Search Results (36,842)

To enhance the operational efficiency of the CMA-BJ3.0 regional numerical model and address the issue of short-term precipitation overforecasting caused by assimilating estimated saturated water vapor, this study investigates the assimilation of radar reflectivity mosaic data by optimizing the configuration of retrieved water vapor in the indirect assimilation scheme. A 1D (one-dimensional) Bayesian method was employed to retrieve and constrain water vapor from reflectivity observations, generating retrieved water vapor for assimilation to mitigate overforecasting biases. A case study of precipitation on 1 August 2022 was analyzed, with particular focus on comparing the innovation vector statistics, spatial patterns of analysis increments, and physical mechanisms underlying forecast differences across multiple data assimilation configurations. Results showed that an observation-background (O-B) statistical distribution closer to a Gaussian unbiased state indicated a better balance between observations and the background field. The optimized scheme corrected systematic positive biases in water vapor, curbed excessive increments, and effectively resolved the overforecasting issue by refining the initial water vapor field. Batch experiments quantitatively demonstrated that assimilating 1D Bayesian-retrieved water vapor significantly improved precipitation forecast scores, particularly for higher magnitudes (≥25.0 mm/3 h), and reduced the over-forecast within the first 6 h. While the study focused on improving short-term precipitation accuracy without considering hydrometeor impacts or convective dynamics, the 1D Bayesian method, despite its background-dependency, proved effective in correcting water vapor biases, making it a promising assimilation scheme. Full article

Background/Objectives: There is clear evidence for the higher prevalence of varicose veins (VVs) among women. In this regard, the research on sex differences affecting this condition is very important for sex-specific health care. We aimed to assess how male or female sex may contribute to the changes to gene expression profiles in the vein wall during varicose transformation. Methods: Paired varicose vein (VV) and non-varicose vein (NV) segments were harvested from patients with VVs after venous surgery. Processed RNAs from those samples were subjected to gene expression analysis by reverse transcription quantitative polymerase chain reaction (RT-qPCR) followed by further data analysis. Multiple linear regression (MLR) analysis was performed to identify and characterize relationships among multiple factors (relative mRNA levels of a gene in NV or VV or their ratio, as dependent variables) and sex (independent variable, used individually or in combination with other patient’s characteristics). For sex-specific gene regulation analysis, all potential binding sites for sex hormone receptors were identified in each gene’s regulatory region sequence. Results: Using the independent method and a replicative patient sample set, we validated our previous data on 23 genes’ differential expression in VVs and obtained insights on their sex-specific regulation. Sex (as an individual independent variable or in combination with other parameters—patient characteristics such as Age, BMI, CEAP class, Height, VVD manifestation and duration) was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the STK38L expression in VVs (with its higher mRNA level in NVs and VVs of women compared to men); sex was a strong predictor (0.6 < R < 0.79; p (R) < 0.05) for the TIMP1 expression in VVs (with its lower mRNA level in VVs of women compared to men); sex was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the EBF1 expression in NVs (with its lower mRNA level in NVs of women compared to men). Conclusions: Confirmed differential expression of the studied genes in VVs indicates their plausible participation in vein wall remodeling. Sex-specific expression in veins for the subset of those genes suggests their hormonal regulation as well as other mechanisms involved in VV pathogenesis. This work enriches our understanding of sex features for the development of VVs and may provide the foundation for future investigations and beneficial treatment options. Full article

This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and Granger causality testing to identify long-term stable causal relationships among variables. These relationships are embedded within the C-mLSTM architecture, enabling the model to effectively capture both temporal dependencies and causal information within the data. Furthermore, the model integrates Bayesian optimization (BO) and twin delayed deep deterministic policy gradient (TD3) algorithms for synergistic optimization. This combined TD3-BO approach achieves an 11.11% improvement in the mean absolute percentage error (MAPE) on average compared to the base model without optimization. For 1–24 h SWH forecasts, the proposed TD3-BO-C-mLSTM outperforms the benchmark models TD3-BO-LSTM and TD3-BO-mLSTM in prediction accuracy. Finally, a Shapley additive explanations (SHAP) analysis was conducted on the input features of the BO-C-mLSTM model, which reveals interpretability patterns consistent with the two-stage causal feature selection methodology. This research demonstrates that integrating causal modeling with optimization strategies significantly enhances time-series forecasting performance. Full article

The necessity to produce new biodegradable polymeric materials, to overcome the economic model, based on the linear economy, and to apply the circular economy model is a global problem. As a result, components unutilized derived from industrial processes are becoming increasingly valuable and useful to create new materials. This work focuses on the production of bioplastics based on poly (vinyl) alcohol (PVA) that have been modified with flavonoid fraction, liquid fraction obtained after digestion with cellulase and pectinase, and the solid material remaining after enzyme treatment, obtained from Citrus bergamia by-product (the so-called “pastazzo”). This last one is an almost completely unutilized product, although it is a potential rich source of biological active compounds. Enzymatic and non-enzymatic green extraction protocol have been employed to separate the different fractions and to make it more suitable to functionalize the PVA, suppling new properties to the bioplastics in a dose-dependent manner. Morpho-functional analysis was conducted by SEM, XRD, colorimetry, UV–visible and ATR-FTIR spectroscopy. Regarding optical properties, the obtained results show that transparency of the film in terms of light transmittance (T%) for PVA alone is very high, but when functionalized it had a reduced T%. From the data obtained, the functionalized films acquire antioxidant activity, as well as good mechanical properties, making them good candidates for biodegradable packaging for preserving the shelf life of different fruits and vegetables as confirmed by the food fresh-keeping test performed on apple samples. Full article

Fiber-reinforced foamed composites have recently attracted growing interest due to their potential in sustainable construction and advanced additive manufacturing. However, their performance strongly depends on the type of matrix and fiber system used. The aim of this study was to perform a comparative analysis of matrix type and fiber composition on the porosity, thermal behavior, and mechanical performance of 3D-printed fiber-reinforced foamed composites. To this end, cementitious mixtures (M1–M3) were compared with alkali-activated hybrid binder systems (M4–M6). The results revealed marked differences in mechanical strength, dimensional stability, moisture transport, and interlayer cohesion. Alkali-activated specimens, particularly M5 and M6, exhibited superior compressive, flexural, and shear strength; reduced water penetration; and improved fiber–matrix bonding, associated with a denser and more homogeneous pore structure. In contrast, cementitious composites showed greater dimensional stability and easier process control, indicating practical advantages for large-scale on-site applications. The results highlight that while alkali activation and hybrid fiber reinforcement enhance structural performance, non-activated foamed concretes remain promising for applications prioritizing simplicity, reproducibility, and thermal insulation. Full article

The greater wax moth Galleria mellonella (Lepidoptera: Galleriinae) represents a ubiquitous apicultural pest that poses significant threats to global beekeeping industries. The larvae damage honeybee colonies by consuming wax combs and tunneling through brood frames, consequently destroying critical hive infrastructure including brood-rearing areas, honey storage cells, and pollen reserves. Larval feeding behavior is critically dependent on chemosensory input for host recognition and food selection. In this study, we conducted a transcriptome analysis of larval heads and bodies in G. mellonella. We identified a total of 25 chemosensory genes: 9 odorant binding proteins (OBPs), 1 chemosensory protein (CSP), 5 odorant receptors (ORs), 4 gustatory receptors (GRs), 4 ionotropic receptors (IRs) and 2 sensory neuron membrane proteins (SNMPs). TPM normalization was employed to assess differential expression patterns of chemosensory genes between heads and bodies. Nine putative chemosensory genes were detected as differentially expressed, suggesting their potential functional roles. Subsequently, we quantified expression dynamics via reverse transcription quantitative PCR in major chemosensory tissues (larval heads, adult male and female antennae), revealing adult antennal-biased expression for most chemosensory genes in G. mellonella. Notably, two novel candidates (GmelOBP22 and GmelSNMP3) exhibited particularly high expression in larval heads, suggesting their crucial functional roles in larval development and survival. These findings enhance our understanding of the chemosensory mechanisms in G. mellonella larvae and establish a critical foundation for future functional investigations into its olfactory mechanisms. Full article

This research evaluates three wind data sources, the Sentinel-1 wind product, the global reanalysis ERA5, and the regional reanalysis CARRA, across Iceland’s North, South, West, and East coastal regions. The analysis mainly focuses on validating Sentinel-1 high-resolution capabilities for capturing fine-scale wind patterns in coastal zones, where traditional reanalyses may have tangible limitations. Performance is evaluated through intercomparison of datasets and analysis of regional wind speed variability, with in situ coastal meteorological observations providing ground-truth validation. The results highlight the relative strengths and limitations of each source, offering guidance for improving wind-driven and wind-dependent applications in Iceland’s coastal regions, such as hazard assessment, marine operations, and renewable energy planning. Full article

This study investigates the effect of hydrogen charging time on the mechanical properties and microstructural evolution of low-carbon ferrite–pearlite steel that has been in service for over 30 years in natural gas transmission. Specimens were subjected to in-situ electrochemical hydrogen charging for varying durations, followed by tensile testing. Detailed microstructural analysis was performed using scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Despite negligible changes in the overall hydrogen content ($C_H$≈ 4.0 wppm), significant alterations in fracture morphology were observed. Fractographic and TEM analyses revealed a clear transition from ductile fracture in uncharged specimens to a predominance of brittle fracture modes (quasi-cleavage, intergranular, and transgranular) in hydrogen-charged samples. The results show time-dependent microstructural changes, including increased dislocation density and the formation of prismatic loop debris, particularly within the ferrite phase. Prolonged charging leads to localized embrittlement, which is explained by enhanced hydrogen trapping at ferrite-cementite boundaries, grain boundaries, and dislocation cores. TEM investigations further indicated a sequential activation of hydrogen embrittlement mechanisms: initially, Hydrogen-Enhanced Localized Plasticity (HELP) dominates within ferrite grains, followed by Hydrogen-Enhanced Decohesion (HEDE), particularly at ferrite-cementite interfaces in pearlite colonies. These findings demonstrate that extended hydrogen charging promotes defect localization, dislocation pinning, and interface decohesion, ultimately accelerating fracture propagation. The study provides valuable insight into the degradation mechanisms of ferrite-pearlite steels exposed to hydrogen, highlighting the importance of charging time. The results are essential for assessing the reliability of legacy pipeline steels and guiding their safe use in future hydrogen transport infrastructure. Full article

The Deliblato Sands Special Nature Reserve encompasses five municipalities and several settlements. This significantly protected region has a strong relationship between its ecosystem and the people who live there. The local population benefits from various advantages provided by this reserve. The residents’ quality of life greatly depends on the reserve’s resources. When used responsibly, they can guarantee a sustainable system with assets that are renewable. Additionally, both locals and tourists benefit from the utilization of forest space for recreation. Above all, endangered plant and animal species are protected in the Deliblato Sands woodlands. Therefore, the role of the local population in protecting this reserve is crucial for the survival of these species. Visitors from both domestic and foreign countries visit this reserve each year in considerable numbers. The study included a quantitative methodology, in which data were collected using questionnaires. The study’s goal is to find out whether the nature reserve has an impact on residents’ lives, activities, and habits, i.e., whether characteristics have an impact on respondents’ contentment. This research aims to examine how the protected area (PA) affects the life of the local community. A total of 1450 residents were surveyed regarding the impact of the Deliblato Sands ecosystem on their habits and activities. Analysis of the data indicates that the inhabitants are significantly impacted by the PA. The strongest impacts are grouped into the ecological and socio-cultural dimensions, while the economic dimension is the one with the weakest impact. The study’s value is evident in the crucial information that was supplied for the creation of national and local planning documents pertaining to the development of rural areas and tourism. The active participation of communities must be the foundation of any planning for tourism growth. Full article

Background: The ESR2 gene encodes Estrogen Receptor-β1 (ERβ1), a putative tumor suppressor in hormone-dependent malignancies. Although ERβ biology has been studied extensively at the expression level, the functional impact of nonsynonymous SNPs (nsSNPs) and untranslated-region (UTR) variants in ESR2 remains underexplored. Methods: We retrieved variants from Ensembl and performed an integrative in silico assessment using PredictSNP, I-Mutant, MUpro, HOPE, MutPred2, and CScape for pathogenicity, oncogenicity and structural stability; STRING/KEGG/GO for pathway context; RegulomeDB and polymiRTS for regulatory effects; and cBioPortal for pan-cancer clinical outcomes (breast (BRCA), endometrial (UCEC), and ovarian (OV)). We evaluated effects of nsSNPs on ERβ1 stability, ligand-binding/DNA-binding domains, co-factor recruitment, and post-transcriptional regulation. Results: Across tools, 93 missense nsSNPs were consistently predicted to be deleterious. Notably, several variants were found to destabilize ERβ1, particularly within the ligand-binding domains (LBD) and DNA-binding domains (DBD). Putative oncogenic drivers R198P and D154N showed high CScape scores and very low population frequencies, consistent with pathogenicity. Several substitutions were predicted to impair coactivator binding and disrupt interactions with key transcriptional partners, including JUN, NCOA1, and SP1. At the post-transcriptional level, rs139004885 was predicted to disrupt miRNA binding, while 3′UTR rs4986938 showed strong regulatory potential and comparatively high population frequency; by contrast, most other identified SNPs were rare. Clinically, pan-cancer survival analyses indicated worse overall survival (OS) in BRCA for ESR2-Altered cases (HR ≈ 2.25; q < 0.001), but better OS in UCEC (HR ≈ 0.24; q ≈ 0.014) and OV (HR ≈ 0.29; q < 0.001), highlighting a tumor-type-specific association. Conclusions: This integrative analysis prioritizes high-impact ESR2 variants that likely impair ERβ1 structure and shows context-dependent clinical effects. Despite their generally low frequency (except for rs4986938), prospective validation linking variant class to ERβ expression and survival outcomes is needed to support biomarker development and therapeutic applications. Full article

This study investigates sustainable development in Europe, moving beyond simple SDG scores and linear models. Using data from 2000 to 2024 across European countries, Principal Component Analysis (PCA) was employed to create indices for the People, Planet, and Prosperity domains of sustainable development. Multivariate Adaptive Regression Splines (MARSs) captured nonlinearities and interactions in the influence of these domains on sustainable development on the European continent. The results show that progress depends on crossing key thresholds rather than gradual change. In the People domain, exceeding minimum levels leads to significant gains. The Planet domain acts as both stabiliser and constraint: moderate ecological performance fosters progress, while extremes hinder it. Prosperity has modest direct effects but becomes influential when combined with social and environmental factors. Interactions reveal that sustainable development arises from domain synergies, with social progress offering the most significant leverage. The findings indicate the need to target lagging regions to surpass social thresholds, protect ecological stability, and ground prosperity in inclusive, eco-friendly frameworks. Overall, the study highlights how data-driven methods can identify tipping points in sustainability and provides a transferable framework for achieving the 2030 Agenda. Full article

Cataract surgery is the most common eye operation worldwide and is regarded as one of the safest procedures in medicine. Yet, despite its low complication rates, it generates a disproportionate share of litigation. The gap between excellent safety profiles and rising medico-legal claims is driven less by surgical outcomes than by patient expectations, often shaped by healthcare marketing and the promise of risk-free recovery. This narrative review explores the clinical and legal dimensions of cataract surgery, focusing on complications, perioperative risk factors, and medico-legal concepts of predictability and preventability. Particular emphasis is given to European frameworks, with the Italian Gelli-Bianco Law (Law No. 24/2017) providing a model of accountability that balances innovation and patient safety. Analysis shows that liability exposure spans all phases of surgery: preoperative (inadequate consent, poor documentation), intraoperative (posterior capsule rupture, zonular instability), and postoperative (endophthalmitis, poor follow-up). Practical strategies for risk reduction include advanced imaging such as macular OCT, rigorous adherence to updated guidelines, systematic video recording, and transparent perioperative communication. Patient-reported outcomes further highlight that satisfaction depends more on visual quality and dialogue than on spectacle independence. By translating legal principles into clinical strategies, this review offers surgeons actionable “surgical–legal pearls” to improve outcomes, strengthen patient trust, and reduce medico-legal vulnerability in high-volume cataract surgery. Full article

Gamma-aminobutyric acid (GABA) is a bioactive amino acid with anti-inflammatory and neurotransmitter properties, yet limited information exists regarding its production by Lactiplantibacillus paraplantarum. We evaluated factors that influence GABA synthesis by L. paraplantarum HK-1 and assessed its production in vitro and under simulated gastrointestinal conditions. GABA production was analyzed using HPLC with pre-column derivatization, gene expression was assessed through RT-qPCR, and probiotic characteristics were evaluated using standard microbiological methods. L. paraplantarum HK-1 demonstrated dose-dependent GABA production with monosodium glutamate (MSG) supplementation, achieving maximum levels at 500 mM MSG (161.1 µg/mL), which was significantly higher than those in other treatments (p < 0.01). A strong positive correlation was observed between MSG concentration and GABA production (r = 0.908, p = 0.002). Gene expression analysis revealed a 61.6-fold higher gadB expression at 500 mM MSG compared to 250 mM, though statistical significance with GABA production was not achieved (r = 0.741, p = 0.259). The strain exhibited appropriate probiotic characteristics including γ-hemolytic activity, bile salt tolerance, and acid resistance. Under simulated gastrointestinal conditions, maximum GABA production occurred in the distal colon (148.3 ± 19.0 µg/mL with probiotic vs. 7.2 ± 6.2 µg/mL control), with overall production significantly higher in probiotic-treated groups (p < 0.001). Overall, L. paraplantarum HK-1 produced GABA throughout gastrointestinal phases and showed traits consistent with probiotic use. These results position HK-1 as a promising GABA-producing candidate for functional food applications, pending in vivo validation. Full article

The widespread emergence of multimodal data on social platforms has presented new opportunities for sentiment analysis. However, previous studies have often overlooked the issue of detail loss during modal interaction fusion. They also exhibit limitations in addressing semantic alignment challenges and the sensitivity of modalities to noise. To enhance analytical accuracy, a novel model named MAHFNet is proposed. The proposed architecture is composed of three main components. Firstly, an attention-guided gated interaction alignment module is developed for modeling the semantic interaction between text and image using a gated network and a cross-modal attention mechanism. Next, a contrastive learning mechanism is introduced to encourage the aggregation of semantically aligned image-text pairs. Subsequently, an intra-modality emotion extraction module is designed to extract local emotional features within each modality. This module serves to compensate for detail loss during interaction fusion. The intra-modal local emotion features and cross-modal interaction features are then fed into a hierarchical gated fusion module, where the local features are fused through a cross-gated mechanism to dynamically adjust the contribution of each modality while suppressing modality-specific noise. Then, the fusion results and cross-modal interaction features are further fused using a multi-scale attention gating module to capture hierarchical dependencies between local and global emotional information, thereby enhancing the model’s ability to perceive and integrate emotional cues across multiple semantic levels. Finally, extensive experiments have been conducted on three public multimodal sentiment datasets, with results demonstrating that the proposed model outperforms existing methods across multiple evaluation metrics. Specifically, on the TumEmo dataset, our model achieves improvements of 2.55% in ACC and 2.63% in F1 score compared to the second-best method. On the HFM dataset, these gains reach 0.56% in ACC and 0.9% in F1 score, respectively. On the MVSA-S dataset, these gains reach 0.03% in ACC and 1.26% in F1 score. These findings collectively validate the overall effectiveness of the proposed model. Full article

The scaling of microwave plasma technologies from successful laboratory demonstrations to larger industrial applications usually involves an increase in microwave power. This upgrade is accompanied by a higher electron density (and electric conductivity) of the plasma that often limits the power efficiency of the device. In this paper, we address this issue through a focused computational study of electromagnetic characteristics of a microwave system containing plasma. Our approach employs finite-different time-domain analysis supported by a simple model which characterizes the plasma medium using plasma frequency and the frequency of electron-neutral collisions. Based on experimental data for electron density with respect to power, the plasma frequency is generated as a linear function of power, thus enabling a direct understanding of how frequency characteristics of the reflection coefficient and patterns of the electric field may vary for different power levels in a variety of plasma scenarios. For a cavity modeled after conventional plasma applicators, computational results illustrate complex behavior of the field with respect to power. When the power is increased, energy efficiency may decrease, remain low, or increase depending on where the operating frequency stands with respect to the system’s resonances. The proposed modeling approach identifies the system parameters which are most impactful in tuning the system to resonance, thus informing the design variables for subsequent computer-aided design of the scaled system. Full article