Search Results (23,170)

Modern artificial intelligence methods are increasingly applied in hydrology, particularly for forecasting water inflow into reservoirs. However, their limited interpretability constrains practical deployment in critical water resource management systems. Explainable AI offers solutions aimed at increasing the transparency of models, which makes the topic relevant in the context of developing sustainable and trusted AI systems in hydrology. Articles published in leading scientific journals in recent years were selected for the review. The selection criteria were the application of XAI methods in hydrological forecasting problems and the presence of a quantitative assessment of interpretability. The main attention is paid to approaches combining LSTM, GRU, CNN, and ensembles with XAI methods such as SHAP, LIME, Grad-CAM, and ICE. The results of the review show that XAI mechanisms increase confidence in AI forecasts, identify important meteorological features, and allow analyzing parameter interactions. However, there is a lack of standardization of interpretation, especially in problems with high-dimensional input data. The review emphasizes the need to develop robust, unified XAI approaches that can be integrated into next-generation hydrological models. Full article

Geraniol (GA) is a terpene compound of natural origin that exhibits strong biological activity. The possibility of using GA as a potential compound with antimicrobial activity is currently of great interest to scientists. The aim of the present study was to comprehensively evaluate the activity of GA against selected strains of Gram-positive bacteria, Gram-negative bacteria, and fungi that pose a significant threat in clinical practice. Among the Gram-positive bacteria studied were Streptococcus spp., Neisseria gonorrhoeae, and Listeria monocytogenes. Among the Gram-negative bacteria tested were Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae. The fungal pathogens analyzed included Candida albicans and Candida glabrata. The results showed that GA exhibited strong antimicrobial activity against most of the microorganisms tested. Gram-positive strains were more susceptible to GA compared to Gram-negative strains, probably due to differences in cell wall structure. In the case of fungi, significant efficacy was noted against Candida albicans. This study confirms the potential of GA as an alternative antimicrobial agent, especially against antibiotic-resistant bacterial strains and fungal pathogens. These results open up new perspectives for the application of GA in medicine and the pharmaceutical industry. The study on creams demonstrated that GA possesses strong antimicrobial properties, effectively inhibiting bacterial growth regardless of the concentration used (0.5–12%) and the type of culture medium, confirming its potential as a natural preservative agent in the cosmetic and pharmaceutical industries. Moreover, the research on the anticancer activity of GA revealed its cytotoxic effects against colon cancer cells (LoVo cell line, IC₅₀ = 32.1 μg/mL) and glioma cells (U87 cell line, IC₅₀ = 41.3 μg/mL), particularly at higher concentrations, indicating its promising therapeutic potential. Full article

Although colored-leaf tea germplasms can broaden product diversity and functional potential, such resources have been rarely reported in Korea. Herein, we comprehensively characterized Ku2, a newly discovered purple-green line of Camellia sinensis, and benchmarked it against the conventional green-leaf ‘Sangmok’. Five-year-old plants grown under identical open-field conditions were evaluated for growth characteristics, leaf pigmentation, biochemical composition, and antioxidant capacity. Ku2 exhibited a more vigorous growth habit with denser branching and produced leaves that were 11% longer and 17% wider than those of ‘Sangmok’, but chlorophyll concentrations were 29–33% lower. Young shoots of Ku2 in the first flush accumulated markedly higher levels of total polyphenols (+38%), anthocyanins (+78%), and total catechins (+35%), including a 70% increase in epigallocatechin-3-gallate. But amino acid and theanine contents were reduced to 30% and 25% of those in ‘Sangmok’, respectively. Consistent with its polyphenol enrichment, Ku2 extracts displayed superior radical-scavenging activity, with lower DPPH and ABTS IC₅₀ values (7.6 ± 0.5 and 11.6 ± 0.2 µg·mL⁻¹) than ‘Sangmok’ (10.1 ± 0.4 and 15.1 ± 0.1 µg·mL⁻¹), approaching ascorbic acid and Trolox standards. These findings highlight Ku2 as a valuable germplasm for developing premium Korean teas and for breeding colored-leaf cultivars enriched with health-promoting metabolites. Full article

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm’s global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm’s efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain. Full article

The search for bioactive compounds with antioxidant properties is critical in combating oxidative stress-related diseases and advancing novel therapeutic agents. Azo dyes, traditionally used in textiles, food, and cosmetics, have recently attracted attention due to their emerging biological activities, including antioxidant potential. In this study, we synthesized and characterized 267 azo dyes derived from natural phenolic cores such as salicylic acid, syringol, and 5,6,7,8-tetrahydro-2-naphthol. Eighteen of these compounds are novel. Structural characterization was performed using NMR, UV-Vis, IR spectroscopy, and mass spectrometry. Antioxidant activity was assessed using in vitro assays with ABTS radical scavenging method. SAR analysis revealed that dyes derived from syringol and 5, 6, 7, 8-tetrahydro-2-naphthol showed the most consistent and potent antioxidant activity. Notably, azo dyes bearing fluoro and nitro substituents in the para position exhibited the lowest IC₅₀ values, highlighting the influence of electron-withdrawing groups and substitution patterns on antioxidant behavior. This work establishes a precedent for SAR-driven evaluation of azo dyes using ABTS and supports their further exploration as functional antioxidant agents in medicinal chemistry. Full article

The study of various microorganisms isolated from an Indian Ocean sponge, Scopalina hapalia ML-263, led to the selection of a promising Actinobacteria strain, Micromonospora sp. SH-82. Genomic analysis identified this strain as a new species, revealing the presence of 23 biosynthetic gene clusters (BGCs), some of which are associated with the synthesis of specialized metabolites such as polyketides deriving from polyketide synthases (PKSs). The strain was cultivated under favorable conditions for the production of bioactive molecules, resulting in the isolation and identification of seven microbial metabolites. Three of them are potentially novel, two erythronolides and one erythromycin, all characterized by a rare C10–C11 double bond. Some of these compounds also display atypical conformations, forming hemiacetals or spiroacetals. Their identification was achieved through detailed chemical analyses (NMR and ESI⁺-HRMS). A molecular networking approach was employed to assess the presence of potentially novel molecules in the microbial crude extract, supported by the identification of isolated molecules. Four molecules (1, 2, 3 and 5) were evaluated for their cytotoxic activities against cancer cell lines (HCT-116 and MDA-MB-231) and the immortalized retinal pigment epithelial RPE1 cells. No activity was observed in the latter, suggesting a lack of toxicity toward healthy cells. Moreover, megalomicin C1 (3), one of the isolated compounds, showed interesting antiplasmodial activity against Plasmodium falciparum 3D7, with an IC₅₀ of 6.37 ± 2.99 µM. Full article

F_TIDE has been proven to be effective in obtaining the time-varying harmonic parameters of nonstationary tidal signals, and the results near the two endpoints of the analyzed time series are more accurate than those obtained by S_TIDE, which provides good conditions for the prediction of future sea levels. In this paper, F_TIDE is used for the short-term prediction of nonstationary tides in Nome (Alaska) and South Beach (Oregon). The significance of each standard parameter of F_TIDE is quantified by calculating its signal-to-noise ratio to determine the appropriate parameters that can be used for prediction. F_TIDE performs well in forecasting the sea level for three weeks at the Nome gauge and one week at the South Beach gauge. F_TIDE causes 30.1% and 42.0% decreases in the mean absolute errors between the forecasts and the observations compared to T_TIDE. F_TIDE is applied to the original signal at the Nome gauge, and the results show a strong correlation between the variation in M₂ amplitude and the variation in the mean sea level. A potential mechanism is speculated in that changes in tides are affected by the changes in water depth on different time scales, which the sea level pressure, wind, sea ice, and other marine motions may contribute to. Full article

Photosynthetic characteristics are critical for grape growth and development. Drought conditions in arid regions significantly affect these characteristics. To identify grape varieties better suited for cultivation in arid environments, this study evaluated the leaf phenotypes and photosynthetic characteristics of 27 table grape varieties in Hotan Prefecture, China. Results revealed significant variations in leaf phenotypes and chlorophyll content (SPAD) among varieties under Hotan’s drought conditions. ‘Kyoho’ exhibited the largest leaf area (254.34 cm²), while ‘Munage’ had the smallest (112.43 cm²), and ‘Manaizi’ showed the highest chlorophyll content (SPAD = 44.21). ‘Munage’ and ‘Flame Seedless’ recorded the highest net photosynthetic rates (P_Nmax = 16.24 and 16.23 μmol·m⁻²·s⁻¹, respectively), while ‘Thompson Seedless’ had the lowest respiratory loss (R_D = 1.15 μmol·m⁻²·s⁻¹) and light compensation point (I_c = 22.41 μmol·m⁻²·s⁻¹), with a highly significant positive correlation between R_D and I_c. ‘Crimson Seedless’ exhibited the highest light saturation point (I_sat = 2745.15 μmol·m⁻²·s⁻¹). Chlorophyll fluorescence analysis indicated that ‘Autumn Black’ had the highest PSII photochemical yield (F_v/F_m = 0.84), while ‘Zicuiwuhe’ showed high energy transfer indices (PI_abs = 1.78, PI_total = 1.66) and electron transfer efficiency (φ_Eo = 0.39). PIabs was significantly correlated with F_v/F_m, F_v/F_o, and energy flux parameters. ‘Molixiang’ demonstrated superior energy utilization, with the highest light absorption (ABS/CSm = 2440.8) and electron transfer flux (ETo/CSm = 874) and the lowest energy dissipation (DIo/CSm = 455.8), supported by a negative correlation between energy dissipation (DIo/CSm) and photochemical efficiency (φ_Eo). Principal component analysis revealed that ‘Molixiang’ had the highest comprehensive photosynthetic adaptability score (0.97), followed by ‘Zicuiwuhe’ (0.79) and ‘Hetianhong’ (0.73), under Hotan’s drought stress conditions. These findings provide valuable insights for selecting and breeding grape varieties adapted to arid environments and climate change. Full article

Plants are capable of responding to various environmental stresses by initiating the expression of genes that encode proteins involved in plant growth, fruit ripening, maintaining protein homeostasis, and combating heat stress (HS) by activating heat tolerance systems. The mechanism of resisting against HS is very intricate, and the molecular basis and involvement of the related gene network in Capsicum annuum L. are not fully understood. There are five different heat shock proteins (HSPs) reported in the literature, namely, small HSPs (sHSPs), CaHSP60s, CaHSP70s, CaHSP90s, and CaHSP100s, which play a pivotal role in heat stress response (HSR) in C. annuum. Heat shock factors (HSFs) and heat stress elements (HSEs) govern the transcriptional modifications and control the relative expression of heat shock proteins (HSPs). The heat stress response is the reprogramming of the molecular cascades involving the cell stress responses against the HSR, which is characterized by the increased production of molecular chaperones, which help the plants to counter the negative physiological impacts on proteins, induced by heat and other abiotic stresses. Therefore, understanding the detailed molecular mechanisms of C. annuum in response to extreme temperatures is critical for exploring how they will be affected by climate change and how they behave to cope with these varied climate extremes. This study is focused on providing a complete understanding of the molecular cascades in C. annuum L.’s response to HS, which starts with the sensation of HS signals and activation of the relative molecular cascades that are responsible for the activation of HSFs and initiate their primary targets, e.g., HSPs. Overall, this review provides deep insights into all the cellular responses during HS with a special focus on categorization and physiological aspects of HSPs and HSFs. Full article

When unmanned aerial vehicles (UAVs) perform transmission line ice cover detection, it is often due to the variable shooting angle and complex background environment, which leads to difficulties such as poor ice-covering recognition accuracy and difficulty in accurately identifying the target. To address these issues, this study proposes an improved icing detection model based on HCDFI–You Only Look Once version 8 (HCDFI-YOLOv8). First, a cross-dense hybrid (CDH) parallel heterogeneous convolutional module is proposed, which can not only improve the detection accuracy of the model, but also effectively alleviate the problem of the surge in the number of floating-point operations during the improvement of the model. Second, deep and shallow feature weighted fusion using improved CSPDarknet53 to 2-Stage FPN_Dynamic Feature Fusion (C2f_DFF) module is proposed to reduce feature loss in neck networks. Third, optimization of the detection head using the feature adaptive spatial feature fusion (FASFF) detection head module is performed to enhance the model’s ability to extract features at different scales. Finally, a new inner-complete intersection over union (Inner_CIoU) loss function is introduced to solve the contradiction of the CIOU loss function used in the original YOLOv8. Experimental results demonstrate that the proposed HCDFI-YOLOv8 model achieves a 2.7% improvement in mAP@0.5 and a 2.5% improvement in mAP@0.5:0.95 compared to standard YOLOv8. Among twelve models for icing detection, the proposed model delivers the highest overall detection accuracy. The accuracy of the HCDFI-YOLOv8 model in detecting complex transmission line environments is verified and effective technical support is provided for transmission line ice cover detection. Full article

Background: miRNA profiling across different stages of laryngeal carcinogenesis explores dysregulated molecules relevant to engaged gene pathways and identifies markers for differential diagnosis and prognosis in early mucosal lesions of the larynx. Methods: Tissue samples were prospectively collected from 28 patients with hypertrophic vocal fold lesions: no dysplasia (ND), low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive cancer (IC), as well as from 3 patients with vocal fold polyps. miRNA profiling of the samples was performed using microfluidic cards—TaqMan^® Human MicroRNA Array A. A comparative analysis of ΔCt (dCt) miRNA expression levels was conducted between groups. Results: hsa-miR-216a-5p and hsa-miR-488-3p were selectively expressed in control tissues, while hsa-miR-105-5p and hsa-miR-516a-5p were exclusively detected in HGD and IC samples. Significant differences in miRNA expression were identified across 4, 16, 17, and 38 miRNA types between control and ND, LGD, HGD, and IC groups, respectively. hsa-miR-185-5p and hsa-miR-21-5p showed significantly altered expression between ND and LGD, HGD, and IC (p = 0.026, 0.001, 0.002; and p = 0.021, 0.002, 0.001, respectively). Twenty-five miRNAs were differentially expressed between LGD and both HGD and IC, while eleven miRNAs distinguished HGD from IC. Notably, hsa-miR-503-5p expression decreased progressively with increasing histological severity. Conclusions: Distinct miRNA expression profiles are associated with progressive stages of laryngeal mucosal lesions. Specific miRNAs may serve as valuable biomarkers for early detection, risk stratification, and prognosis in vocal fold carcinogenesis. Full article

The plants from the Ocimum genus, belonging to the Labiatae family, serve as important bioresources of essential oils (EOs) rich in biologically active secondary metabolites, widely used in medicine, food, and cosmetics. This study explored the volatile composition, enantiomeric distribution, and in vitro biological activities of EOs from three Ocimum species native to Nepal: O. tenuiflorum L., O. basilicum L., and O. americanum L. EOs were extracted via hydro-distillation and analyzed using gas chromatography–mass spectrometry (GC-MS) for chemical profiling and chiral GC-MS for enantiomeric composition. Hierarchical cluster analysis was performed for major chemotypes. Antioxidant activity was assessed using DPPH and ABTS assays. Antimicrobial efficacy was evaluated using the microbroth dilution method, and cytotoxicity was tested on NIH-3T3 (normal) and MCF-7 (breast cancer) cell lines via the Cell Counting Kit-8 assay. EO yield was highest in O. tenuiflorum (1.67 ± 0.13%) during autumn and lowest in O. americanum (0.35 ± 0.02%) during winter among all Ocimum spp. The major compounds identified in O. tenuiflorum were eugenol (32.15–34.95%), trans-β-elemene (29.08–32.85%), and β–caryophyllene (19.85–21.64%). In O. americanum, the major constituents included camphor (51.33–65.88%), linalool (9.72–9.91%), germacrene D (7.75–1.83%), and β–caryophyllene (6.35–3.97%). For O. basicilum, EO was mainly composed of methyl chavicol (62.16–64.42%) and linalool (26.92–27.05%). The oxygenated monoterpenes were a dominant class of terpenes in the EOs except for O. tenuiflorum (sesquiterpene hydrocarbon). A hierarchical cluster analysis based on the compositions of EOs revealed at least three different chemotypes in Ocimum species. Chiral GC-MS analysis revealed β-caryophyllene and germacrene D as enantiomerically pure, with linalool consistently dominant in its levorotatory form. O. tenuiflorum exhibited the strongest antimicrobial activity, particularly against Candida albicans, and showed notable anticancer activity against MCF-7 cells (IC₅₀ = 23.43 µg/mL), with lower toxicity to normal cells. It also demonstrated the highest antioxidant activity (DPPH IC₅₀ = 69.23 ± 0.10 µg/mL; ABTS IC₅₀ = 9.05 ± 0.24 µg/mL). The EOs from Ocimum species possess significant antioxidant, antimicrobial, and cytotoxic properties, especially O. tenuiflorum. These findings support their potential application as natural agents in medicine, food, and cosmetics, warranting further validation. Full article

ECIS-based impedance biosensors have been extensively studied in various fields including cancer research, microbiology, and immunology. However, most studies have primarily focused on monitoring cellular behavior through impedance changes, with relatively less emphasis on interpreting the biological significance of impedance signals. In this study, we employed a multi-well array impedance biosensor to conduct IC₅₀ (half-maximal inhibitory concentration) analysis, a widely used metric for evaluating drug efficacy and toxicity in biological and pharmacological research. Specifically, we assessed the IC₅₀ values of puromycin, an aminonucleoside antibiotic known to inhibit protein synthesis. NIH/3T3 fibroblasts were exposed to various concentrations of puromycin, and real-time impedance monitoring was performed. Cell viability was assessed, and the IC₅₀ value of puromycin for NIH/3T3 cells was determined to be 3.96 µM using capacitance-based impedance analysis. Our findings demonstrate that the multi-well array impedance biosensor provides a rapid and quantitative method for drug toxicity evaluation, offering a valuable platform for drug screening and biocompatibility assessment. Full article

The harsh environment in Arctic regions presents significant challenges to ship stability, particularly when ice accumulation and hull damage occur simultaneously, potentially increasing the risk of instability. This study addresses this critical issue by proposing a comprehensive stability assessment framework for ships operating in Arctic regions. Utilizing the DTMB-5415 ship model, the evaluation integrates both static and dynamic stability under combined ice accumulation and damage conditions. Firstly, an ice accumulation prediction model was developed to estimate ice accumulation over various durations. Subsequently, the static stability of damaged ships with ice accumulation was evaluated. Computational Fluid Dynamics (CFD) simulations were then conducted to calculate roll damping coefficients and analyze the effects of damage location and ice accumulation on free roll decay behavior. A single-degree-of-freedom (SDOF) roll motion model was constructed, incorporating roll damping coefficients and wave excitation moments to simulate roll responses in random wave environments. Extreme value prediction was employed to estimate the short-term extreme response distribution of roll motions. The results indicate that ship stability decreases significantly when ice accumulation and hull damage occur simultaneously. This integrated framework provides a systematic foundation for evaluating ship stability in the Arctic environment, specifically accounting for the combined effects of ice accretion and hull damage. Full article

As extreme weather events become more frequent, the icing of transmission lines in winter has become more common, causing significant economic losses to power systems and drawing increasing attention. However, owing to the complexity of the conductor icing process, establishing high-precision ice thickness prediction models is vital for ensuring the safe and stable operation of power grids. Therefore, this paper proposes a hybrid model combining an improved snake optimization (ISO) algorithm, deep extreme learning machine (DELM), and hybrid kernel extreme learning machine (HKELM). Firstly, based on the analysis of the factors that influence the icing, the temperature, the humidity, the wind velocity, the wind direction, and the precipitation are selected as the weather parameters for the prediction model of the transmission line icing. Secondly, the HKELM is introduced into the regression layer of DELM to obtain the deep hybrid kernel extreme learning machine (DHKELM) model for ice thickness prediction. The SO algorithm is then augmented by incorporating the Latin hypercube sampling technique, t-distribution mutation strategy, and Cauchy mutation, enhancing its convergence. Finally, the ISO-DHKELM model is applied to the icing data of transmission lines in Sichuan Province for experiments. The simulation results indicate that this model not only performs well, but also enhances the accuracy of ice thickness predictions. Full article