Search Results (45,164)

Employing the WaveMIMO methodology, the present numerical study evaluates a submerged horizontal plate (SHP) device under the incidence of representative regular and realistic irregular waves associated with the sea state off the coast of Rio Grande, Brazil. The dual functionality of the SHP device is investigated, considering its operation as a breakwater (BW) and as a wave energy converter (WEC). The main focus of this study is to investigate the effects of numerical beach (NB) positioning on the hydrodynamic response of the SHP. The governing equations for mass, momentum, and volume fraction are solved using the finite volume method (FVM), while the water–air interaction is modeled through the volume of fluid (VOF) approach. The analysis assessed the influence of SHP length (L_p) using five different values. For the tested Rio Grande sea state, SHP geometry, two-dimensional numerical model, and adopted hydrodynamic indicators, the results show that the exclusive use of representative regular waves was not sufficient to reproduce the hydrodynamic trends obtained under realistic irregular waves. The SHP demonstrates its highest BW performance in reducing the significant wave height at 3L_p for representative regular waves and realistic irregular waves. As a WEC, it achieves its highest axial velocity at 3L_p for representative regular waves and 1.5L_p and 2L_p for realistic irregular waves. The performance of the SHP as BW-WEC is the highest at 3L_p for regular waves and 2.5L_p for realistic irregular waves. In contrast to previous work, in which the NB was kept at a fixed position, the present study indicates that the downstream computational-domain configuration, including the relative positioning between the SHP and the NB, is an important factor affecting the monitored hydrodynamic response and should be carefully defined in CFD wave-flume simulations. Full article

Rainfall is a critical factor inducing slope instability, and accurate prediction of the factor of safety (FOS) of slopes under rainfall conditions is of paramount importance for disaster prevention and mitigation. Conventional numerical simulation methods incur high computational costs, while individual machine learning models are often insufficient to adequately capture the nonlinear spatiotemporal evolution characteristics of multiple factors under coupled multi-physics fields. To address these limitations, this paper proposes a Transformer–LSTM prediction framework. First, a fluid–structure coupling model for rainfall-affected slopes is constructed using COMSOL, and multi-factor orthogonal experiments are performed to generate multi-dimensional time-series data. Subsequently, a Transformer–LSTM fusion deep learning model is built, in which LSTM is employed to extract the temporal dynamic characteristics of rainfall infiltration, and the self-attention mechanism of the Transformer is leveraged to enhance feature extraction and global dependency modeling of key disaster-causing factors. Experimental results demonstrate that the Transformer–LSTM model significantly outperforms traditional PSO-LSTM, PSO-SVM, and standalone Transformer or LSTM models in terms of both prediction accuracy and generalization capability. Its coefficient of determination (R²) remains above 0.94, and key evaluation metrics—including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—attain the lowest values among the compared models. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability framework is introduced to quantitatively elucidate the model’s predictive decision-making and to establish a physically grounded causal mapping with geotechnical mechanisms. It is confirmed that effective cohesion and slope angle exert a dominant interactive effect on the degradation of slope stability, providing data-driven support for wide-area monitoring of rainfall-induced landslides. Full article

The diversity and abundance of the brain virome is an active field of investigation. However, how the brain virome relates to the presence of viruses outside of the nervous system remains unclear. The rationale for this study is that analyses across multiple biologically linked compartments within the same individuals provide an important opportunity to evaluate virome discordance and viral burden. To characterize viral prevalence and burden across anatomical compartments, we applied the targeted viral enrichment method ViroFind to matched postmortem brain (n = 66), cerebrospinal fluid (CSF; n = 24), and peripheral samples (spleen, peripheral blood mononuclear cells, and lymph nodes; n = 66) from individuals with and without human immunodeficiency virus (HIV) infection and substance use disorder (SUD) in the National NeuroAIDS Tissue Consortium. We detected nucleic acids from 27 viruses representing 12 taxa. Several viruses, including adenovirus, torque teno virus, Epstein–Barr virus, human herpesvirus 6 and 7, cytomegalovirus, parvovirus, and JC polyomavirus, showed significant inter-compartment differences in prevalence or burden. CSF exhibited lower overall viral diversity than brain or peripheral samples, whereas peripheral samples showed the highest viral burden. CNS viral detection was more likely when the same virus was also detected in the periphery. We also detected HBV and HCV in CNS samples despite them not being classically regarded as neurotropic. Broader virome profiling showed greater peripheral viral burden and diversity in HIV-positive than HIV-negative individuals, whereas SUD was not associated with overall viral burden differences. These findings highlight important cross-compartment differences in viral detection, including occurrence of occult HBV infection within the CNS, and support the value of CNS–periphery comparisons in virome studies. These findings can contribute to improved diagnosis and management of viral infections. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

Background and Objectives: Maintenance of condylar position during bilateral sagittal split ramus osteotomy (BSSRO) is important for preserving temporomandibular joint biomechanics and skeletal stability. During surgery, loss of muscle tone under general anesthesia may alter the condyle–fossa relationship, making accurate repositioning of the proximal segment challenging. Although manual positioning remains the most commonly used intraoperative approach, evidence regarding its ability to preserve the preoperative condyle–fossa relationship remains limited. This study evaluated changes in the condyle–fossa relationship following BSSRO performed with manual proximal segment positioning. Materials and Methods: This single-center retrospective study included lateral cephalometric radiographs of 14 patients (8 females, 6 males; aged 19–29 years) with skeletal Class III malocclusion treated with combined orthodontic treatment and BSSRO. Radiographs were obtained preoperatively (T0), immediately postoperatively (T1), and at the final follow-up examination (T2). Condylar position was assessed using a Cartesian coordinate system, joint space measurements, and the Condyle Position Index (CPI). Statistical analyses were performed using the Friedman and Wilcoxon signed-rank tests (p < 0.05). Results: Significant differences were observed in CPI and anterior joint space measurements across the observation periods. Interval analysis demonstrated increased CPI values and decreased anterior joint space measurements between T1 and T2, whereas no significant immediate postoperative changes were observed. Intra-observer reliability was excellent, with intraclass correlation coefficients exceeding 0.90 for all variables. Conclusions: Manual positioning of the proximal segment during BSSRO may provide acceptable immediate postoperative condyle–fossa stability but may not completely maintain the preoperative condyle–fossa relationship over time. Although no significant immediate postoperative changes were observed, significant changes in the condyle–fossa relationship were identified at the final follow-up examination. These findings support the need for further prospective studies incorporating clinical temporomandibular joint assessment and three-dimensional imaging. Full article

Macrobrachium nipponense is an economically important freshwater prawn species in China, where larger individuals have higher commercial value than smaller ones. Previous studies indicated that bestrophin 3 (BEST3) may play a regulatory role in the growth performance of this species. Therefore, the present study investigated the potential functions of the BEST3 gene in the growth of M. nipponense by using quantitative real-time PCR (qPCR) and RNA interference (RNAi), and also searched for growth-related single-nucleotide polymorphisms (SNPs) within this gene. qPCR results revealed that Mn-BEST3 expression was widely detected across all tested tissues, suggesting that this gene may serve multiple functions in M. nipponense. Notably, its highest expression was observed in muscle tissue, which was significantly greater than that in all other tested tissues (p < 0.05), implicating a potential role for this gene in growth regulation. Further qPCR analysis confirmed that the synthesized dsBEST3 effectively reduced Mn-BEST3 expression. The body mass gain percentage in the dsBEST3-injected group was significantly lower than that in the dsGFP-injected control group, with differences becoming significant from Day 12 onward in both males and females (p < 0.05). These findings indicate that Mn-BEST3 plays a positive role in regulating growth in M. nipponense. Finally, three SNPs were identified in the coding region of this gene. The associations of these three SNPs with growth performance, including body weight and total length, were further validated using 50 male and 50 female prawns derived from a full-sib family at approximately 5 months post-hatching. Among them, one SNP (S31_23192836) was found to be associated with growth performance in both male prawns and female prawns. Overall, this study confirmed the positive regulatory role of BEST3 in the growth of M. nipponense and identified growth-related SNPs within this gene. These results improve our understanding of the molecular mechanisms underlying growth regulation and support the production of populations with superior growth traits through marker-assisted selection. Full article

Seasonal variation in aroma quality is critical for commercial grading of Xinyang Maojian (XYMJ) green tea, and how seasonal changes shape its volatile composition and aroma profile remains unclear. This study investigated the volatile profiles of XYMJ harvested in spring, summer, and autumn using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and odor activity value (OAV) analysis. A total of 93 volatile compounds were identified, with alkenes, alcohols, and esters being the most numerous chemical classes. Total volatile content decreased significantly seasonally (p < 0.05), being highest in spring (1716.68 μg/kg), followed by summer (1566.72 μg/kg) and autumn (1378.21 μg/kg). PCA and PLS-DA clearly distinguished seasons. Using a dual-filtering strategy (variable importance in the projection > 1.0 and p < 0.01), 14 differential volatile metabolites were identified as core seasonal markers. Geraniol, cis-jasmone, and indole were identified as key drivers of the premium floral fragrance in spring XYMJ, while cis-3-hexenyl hexanoate and linalool peaked in the summer harvest. OAV results and cross-modal sensory interaction principles suggest that the superior flavor of spring XYMJ arises from both higher aromatic intensity and an optimized aroma-taste balance. These findings provide useful insights into the seasonal variations in the metabolic and chemical profiles of XYMJ, enhancing our understanding of the biochemical markers associated with its production timeline. Full article

Background: Extremely and very preterm infants are at high risk for adverse neurodevelopmental outcomes. Early prediction remains challenging when relying on static clinical markers or single time-point neuroimaging. Serial cranial ultrasound (CUS) enables repeated bedside assessment of cerebral growth and may provide longitudinal trajectory biomarkers integrable with routine clinical data. Methods: In this retrospective two-center cohort study, 89 preterm infants (<32 weeks’ gestation and/or <1500 g birth weight) were assessed using the Bayley-III at 24 months corrected age. Brain volume trajectory features were derived from serial CUS using a standardized ellipsoid model. A three-level analytical framework was applied as follows: univariate regression (62 models, Bonferroni and Benjamini–Hochberg correction), multivariate SVM classification with five-fold GroupKFold cross-validation, ensuring patient-level data separation and feature importance analysis with interaction characterization using stratified Spearman correlation and two-dimensional partial dependence plots. Results: Multivariate classification yielded modest but above-chance performance (balanced accuracy 0.277–0.463, Cohen’s κ 0.042–0.152). Respiratory morbidity duration—mechanical ventilation and BPD severity—were the most robustly associated univariate predictors, surviving Bonferroni correction. Brain volume trajectory features showed no significant univariate associations but contributed conditionally within the multivariate framework as follows: the interaction between brain volume slope and trajectory linearity was the strongest for cognitive outcome (Δr = 0.47), and postnatal growth restriction showed amplified adverse effects at lower birth weight for motor outcome (Δr = 0.47). Conclusions: This study demonstrates the value of ML methods as structured analytical tools for characterizing predictor–outcome relationships in preterm neurodevelopment; respiratory morbidity and brain volume trajectory features emerged as the most informative predictor classes. Prospective multicenter validation is required before clinical translation. Full article

The rapid adoption of Generative Artificial Intelligence (GenAI) tools is transforming learning practices in higher education, raising important questions about their educational value and impact on student learning. This study examines how university students use GenAI tools in both academic and everyday contexts, with emphasis on usage patterns, applications and motivations. A large-scale voluntary survey was conducted with 788 undergraduate students from a single public university in Greece, with respondents drawn from multiple schools and disciplines. Data were collected through an online questionnaire and analyzed using descriptive and inferential statistical methods to explore frequency of use, application categories and motivations for engagement with GenAI tools. The results indicate a high level of reported GenAI engagement among the participants, with ChatGPT emerging as the most frequently used tool. Students primarily rely on GenAI tools for information searching, understanding academic content and supporting academic tasks, while creative and entertainment-related uses are less frequent. Overall, the findings suggest that students perceive GenAI tools as useful for learning support and efficiency improvement. The results indicate that GenAI tools are becoming integrated into students’ reported learning practices. They also highlight the need for clear pedagogical guidelines and systematic AI literacy integration in teaching and learning. Full article

Over the last two decades, social enterprises in Romania have taken on an increasingly important role in the production and provision of social goods and services for vulnerable groups. Although forms of the social economy have long existed in Romanian society, sustainability remains a constant concern, particularly in the context of dependence on European Union structural funds. This study identifies the multidimensional factors influencing the sustainability of social enterprises in Romania, combining a quantitative analysis of 121 certified social enterprises from the National Register (2016–2022) with qualitative case studies of 15 selected organisations. Revenue diversification was significantly associated with financial sustainability (β = −0.28, p < 0.01), whilst high dependence on EU funding (>50% of revenue) was negatively associated with long-term viability (HR = 2.18, p = 0.002). Participation in networks was associated with markedly higher five-year survival rates (87.2% for network members versus 69.5% for non-members). Six key sustainability strategies were identified: hybrid revenue models, integration into the value chain, community inclusion, adaptive leadership, strategic partnerships, and effective communication of results and impact. Environmental sustainability is addressed with preliminary proxy evidence from the qualitative component; systematic measurement of this dimension represents a priority for future research. The findings confirm the absence of an integrated support framework for the sustainable activities of the social economy and, in some cases, the limited capacity of public institutions to support vulnerable groups. Policy recommendations include phased funding mechanisms, transitional support instruments and the systematic development of regional ecosystems. Full article

Tetranychus urticae is a highly destructive, polyphagous mite that has developed resistance to multiple acaricides, necessitating the evaluation of new compounds. Isocycloseram is a novel insecticide with potential to control this mite; the effects of its sublethal concentrations are still uninvestigated. In this study, an age-stage, two-sex life table model was used to evaluate the sublethal effects of isocycloseram concentrations (LC₁₀ and LC₃₀) on population growth, reproduction, and development of the two-spotted spider mite. The results showed that the LC₁₀ and LC₃₀ values were 0.012 mg/L and 0.022 mg/L, respectively. Sublethal concentrations of LC₁₀ significantly affected population growth by reducing fertility, while LC₃₀ significantly prolonged the immature stage and reduced average fecundity by 37%. With the LC₃₀ treatment, the net reproductive rate R₀ decreased by 43%, and the intrinsic rate of increase r decreased significantly, from 0.152 day⁻¹ to 0.117 day⁻¹. The doubling time DT was extended by 30%, from 4.55 days to 5.92 days. This study covers the importance of life table analysis for investigating sublethal effects and for ensuring that, when isocycloseram is incorporated into integrated pest management, both its direct toxicity and its effects on population dynamics are considered. Full article

Podophyllum hexandrum Royle (1834) (Himalayan Mayapple), a key Himalayan medicinal plant and source of podophyllotoxin for anticancer drugs, is declining due to overharvesting, habitat loss, and climate change. This study, conducted from May to September 2024 across nine populations in Swat, Pakistan, assessed its ethnobotanical importance and conservation status. A total of 331 participants (270 individual surveys + 61 group discussions) were included. Using ethnobotanical surveys, IUCN-CMP threat frameworks, and spatial analysis, results showed high cultural value (Use Value = 0.63–0.92) and strong consensus for rheumatism (ICF = 0.91) and fever (ICF = 0.89). Fidelity levels were 94% for rheumatism and 88% for fever. Only 35% of respondents demonstrated conservation awareness. Overharvesting was the main threat, followed by habitat degradation and climate change. The species showed restricted distribution (EOO = 4250 km²; AOO = 295 km²), high fragmentation (0.68), and a 35% population decline over 10 years. It is assessed as Endangered (EN B1ab (iii, v)). This study provides the first integrated ethnobotanical–GIS assessment of P. hexandrum in the Hindu Kush–Himalaya region of Pakistan, offering measurable conservation baselines and community perception data previously unavailable. Findings align with global medicinal plant decline trends and support integration with CBD, SDGs (3 and 15), and potential CITES listing. Urgent conservation actions are required, including community-based management, habitat restoration, sustainable harvesting, ex situ conservation, and policy enforcement. Full article

Students with disabilities, particularly those needing additional support or intervention to manage emotions and behaviors, build healthy relationships, and navigate social and academic demands, face heightened risks of high school pushout that can be traced back to their transition into high school. Project Elevate (PE) is a multi-component intervention that strategically invests in early coordinated student, family, and school supports to prevent barriers associated with high school pushout, such as a lack of continuity of effective services across school sites. This mixed-methods pilot study examined the implementation of PE with three 8th-grade students and their parents during their last term in middle school. This study includes quantitative pre–post descriptive analyses of multi-informant reports of students’ social, emotional, and behavioral skills, as well as descriptive analyses of weekly teacher- and parent-reported behavior and student attendance. Qualitative analysis using the Framework Method was applied to student and parent interviews and open-ended responses on a satisfaction questionnaire to understand their experience receiving PE support. Session case notes were also used as contextual data to describe implementation processes and contextualize findings. Results indicated improvements in student attendance and reductions in home-based behavioral concerns, with mixed findings across school-based outcomes. Students and parents reported high satisfaction with the intervention, highlighting the value of individualized support, goal setting, and strengthened communication with schools. Findings from this intervention development pilot study provide preliminary evidence regarding the implementation and perceived value of PE. Results also highlight the importance of culturally responsive, relationship-centered practices that affirm student strengths and support access to educational opportunities. Further investigation of PE in larger studies is warranted. Full article

This study proposes a hybrid forecasting framework that integrates Kalman Filtering (KF), Markov Switching (MS), and nonlinear recurrent learning for stock-index prediction. The KF component smooths short-term price noise, the MS model identifies latent return–volatility regimes, and the LSTM/GRU components learn nonlinear temporal patterns from regime-conditioned information. The framework is evaluated using the CSI 300, S&P 500, and Nikkei 225 indices through forecasting-accuracy measures, Bootstrap Diebold–Mariano tests with Modified Bayes Factor evidence, out-of-sample trading simulations, and robustness checks. The empirical results show that regime conditioning is the primary source of forecasting and economic improvement. KF–MS–LSTM performs best for the CSI 300 and Standard MS performs strongest for the S&P 500, while KF–MS–LSTM and KF–MS–GRU are more competitive for the Nikkei 225. In contrast, models without regime information, including pure LSTM/GRU and the standalone Transformer, generally exhibit weaker forecasting and trading performance. The findings suggest that latent market-state information is more important than neural-network complexity alone for robust financial forecasting, while the incremental value of Kalman filtering and recurrent learning remains market dependent. Overall, the results support regime-aware forecasting as an interpretable and economically meaningful approach for stock-index prediction under heterogeneous market environments. Full article

Aluminum is undoubtedly a key material in modern industry. Flat plates made of aluminum alloys are widely used in construction, aeronautics, automotive, and others. The current paper presents an analysis of the behavior of a thin plate made of Al7075-T651 aluminum alloy, subjected to a uniaxial stress, and clamped at one end. The results of the numerical simulation with FRANC2D software have been used for accurate determination of the stress intensity factors (K_I, K_II) and being validated for the simple cases using analytical calculations. The Failure Assessment Diagram (FAD) based on the toughness ratio Kr and the load ratio Lr has been used to evaluate the structural integrity of cracked components based on the load, its position, crack size, and the fracture properties of the material. The FAD analysis results highlight the significant influence of crack position on the values of the K factor. The edge and inclined cracks lead to increases in stress intensity factors and to the occurrence of mixed-mode loading conditions. The study demonstrates the effectiveness and usefulness of the proposed methodology in the analysis of structures with discontinuities and emphasizes the importance of crack positioning in assessing the safety of engineering components. Full article