Search Results (4,142)

Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R² = 0.71, RMSE = 0.0295 m³/m³) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling. Full article

Ultra-short microwave pulses with durations of 1 ns or less can go through a PIN limiter with low attenuation before the limiter responds and can therefore be directly injected into the subsequent low-noise amplifier (LNA). Based on this consideration, this paper reports the first investigation of degradation and damage in the GaAs pseudomorphic high-electron-mobility transistors (pHEMT) LNA induced by high-power ultra-short microwave pulses. The effects of pulse power, frequency, and repetition rate on the degradation and damage mechanisms of the LNA are investigated. The results show that, when exposed to a 45 dBm ultra-short microwave pulse at 2 GHz, the region beneath the transistor gate on the drain side experiences the most rapid burnout within 43 ns at a repetition rate of 200 MHz. Further analysis reveals that the negative half-cycle of the high-power microwave pulse depletes the electron concentration in the transistor. Consequently, excessively high repetition rates of high-power ultra-short microwave pulses are less likely to induce transistor burnout. Furthermore, the gain of the LNA is affected by both the electron concentration and the electron mobility in the transistor. The injected high-power ultra-short microwave pulses disturb the electron concentration, while the temperature rise reduces electron mobility. As a result, the transistor performance degrades, leading to a decrease in the LNA gain. Full article

Traditional microwave-absorbing metasurfaces struggle to integrate optical transparency with ultra-wideband and high-efficiency microwave absorption, severely limiting their deployment in optoelectronics-compatible electromagnetic protection. To address this constraint, we propose a transparent ultra-wideband microwave-absorbing metasurface based on multilayered indium tin oxide (ITO) films. The unit cell is constructed using a multilayered PMMA dielectric configuration, where ITO conductive layers are patterned as a top square patch, middle square rings, and a continuous bottom film. Full-wave parametric optimization, combined with multilayer resonant coupling, effectively extends the absorption bandwidth. We elucidate the underlying broadband absorption mechanism by analyzing electromagnetic field and surface current distributions at typical resonant frequencies. Furthermore, we systematically investigate the impacts of ITO sheet resistance, incident angle, and polarization state on absorption performance. A 6 × 6 array prototype is fabricated and experimentally characterized in a microwave anechoic chamber. The measured results demonstrate that the proposed metasurface achieves an absorptance exceeding 90% across 9.2–40.2 GHz, delivering a fractional bandwidth of 125.5%. The experimental responses are in good agreement with numerical simulations, and the fabricated prototype retains good optical transparency. Benefiting from the synergistic integration of ultra-wideband microwave absorption and superior optical transmissivity, this multilayer stacked metasurface offers a promising strategy for advanced optoelectronics-compatible stealth and transparent electromagnetic shielding applications. Full article

Pomegranate is increasingly being recognized as a versatile source of bioactive compounds with antimicrobial, antioxidant, anti-inflammatory, and microbiota-modulating potential. Beyond the edible arils and juice, peel, seeds, leaves, flowers, and other agro-industrial by-products are rich in ellagitannins, particularly punicalagins, as well as ellagic acid, gallic acid, flavonoids, anthocyanins, fatty acids, and related phytochemicals. Their recovery depends strongly on plant fraction, cultivar, solvent, and extraction technology, including conventional hydroalcoholic extraction, ultrasound- and microwave-assisted processes, high-pressure treatments, and enzyme-assisted methods. Pomegranate-derived preparations exhibit activity against Gram-positive and Gram-negative bacteria, fungi, multidrug-resistant pathogens, and microbial biofilms, while selected compounds may enhance the efficacy of conventional antimicrobials. Emerging evidence also indicates bidirectional interactions with microbial communities, including microbial biotransformation of ellagitannins into urolithins and modulation of beneficial taxa and microbial metabolites. This narrative review integrates agronomic and phytochemical diversity, extraction and standardization strategies, mechanisms of antimicrobial action, synergistic interactions, microbiota-related effects, and applications in food preservation, biomedicine, animal nutrition, agriculture, and environmental sustainability. Key translational limitations include compositional variability, methodological heterogeneity, insufficient standardization, limited in vivo validation, and scarce evidence from realistic application models and clinical studies. Addressing these gaps is essential for developing safe, reproducible, and scalable pomegranate-derived preparations. Full article

Accurate prediction of qubit–qubit exchange coupling is essential for superconducting processor design. We present and benchmark an automated full-wave electromagnetic (EM) workflow in Keysight Advanced Design System (ADS) and QuantumPro that evaluates pairwise exchange couplings from layout-level multiport responses using the established transfer-impedance relation of Solgun et al. Because many scalable superconducting processors employ repeated or near-repeated coupling motifs, reduced EM models can lower computational cost if their error is quantified. We study nearest-neighbor couplings in a reconstructed geometry based on the IBM Q 16 Rueschlikon processor using a sixteen-qubit (16Q) coupling-network model, four-qubit (4Q) unit-cell models, and pair-specific two-qubit (2Q) sub-blocks. The 4Q models provide a like-for-like external benchmark because they match the model extent used by Solgun et al. for the published full-wave coupling calculations. The 4Q models reproduce the published simulated couplings with a 3.70% mean absolute percentage error and differ from 16Q by 3.62% on average. For the representative single solves reported here, the 16Q solve takes approximately 7.2 times as long as 4Q and requires 5.7 times the peak memory. This supports 4Q as a practical reduced model for quantitative local coupling prediction. Further reduction to 2Q changes the couplings by 6.17% on average but remains effective for inexpensive mesh-convergence screening. The resulting workflow provides automated exchange-coupling extraction together with model-extent and convergence assessment in a commercial microwave EDA environment. Full article

Objective: This study aimed to develop and optimize a rapid, eco-friendly microwave-assisted extraction (MAE) process for recovering total phenolic content (TPC) and total flavonoid content (TFC) from garlic (Allium sativum L.), while evaluating the predictive performance of response surface methodology (RSM) versus artificial neural networks (ANNs) and assessing the in vitro gastrointestinal stability of key polyphenols. Methodology: A four-factor, three-level central composite design (CCD) was implemented to evaluate the effects of extraction time, temperature, ethanol concentration, and solvent-to-solid ratio. A second-order polynomial RSM model was benchmarked against a 4-10-2 multilayer perceptron ANN trained by backpropagation. Optimal conditions were derived using the Derringer–Suich desirability function and confirmed experimentally. Individual polyphenols were profiled via LC-MS/MS and monitored across simulated oral, gastric, and intestinal digestion phases. Principal Results: The ANN model demonstrated superior predictive performance (R² = 0.9999 training, 0.9974 validation, 0.9939 testing) compared to the RSM model (R² = 0.9721 for TPC and 0.9925 for TFC). Experimental validation under optimal conditions—1.50 min, 55 °C, 75% ethanol, and a 29 mL/g ratio—yielded a TPC of 2.487 mg GAE/g FW and a TFC of 21.356 mg QUE/g FW. During simulated gastrointestinal digestion, significant degradation occurred during the intestinal phase, resulting in low final recoveries for gallic acid (16.9%), caffeic acid (19.9%), and luteolin (22.6%). Conclusions: MAE coupled with ANN modeling provides a highly accurate, rapid, and green extraction strategy for garlic polyphenols. However, the marked degradation of target compounds during intestinal digestion highlights the necessity of encapsulation or protective delivery systems to preserve their biological functionality in food applications. Full article

Multi-objective optimization has become an indispensable tool for solving engineering design and manufacturing problems involving multiple conflicting objectives. This paper presents a novel parameter-less multi-objective optimization (MOO) framework that combines the strengths of evolutionary MOO techniques with the parameter-free search philosophy of the Jaya and Rao algorithms. The proposed framework incorporates non-dominated sorting, elite archiving, and crowding-distance mechanisms to achieve an effective balance between convergence and diversity while eliminating the need for algorithm-specific control parameters. The proposed framework is first validated on sixteen widely used unconstrained benchmark problems comprising five ZDT, seven DTLZ, two IDTLZ, and two SDTLZ test suites using the maximum number of function evaluations reported in the literature. Its performance is evaluated using five widely accepted quality indicators, namely Generational Distance (GD), Inverted Generational Distance (IGD), Hypervolume (HV), Spacing (SP), and Spread (SD). The benchmark results demonstrate that the proposed framework produces competitive Pareto-optimal fronts and exhibits excellent convergence, diversity, and solution distribution compared with several state-of-the-art evolutionary multi-objective optimization algorithms. The practical applicability of the proposed framework is demonstrated through five representative manufacturing optimization problems involving Selective Laser Melting, Microwave Hybrid Heating, Sustainable Machining, Wire Electrical Discharge Machining, and Wire Arc Additive Manufacturing. These case studies encompass additive, thermal, subtractive, and many-objective manufacturing optimization problems with conflicting performance measures. The generated Pareto-optimal solutions are subsequently ranked using the recently developed BHARAT (Best Holistic Adaptable Ranking of Attributes Technique) multi-attribute decision-making method to identify the most suitable compromise solutions. The obtained results demonstrate that the proposed parameter-less MOO framework provides a simple approach with competitive convergence, diversity, and decision-support capabilities for complex manufacturing optimization problems. Full article

This study utilizes the 89 GHz dual-polarization channel of the Ground-Based Multi-Frequency and Dual-Polarization Microwave Radiometer (GMD-MR) to overcome the challenges posed by the insensitivity of low-frequency microwave channels to cloud ice particles. By integrating data from Micro-Rain Radars (MRRs), we developed and implemented advanced convolutional and deep learning models. These models leverage brightness temperature, polarization differences, and constraints from cloud and precipitation data to quantitatively estimate cloud ice content, cloud water content profiles, rainwater content profiles, and precipitation rates, achieving correlation coefficients of 0.6, 0.7, 0.84, and 0.84, respectively. Our analysis of the spatiotemporal dynamics of ice water, cloud liquid water, and rain liquid water paths during precipitation events highlights their predictive value for precipitation occurrence. With a prediction accuracy of 97% and a temporal correlation coefficient of 0.9, our findings affirm the effectiveness of ground-based radiometers and micro-rain radars in precipitation detection. This study demonstrates the capability of multi-instrument joint retrieval for various meteorological parameters, highlighting the significant potential of multi-source microwave data fusion in quantitative precipitation estimation. It establishes and reinforces the foundation for future investigations into the physical processes of precipitation evolution. Full article

This study proposes a 2.45 GHz low-power microwave drilling system for precise bone drilling near sensitive organs with a tunable parallel metal impedance-matching sleeve. The system is based on a quarter-wavelength coaxial resonant cavity integrated with a movable parallel impedance tuning sleeve, which can mitigate impedance mismatch caused by dielectric variations in ablated tissue during drilling. To further ensure matching accuracy and stability, a solid-state source is employed to track the minimum reflection frequency within the operating bandwidth. Broadband electromagnetic and transient thermal simulations are conducted to evaluate the impedance response and thermal characteristics. A prototype microwave drilling system is fabricated and experimentally validated in fresh cartilage tissue, fresh cortical bone, and cooked bovine femur samples. The results demonstrate that the proposed tuning sleeve and adaptive source effectively regulate the response to the reflection coefficient. At 2.45 GHz and an input power of 20 W, the proposed system achieves a measured reflection coefficient below −12 dB and produces a localized drilled region within 10 s. Compared with previously reported microwave drilling systems operating at power levels of up to approximately 200 W and recent microwave drilling studies typically employing tens to hundreds of watts, the proposed system demonstrates bone drilling at a substantially reduced input power, while the integrated tunable sleeve provides structural impedance matching without relying solely on conventional external impedance tuners. Full article

Microwave-driven thermochemical cycles can split water for hydrogen production at temperatures far below those of conventional solar thermochemical routes, yet the responsible physical mechanisms remain unclear and numerical models for the coupled solar-microwave hybrid system are still scarce. Building on previous experimental work, we developed a coupled numerical model that integrates impedance matching, non-thermal enhancement, two-stage Arrhenius kinetics, and energy conservation to systematically investigate the interplay between microwave power, temperature evolution, and reaction progress. The model predictions agree well with experimental data in terms of temperature evolution trends, power threshold ranges, and reaction timescales. The results indicate that, within the present modeling framework, the effective microwave absorption efficiency increases from 1.2% at low temperatures to approximately 14% near 85 °C, with the non-thermal enhancement factor contributing as an empirical parameter. Under pure microwave mode, the required power threshold for reaction initiation is approximately 120 W; the solar-microwave synergistic mode reduces this threshold to about 70 W, a 42% reduction. At an input power of 100 W, the energy conversion efficiency reaches a maximum of 42%. Analysis of the sudden temperature change identifies 85 °C as the critical triggering temperature: below it, the system remains in a low-absorption cold state, while once crossed, a positive feedback mechanism rapidly propels the system into the high-temperature reaction regime. This study provides a numerical modeling framework for describing the coupled solar-microwave thermal behavior of the system and for guiding the optimization of its operational parameters. Since the available measurements cannot independently separate the thermal and non-thermal contributions, the non-thermal enhancement remains an empirically introduced factor rather than an experimentally established physical effect. Full article

Barley (Hordeum vulgare L.) is rich in β-glucan and resistant starch, key functional carbohydrates that are determinants of metabolic health. However, evidence on the effects of household processing on these fractions across barley forms remains limited. This study evaluated soaking and four household thermal processing methods (pressure cooking, microwave cooking, boiling, and dry roasting) on extractable β-glucan and total resistant starch in pearled barley, barley grits and barley flour. Pressure cooking produced the highest observed extractable β-glucan in pearled barley (6.40 ± 0.22 vs. raw 4.24 ± 0.17 g/100 g) and grits (5.80 ± 0.17 vs. raw 5.31 ± 0.27 g/100 g), whereas boiling was associated with highest extractability in flour (5.20 ± 0.17 vs. raw 4.59 ± 0.23 g/100 g). Soaked pressure cooking resulted in greater total resistant starch content in pearled barley (8.02 ± 0.11 vs. raw 5.24 ± 0.04 g/100 g) and grits (5.33 ± 0.09 vs. raw 2.10 ± 0.13 g/100 g), while non-soaked boiling showed a greater response in flour (1.07 ± 0.04 vs. raw 0.22 ± 0.01 g/100 g). The exploratory findings suggest that barley form may influence the response of functional carbohydrates to household thermal processing. Translating these outcomes into metabolic benefits requires further physicochemical, metabolic, and physiological validation. Full article

This paper addresses the need for compact, low-cost soil moisture sensors operating at low microwave frequencies that can provide accurate, texture-aware (i.e., sensitive to different soil particle-size distributions such as sand and loam) characterization of soil permittivity and moisture content. Conventional techniques and many existing microwave resonator sensors are constrained by limited penetration depth, relatively large or complex structures, and calibration procedures that do not robustly account for different soil textures and moisture ranges. A dual-port microstrip square split ring resonator (SRR) sensor on Rogers RO3010 (Rmit University, Melbourne, Australia) is designed for operation at 1.3 GHz and analyzed using full-wave 3D electromagnetic simulations. The structure employs a T-shaped feedline and a shunt quarter-wavelength matching section to achieve strong field confinement in the sensing region and effective impedance matching. Soil is modeled as sandy and loamy superstrates over practical agricultural moisture ranges, with their complex permittivities drawn from reference datasets. Empirical calibration models are then developed, including polynomial curve fitting between resonance frequency shift and real permittivity, machine-learning-based calibration using resonance frequency and transmission loss features, and multiple linear regression linking moisture content to both real and imaginary permittivity components. The sensor exhibits a resonance frequency shift of about 115 MHz over 0–30% moisture for sand and 0–40% for loam, with a maximum sensitivity of 3.4%. Calibration models achieve mean absolute error below 1.22%, root mean square error under 1.58%, and coefficients of determination R² > 0.98 for both soil textures. These results demonstrate that a compact 1.3 GHz square SRR sensor with data-driven calibration, i.e., empirical models learned from simulated and measured S-parameters, enables sensitive, reproducible, and texture-aware soil moisture estimation suitable for agricultural and environmental monitoring. Full article

A CMY palette of ceramic pigments was synthesized using both microwave-assisted firing (800 W, 30 min) and conventional electric firing (1000 °C for 3 h). For the allochromatic vanadium-zircon system (including the non-mineralized green and halide-mineralized blue compositions), as well as the chromium-doped scheelite yellow pigment, microwave firing does not outperform conventional calcination. Although comparable reactions occur during synthesis, microwave firing produces powders with lower colour performance. Nevertheless, these differences become visually negligible after incorporation into glazes. This behaviour can be attributed to the low dopant concentration and the localized, selective heating characteristic of microwave irradiation. In the vanadium-zircon system, the microwave-mineralized sample exhibits features similar to those of the non-mineralized compositions, including lower reactivity, smaller crystallite size, and enhanced blue colour development when applied in glazes. In contrast, for the idiochromatic Zn(Al_1.3Fe_0.5Cr_0.2)O₄ spinel red-brown pigment, microwave firing yields superior colour performance compared with conventional electric firing, producing higher chroma and greater colour intensity. In idiochromatic pigments, the relatively high proportion of chromophore components promotes more homogeneous microwave absorption and heating throughout the precursor mixture. The enhanced colour properties achieved through microwave synthesis may indicate the presence of a beneficial non-thermal microwave effect, leading to improved chromatic performance. Full article

Food processing can modify legume structure and improve its applicability in protein-fortified foods. This study investigated the effects of six pretreatments, including peeling, roasting, atmospheric steaming, high-pressure steaming, sprouting, and microwaving, on the processing characteristics and quality attributes of wheat bread containing 50% red kidney bean flour (RKBF). Results showed that thermal pretreatments reduced the gelatinization enthalpy (ΔH) of composite flours by 4.6–13.2% and increased dough water absorption rate by 5.7–9.7% compared with untreated RKBF. Hydrothermal treatments significantly improved dough rheology and structural integrity by increasing glutenin macropolymer (GMP) content, reducing free sulfhydryl levels, altering protein secondary structure, and optimizing starch and gluten distribution. Atmospheric steaming increased the bread specific volume from 3.37 to 3.53 cm³/g and reduced hardness by 16%. Incorporation of RKBF also reduced rapidly digestible starch from 64.91% in wheat bread to 48.99–56.16% and increased resistant starch from 11.76% to 21.27–25.55%. Moreover, volatile analysis identified 42 compounds, with thermal pretreatments effectively reducing beany flavor compounds. Peeling alleviated dark coloration but further reduced bread volume, whereas sprouting-induced protein degradation adversely affected dough formation and gluten structure. Overall, atmospheric steaming exhibited the best balance between processing performance and nutritional quality, showing a significant improvement in the bread quality containing 50% RKBF. Full article

Nitrogen-containing fused tetracyclic systems, exemplified by the synthetic 11H-indeno[1,2-b]quinoxalin-11-one core and the natural alkaloid tryptanthrin (indolo[2,1-b]quinazolin-6,12-dione), constitute structural scaffolds whose rigid, planar architecture enables high-affinity interaction with nucleic acids and kinase active sites. Converting the exocyclic carbonyls at C-11 and C-6, respectively, into oximes has become a productive strategy in medicinal chemistry. This transformation modulates frontier orbital energies, installs N,O- and N,N-chelating pharmacophores, and enables nitric oxide (NO) release. Here, we summarize current knowledge of the synthesis, stereochemical characterization, and diverse biological activities of these tetracyclic ketoximes and related derivatives. Microwave, sonochemical, visible-light photocatalytic, and multicomponent methods now afford efficient, economical routes to the parent ketones and their oximes. X-ray crystallography, spectroscopy, and density functional theory have firmly established the thermodynamic preference for the E-oxime configuration and clarified how this geometry, along with potential target-induced isomerization, shapes binding. The oximes bind c-Jun N-terminal kinases (JNK1–3) with high affinity, a property that accounts for their neuroprotective effects in models of cerebral ischemia and Alzheimer-like pathology, their dual JNK inhibition and NO-mediated cardioprotection in hypertension and myocardial infarction, and their anti-inflammatory activity via suppression of NF-κB/AP-1 signaling. Broader studies also document anticancer, antimicrobial, antiviral, and antidiabetic activities arising from DNA intercalation, topoisomerase inhibition, metal-ion coordination, and kinase blockade. Compelling preclinical profiles notwithstanding, low oral bioavailability and rapid hepatic clearance remain major pharmacokinetic obstacles. Ongoing work on new formulations, prodrug strategies, and structure–activity optimization seeks to slow systemic elimination. Precise stereochemical definition combined with pleiotropic pharmacology positions tetracyclic ketoximes as attractive candidates for next-generation agents against complex multifactorial diseases. Full article