Search Results (45,105)

To compensate for the instability of renewable energy sources during China’s energy transition, large thermal power plants must provide critical operational flexibility, primarily through deep peaking. To investigate the combustion performance and wear and tear of a 600 MW pulverized coal boiler under deep peaking, the gas–solid flow characteristics and distributions of flue gas temperature, wall heat flux, and wall wear rate in a 600 MW tangentially fired pulverized coal boiler under variable loads (353 MW, 431 MW, 519 MW, and 600 MW) are investigated in this study employing computational fluid dynamics numerical simulation method. Results demonstrate that increasing the boiler load significantly amplifies gas velocity, wall heat flux, and wall wear rate. The maximum gas velocity in the furnace rises from 20.9 m·s⁻¹ (353 MW) to 37.6 m·s⁻¹ (600 MW), with tangential airflow forming a low-velocity central zone and high-velocity peripheral regions. Meanwhile, the tangential circle diameter expands by ~15% as the load increases. The flue gas temperature distribution exhibits a “low-high-low” profile along the furnace height. As the load increases from 353 MW to 600 MW, the primary combustion zone’s peak temperature rises from 1750 K to 1980 K, accompanied by a ~30% expansion in the coverage area of the high-temperature zone. Wall heat flux correlates strongly with temperature distribution, peaking at 2.29 × 10⁵ W·m⁻² (353 MW) and 2.75 × 10⁵ W·m⁻² (600 MW) in the primary combustion zone. Wear analysis highlights severe erosion in the economizer due to elevated flue gas velocities, with wall wear rates escalating from 3.29 × 10⁻⁷ kg·m⁻²·s⁻¹ (353 MW) to 1.23 × 10⁻⁵ kg·m⁻²·s⁻¹ (600 MW), representing a 40-fold increase under full-load conditions. Mitigation strategies, including ash removal optimization, anti-wear covers, and thermal spray coatings, are proposed to enhance operational safety. This work provides critical insights into flow field optimization and wear management for large-scale coal-fired boilers under flexible load operation. Full article

Magnetic nanowires with domain walls (DWs) play a crucial role in the advancement of next-generation memory and spintronic devices. Understanding the thermal effects on domain wall behavior is essential for optimizing performance and stability. This study investigates the thermal chirality-dependent dynamics and pinning of transverse domain walls (TDWs) in Z-junction nanowires using micromagnetic simulations. The analysis focuses on head-to-head (HHW) and tail-to-tail (TTW) domain walls with up and down chirality under varying thermal conditions. The results indicate that higher temperatures reduce the pinning strength and depinning current density, leading to enhanced domain wall velocity. At 200 K, the HHW_down domain wall depins at a critical current density of 1.2 × 10¹¹ A/m², while HHW_up requires a higher depinning temperature, indicating stronger pinning effects. Similarly, the depinning temperature (T_d) increases with Z-junction depth (d), reaching 300 K at d = 50 nm, while increasing Z-junction (λ) weakens pinning, reducing T_d to 150 K at λ = 50 nm. Additionally, the influence of Z-junction geometry and magnetic properties, such as saturation magnetization (M_s) and anisotropy constant (K_u), is examined to determine their effects on thermal pinning and depinning. These findings highlight the critical role of chirality and thermal activation in domain wall motion, offering insights into the design of energy-efficient, high-speed nanowire-based memory devices. Full article

Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020–2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from ${10}^{-3}$ to ${10}^{-6}$. Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional–recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems. Full article

Metabolome studies have already been carried out in patients with systemic sclerosis (SSc). However, polyneuropathy (PNP) as a complication of SSc has been overlooked in these studies. To the best of our knowledge, this is the first study to examine metabolic changes in SSc patients with PNP. Patients with SSc (n = 62) and a healthy control group (HC) (n = 72) were recruited from two Latvian hospitals. Blood plasma samples were collected and analyzed using an LC-MS-based targeted metabolomics workflow. Our plasma sample cohort consisted of 62 patients with SSc, 42% of whom had PNP. Differences between SSc patients and the HC group with fold changes > 2 were observed for aspartic acid, glutamic acid, valine, and citrulline, all of which were reduced. In contrast to the SSc to HC discrimination, no metabolites had a high fold change; only minor changes were observed using FC > 1.3. We identified elevated concentrations of kynurenine, asparagine, and alanine. Changes in metabolite regulation in patients with SSc, compared to controls, are not identical to those observed in SSc patients with PNP, with elevated concentrations of kynurenine and alanine specific to the SSc subgroup. SSc patients with PNP should probably be considered a distinct population with important metabolomic features. Full article

Background/Objectives: The rapid emergence of antibiotic-resistant Escherichia coli in food and clinical environments necessitates new, clean-label antimicrobials. This study assessed eight Greek native essential oils—oregano, thyme, dittany, rosemary, peppermint, lavender, cistus and helichrysum—for activity against six genetically and phenotypically diverse E. coli strains (reference, pNorm, mecA, mcr-1, blaOXA and O157:H7). We aimed to identify oils with broad-spectrum efficacy and clarify the chemical constituents responsible. Methods: Disk-diffusion assays measured inhibition zones at dilutions from 50% to 1.56% (v/v). MIC and MBC values were determined by broth microdilution. GC–MS profiling identified dominant components, and Spearman rank-order correlations (ρ) linked composition to activity. Shapiro–Wilk tests (W = 0.706–0.913, p ≤ 0.002) indicated non-normal data, so strain comparisons used Kruskal–Wallis one-way ANOVA with Dunn’s post hoc and Bonferroni correction. Results: Oregano, thyme and dittany oils—rich in carvacrol and thymol—exhibited the strongest activity, with MIC/MBC ≤ 0.0625% (v/v) against all strains and inhibition zones > 25 mm at 50%. No strain-specific differences were detected (H = 0.30–3.85; p = 0.998–0.571; p_adj = 1.000). Spearman correlations confirmed that carvacrol and thymol content strongly predicted efficacy (ρ = 0.527–0.881, p < 0.001). Oils dominated by non-phenolic terpenes (rosemary, peppermint, lavender, cistus, helichrysum) showed minimal or no activity. Conclusions: Phenolic-rich EOs maintain potent, strain-independent antimicrobial effects—including against multidrug-resistant and O157:H7 strains—via a multi-target mode that overcomes classical resistance. Their low-dose efficacy and GRAS status support their use as clean-label food preservatives or adjuncts to antibiotics or bacteriophages to combat antimicrobial resistance. Full article

This study presents a scenario-based, two-dimensional flood modeling approach to assess the potential downstream impacts of a piping-induced dam failure triggered by seismic activity. The case study focuses on the Doğantepe Dam in northwestern Türkiye, located near an active branch of the North Anatolian Fault. Critical deformation zones were previously identified through PLAXIS 2D seismic analyses, which served as the physical basis for a dam break scenario. This scenario was modeled using the HEC-RAS 2D platform, incorporating high-resolution topographic data, reservoir capacity, and spatially varying Manning’s roughness coefficients. The simulation results show that the flood wave reaches downstream settlements within the first 30 min, with water depths exceeding 3.0 m in low-lying areas and flow velocities surpassing 6.0 m/s, reaching up to 7.0 m/s in narrow sections. Inundation extents and hydraulic parameters such as water depth and duration were spatially mapped to assess flood hazards. The study demonstrates that integrating physically based seismic deformation data with hydrodynamic modeling provides a realistic and applicable framework for evaluating flood risks and informing emergency response planning. Full article

Background: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain–computer interface (BCI), functional electrical stimulation (FES), and a robotic device. Methods: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions. Results: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system. Conclusions: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system. Full article

This study introduces an algorithm for quantifying oceanic wind speed and identifying sound sources in the local underwater soundscape. Utilizing low-complexity metrics like one-minute spectral kurtosis and power spectral density levels, the algorithm categorizes different soundscapes and estimates wind speed. It detects rain, vessels, fin and blue whales, as well as clicks and whistles from dolphins. Positioned as a foundational tool for implementing the Ocean Sound Essential Ocean Variable (EOV), it contributes to understanding long-term trends in climate change for sustainable ocean health and predicting threats through forecasts. The proposed soundscape classification algorithm, validated using extensive acoustic recordings (≥32 kHz) collected at various depths and latitudes, demonstrates high performance, achieving an average precision of $89\%$ and an average recall of $86.59\%$ through optimized parameter tuning via a genetic algorithm. Here, wind speed is determined using a cubic function with power spectral density (PSD) at 6 kHz and the MASLUW method, exhibiting strong agreement with satellite data below 15 m/s. Designed for compatibility with low-power electronics, the algorithm can be applied to both archival datasets and real-time data streams. It provides a straightforward metric for ocean monitoring and sound source identification. Full article

Background: Diabetes and cancer are two of the most life-threatening disorders affecting individuals of all ages worldwide. This study aimed to develop a novel Acrocomia aculeata (bocaiuva) fruit pulp oil-loaded nanoemulsion and evaluate its inhibitory effects on α-glucosidase and pancreatic lipase, as well as its antiglycant activity and cytotoxicity against cancer cells. Additionally, this study assessed the impact of both the oil and the nanoemulsion on blood cells. Methods: The pulp oil was extracted by cold pressing. The oil’s physicochemical properties were determined according to the AOAC and the Brazilian Pharmacopeia. The lipid profile was performed by GC-MS. The nanoemulsion was prepared by the phase inversion method using ultrasonic stirring for particle size reduction and for homogenization. Response Surface Methodology was used for optimizing nanoemulsion preparation. Enzyme inhibition tests were conducted using assay kits. Cytotoxicity in cancer cells was evaluated using the Sulforhodamine B assay. Results: Comprehensive physicochemical and chemical characterization of bocaiuva oil was performed, identifying oleic acid (71.25%) as the main component. The oil contains 23.04% saturated fatty acids, 73.79% monounsaturated acids, and 3.0% polyunsaturated fatty acids. The nanoemulsion (particle size 173.6 nm; zeta potential −14.10 mV) inhibited α-glucosidase (IC₅₀: 43.21 µg/mL) and pancreatic lipase (IC₅₀: 41.99 µg/mL), and revealed a potent antiglycation effect (oxidative IC₅0: 18.36 µg/mL; non-oxidative pathway IC₅₀: 16.33 µg/mL). The nanoemulsion demonstrated good cytotoxicity and selectivity against prostate cancer cells (IC₅₀: 19.13 µg/mL) and breast cancer cells (IC₅₀: 27.22 µg/mL), without inducing hemolysis, platelet aggregation, or anticoagulant effects. Conclusions: In this study, a comprehensive physical and chemical characterization of bocaiuva fruit pulp oil was conducted for the first time as a preliminary step toward its future standardization as an active ingredient in cosmetic and pharmaceutical formulations. The resulting nanoemulsion represents a novel alternative for managing diabetes and cancer. Although the nanoemulsion exhibited lower cytotoxicity compared to doxorubicin, it remains promising due to its composition of essential fatty acids, phenols, and carotenoids, which offer multiple health benefits. Further studies are needed to validate its efficacy and safety in clinical applications. Full article

Background/Objectives: Gut dysbiosis has been implicated in multiple sclerosis (MS), but microbial signatures remain inconsistent across studies. Machine learning (ML) algorithms based on global microbiome data integration can reveal key disease-associated microbial biomarkers and new insights into MS pathogenesis. This study aimed to investigate gut microbial signatures associated with MS and to evaluate the potential of ML for diagnostic applications. Methods: Fecal samples from 29 relapsing–remitting MS patients during exacerbation and 27 healthy controls were analyzed using 16S rRNA gene sequencing. Differential abundance analysis was performed, and data were integrated with 29 published studies. Four ML models were developed to distinguish MS-associated microbiome profiles. Results: MS patients exhibited reduced levels of Eubacteriales (p = 0.037), Lachnospirales (p = 0.021), Oscillospiraceae (p = 0.013), Lachnospiraceae (p = 0.012), Parasutterella (p = 0.018), Faecalibacterium (p = 0.004), and higher abundance of Lachnospiraceae UCG-008 (p = 0.045) compared to healthy controls. The Light Gradient Boosting Machine classifier demonstrated the highest performance (accuracy: 0.88, AUC-ROC: 0.95) in distinguishing MS microbiome profiles from healthy controls. Conclusions: This study highlights specific microbiome dysbiosis in MS patients and supports the potential of ML for diagnostic applications. Further research is needed to elucidate the mechanistic role of these microbial alterations in MS progression and their therapeutic utility. Full article

Two-component dental materials are commonly used by the dentist for various applications (cementation of indirect restorations, filling of a cavity without layering, etc.). These materials are cured by redox polymerization. The (hydro)peroxide/thiourea/copper salt redox initiator system is well established and can be found in a wide range of commercially available dental materials. The thiourea is a key component of the initiator system. This study explores the influence of the nature of the thiourea reducing agent on the reactivity and efficiency of redox initiator systems. In this work, six different thiourea structures were investigated, in combination with copper(II) acetylacetonate and cumene hydroperoxide (CHP), to understand their impact on polymerization kinetics and mechanical properties of methacrylate-based materials. Various experimental techniques, including mass spectrometry (MS) and spectroscopic analyses, were employed to elucidate the underlying mechanisms governing these redox systems. The results highlight that thiourea plays a dual role, acting both as a reducing agent and as a ligand in copper complexes, affecting radical generation and polymerization efficiency. Structural modifications of thiourea significantly influence the initiation process, demonstrating that reactivity is governed by a combination of factors rather than a single property. Self-cure dental flowable composites exhibiting excellent flexural strength (>100 MPa) and modulus (>6000 MPa) were obtained using hexanoyl thiourea, N-benzoylthiourea, or 1-(pyridin-2-yl)thiourea as a reducing agent. The adjustment of the Cu(acac)₂ enables to properly set the working time in the range of 100 to 200 s. These findings provide valuable insights into the design of the next generation of redox initiating systems for mild and safe polymerization conditions. Full article

Aftershocks are inevitable phenomena following a mainshock, especially after a major earthquake. However, the cumulative damage caused by aftershocks and its impact on structural performance evaluation has only recently received significant attention. This study explores the effects of mainshock–aftershock (MS–AS) sequences, including multiple consecutive aftershocks, acting on 3D steel moment-resisting frame structures. Following nonlinear time history analysis, several fundamental variables such as residual interstory drift, maximum displacement, plastic hinge formation, and base shear are evaluated to examine cumulative damage. In this context, the findings depicted in terms of aftershocks play a significant role in exacerbating plastic deformations and damage accumulation in steel moment frames. Subsequently, to mitigate cumulative damage on steel moment frames, retrofitting strategies were implemented. Retrofitting strategies effectively reduce cumulative damage and improve seismic resilience under multiple earthquake events. This research highlights the limitations of single-event seismic assessments and the need to incorporate sequential earthquake effects in design and retrofit practices. Furthermore, it provides new insights into mitigating further damage by retrofitting existing structures under multiple earthquakes. Full article

The holistic use of Moringa oleifera Lam. seeds is not as popular amongst rural South Africans. This study screened for the phytochemicals, antimicrobial, and antioxidant potentials as well identifying the compounds in the oils of South African Moringa seed oils using cost-effective thin layer chromatography bioautography and dot blot assays, because fewer studies have been conducted using seed samples from this country. The results obtained indicated that the best oil extract yield (24.04%) was obtained for hexane from 60.10 g of powdered seeds. The yield of the other extracts ranged from 6.2 to 9.5%. Positive test results were obtained for terpenoids, steroids, alkaloids, flavonoids, phenols, and tannins, with potentially good antioxidant properties for scavenging free radicals from 2,2-diphenyl-1-picrylhydrazyl (DPPH) and good antimicrobial activity against Acinetobacter baumannii (BAA 747), Escherichia coli (ATCC 25922), Klebsiella pneumoniae (ATCC 27853), and Pseudomonas aeruginosa (ATCC 27853), with the best zone of inhibition of 314.2 mm² obtained for oil extracted with hexane, followed by dichloromethane, methanol, and acetone oil extracts, respectively. The best minimum inhibitory concentration (MIC) of 0.032 mg/mL against P. aeruginosa was recorded for the hexane oil, compared with ciprofloxacin, which had an MIC of 0.0039 mg/mL against the same pathogen. The identification of the in-oil compounds proposed to mitigate inhibitory activity against the test microbes was carried out through GC-MS analysis matching our results with the GC-MS library. These compounds included ursane-3,16-diol, azetidin-2-one, 1-benzyl-4à-methyl, dibutyl phthalate, 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene, 1H-pyrrole-2,5-dione, 3-ethyl-4-methyl, octopamine rhodoxanthin, 29,30-dinorgammacerane-3,22-diol, 21,21-dimethy, cholan-24-oic acid, 3,7-dioxo, and benzyl alcohol. These are in addition to the stability-indicating marker compounds like oleic acid (54.9%), 9-Octadecenoic acid (z)-, methyl ester (23.3%), n-hexadecanoic acid (9.68%), among others observed over a five year period. Full article

Aquilaria sinensis (Lour.) Gilg, the exclusive botanical source of Chinese agarwood, holds significant medicinal value. This study investigated the agarwood-inducing potential of a Fusarium strain obtained through prior isolation work. Through integrated morphological characterization and molecular phylogenetic analysis, the strain was conclusively identified as Fusarium equiseti. GC-MS analysis revealed that fungal inoculation induced the synthesis of characteristic sesquiterpenes and aromatic compounds consistent with natural agarwood profiles. Quantitative determination demonstrated progressive accumulation of agarotetrol, a key quality marker, reaching 0.034%, 0.039%, and 0.038% at 2, 4, and 6 months post-inoculation, respectively—significantly exceeding levels from physical wounding (p < 0.05) and PDA control treatments. Histological examination showed characteristic yellow-brown oleoresin deposits concentrated in the inner phloem, mirroring the anatomical features of wild-type agarwood. Critical quality parameters measured in December-harvested samples included ethanol extractives (17.69%), chromone derivatives 2-[2-(4-methoxyphenyl) ethyl] chromone, and 2-(2-phenylethyl) chromone (2.13%), all meeting or surpassing the specifications outlined in the National Standard for Agarwood Classification (LY/T 3223-2020). These comprehensive findings establish F. equiseti as a promising microbial agent for sustainable agarwood production in A. sinensis plantations. Full article

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article