAs. The results reveal that, despite InGaAs mixers exhibiting higher conversion loss, the bit error rate (BER) can be as low as that with GaAs. This is attributed to the purity of optically generated LO signals in the receiver. This work positions InGaAs Schottky technology as a compelling candidate for terahertz reception in the context of optical wireless communication systems. Full article