Search Results (91,393)

The growing demand for gold in various high-technology applications necessitates the development of efficient and selective methods for its recovery and analysis, which can be achieved using such macrocyclic ligands as crown esters and their aza-substituted derivatives. The present paper reports on the equilibrium constants for the formation of gold(III) complexes with 18-crown-6, 1-aza-18-crown-6, 1,10-diaza-18-crown-6, and the cryptand 4,7,13,16,21,24-hexaoxa-1,10-diazabicyclo[8.8.8]hexacosane (Kryptofix 222) in aqueous solution at T = 298.2 K, p = 0.1 MPa, I → 0. The equilibrium constants (log β) for the substitution of chloride ions by macrocycles were determined to be 4.52 ± 0.04, 9.15 ± 0.03, 9.08 ± 0.07, and 11.51 ± 0.08, respectively. Equilibrium constants for protonated and polyligand species are also provided. The complexation mechanism was elucidated using a combination of spectroscopic techniques. UV-Vis and IR spectroscopy confirm the substitution of chloride ligands by the nitrogen donor atoms of the aza-macrocycles within the tetrachloroaurate(III) ion. Furthermore, ¹H NMR analysis reveals that the diaza-substituted ligands can form both inclusion complexes, where the gold cation is encapsulated within the macrocyclic cavity, and exclusion complexes. These findings provide a quantitative foundation for the design of novel macrocycle-based extractants and sensors for gold(III). Full article

In this study, NH₂-MgO was employed as a crosslinking agent to covalently link boron nitride (BN) and silicon carbide whiskers (SiC_w) via an amidation reaction, yielding the BN-MgO-SiC_w hybrid filler. The BN-MgO-SiC_w/PBz composites were fabricated using a ball-milling-assisted solution mixing method combined with hot-press molding, and their comprehensive properties were systematically evaluated. The results demonstrate that the BN-MgO-SiC_w/PBz composite exhibits excellent thermal conductivity, favorable dielectric properties, superior thermal stability, and outstanding mechanical performance. At a filler loading of 50 wt%, the composite achieved a thermal conductivity of 1.41 W/mK, which is substantially higher than that of the KH560-BN/PBz composite (0.91 W/mK) and approximately 5.2 times that of the neat PBz matrix. The dielectric constant (ε) and dielectric loss (tan δ) of the BN-MgO-SiC_w/PBz composite were 6.81 and 0.013, respectively, remaining at relatively low levels. The thermal degradation temperature at 30% weight loss (T₃₀) and the heat resistance index temperature (T_HRI) reached 572 °C and 244 °C, respectively, both higher than those of the KH560-BN/PBz composite at the same filler loading (511 °C and 224 °C). The tensile strength and flexural strength of the BN-MgO-SiC_w/PBz composite were 50.0 MPa and 72.3 MPa, respectively, exceeding those of the KH560-BN/PBz composite (39.4 MPa and 56.2 MPa) while remaining slightly below those of the neat PBz matrix. Collectively, these findings indicate that the BN-MgO-SiC_w/PBz composite holds great promise as a novel material with well-balanced comprehensive properties, making it a strong candidate for applications in fields such as electronic packaging. Full article

Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km² area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article

The reconstruction of detrital flux, paleoclimate, paleosalinity, paleo-primary productivity, paleohydrodynamic conditions, and paleo-water depth enhances understanding of sedimentary processes and their drivers during deep-time greenhouse-icehouse transitions, such as the Eocene–Oligocene transition. This study uses detailed geochemical analyses of major oxides and trace elements in sediment samples collected from the Beni Suef Formation (Bartonian–Priabonian) and the Maadi Formation (Priabonian) in the southern Tethys shelf (Egypt, northeastern Desert). Detrital proxies, including Si/Al, Ti/Al, and Zr/Al, indicate an enhanced influx of terrigenous sediments in the middle portion of the Qurn Member of the Beni Suef Formation, as further supported by noticeable facies variations, particularly the transition from shale to coarser silt- and sand-sized fractions. Paleoclimate indicators (Sr/Ba, Rb/Sr, K₂O/Al₂O₃, and Sr/Cu) point to a climatic shift from humid to arid conditions, consistent with the regional Late Eocene aridification across the Tethyan realm. Paleosalinity proxies (Sr/Ba, Ca/Al, and Mg/Al×100) suggest episodic intensification of open-marine influence and a reduction in freshwater input, with an upsection increase in Sr/Ba ratios, reflecting phases of enhanced marine water settings or decreased terrestrial runoff. Primary productivity was evaluated using multiple geochemical proxies, including P, Ni/Al, Cu/Al, P/Al, P/Ti, and Babio ratios. These collectively indicate generally low primary productivity interrupted by intervals of enhanced paleoproductivity or increased organic matter export to the sediments. This interpretation is further supported by the low total organic carbon (TOC) values. These results highlight the sensitivity of the southern Tethys shelf to Middle–Late Eocene climatic variability and the key role of prevailing paleoenvironmental conditions in controlling sediment supply, water chemistry, and biological productivity. Full article

With the rapid advancement of information technology, the energy consumption of data centers has become a critical issue. Accurate cooling load prediction is essential for optimizing cooling system operations and improving energy efficiency. However, conventional models often struggle to capture the complex nonlinearities and multi-variable coupling effects inherent in data centers. To address the limitations of existing models in terms of training efficiency and generalization performance, this study proposes a cooling load prediction model that integrates the light gradient boosting machine (LightGBM) algorithm with Bayesian optimization. The model was validated using data generated from an EnergyPlus simulation of a representative medium-scale data center. Comparative analysis demonstrates that the proposed model surpasses naive benchmarks (T-1, T-24, and T-168) and other machine learning models (SVR, XGBoost, and LSTM), achieving superior performance with a Root Mean Squared Error (RMSE) of 4.3234 kW, R² of 0.9999, and Mean Absolute Percentage Error (MAPE) of 0.07%. A noise robustness analysis further reveals that the model maintains excellent performance under realistic uncertainties, achieving an R² above 0.99 and an RPD exceeding 12 even at high noise levels (SNR = 20 dB). The total runtime and Relative Prediction Deviation (RPD) were 33.45 s and 86.2685, respectively, indicating an excellent balance between computational efficiency and robust predictive reliability. The key contribution of this research is the effective integration of LightGBM and Bayesian optimization to provide a highly accurate and efficient tool for data center cooling load prediction. This approach offers a scientific foundation for the intelligent control of cooling systems and energy efficiency optimization in data centers, with direct practical implications for building energy management. Full article

In this article, we introduce and investigate a novel class of graphs that are called Power Congruence Graph PCGs, which are defined over the vertex set V$=\{0,1,2,\dots ,n-1\}$ where two vertices $a,b\in V$ are adjacent if $a^k\equiv b^k(modm)$ for some modulus $m\in M_p$, where $M_p=\{p,p^2,\dots ,p^t\mid p^t<n\}$. We thoroughly characterize the structural features of these graphs, establishing that each PCG decomposes into a union of $d+1$ complete components, where $d={\displaystyle \frac{p-1}{gcd(k,p-1)}}$. The component sizes are explicitly given for n, p, and k. This decomposition highlights symmetry patterns in the component arrangement, emphasizing connectedness and structural balance. We derive key graph-theoretic metrics such as degree distribution, size, chromatic number, clique number and domination number. We also compute the adjacency and Laplacian matrices, as well as their spectra and associated graph energies to better understand the structural similarities and differences among PCGs with different exponents and prime moduli. This paper offers a systematic framework for comprehending power congruence based graph constructs, integrating number theory with structural and spectral graph theory and illustrating the natural symmetry that underpins these combinatorial structures. Full article

Background: Genetic diversity and genetic differentiation between Gossypium hirsutum-Gossypium barbadense introgression lines (ILs) and early-maturing upland cotton lines are critical for resolving the core breeding contradiction in Xinjiang cotton region: narrow genetic basis of early-maturing cultivars and late maturity of ILs with superior fiber quality. Xinjiang is one of the major cotton-producing regions in China, and breeding high-quality early-maturing upland cotton adapted to local ecological conditions is essential for improving cotton yield and quality. However, the genetic relationship and differentiation between the two types of cotton germplasm remain unclear, which hinders the efficient utilization of germplasm resources in breeding. Therefore, this study aimed to clarify the genetic diversity and differentiation between the two germplasm types and identify key candidate loci related to early maturity and fiber quality, providing support for cotton breeding. Results: Here, we used a 40K Single Nucleotide Polymorphism chip to genotype core cotton germplasm in northern Xinjiang, and analyzed their population structure, genetic diversity and functional SNP loci associated with early maturity and fiber quality. The tested materials were clearly divided into two subgroups (ILs and early-maturing lines). Genetic diversity analysis revealed a significantly narrow genetic basis in the early-maturing subgroup, while the IL subgroup had higher genetic diversity. Specifically, the early-maturing subgroup showed lower nucleotide diversity and polymorphism information content compared with the IL subgroup, indicating that the genetic variation of early-maturing cotton germplasm in northern Xinjiang is relatively limited. A total of 25 non-synonymous SNPs were identified, among which the c.A613G:p.T205A mutation in GH_D09G1484 (mRNA-decapping enzyme 1, DCP1) was a characteristic variation of early-maturing cotton, and a possible non-synonymous mutation in GH_A09G2400 (Heat shock transcription factor A6b, HSFA6B) was associated with fiber development. These two candidate genes were annotated to be involved in plant growth and development, further supporting their potential roles in regulating cotton early maturity and fiber quality. Conclusions: This study clarified the genetic differentiation between the two types of germplasms and identified key candidate loci for early maturity and fiber quality, providing precise molecular markers and theoretical support for breeding high-quality early-maturing upland cotton adapted to Xinjiang’s ecological conditions. The results also highlight the value of Gossypium hirsutum–Gossypium barbadense introgression lines in enriching the genetic basis of early-maturing cotton, which can be further utilized to solve the core breeding contradiction in the Xinjiang cotton region. Full article

Internal reconnection events (IREs) are rapid magnetohydrodynamic phenomena that play an important role in the confinement and stability of spherical tokamak plasmas. Reliable identification of IREs in experimental data is challenging due to short discharge durations, ambiguous event boundaries, and the limited availability of labeled data. In this study, we propose an unsupervised, event-level IRE detection framework based on anomaly detection techniques and apply it to experimental data from the VEST spherical tokamak. The proposed framework combines a two-stage detection strategy using plasma current and $H_{\alpha }$ emission signals with sliding-window segmentation and event-level evaluation, enabling physically meaningful IRE identification without labeled training data. Three unsupervised models—K-Nearest Neighbors (KNN), One-Class Support Vector Machine (OCSVM), and an autoencoder (AE)—are evaluated within a unified framework. All models achieve stable detection performance, with precision exceeding 80% and recall above 70% under a precision-oriented operating point. To enhance detection robustness, a KNN-based cleaning procedure is introduced during training to remove noise-driven, locally isolated windows, significantly reducing spurious detections while preserving physically meaningful IRE signatures. Event-level analysis indicates that missed detections under this operating regime predominantly correspond to weak events with limited impact on global plasma behavior. The proposed framework is fully unsupervised, computationally efficient, and readily extensible to other spherical tokamak devices, providing a flexible foundation for incorporating additional diagnostics, such as Mirnov coil signals, toward precursor-aware detection and future predictive modeling of IRE activity. Full article

Climate change poses an urgent challenge to Canada’s sustainable development. The country experiences increasing extreme weather events, rising temperatures, and pressures on energy systems—particularly in remote northern regions. In Newfoundland and Labrador, isolated communities are vulnerable because reliance on diesel-based electricity increases greenhouse gas emissions, energy costs, and environmental risks, highlighting the need for resilient energy solutions. This study uses a systematic methodology combining literature review, local energy demand data, and site-specific wind resources to design and optimize hybrid renewable energy systems (HRESs) for Makkovik. It employs HOMER Pro and the Monte Carlo method to evaluate uncertainties in cost, fuel consumption, and renewable fraction. The objectives are to quantify how renewable integration can reduce emissions, improve energy reliability, and support sustainable development in remote communities. The novelty lies in combining location-specific modeling with probabilistic Monte Carlo analysis and providing robust, system-level insights into environmental and economic outcomes while guiding climate-resilient energy planning. The proposed HRES significantly mitigates climate change impacts, reducing annual CO₂ emissions from 72,500 kg/year to 15,190 kg/year. Monte Carlo analysis indicates economic feasibility with a net present cost of $14.5 million, a levelized cost of electricity of 0.256 $/kWh, and diesel consumption reduced from 29,970 L/year to 5854 L/year. Wind energy provides 99.6% of total annual electricity, ensuring a high renewable fraction and reliable power, enhancing energy resilience and adaptation potential. This study demonstrates that a well-designed hybrid renewable energy system can deliver measurable emission reductions, economic feasibility, and enhanced energy resilience. It supports sustainable development and climate change mitigation in remote Canadian communities. Full article

The East China Sea represents a critical coastal wetland region, characterized by complex geomorphology, heterogeneous land-cover composition, and diverse wetland types. Accurate delineation of coastal wetland extent is essential for ecosystem service assessment and sustainable coastal management, directly contributing to wetland-related Sustainable Development Goals (SDGs), particularly SDG 15, on ecosystem conservation and biodiversity protection. However, pronounced spectral similarity and structural heterogeneity among wetland classes pose substantial challenges to reliable classification. To address these challenges, this study developed a hierarchical classification framework integrating Random Forest, K-means clustering, and a decision tree classifier based on multi-source Sentinel-1 and Sentinel-2 imagery. Spectral, polarimetric, texture, and morphological features were systematically constructed to enhance class separability. Using this framework, a 10 m resolution coastal wetland map of the East China Sea was generated for 2023. The proposed approach achieved an overall accuracy of 91.32% and improved the discrimination of spectrally similar wetland types. Feature fusion reduced confusion among water-related classes, while object-based clustering improved the extraction of linear riverine wetlands. The resulting 10 m wetland map provides updated spatial information for ecological assessment and coastal management in the East China Sea. Full article

Sustainable ridge-based peanut production following winter wheat requires soil preparation technologies capable of simultaneously ensuring precise ridge formation, reduced energy consumption and efficient in situ utilisation of crop residues. This study aimed to develop and experimentally validate a combined soil preparation technology integrating shallow tillage, deep loosening and ridge formation within a single field pass, and to quantify its technological and biological performance. Field experiments were conducted using a prototype combined machine with analytically justified geometric parameters of the working tools, followed by multifactor optimisation and statistical modelling. Technological performance was assessed by soil fragmentation degree and draft resistance, while biological effects were evaluated using residue incorporation (Pz), biodegradation coefficient after 60 days (k₆₀) and dehydrogenase activity after 30 days (DHA₃₀). The results showed statistically significant nonlinear relationships between tool parameters and technological responses, with coefficients of determination exceeding 0.94 for soil fragmentation and 0.97 for draft resistance. The proposed technology increased residue incorporation efficiency by 15–20%, enhanced biodegradation intensity (k₆₀) by up to 18%, and reduced energy consumption due to single-pass operation compared with conventional multi-pass systems. A strong relationship between Pz and biological indicators confirmed the key role of residue placement in controlling microbial processes. These findings demonstrate that integrated control of soil processing and residue placement enables energy-efficient single-pass technologies for ridge-based peanut production systems. Full article

Magnesium potassium phosphate cement (MKPC) is a promising bone repair material but suffers from excessively rapid setting time (typically within minutes) that limits clinical application. This study systematically investigates trimagnesium phosphate (TMP) as a candidate retarding additive for MKPC. TMP was used to partially replace dead-burned magnesium oxide at replacement levels of 0%, 10%, and 15% by mass. The effects of TMP content, water-to-cement ratio (0.17–0.23), and magnesium-to-phosphate molar ratio (4–10) on setting time, fluidity, hydration kinetics, compressive strength, and microstructure were comprehensively evaluated. Results show that TMP effectively extends the setting time from 9–13 min (without TMP) to 10–19 min, providing a working window that may be suitable for biomedical applications requiring extended handling time. Notably, 10% TMP incorporation enhances early compressive strength, with 1-day strength reaching 35.2 MPa compared to 28.5 MPa for control samples. Hydration heat analysis reveals TMP moderates the acid-base reaction kinetics through its slower dissolution rate compared to MgO. Microstructural characterization shows TMP promotes the formation of denser K-struvite crystals with refined microstructure. The optimal TMP dosage of 10% achieves a balanced performance, extending setting time while improving early strength and microstructural densification. These findings establish TMP as an effective retarder for developing MKPC-based materials with potential for biomedical applications, pending further biological validation. Full article

Background: Klebsiella pneumoniae is a Gram-negative bacterium that is found in human microbiota and in diverse environments. This opportunistic pathogen exhibits a highly variable genetic background and is responsible for a broad range of hospital- and community-acquired, multidrug-resistant infections worldwide. To track transmission pathways and understand genetic diversity, single-nucleotide polymorphism (SNP) clustering has become an essential tool. Methods: This study examines data from 2018 to 2024 in the NCBI Pathogen Detection database to determine the temporal and spatial distribution of SNP clusters in clinical K. pneumoniae across Middle East and North Africa (MENA) countries. Results: Among 1858 isolates, a heterogeneous population structure was observed. Of the 478 identified SNP clusters, a few dominant clusters accounted for 37% of the isolates, and numerous low-frequency lineages were detected. The descriptive yearly snapshot revealed a diverse representation of top clusters. Geographical analysis showed the presence of both localized and limited cross-border distribution patterns. Countries with diverse clusters also exhibit higher diversity of carbapenem- and ESBL-resistant genes. Conclusions: These findings provide valuable insights into the dominant, regionally concentrated K. pneumoniae lineage across MENA countries, assisting future genomic surveillance and efforts to combat clinical K. pneumoniae infections in this region. Full article

Isoprene is a significant source of secondary organic aerosol (SOA) in the atmosphere. This study investigates the physicochemical properties of isoprene-derived SOA formed through ozonolysis and photooxidation under varying NO_x conditions in an environmental chamber. SOA produced by dark ozonolysis and under low NO_x conditions had a density of 1.35–1.38 g cm⁻³ and an organic-to-carbon (O:C) ratio of 0.89–0.97. It was relatively volatile, consisting of semi-volatile organic compounds (SVOCs, 40%) and low-volatility organic compounds (LVOCs, 52%), with a small fraction of extremely low-volatility organic compounds (ELVOCs, ~7%); its vaporization enthalpy (ΔH_vap) was 90–106 kJ mol⁻¹. Under high NO_x conditions (isoprene/NO_x ratios = 1.2–6.8, with isoprene units in ppbC), SOA exhibited lower density (1.26–1.29 g cm⁻³) and lower O:C ratios (0.62–0.72). It was also less volatile than SOA formed under dark ozonolysis and low NO_x conditions; volatility decreased with decreasing isoprene/NO_x ratio, while ΔH_vap increased from 65 to 95 kJ mol⁻¹. SOA formed under very high NO_x conditions (isoprene/NO_x ratio = 0.6) was characterized by a higher density (1.34 g cm⁻³) and O:C ratio (0.88). However, it was the least volatile, comprising 68% LVOCs and 32% ELVOCs, and had the highest ΔH_vap of 114 kJ mol⁻¹. At low isoprene/NO_x ratios (0.6–1.2) yields were suppressed (0.6%) in comparison to those (6.8%) at higher isoprene/NO_x ratios (5–7). Full article

Background: To evaluate the potential of cefiderocol as a topical ophthalmic antibiotic by determining the susceptibility of keratitis isolates from an extensive panel of Gram-negative bacterial species to this siderophore-cephalosporin class antibiotic. Methods: Minimum Inhibitory Concentrations (MICs) of cefiderocol were determined by the broth dilution method using iron-depleted, cation-adjusted Mueller–Hinton broth. The following Gram-negative bacteria were included: Acinetobacter baumannii (n = 13), Achromobacter xylosoxidans (n = 14), Escherichia coli (n = 15), Klebsiella aerogenes (n = 14), Klebsiella pneumoniae (n = 13), Klebsiella oxytoca (n = 14), Moraxella spp. (n = 15), Proteus mirabilis (n = 13), Pseudomonas aeruginosa (n = 17), Serratia marcescens (n = 14) and Stenotrophomonas maltophilia (n = 12). MIC₉₀ values were calculated for each of the species. Results: MIC₉₀ values (µg/mL): A. baumannii (0.5), A. xylosoxidans (0.25), E. coli (0.5), K. aerogenes (1.0), K. oxytoca (0.5), K. pneumoniae (0.5), Moraxella spp. (0.5), P. mirabilis (0.25), P. aeruginosa (0.5), S. marcescens (0.5), and S. maltophilia (0.25). In total, 100% of the isolates were determined to be susceptible to cefiderocol in vitro except for A. xylosoxidans and Moraxella spp., for which there are no established breakpoints for cefiderocol. Conclusions: Cefiderocol demonstrated in vitro activity against the tested panel of Gram-negative keratitis isolates. The results of this study suggest cefiderocol may be useful for the treatment of keratitis caused by numerous Gram-negative pathogens. Further development of cefiderocol for the topical treatment of Gram-negative keratitis is indicated. Full article