Search Results (84)

To mitigate the reduced accuracy of direction-of-arrival (DOA) estimation in scenarios with low signal-to-noise ratios (SNR) and multiple interfering sources, this paper proposes an Auxiliary Classifier Generative Adversarial Network (ACGAN) architecture that integrates a Squeeze-and-Excitation (SE) attention mechanism and a Multi-scale Dilated Feature Aggregation (MDFA) module. In this neural network, a vector hydrophone array is employed as the receiving unit, capable of simultaneously sensing particle velocity signals in three directions $\left(v_x,v_y,v_z\right)$ and acoustic pressure $p$, thereby providing high directional sensitivity and maintaining robust classification performance under low-SNR conditions. The MDFA module extracts features from multiple receptive fields, effectively capturing cross-scale patterns and enhancing the representation of weak targets in beamforming maps. This helps mitigate estimation bias caused by mutual interference among multiple targets in low-SNR environments. Furthermore, an auxiliary classification branch is incorporated into the discriminator to jointly optimize generation and classification tasks, enabling the model to more effectively identify and separate multiple types of labeled sources. Experimental results indicate that the proposed network is effective and shows improved performance across diverse scenarios. Full article

The increasing awareness of the need for sustainable solutions to secure future energy supplies has spurred the search for innovative approaches. Energo-Efekt Sp. z o.o. has prepared a project for the green transformation of the energy system at a producer of spirits through the rectification of raw alcohol. An installation was conceptualised to develop the system to convert energy from biomass fuels into electricity and heat. The innovation of the installation is the use of an expander—a Heliex system which is the twin-screw turbine generator converting energy in the form of wet steam into electrical power integrated with pressure-reducing valve. This system captures all or part of the available steam flow and reduces the steam pressure, not only delivering steam at the same, lower pressure but also generating rotary energy that can be used to produce electricity with the power output range of 160 to 600 kWe. Currently, the company utilises natural gas as a fuel source and acquires electricity from the external grid. Implementing the system could reduce the carbon footprint associated with the production of vodka at the plant by 97%, to 102 t CO₂ annually. This reduction would account for approximately 21% of the total carbon footprint of the entire alcohol production process. The system could also be applied to other low-power systems that produce < 250 kW, making it a viable option for use in distributed energy networks, and can be used as a model solution for other distillery plants. The transformation project dedicated to Polmos Żyrardów involves a comprehensive change in both the energy source and its management. The fossil fuels used until now are being replaced with a renewable energy source in the form of biomass. The steam and electricity cogeneration system meets the rectification process’s energy demand and can supply the central heating node. Heat recovery exchangers recuperate heat from the boiler room exhaust gases and the rectification cooling process. Potentially, all of these changes lead to the company’s energy self-sufficiency and reduce its overall environmental impact with almost zero CO₂ emissions. Full article

The widespread integration of power electronic devices and renewable energy sources into power systems has significantly exacerbated voltage and current waveform distortion issues, where asymmetric loads—including single-phase nonlinear equipment and unbalanced three-phase power electronic installations—serve as critical harmonic sources whose inherent nonlinear and asymmetric characteristics increasingly compromise power quality. To enhance power quality management, this paper proposes a universal harmonic source modeling and operational state identification methodology integrating physical mechanisms with data-driven algorithms. The approach establishes an RL-series equivalent impedance model as its physical foundation, employing singular value decomposition and Z-score criteria to accurately characterize asymmetric load dynamics; subsequently applies Variational Mode Decomposition (VMD) to extract time-frequency features from equivalent impedance parameters while utilizing Density-Based Spatial Clustering (DBSCAN) for the high-precision identification of operational states in asymmetric loads; and ultimately constructs state-specific harmonic source models by partitioning historical datasets into subsets, substantially improving model generalizability. Simulation and experimental validations demonstrate that the synergistic integration of physical impedance modeling and machine learning methods precisely captures dynamic harmonic characteristics of asymmetric loads, significantly enhancing modeling accuracy, dynamic robustness, and engineering practicality to provide an effective assessment framework for power quality issues caused by harmonic source integration in distribution networks. Full article

Background: hMPXV poses a major public health risk due to its human-to-human transmissibility, severe complications, especially in immunocompromised individuals, and global spread, necessitating effective surveillance and stringent prophylactic measures to mitigate its colossal impact. Objective: The study aimed to annotate hMPXV(IMV) proteins to propose a potential reverse vaccinology-based vaccine against hMPXV. Methods: The target MPXV(IMV) protein’s sequences, formatted in FASTA, were sourced from genome/proteome databases (BV-BRC and UniProt) (accessed on 6 November 2024), followed by CD-Hit-based redundancy removal. Epitope prediction for B-cells (lymphocytes), cytotoxic T-cells or cytotoxic T-lymphocytes (CTLs), and helper T-cells (HTLs) was executed using ABCpred, IEDB’s ANNs 4.0, and an artificial neural network-based alignment tool (NN-align 2.3)/ML-based tool (NetMHCII 2.3). Various immunoinformatics filters (antigenicity, toxicity, and allergenicity) were applied to substantiate the potency and safety of the formulated vaccine candidate. The constructed vaccine’s physiochemical and structural features (secondary and tertiary), with structural stability (confirmed by molecular docking followed by dynamic simulation with TLRs (TLR4 & TLR2) and MHCs), were determined. Additionally, cloning (using pET-28a(+) vector) was conducted to verify the vaccine’s expression potential and translation efficiency. The construct’s population coverage was also ascertained. Results: The MPXV-2-Beta vaccine constructs, of the six initially designed constructs, was identified as the most promising candidate, signifying nonallergenic profile and nontoxic features, with a predicted antigenicity score (PAS) = 0.7202, 407 residues, a molecular weight of 43,102.1 Da, pI of 9.2, and favorable stability parameters (AI: 65.65, GRAVY: −0.597, I-i: 25.92). It showed high solubility (score: 0.942). The ProSA Z-score of −9.38 confirmed the structural stability, reliability, and precision of the MPXV-2-Beta 3D model, which is comparable to experimental structures. Furthermore, 98.8% of all the residues nested within favored or allowed regions in a critical Ramachandran plot signified the model’s exceptional structural integrity and quality. Docking and dynamic simulation of MPXV-2-Beta with TLRs (TLR4 & TLR2) and MHCs demonstrated stiffer docking stability (strong polar and nonpolar interaction) and negative eigenvalue value (during dynamic simulation), suggesting its ability to enhance immune receptor activation under physiological conditions. MPXV-2-Beta was predicted to trigger a robust immune response (IR) with comprehensive world population coverage (98.55%, SD = 10.41). Conclusions: Based on the evaluated parameters, the MPXV-2-Beta designed in this study exhibited significant potential as an effective candidate against hMPXV. This study establishes a foundation for developing an efficient vaccine against hMPXV, requiring further experimental and clinical validation to confirm computational findings. Full article

Over the past few decades, awareness has risen substantially about the limitations of non-renewable resources and the environmental challenges facing the chemical industry. This has necessitated a transition toward renewable resources, such as lignocellulosic biomass, which is among the most abundant renewable carbon sources on the planet. Lignocellulosic biomass represents a significant yet often underutilized source of fermentable sugars and lignin, with potential applications across multiple sectors of the chemical industry. The formation of humins (polymeric byproducts with a complex conjugated network, comprising furanic rings and various functional groups, including ketones) occurs inevitably during the hydrothermal processing of lignocellulosic biomass. This study presents the use of humin byproducts derived from soybean peels for the production of fluorescent carbon dots (CDs). A comparison between sonochemical and thermochemical methods was conducted for the synthesis of this nanomaterial. The obtained nanoparticles were characterized in terms of size, morphology (TEM, DLS), and Z-potential. Subsequently, the spectroscopic properties of the prepared CDs were studied using absorption and emission spectroscopy. In particular, the CDs displayed a blue/cyan fluorescence under UV irradiation. The emission properties were found to be dependent on the excitation wavelength, shifting to longer wavelengths as the excitation wavelength increased. The carbon dots that exhibited the most favorable photochemical properties (QY = 2.5%) were those produced through a sonochemical method applied to humins obtained from the dehydration of soybean husks with phosphoric acid and prior treatment. Full article

Sinopodophyllum hexandrum (Royle) Ying, the only species of Sinopodophyllum in Berberidaceae, is an endangered traditional Tibetan medicine. The harsh plateau growth environment makes S. hexandrum tough to breed and meet the global demand for clinical medications such as podophyllotoxin (PTOX) and etoposide. Jasmonic acid (JA) is acknowledged as a key phytohormone that modulates stress responses by activating defense mechanisms and promoting the production of specialized metabolites, which offers valuable insights for developing varieties that are more resilient to stress or yield higher amounts of secondary metabolites. In this study, JA treatment was used as a simulated source of stress to investigate the spatiotemporal changes in phytohormones, such as JA, cis-(+)-12-oxo-10, 15(Z)-phytodienoic acid (cis-(+)-OPDA), and abscisic acid (ABA), and transcriptional regulation following hormonal regulation in intact plants. Some correlations through changes in phytohormone levels and the expression level of related signaling pathway genes were observed to confirm the overall regulatory effect after the JA treatment. Furthermore, the JA treatment caused the differential expression of various genes including transcription factors (TFs), of which the most typical one is myelocytomatosis oncogene like protein 2 (MYC2), ShMYC2_3. Therefore, we proposed that a plant hormone-mediated regulatory network exists endogenously in S. hexandrum, enabling it to respond to JA treatment. This study provides a new direction for the germplasm improvement and the sustainable utilization of S. hexandrum when facing exogenous stimulation. Full article

The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a constrained muon flux, leading to prolonged acquisition times, noisy reconstructions, and challenges in image interpretation. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images were able to match the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated a noise improvement to that of 31 days worth of sampling. A second cWGAN-GP model, trained for semantic segmentation, was developed to quantitatively assess the upsampling model’s impact on each of the features within the concrete samples. This model was able to achieve segmentation of rebar grids and tendon ducts embedded in the concrete, with respective Dice–Sørensen accuracy coefficients of 0.8174 and 0.8663. This model also revealed an unexpected capability to mitigate—and in some cases entirely remove—z-plane smearing artifacts caused by the muography’s inherent inverse imaging problem. Both models were trained on a comprehensive dataset generated through Geant4 Monte Carlo simulations designed to reflect realistic civil infrastructure scenarios. Our results demonstrate significant improvements in both acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications. Full article

The radio source J0028+0035 is a recently discovered double–double radio galaxy at redshift $z=0.398$. Its relic outer lobes are separated by about $3^{\prime }$ in the sky, corresponding to ∼1 Mpc projected linear size. Inside this large-scale structure, the inner pair of collinear lobes span about 100 kpc. In the arcsec-resolution radio images of J0028+0035, there is a central radio feature that offers the intriguing possibility of being resolved into a pc-scale, third pair of innermost lobes. This would make this radio galaxy a rare triple–double source where traces of three distinct episodes of radio activity could be observed. To reveal the compact radio structure of the central component, we conducted observation with the European Very Long Baseline Interferometer Network and the enhanced Multi Element Remotely Linked Interferometer Network. Our $1.66$ GHz image with high (∼5 milliarcsec) resolution shows a compact central radio core with no indication of a third, innermost double feature. The observation performed in multi-phase-centre mode also revealed that the physically unrelated but in projection closely separated background source 5BZU J0028+0035 has a single weak, somewhat resolved radio feature, at odds with its blazar classification. Full article

Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m² solar farm under shaded conditions. Full article

To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a dead zone setting. Additionally, its cascaded multilevel structure enables independent control of each power unit structure without capacitor voltage sharing problems. Secondly, this study proposes a current-predictive control strategy to reduce current harmonics on the grid side. Moreover, the feedback model of current and system state is established, and the fast control of grid-connected current is realized with the deadbeat control weighted by the predicted current deviation. And a grid-side inductance parameter identification is added to improve control accuracy. Also, an improved multi-carrier phase-shifted sinusoidal PWM method is adopted to address the issue of switching frequency doubling, which is caused by the shoot-through zero vector in quasi-Z-source inverters. Finally, the problems of switching frequency doubling and high harmonics on the grid side are solved by the improved deadbeat control strategy with an improved MPSPWM method. And a seven-level simulation model is built in MATLAB (2022b) to verify the correctness and superiority of the above theory. Full article

Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models. Full article

To reduce the burden of electric vehicle (EV) charging power requirements, photovoltaic (PV) infrastructure EV charging has grown in recent years. The Z-Source Inverter (ZSI) allows tapping the boosted DC and AC by adjusting the switching shoot-through. However, it has only one DC tapping, thus limiting multiport charging options. This can be overcome by splitting the boosting capacitors used at the load terminal, which supports multiple charging ports, enabling simultaneous charging of multiple EVs, thereby increasing capacity and improving overall system efficiency. This paper presents a novel PV-tied Adaptable Z-Source Inverter (AZSI) for multiport EV charging. The modified split capacitor Z-source impedance networks ensure power availability at the charging station by regulating PV generation and grid supply. The performance of the AZSI was evaluated with experimentations that achieved an efficiency of 93.8% with three charging ports. This work contributes to developing sustainable and efficient charging infrastructure to meet the growing demands of the electric vehicle market. Full article

This article presents the basic principles for an Extremely Low Frequency (ELF) IoT-based isotropic meter implementation, which can measure magnetic flux density from 100 nT up to 10 μT. The identical sensor probes are used for isotropic field measurements in the X, Y, and Z planes. The prototype has a flat response across the frequency range from 40 Hz to 10 kHz, detecting and measuring several magnetic field sources. The proposed low-cost meter can measure fields from the power supply network and its harmonic frequencies in the operating frequency band. The proposed magnetic flux density meter circuit is simple to implement and the measured field can be displayed on any mobile device with Wi-Fi connectivity. An Arduino board with the embedded Wi-Fi Nina module is responsible for data transferring from the sensor to the cloud as a complete IoT solution, supported by the Blynk application via Android and iOS operating systems or web interface. In addition, an ELF energy harvesting (EH) circuit was also proposed in our study for the utilization of the alternating magnetic fields (50 Hz) derived from the operation of several consumer devices such as transformers, power supplies, hair dryers, etc. using low-consumption applications. Experimental measurements showed that the (DC) harvesting voltage can reach up to 4.2 volts from the magnetic field of 33 μΤ, caused by the operation of an electric hair dryer and can fully charge the 100 μF storage capacitor (C_s) of the proposed EH system in about 3 min. Full article

Ginkgo biloba L. stands as one of the oldest living tree species, exhibiting a diverse range of biological activities, including antioxidant, neuroprotective, anti-inflammatory, and cardiovascular activities. As part of our ongoing discovery of novel bioactive components from natural sources, we directed our focus toward the investigation of potential bioactive compounds from G. biloba fruit. The profiles of its chemical compounds were examined using a Global Natural Products Social (GNPS)-based molecular networking analysis. Guided by this, we successfully isolated and characterized 11 compounds from G. biloba fruit, including (E)-coniferin (1), syringin (2), 4-hydroxybenzoic acid 4-O-β-D-glucopyranoside (3), vanillic acid 4-O-β-D-glucopyranoside (4), syringic acid 4-O-β-D-glucopyranoside (5), (E)-ferulic acid 4-O-β-D-glucoside (6), (E)-sinapic acid 4-O-β-D-glucopyranoside (7), (1′R,2′S,5′R,8′S,2′Z,4′E)-dihydrophaseic acid 3′-O-β-D-glucopyranoside (8), eucomic acid (9), rutin (10), and laricitrin 3-rutinoside (11). The structural identification was validated through a comprehensive analysis involving nuclear magnetic resonance (NMR) spectroscopic data and LC/MS analyses. All isolated compounds were evaluated using an E-screen assay for their estrogen-like effects in MCF-7 cells. As a result, compounds 2, 3, 4, 8, and 9 promoted cell proliferation in MCF-7 cells, and these effects were mitigated by the ER antagonist, ICI 182,780. In particular, cell proliferation increased most significantly to 140.9 ± 6.5% after treatment with 100 µM of compound 2. The mechanism underlying the estrogen-like effect of syringin (2) was evaluated using a Western blot analysis to determine the expression of estrogen receptor α (ERα). We found that syringin (2) induced an increase in the phosphorylation of ERα. Overall, these experimental results suggest that syringin (2) can potentially aid the control of estrogenic activity during menopause. Full article

This paper proposes a Litz winding numerical-simulation model considering the transposition effect, and uses the transient-plane-source method to verify the numerical-simulation method. In addition, numerical methods were adopted to further investigate the impact of filling rate and epoxy-resin type, and their combined effects, on thermal conductivity. To facilitate engineering design, the discrete data points were fitted using the least square method to obtain a straightforward and application-friendly polynomial empirical formula. On this basis, the GA-BP neural network was used to analyze the data in order to seek out more accurate prediction results for the entire data set. As a result, compared with the least square method, the error between the prediction result and the target value in the $\mathrm{x}$ direction was reduced by 87.04%, and the error in the $\mathrm{z}$ direction was reduced by 84.97%. Full article