Search Results (2,605)

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

The present paper proposes a three-dimensional (3D) spherical shell model for the magneto-electro-elastic (MEE) free vibration analysis of simply supported multilayered smart shells. A mixed curvilinear orthogonal reference system is used to write the unified 3D governing equations for cylinders, cylindrical panels and spherical shells. The closed-form solution of the problem is performed considering Navier harmonic forms in the in-plane directions and the exponential matrix method in the thickness direction. A layerwise approach is possible, considering the interlaminar continuity conditions for displacements, electric and magnetic potentials, transverse shear/normal stresses, transverse normal magnetic induction and transverse normal electric displacement. Some preliminary cases are proposed to validate the present 3D MEE free vibration model for several curvatures, materials, thickness values and vibration modes. Then, new benchmarks are proposed in order to discuss possible effects in multilayered MEE curved smart structures. In the new benchmarks, first, three circular frequencies for several half-wave number couples and for different thickness ratios are proposed. Thickness vibration modes are shown in terms of displacements, stresses, electric displacement and magnetic induction along the thickness direction. These new benchmarks are useful to understand the free vibration behavior of MEE curved smart structures, and they can be used as reference for researchers interested in the development of of 2D/3D MEE models. Full article

Background/Objectives: Neck pain caused by myofascial pain syndrome (MPS) is a highly prevalent musculoskeletal condition. Repetitive peripheral magnetic stimulation (rPMS) is a promising treatment option; however, its therapeutic effect and optimal treatment frequency remain unclear. This study aimed to investigate the therapeutic effect and duration of effect of rPMS in patients with MPS of the neck. Methods: In this randomized, sham-controlled, crossover trial, 27 patients with neck MPS and baseline visual analog scale (VAS) scores ≥ 40 were enrolled. The mean age was 43.8 ± 9.1 years, and 63% were female. Participants were randomly assigned to receive either an initial rPMS treatment (a 10 min session delivering 3900 pulses at 5–10 Hz) or sham stimulation. After 7 days, groups crossed over. Pain intensity (VAS), disability (Neck Disability Index; NDI), and analgesic use were recorded daily for seven consecutive days. A linear mixed-effects model was used for analysis. Results: At baseline, the VAS and NDI scores were 61.8 ± 10.5 and 26.0 ± 6.3, respectively. rPMS produced a significantly greater reduction in both VAS and NDI scores, with the greatest differences observed on Day 4: the differences were −24.1 points in VAS and −8.5 points in NDI compared to the sham group. There was no significant difference in analgesic use between the two groups. Conclusions: A single rPMS session provides short-term improvement in pain and disability in neck MPS. Based on the observed therapeutic window, more frequent sessions (e.g., twice weekly) may provide sustained benefit and should be explored in future studies. Full article

During red wine production, managing the pomace cap is key for a successful fermentation, allowing the extraction of phenolics and other metabolites and providing the necessary oxygen for yeast activity. In recent years, automatic cap management systems based on the injection of gases have gained popularity, despite the limited scientific information regarding the outcomes of their use. This trial aimed to evaluate the composition of wine during industrial red wine fermentations using an automatic sequential air injection system (i.e., AirMixing MI^TM). Fourteen lots of Cabernet Sauvignon grapes were fermented using four air injection regimes, where the intensity and daily frequency of air injections were set to either low or high. As expected, the treatment combining high-intensity and high-frequency air injection produced the largest dissolved oxygen peaks reaching up to 1.9 mg L⁻¹ per cycle, compared to 0.1 mg L⁻¹ in the low-intensity and low-frequency treatment. Yet, in all cases, little to no accumulation of oxygen overtime was observed. Regarding phenolics, the highest intensity and frequency of air injections led to the fastest increase in total phenolics, anthocyanins, short polymeric pigments, and tannin concentration, although compositional differences among treatments equilibrate by the end of fermentation. The main differences in phenolic compounds observed during fermentation were mediated by temperature variation among wine tanks. Based on these findings, it is advisable to keep the characterizing kinetics of phenolic extraction and expand the study to the aroma evolution of wines fermented with this technology. Full article

Accurate estimation of Interrupted Sampling Repeater Jamming (ISRJ) sampling duration is essential for effective radar anti-jamming. However, in complex electromagnetic environments, the simultaneous presence of suppressive and deceptive jamming, coupled with significant signal overlap in the time–frequency domain, renders ISRJ separation and parameter estimation considerably challenging. To address this challenge, this paper proposes a method utilizing the Fractional Fourier Transform (FRFT) and a Hybrid Modality Fusion Network (HMFN) for ISRJ signal separation and sampling-duration estimation. The proposed method first employs FRFT and a time–frequency mask to separate the ISRJ and target echo from the mixed signal. This process effectively suppresses interference and extracts the ISRJ signal. Subsequently, an HMFN is employed for high-precision estimation of the ISRJ sampling duration, offering crucial parameter support for active electromagnetic countermeasures. Simulation results validate the performance of the proposed method. Specifically, even under strong interference conditions with a Signal-to-Jamming Ratio (SJR) of −5 dB for deceptive jamming and as low as −10 dB for suppressive jamming, the regression model’s coefficient of determination still reaches 0.91. This result clearly demonstrates the method’s robustness and effectiveness in complex electromagnetic environments. Full article

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

In this paper, we propose an optoelectronic feed-forward millimeter-wave generator based on the Mach–Zehnder interferometer (MZI) structure. The phase noise of the local oscillation (LO) input is extracted by loop design and used for phase noise suppression of the output, thereby optimizing the phase noise performance of the generator output. The scheme achieves separation of the phase noise by using an MZI structure and a mixing-frequency oscillator to realize the differential and integration process of the phase noise from the LO input source, respectively. Then, it is combined with a feed-forward operation to skillfully realize phase noise rejection of the resulting high-frequency output. The proposed scheme has been demonstrated to facilitate millimeter-wave generation at 40 GHz and 50 GHz. The measured phase noise is as low as −120 dBc/Hz at a 10 kHz offset, and the experimental setup achieves phase noise suppression of up to 36 dB at this frequency offset. Through systematic theoretical analysis and experimental verification, the excellent capabilities of the proposed scheme in high-frequency signal generation and phase noise suppression are fully demonstrated, which provides a new technological path for high-performance millimeter-wave generation, avoiding the deterioration of the phase noise introduced using high-frequency optoelectronic devices other than photodetectors (PDs) to process the signals. Full article

Pyrochlore Bi_1.8Mn_0.5Ni_0.5Ta₂O_9-Δ (sp.gr. Fd-3m, a = 10.5038(9) Å) was synthesized by the solid-phase reaction method and characterized by vibrational and X-ray spectroscopy. According to scanning electron microscopy, the ceramics are characterized by a porous microstructure formed by randomly oriented oblong grains. The average crystallite size determined by X-ray diffraction is 65 nm. The charge state of transition element cations in the pyrochlore was analyzed by soft X-ray spectroscopy using synchrotron radiation. For mixed pyrochlore, a characteristic shift of Bi4f and Ta4f and Ta5p spectra to the region of lower energies by 0.25 and 0.90 eV is observed compared to the binding energy in Bi₂O₃ and Ta₂O₅ oxides. XPS Mn2p spectrum of pyrochlore has an intermediate energy position compared to the binding energy in MnO and Mn₂O₃, which indicates a mixed charge state of manganese (II, III) cations. Judging by the nature of the Ni2p spectrum of the complex oxide, nickel ions are in the charge state of +(2+ζ). The relative permittivity of the sample in a wide temperature (up to 350 °C) and frequency range (25–10⁶ Hz) does not depend on the frequency and exhibits a constant low value of 25. The minimum value of 4 × 10⁻³ dielectric loss tangent is exhibited by the sample at a frequency of 10⁶ Hz. The activation energy of conductivity is 0.7 eV. The electrical behavior of the sample is modeled by an equivalent circuit containing a Warburg diffusion element. Full article

In October 2024, a DANA (Isolated Depression at High Levels) triggered torrential rains across the Valencian Community, causing 227 deaths, severe infrastructure damage, and economic losses estimated at €17.8 billion. In this context of crisis, startups, despite having fewer resources and less experience than large corporations, played a significant role in crisis communication, shaping public perception and operational continuity. This study explores the communication strategies adopted by startups during and after the disaster, focusing on their activity on Instagram, TikTok, and Facebook between October 2024 and January 2025. Using a mixed-methods approach, we conducted a quantitative analysis of digital discourse through the Fanpage Karma tool, assessing metrics such as engagement, reach, and posting frequency. Sentiment analysis was performed using GPT-4, an advanced natural language processing model, and in-depth interviews with startup representatives provided qualitative insights into reputational impacts. The findings reveal that startups which aligned their discourse with the social context, prioritizing transparency and emotional proximity, enhanced their visibility and credibility. These results underscore how effective crisis communication not only mitigates reputational risk but also strengthens the local entrepreneurial ecosystem through trust-building and social responsibility. Full article

In the field of image dehazing, many deep learning-based methods have demonstrated promising results. However, these methods often neglect crucial frequency-domain information and rely heavily on labeled datasets, which limits their applicability to real-world hazy images. To address these issues, we propose a semi-supervised image-dehazing network based on a trusted library (WTS-Net). We construct a dual-branch wavelet transform network (DBWT-Net). It fuses high- and low-frequency features via a frequency-mixing module and enhances global context through attention mechanisms. Building on DBWT-Net, we embed this backbone in a teacher–student model to reduce reliance on labeled data. To enhance the reliability of the teacher network, we introduce a trusted library guided by NR-IQA. In addition, we employ a two-stage training strategy for the network. Experiments show that WTS-Net achieves superior generalization and robustness in both synthetic and real-world dehazing scenarios. Full article

The control of following motion under mesoscale gap flow fields has important applications. The flexible characteristics of the plant, wideband time-varying disturbances caused by the flow field, and requirements of high precision and low overshoot make achieving submicron level accuracy a significant challenge for traditional control methods. This study adopts the control concept of Disturbance Observer Control (DOBC) and uses H_∞ mixed-sensitivity shaping technology to design a Q-filter. Simultaneously, multiple control techniques, such as high-order reference trajectory planning, Proportional-Integral-Derivative (PID) control, low-pass filtering, notch filtering, lead lag correction, and disturbance rejection filtering, are applied to obtain a control system with a high open-loop gain, sufficient phase margin, and stable closed-loop system. Compared to traditional control methods, the new method can increase the open-loop gain by 15 times and the open-loop bandwidth by 8%. We even observed a 150-time increase of the open-loop gain at the peak frequency. Ultimately, the method achieves submicron level accuracy, making important advances in solving the control problem of semiconductor equipment. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

Looking toward the future, governments around the world have started to change their energy mix due to climate change. The new energy mix will consist mainly of Inverter-Based Resources (IBRs), such as wind and solar power. This transition from a synchronous to a non-synchronous grid introduces new challenges in stability, resilience, and synchronization, necessitating advanced control strategies. Among these, Grid-Forming (GFM) control techniques have emerged as an effective solution for ensuring stable operations in microgrids and large-scale power systems with high IBRs integration. This paper presents a systematic review of GFM control techniques, focusing on their principles and applications. Using the PRISMA 2020 methodology, 75 studies published between 2015 and 2025 were synthesized to evaluate the characteristics of GFM control strategies. The review organizes GFM strategies, evaluates their performance under varying operational scenarios, and emphasizes persistent challenges like grid stability, inertia emulation, and fault ride-through capabilities. Furthermore, this study examines real-world implementations of GFM technology in modern power grids. Notable projects include the UK’s National Grid Pathfinder Program, which integrates GFM inverters to enhance stability, and Australia’s Hornsdale Power Reserve, where battery energy storage with GFM capabilities supports grid frequency regulation. Full article