Search Results (573)

Background: Within the framework of social cognition, conservatism can be conceptualized as a strategy for addressing fundamental psychological needs. Therefore, it is hypothesized that individuals with conservative orientations exhibit stronger reactions to perceived threats compared to their less conservative counterparts. Aim: To perform an exploratory scoping systematic review of existing literature examining behavioral, physiological, neurophysiological, and emotional responses associated with the relationship between conservatism and threat perception. Method: Following PRISMA guidelines, a systematic search was conducted using PubMed and Google Scholar primary databases, resulting in the inclusion of 19 relevant articles. Results: Approximately three-fifths (11 of 19 studies; 57.9%) provided empirical support for the hypothesis that conservatism is positively associated with threat sensitivity. These findings reveal a complex and nuanced relationship between conservatism and threat perception, with recent evidence—including large-scale longitudinal data and experimental manipulations of COVID-19–related threats—indicating weak or context-dependent associations. The overall pattern highlights substantial heterogeneity across methodological approaches, with mixed results particularly among physiological and priming studies. Conclusions: While the majority of evidence supports a relationship between political conservatism and threat sensitivity, the magnitude of this association appears modest, emphasizing the importance of considering moderating variables such as cultural context, the type of threat, and methodological variations in measurement in future research. Full article

We investigate the excitation and variability of migrating and non-migrating diurnal and semi-diurnal tides in the mesosphere and lower thermosphere (MLT) during the 2021 Northern Hemisphere sudden stratospheric warming (SSW). Zonal wind data from MERRA-2 reanalysis are decomposed into tidal components using a two-dimensional least-squares harmonic fitting technique. The migrating diurnal tide (DW1) strengthens at low latitudes following the SSW onset, whereas the migrating semi-diurnal tide (SW2) intensifies at high latitudes. Non-migrating diurnal tides (D0, DW2, DW3) arise from nonlinear interactions between DW1 and stationary planetary waves (SPWs), while non-migrating semi-diurnal tides (SW1, SW3) are modulated by stratospheric ozone variability linked to planetary-wave activity. The zonally symmetric semi-diurnal tide (S0) responds primarily to dynamical perturbations associated with the SSW. Eastward non-migrating diurnal tides (DE2, DE3) correlate strongly with total precipitable water vapor (TPWV), indicating tropospheric latent-heat forcing, whereas DE1 exhibits weak coupling. These results reveal distinct, latitude-dependent excitation pathways connecting stratospheric and tropospheric dynamics to tidal variability in the MLT during major SSW events. Full article

Blast damage to structural members poses serious risks to both buildings and people, making it important to understand how these elements behave under extreme loads. Columns in reinforced concrete (RC) structures are especially critical, as their sudden failure can trigger progressive collapse, unlike beams or slabs that have more redundancy. This state-of-the-art review brings together the current knowledge of the blast response of RC columns, focusing on their failure patterns, dynamic behavior, and key loading mechanisms. The studies covered include experiments, high-fidelity numerical simulations, emerging machine learning approaches, and analytical models for columns of different shapes (square, rectangular, circular) and strengthening methods, such as fiber reinforcement, steel-concrete composite confinement, and advanced retrofitting. Composite columns are also reviewed to compare their hybrid confinement and energy-absorption advantages over conventional RC members. Over forty specific studies on RC columns were analyzed, comparing the results based on geometry, reinforcement detailing, materials, and blast conditions. Both near-field and contact detonations were examined, along with factors like axial load, standoff distance, and confinement. This review shows that RC columns respond very differently to blasts depending on their shape and reinforcement. Square, rectangular, and circular sections fail in distinct ways. Use of ultra-high-performance concrete, steel fibers, steel-concrete composite, and fiber-reinforced polymer retrofits greatly improves peak and residual load capacity. Ultra-high-performance concrete can retain a significantly higher fraction of axial load (often >70%) after strong blasts, compared to ~40% in conventional high-strength RC under similar conditions. Larger sections, closer stirrups, higher transverse reinforcement, and good confinement reduce spalling, shear failure, and mid-height displacement. Fiber-reinforced polymer and steel-fiber wraps typically improve residual strength by 10–15%, while composite columns with steel cores remain stiff and absorb more energy post-blast. Advanced finite element simulations and machine learning models now predict displacements, damage, and residual capacity more accurately than older methods. However, gaps remain. Current design codes of practice simplify blast loads and often do not account for localized damage, near-field effects, complex boundary conditions, or pre-existing structural weaknesses. Further research is needed on cost-effective, durable, and practical retrofitting strategies using advanced materials. This review stands apart from conventional literature reviews by combining experimental results, numerical analysis, and data-driven insights. It offers a clear, quantitative, and comparative view of RC column behavior under blast loading, identifies key knowledge gaps, and points the way for future design improvements. Full article

Developing nations, such as the Southern African nations, fail to achieve environmental sustainability because of bad governance and high levels of corruption. The misallocation and misuse of resources and green finance worsen environmental problems in such nations; hence, there is a need for correct policy reforms and improvements in institutional quality if the green transition is to be achieved. However, the literature lacks sound empirical evidence that could unlock this problem and direct us to the adoption of relevant policies. This research is an attempt to examine the role of institutional quality and green financial technology in promoting sustainable environments in Southern African nations with high environmental problems. Therefore, data from the seven Southern African nations from 2000 to 2022 are employed in the analysis. The research model is analyzed with the ‘Methods of Moments Quantile Regression’, which overcomes panel data-related problems such as ‘heterogeneity’ and ‘cross-sectional dependence’. The key findings of this research indicate the symmetric positive influence of institutional quality, green finance and renewable energy in supporting environmental sustainability. Additionally, financial development supports environmental sustainability, but its influence is asymmetric, where positive significant influence is in the lower quantile and weak negative effect in the top quantile. Nonetheless, technological innovation worsens environmental sustainability in the Southern African nations, calling for the need to leapfrog to cleaner technologies that have been adopted in developed nations. Full article

Submerged aquatic macrophytes play a key role in stream ecosystems, but their recovery in historically degraded Flemish streams is often limited. This study investigates whether sediment contamination constrains natural macrophyte germination and early seedling establishment. To address this knowledge gap, we combined a controlled mesocosm experiment with an analysis of long-term monitoring data from Flemish streams. The mesocosms showed that higher levels of sediment contamination reduced seedling emergence, indicating that sediment quality can directly inhibit germination and early establishment. In addition, historical monitoring data revealed only a weak association between sediment quality and macrophyte occurrence, pointing to the importance of interacting drivers such as hydrology, light availability, and habitat structure. Together, these findings highlight sediment contamination as a context-dependent but relevant barrier to macrophyte recruitment, underscoring the need to integrate sediment quality into broader restoration planning for streams in Flanders and abroad. Full article

A seawall settlement is a critical concern in marine engineering, as an excessive or uneven settlement can undermine structural stability and diminish the capacity to withstand marine hydrodynamic actions such as storm surges, waves, and tides. Accordingly, accurate settlement prediction is vital to ensuring seawall safety. To address the lack of clustering methods that capture the time-series characteristics of monitoring points and the limitations of hyperparameter sensitivity of conventional LSTM models, this study proposes a hybrid model integrating Dynamic Time Warping-based Hierarchical Clustering (DTW-HC) and an Adaptive Joint Search Optimization-enhanced Long Short-Term Memory Model (AJSO-LSTM). First, DTW-HC is employed to cluster monitoring points according to their time series characteristics, thereby constructing a spatial panel data structure that incorporates both temporal evolution and spatial heterogeneity. Then, an AJSO-LSTM model is developed within each cluster to capture temporal dependencies and improve prediction performance by overcoming the weaknesses of a conventional LSTM. Finally, using seawall settlement monitoring data from a real engineering case, the proposed method is validated by comparing it with a statistical model, a back-propagation Neural Network (BP-ANN), and a conventional LSTM. Results demonstrate that the proposed model consistently outperforms these three benchmark methods in terms of prediction accuracy and robustness. This confirms the potential of the proposed framework as an effective tool for seawall safety management and long-term service evaluation. Full article

Accurate prediction of the rate of penetration (ROP) in deep and ultra-deep wells remains a major challenge due to complex downhole conditions and limited real-time data. To address the issues of physical inconsistency and weak generalization in conventional da-ta-driven approaches, this study proposes a mechanism-guided and attention-enhanced deep learning framework. In this framework, drilling physical principles such as energy balance are reformulated into differentiable constraint terms and directly incorporated in-to the loss function of deep neural networks, ensuring that model predictions strictly ad-here to drilling physics. Meanwhile, attention mechanisms are integrated to improve feature selection and temporal modeling: for tree-based models, we investigate their implicit attention to key parameters such as weight on bit (WOB) and torque; for sequential models, we design attention-enhanced architectures (e.g., LSTM and GRU) to capture long-term dependencies among drilling parameters. Validation on 49,284 samples from 11 deep and ultra-deep wells in China (depth range: 1226–8639 m) demonstrates that the synergy between mechanism constraints and attention mechanisms substantially improves ROP prediction accuracy. In blind-well tests, the proposed method achieves a mean absolute percentage error (MAPE) of 9.47% and an R² of 0.93, significantly outperforming traditional methods under complex deep-well conditions. This study provides reliable intelligent decision support for optimizing deep and ultra-deep well drilling operations. By improving prediction accuracy and enabling real-time anomaly detection, it enhances operational safety and efficiency while reducing drilling risks. The proposed approach offers high practical value for field applications and supports the intelligent development of the oil and gas industry. Full article

Cognitive diagnostic models (CDMs) are commonly used in educational assessment to uncover the specific cognitive skills that contribute to student performance, allowing for precise identification of individual strengths and weaknesses and the design of targeted interventions. Traditional CDMs, however, depend heavily on a predefined Q-matrix that specifies the relationship between test items and underlying attributes. In this study, we introduce a hidden Markov log-linear additive cognitive diagnostic model (HM-LACDM) that does not require a Q-matrix, making it suitable for analyzing longitudinal assessment data without prior structural assumptions. To support scalable applications, we develop a variational Bayesian inference (VI) algorithm that enables efficient estimation in large datasets. Additionally, we propose a method to reconstruct the Q-matrix from estimated item-effect parameters. The effectiveness of the proposed approach is demonstrated through simulation studies. Full article

This paper examines the asymptotic behavior of the conditional hazard function using kernel-based methods, with particular emphasis on functional weakly dependent data. In particular, we establish the asymptotic normality of the proposed estimator when the covariate follows a functional quasi-associated process. This contribution extends the scope of nonparametric inference under weak dependence within the framework of functional data analysis. The estimator is constructed through kernel smoothing techniques inspired by the classical Nadaraya–Watson approach, and its theoretical properties are rigorously derived under appropriate regularity conditions. To evaluate its practical performance, we carried out an extensive simulation study, where finite-sample outcomes were compared with their asymptotic counterparts. The results showed the robustness and reliability of the estimator across a range of scenarios, thereby confirming the validity of the proposed limit theorem in empirical settings. Full article

The shipping industry remains heavily dependent on heavy fuel oils, which account for approximately 77% of fuel consumption and contribute significantly to greenhouse gas (GHG) emissions. In line with the IMO’s decarbonization targets, ammonia has emerged as a promising carbon-free alternative. This study evaluates the strategic viability of ammonia, especially green production, as a marine fuel through a hybrid SWOT–Best–Worst Method (BWM) analysis, combining literature insights with expert judgment. Data were collected from 17 maritime professionals with an average of 15.7 years of experience, ensuring robust sectoral representation and methodological consistency. The results highlight that opportunities hold the greatest weight (0.352), particularly the criteria “mandatory carbon-free by 2050” (O3:0.106) and “ammonia–hydrogen climate solution” (O2:0.080). Weaknesses rank second (0.270), with “higher toxicity than other marine fuels” (W5:0.077) as the most critical concern. Strengths (0.242) underscore ammonia’s advantage as a “carbon-free and sulfur-free fuel” (S1:0.078), while threats (0.137) remain less influential, though “costly green ammonia” (T3:0.035) and “uncertainty of green ammonia” (T1:0.034) present notable risks. Overall, the analysis suggests that regulatory imperatives and environmental benefits outweigh safety, technical, and economic challenges. Ammonia demonstrates strong potential to serve as viable marine fuel in achieving the maritime sector’s long-term decarbonization goals. Full article

Background/Objectives: The medial longitudinal arch (MLA) is initially masked by a fat pad that makes the foot appear flat. In preschool age, this fat pad resorbs, and the arch becomes more defined. The exact age at which the arch attains its final form remains uncertain due to high inter-individual variability and differing assessment methods, which complicates the distinction between physiological development and potential abnormalities. Moreover, commonly used classification terms such as “flat” or “normal” do not adequately reflect the developmental progression and may be misleading in young children. This study aimed to describe the MLA developmental patterns and propose an adjusted classification terminology to improve clinical differentiation between feet undergoing normal developmental changes and cases requiring intervention. Methods: The present study employs both cross-sectional (285 children aged 4.00–8.99 years) and longitudinal (50 children measured annually between ages 4–6) designs. Foot dimensions were assessed using standard anthropometry, and the MLA was assessed via podograms using the Chippaux–Smirak index (CSI). To better reflect the developmental nature of the MLA, the arch was categorized as “formed” and “unformed”. Cross-sectional data were analyzed with ANOVA and visualized using LOESS regression, longitudinal data with linear mixed models, and relationships between CSI and foot dimensions with Spearman’s correlation. Results: MLA development showed significant changes up to age 6, with the most pronounced changes occurring between ages 4 and 5 and slowing thereafter. Children with an unformed arch at age 4 exhibited a steeper developmental trajectory than those with an already advanced arch form. Correlations between arch shape and foot dimensions were statistically significant but weak. No significant between-sex differences were observed. Conclusions: The timing of the most pronounced phase of medial longitudinal arch (MLA) development varies between individuals and is typically completed by 6 years of age, with no sex-dependent differences. Age 6 therefore represents a practical milestone for reliable clinical assessment, since earlier classifications risk misinterpreting normal developmental variation as pathology. Full article

Monitoring air pollution remains a significant challenge for both environmental policy and public health, particularly in parts of Eastern Europe where industrial structures are undergoing transition. In this paper, we examine long-term air quality trends in Poland between 1990 and 2023, drawing on multiple sources: satellite observations (from 2019 to 2025), ground-based stations, and official national emission inventories. The analysis focused on sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM₁₀, PM_2.5). Data were obtained from the Sentinel-5P TROPOMI sensor, processed through Google Earth Engine, and monitored by the Chief Inspectorate of Environmental Protection (GIOŚ, Warsaw, Poland) and the National Inventory Report (NIR, Warsaw, Poland), compiled by KOBiZE (The National Centre for Emissions Management, Warsaw, Poland). The results show a decline in emissions. SO₂, for instance, dropped from about 2700 kilotons in 1990 to under 400 kilotons in 2023. Ground-based measurements matched well with inventory data (correlations around 0.75–0.85), but the agreement was noticeably weaker when satellite estimates were compared with surface monitoring. In addition to analyzing emission trends, this study examined the relationship between pollution levels and meteorological conditions across major Polish cities from 2019 to mid-2024. Pearson’s correlation analysis revealed strong negative correlations between temperature and pollutant concentrations, especially for SO₂, reflecting the seasonal nature of pollution peaks during colder months. Wind speed exhibited ambiguous relationships, with daily data indicating a dilution effect (negative correlations), whereas monthly averages revealed positive associations, likely due to seasonal confounding. Higher humidity was consistently linked to higher pollution levels, and precipitation showed weak negative correlations, likely influenced by seasonal weather patterns rather than direct atmospheric processes. These findings suggest that combining different monitoring methods, despite their quirks and mismatches, provides a fuller picture of atmospheric pollution. They also point to a practical challenge. Further improvements will depend less on sweeping industrial reform and more on shifting everyday practices, like how homes are heated and how people move around cities. Full article

Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using $R^2$, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to $R^2=0.86$ for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability. Full article

Breast cancer is one of the most common malignancies worldwide, affecting 2.3 million and causing 670,000 deaths in women annually. However, data indicate that the risk of developing breast cancer decreases with pregnancy at a young age, and each subsequent pregnancy further reduces the risk by approximately 10%. One of the characteristics inherent in both the mammary gland epithelium in pregnant women and luminal epithelial adenocarcinomas is the increased expression of NIS—the sodium/iodide symporter, whose defective cytoplasmic forms possess pro-oncogenic properties. Therefore, the analysis of the degree of influence of pregnancy on NIS expression in breast cancer cells is of medical interest. The aim of this study is to conduct a comparative morphological analysis of NIS expression in breast cancer cells according to the number of pregnancies of each patient. This study included 161 patients with triple-negative breast cancer who visited the P.A. Herzen Moscow Oncology Research Institute from 2020 to 2023. Immunohistochemical examination was performed using antibodies to NIS. The gravidity status of women was determined based on provided medical documentation. The degree of NIS expression was assessed using a modified Gainor scale. Statistical analysis was performed using mean and standard deviation (SD) depending on the normality of the distribution (Lilliefors test: p > 0.20); a p-value ≤ 0.05 was considered statistically significant. The degree of correlation between variables was assessed using Kendall’s tau rank correlation coefficient. A weak to moderate negative correlation (τ: −0.369) was found between the number of pregnancies and the degree of NIS expression in triple-negative breast cancer cells. In patients with triple-negative breast cancer, a weak to moderate negative correlation was found between the degree of NIS expression and gravidity status. The discovered phenomenon is likely due to the terminal differentiation of the mammary gland epithelium that occurs during pregnancy. This may potentially indicate the suppression of pro-oncogenic properties of atypical cells developed from the epithelium that has undergone terminal differentiation. Full article

This study presents a novel and efficient iterative scheme in the setting of CAT(0) spaces and investigates the convergence properties for a generalized class of mappings satisfying the Garcia–Falset property using the proposed iterative scheme. Strong and weak convergence results are established in CAT(0) spaces, generalizing many existing results in the literature. Furthermore, we discuss the stability and data dependence of the new iterative process. Numerical experiments include an analysis of error values, the number of iterations, and computational time, providing a comprehensive assessment of the method’s performance. Moreover, graphical comparisons demonstrate the efficiency and reliability of the approach. The obtained results are utilized in solving integral equations. Additionally, the paper concludes with a polynomiographic study of the newly introduced iterative process, in comparison with standard algorithms, such as Newton, Halley, or Kalantari’s $B_4$ iteration, emphasizing symmetry properties. Full article