Search Results (6,899)

Losing a sibling to a drug-related death can lead not only to profound grief but also to unexpected psychological growth. This mixed-method study examined such growth among siblings bereaved by a drug-related death in Norway, combining survey data from 78 participants with interviews from ten siblings. Quantitative findings showed that appreciation of life and personal strengths were the most prominent domains of growth. Regression analysis indicated that self-efficacy explained most of the variance in growth when controlling for time since death, whereas social support did not make a unique contribution. Qualitative findings added depth by revealing how growth was experienced through closer family relationships and a heightened sense of empathy toward people in vulnerable situations. These accounts suggest that growth may involve a reorientation of values and deeper relational ties, aspects that standardized measures may not fully capture. Although based on a small and relatively homogeneous sample, the integrated results point to the importance of internal coping resources and family connectedness in fostering growth after a stigmatized loss. Further research should explore these mechanisms in more diverse populations and examine how they evolve over time. Full article

Due to long-term burial underground, oil and gas pipelines are susceptible to external surface corrosion influenced by time and soil conditions, which can lead to leakage and burst failures. Pipeline failure not only results in significant economic losses but also has catastrophic impacts on human safety and the environment. Therefore, modeling and analyzing the corrosion failure of these pipelines is of critical practical importance to ensure their safe operation during service. Addressing the insufficient research on correlation effects in current reliability evaluations of corroded pipelines, this paper proposes a calculation method for the failure probability of corroded oil and gas pipelines that considers the influence of two-layer correlations. Taking a specific segment of the Shaanxi–Beijing pipeline as a case study, the Monte Carlo sampling algorithm is employed to calculate the impact of two-layer correlations and the quantity of defect on the pipeline’s failure probability. Furthermore, a sensitivity analysis of the correlation coefficients is conducted. The results indicate that the influence of defect correlation on pipeline failure probability is significantly more pronounced than that of random variable correlation. The probabilities of pinhole leakage and burst failure decrease as the correlation coefficient between defects increases, while they increase with the number of defects. Random variable correlation exhibits no impact on pinhole leakage probability; however, the burst failure probability decreases with an increasing correlation coefficient between wall thickness and pipe diameter, but increases as the correlation between initial defect length and depth grows. Furthermore, the correlation coefficient between axial and radial defect growth rates exerts a bidirectional effect on burst failure probability: during the first 25 years of the prediction period, the failure probability increases with the correlation coefficient, whereas it subsequently decreases after approximately 25 years. These findings are applicable to the reliability evaluation of oil and gas pipelines containing multiple corrosion defects, providing valuable technical references for ensuring safe operation and the steady supply of energy resources. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

Background: Severe burn injuries are associated with prolonged consequent wound healing, substantial symptoms burden, and delayed, sometimes incomplete, functional recovery. Photobiomodulation using Multiwave Locked System (MLS) LASER therapy has been proposed as an adjunctive intervention to support tissue repair and thereby improve rehabilitation outcomes, but related clinical evidence in burn populations remains limited. Materials and Methods: This comparative study included 65 patients with severe burn injuries, of whom 35 were prospectively treated with adjunctive MLS LASER therapy, in addition to standard care, and 30 retrospectively identified patients, who received standard care alone, served as controls. The primary outcome was the time until complete epithelialization, while secondary outcomes included: reduction in wound surface, pain intensity, pruritus severity, scar quality, and functional improvements. Assessments were performed at baseline and after a standardized follow-up period of up to 20 days. Results: Patients treated with MLS LASER therapy achieved complete epithelialization significantly earlier than controls (median 40 vs. 73 days, p < 0.001) and demonstrated greater wound area reduction (median 434 vs. 137 cm², p = 0.0012). In multivariable analyses adjusted for burn extent, burn depth, age, and diabetes mellitus, considered as factors worsening evolution, MLS LASER therapy remained independently associated with shorter time to epithelialization and greater reduction in wound dimension. Significant improvements favoring the MLS group were also observed regarding pain, pruritus, scar quality, and functional outcomes, all assessed using specific evaluation tools (p < 0.001). Conclusions: Adjunctive MLS LASER therapy appears to be associated with improved wound healing dynamics and enhanced rehabilitation outcomes in patients with severe burn injuries. These findings should be interpreted with caution given the study limitations, including the non-randomized design and relatively small sample size. MLS LASER therapy may represent a promising adjunctive option in the conservative management of burn injuries; however, further prospective randomized studies are required to confirm these results and to define optimal treatment protocols. Full article

Urban coastal wetlands along the Peruvian Pacific coast are increasingly affected by urban expansion, pollution, and hydrological alterations, compromising their ecological integrity. In this context, the spatiotemporal variation of the aquatic macrophyte community and its relationship with limnological conditions and drivers of change were evaluated in the Santa Rosa wetland (Chancay, Lima). The objective is to evaluate the spatiotemporal variation of the aquatic macrophyte community in the Santa Rosa wetland and analyze its relationship with physicochemical limnological variables and drivers of change. Sampling was conducted during two contrasting hydrological seasons in 2022: T1 (low-water season) and T2 (high-water season), at six sampling points (P1–P6). Physicochemical variables (water depth, temperature, pH, conductivity, total dissolved solids—TDS, total suspended solids—TSS, dissolved oxygen—DO, turbidity, nitrate—NO₃⁻, ammonium—NH₄⁺, phosphate—PO₄³⁻, and dissolved organic matter—DOM) were measured, and the relative abundance of aquatic macrophytes was evaluated. Drivers of change were identified through direct observation and a structured matrix, with phosphate a PCoA performed to summarize spatiotemporal trends. Data were analyzed using Principal Component Analysis (PCA), Co-inertia analysis, and Multi-Response Permutation Procedures (MRPP). Significant spatiotemporal variation was observed in physicochemical parameters (p < 0.05), with moderate covariation between the two matrices (RV = 0.47). A total of ten aquatic macrophyte species were recorded, with higher abundance of Pontederia crassipes and Pistia stratiotes in T1, and Hydrocotyle ranunculoides and Bacopa monnieri in T2. The most relevant drivers of change were solid waste, livestock grazing, organic contamination, and urban expansion. Spatial heterogeneity was observed in the drivers of change affecting the Santa Rosa wetland, forming a mosaic of areas with different impact profiles. Despite multiple anthropogenic pressures, the Santa Rosa wetland maintains a limnological structure and a functionally coupled macrophyte community, suggesting that essential ecological processes are maintained within the temporal scope of this study. The observed covariation between physicochemical conditions and vegetation confirms the persistence of essential ecological processes, even within an altered urban context. This study demonstrates that integrating biotic components, limnological variables, and drivers of change is fundamental to understanding and monitoring the ecological dynamics of urban wetlands along the Peruvian coast. Full article

This study examines the firm-specific factors associated with the level and quality of compliance with the International Integrated Reporting Framework (IIRF) among companies in the United Arab Emirates (UAE), an emerging economy characterized by a growing sustainability-oriented institutional context. Although the Securities and Commodities Authority (SCA) mandates listed companies to publish an integrated report, it does not prescribe a specific reporting framework. As a result, alignment with the IIRF and the depth of disclosure remain largely discretionary. Using a sample of 89 non-financial firms listed on the Dubai Financial Market (DFM) and Abu Dhabi Securities Exchange (ADX), an Integrated Reporting Disclosure Score (IRDS) was constructed through content analysis based on 43 criteria derived from the IIRF. Regression and dominance analyses were employed to examine the relationship between firm characteristics and the level of IIRF compliance. The results indicate that firm size, profitability, board size, and gender diversity are positively associated with higher levels of IIRF alignment and disclosure quality, while financial leverage and board independence are not significantly associated with disclosure levels. The dominance analysis further shows that firm size, board size, gender diversity, and profitability account for the majority of the model’s explanatory power. Overall, the findings contribute to the literature by providing empirical evidence on voluntary compliance with international integrated reporting standards beyond mandatory reporting requirements in an emerging market context. Full article

Human infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. Identifying reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. This study established and refined an untargeted high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (HPLC-ESI-MS/MS) protocol for a comprehensive lipidomic and metabolomic analysis of human spermatozoa, using only 1.25 million cells per sample. Compared with previous reports, our optimized method achieved an unparalleled level of analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites. This corresponds to nearly a 7600-fold improvement in detection efficiency per cell compared with previously published approaches. Lipidomic analysis revealed that the most abundant lipid classes were glycerophospholipids (39%), cholesterol (20%) and fatty acids (19%), with cholesterol representing the single most abundant compound. This observation is consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling similarly identified glycerophospholipids (44%), eicosanoids (14%) and N-acyl amino acids (12%) as the major metabolite classes. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. Overall, this work establishes a robust, sensitive, and scalable analytical framework that enables the high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. Full article

Topsoil features within a depth of 0–10 cm are vital for making soil management decisions that affect crop production. However, the use of these soil features to predict soil organic carbon (SOC) at 10–20 cm requires further investigation. The study aims to predict SOC at 10–20 cm using total nitrogen (total N), pH, cation exchange capacity (CEC), and SOC at 0–10 cm and select a suitable model for predicting SOC. This study was conducted using data from a long-term tillage, winter wheat (Triticum aestivum L.)-fallow experiment established in autumn 1970. Treatments included moldboard plow, stubble mulch, no-till, and native sod, each replicated three times. Soil samples were collected from each plot at depths of 0–10 cm and 10–20 cm in April of 2010 and 2011. Models were fit using ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), random forests, and Bayesian additive regression trees (BART). Using root mean square error (RMSE), SOC was predicted with an accuracy of 1.44 g kg⁻¹ or relative RMSE (rRMSE) of 13.5%. This was achieved with the OLS model that used total N, pH, and CEC as predictors. The good performance of the OLS model over more flexible machine learning approaches suggests that the information predictors provide about the response variable (SOC) is approximately linear. As the agricultural dataset was small (n = 24), the less complex model reduced the chances of overfitting while keeping the variance relatively low. Random forests and BART had an rRMSE greater than 21%. Statistical models could be used to estimate SOC at 10–20 cm and reduce destructive soil analysis methods. Full article

In our previous analysis of the eclipse timing variation patterns of eclipsing binaries located in and near the Northern Continuous Viewing Zone (NCVZ) of the TESS space telescope, 135 hierarchical triple star candidates were found. Now, two additional years of TESS observations are available and, hence, we have extended the former analysis with the use of the new observational data. We now detect 168 triple star candidates in the updated and reanalyzed sample. The majority (∼74%) of them are identical to the former triples candidates. For many of them, our new solutions are more certain than the original ones. Therefore, we can now conclude that we have identified at least 66 short-period hierarchical triple stellar systems in the NCVZ with full confidence. In the case of the majority of the remaining systems in our sample, the presence of a close third stellar component appears to be very likely. We also identify additional, longer timescale period variations in 34 systems (20% of the total sample) and conclude that in at least three systems the presence of a fourth stellar component is quite plausible. Finally, we report the complete disappearance of the eclipses in two former EBs and detect eclipse depth variations in seven other EBs as well. We interpret this effect as the consequence of changing orbital inclination caused by a non-coplanar third body. Full article

Nature-based tourism in protected areas brings economic benefits but can also lead to negative environmental impacts, such as trail degradation. This study aimed to quantify hiking-induced degradation on the Mvubu and Nkonkoni trails in Enseleni Nature Reserve, South Africa. Data were collected through systematic sampling at 20 points along each trail, with 50-m intervals between sampling locations. Several trail degradation indicators were recorded, including: trail grade (TG), landform grade (LG), cross-sectional area (CSA), soil compaction, surface composition, soil texture, and soil moisture. Maximum incision depth (MID) and trail width (WID) were treated as response variables. Statistical relationships between degradation indicators and response variables were analysed using linear regression and partial least squares regression (PLSR). The results indicated significant differences (p < 0.05) between the two trails for several degradation indicators, including surface composition (specifically soil cover), soil compaction, soil texture, and soil moisture. PLSR models explained 19–20% of the variance in MID and 12–55% of the variance in WID. Such weak model performance suggests that trail degradation may be influenced by additional factors not measured in this study. In particular, human behavioural factors, such as hiker avoidance of muddy sections, may play an important role in shaping patterns of trail degradation beyond the measured environmental variables. Early signs of rill erosion were observed on the Mvubu Trail, while informal trail formation was evident on the Nkonkoni Trail. Consequently, the study recommends a dual-track strategy involving revegetation along with the installation of water bars and check dams on the Mvubu Trail to prevent rilling, and “Leave-No-Trace” visitor education for the Nkonkoni Trail to reduce informal path formation. Full article

Background: The Mediterranean diet (MD) represents a nutritionally balanced eating pattern characterized by high consumption of fruits, vegetables, legumes, nuts, whole grains, olive oil, fish, and extra-virgin olive oil as the principal fat source and limited intake of red meat and refined sugars. Emerging evidence indicates that the MD’s anti-inflammatory and antioxidant properties extend beyond systemic health, potentially reducing the risk and severity of periodontitis. This narrative review aimed to synthesize current evidence on the relationship between adherence to the MD and periodontal health outcomes. Methods: A comprehensive electronic literature search was conducted in PubMed without restrictions on publication date. Fourteen studies, ranging from 2019 to 2025, were included, encompassing human, clinical, experimental, and review designs that examined MD adherence and its effects on periodontal parameters. Eligible studies included cross-sectional, cohort, randomized controlled trials; systematic reviews; and animal models assessing clinical periodontal indices, inflammatory biomarkers, or microbial composition. Extracted data included study design, population characteristics, dietary assessment methods, and primary periodontal findings. Results: Most studies demonstrated that greater adherence to the MD was associated with improved periodontal parameters, including reduced probing pocket depth, clinical attachment loss, and bleeding on probing. Interventional trials showed significant reductions in systemic inflammatory markers such as IL-1β, TNF-α, and CRP, along with decreased counts of periodontopathogenic bacteria. Experimental studies further revealed the protective role of oleic acid and polyphenols in regulating macrophage activity, suppressing osteoclastogenesis, and enhancing IL-10 expression via epigenetic modulation. However, heterogeneity in dietary scoring systems, sample characteristics, and follow-up duration limited direct comparison, and not all associations reached statistical significance. Conclusions: Current evidence supports a beneficial association between MD adherence and periodontal health, mediated through anti-inflammatory, antioxidant, and microbiome-stabilizing mechanisms. Further standardized longitudinal and interventional studies are needed to confirm causality and refine nutritional strategies for periodontal disease prevention and management. Full article

The Digital Elevation Model (DEM), a crucial data source for waterlogging simulations, significantly influences the accuracy of the results. In complex urban environments, low-resolution DEMs cannot accurately capture the depressional characteristics of city roads or water levels during river floods, leading to distorted urban flooding simulations. To this end, this study developed a novel technique to refine the public 30 m resolution DEM to 1 m resolution for the urban area. The method establishes a zero-flood-depth baseline by correcting the elevations of key elements to improve the accuracy of urban inundation simulations. This is achieved through a semi-automated vector–raster integration workflow, which includes (1) road elevation correction that classifies road vectors, samples elevation at end points, and applies linear interpolation to depict roads as depressions and (2) waterway elevation correction that raises riverbed levels to match adjacent banks, simulating a pre-flood critical state. Polk County in Florida, USA, and the Central Business District (CBD) in Beijing, China, were selected as the research areas. In Polk County, we directly verified its accuracy using the official 1m LiDAR DEM. The results show that the mean error (ME), the root mean square error (RMSE), and the Standard Deviation (SD) improved by approximately 9%, 20%, and 65%, respectively, compared with previous methods. In Beijing, we used a volume matching algorithm to simulate urban flood depths under different rainfall scenarios, indirectly validating the results by comparing the simulated inundation volumes with the theoretical rainfall amounts. The refinement of the DEM significantly improved the topological accuracy of the river channels and the reliability of flood depths, and we analyzed two types of water accumulation behavior patterns. Overall, this study innovatively integrates public raster and vector data, utilizing known attribute information to refine public datasets and construct a highly precise water accumulation model. Full article

Statistical depth provides a center–outward ordering of multivariate observations and is widely used in nonparametric inference. We study depth-based tests for multivariate two-sample problems and examine the behaviour of different depth notions using the DD plot (data-depth plot) across a variety of distributional space. The DD plot illustrates that depth functions differ in their sensitivity to distributional differences, emphasizing the importance of depth selection in two-sample testing. We propose a new two-sample test statistic, log DDR, constructed from ratios of numerical depth values rather than depth-induced ranks. Simulation studies under multiple scenarios and for three representative depth functions indicate that log DDR achieves improved power relative to several competing depth-based nonparametric tests. The results further demonstrate that the performance of log DDR and existing methods depends strongly on the chosen depth function, consistent with insights from the DD plot. These findings support a two-stage testing approach in which the DD plot is used to guide the choice of depth notion before applying log DDR for homogeneity testing. Full article

Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article

Matrix Product States (MPSs) have become an indispensable symmetry-based representation for simulating quantum systems on near-term hardware by constraining entanglement entropy through a fixed bond dimension $\mathsf{\chi }$. This study identifies a critical “rank explosion” phenomenon that destabilizes this low-rank manifold during conditional quantum diffusion processes. We empirically demonstrate that the introduction of conditional guidance—essential for semantic control—injects global correlations that drive the effective Schmidt rank to increase by $4\times$ (from $\mathsf{\chi }=4$ to $16$), saturating the simulation limits and necessitating quantum circuits with approximately $1.8\times {10}^3$ Controlled-NOT (CNOT) gates. Such circuit depths fundamentally exceed the operational coherence budgets of Noisy Intermediate-Scale Quantum (NISQ) devices. To mitigate this structural instability, we propose Rank-Aware Conditional Synthesis (RACS), a sampling framework that maintains the latent trajectory within a prescribed MPS manifold through step-wise manifold projection and time-shift error correction. Experimental results on real-world semantic data reveal that RACS reduces reconstruction error, or Mean Squared Error (MSE) by 30.8% and enhances latent trajectory smoothness by 36.8% compared to conventional post hoc truncation. At a fixed hardware-efficient rank of $\mathsf{\chi }=4$, RACS achieves a +4.8% fidelity gain and exhibits superior robustness against depolarizing noise. By resolving the tension between conditional expressivity and entanglement constraints, RACS provides a principled, hardware-aware methodology for high-fidelity quantum generative modeling. Full article