Search Results (6,235)

Analyzing large datasets of animal vocalizations requires efficient bioacoustic methods for categorizing the vocalization types. This study evaluates the effectiveness of different vocalization clustering methods, comparing fully automated and semi-automated methods against the gold standard of manual expert annotations. Effective methods achieve good clustering performance whilst minimizing human effort. We release a new dataset of 1454 zebra finch vocalizations manually clustered by experts, on which we evaluate (i) fully automated clustering using off-the-shelf methods based on sound embeddings and (ii) a semi-automated workflow relying on refining the embedding-derived clusters. Clustering performance is assessed using the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Results indicate that while fully automated methods provide a useful baseline, they generally fall short of human-level consistency. In contrast, the semi-automated workflow achieved agreement scores comparable to inter-expert reliability, approaching the levels of expert manual clustering. This demonstrates that refining embedding-derived clusters reduces annotation time while maintaining gold standard accuracy. We conclude that semi-automated workflows offer an optimal strategy for bioacoustics, enabling the scalable analysis of large datasets without compromising the precision required for robust behavioral insights. Full article

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

Ecosystem health assessment is essential for informing ecological protection and sustainable management, yet current evaluation frameworks often overlook the foundational role of natural background conditions and struggle with methodological uncertainties in indicator weighting, particularly in ecologically fragile regions. To address these dual challenges, this study proposes a novel Base–Pressure–State–Response (BPSR) framework that systematically integrates key natural background factors as a fundamental “Base” layer. Focusing on the Qinling Mountains—a critical ecological barrier in China—we implemented this framework at the county scale using multi-source data (2000–2023) and introduced a Monte Carlo simulation with triangular probability distributions to quantify and synthesize weight uncertainties from multiple methods, thereby enhancing assessment robustness. Furthermore, the Geodetector method was employed to quantitatively identify the driving forces behind the spatiotemporal heterogeneity of ecosystem health. Supported by 3S technology, our analysis demonstrates a sustained improvement in ecosystem health: the composite index rose from 0.723 to 0.916, healthy areas expanded from 60.17% to 68.48%, and nearly half of the region achieved a higher health grade. Spatially, a persistent “low–south, high–north” pattern was observed, shaped by human disturbance gradients, while temporally, the region evolved from localized improvement (2000–2010) to broad-scale recovery (2010–2023), despite lingering degradation in human-dominated zones. Driving force analysis revealed a shift from early dominance by natural and land use factors to a later complex interplay where urbanization pressure and climatic conditions jointly shaped the health pattern. The BPSR framework, combined with probabilistic weight optimization and driving force quantification, offers a methodologically robust and spatially explicit tool that advances ecosystem health evaluation and supports targeted ecological governance, policy formulation, and sustainable management in fragile mountain ecosystems, with transferable insights for similar regions globally. Full article

In a high-dimensional classification dataset, feature selection is crucial for improving classification performance and computational efficiency by identifying an informative subset of features while reducing noise, redundancy, and overfitting. This study proposes a novel metaheuristic-based ensemble feature selection approach by combining the complementary strengths of Modified Binary Particle Swarm Optimization (MBPSO) and Binary Dynamic Grey Wolf Optimization (BDGWO). The proposed MBPSO–BDGWO ensemble method is specifically designed for high-dimensional classification problems. The performance of the proposed MBPSO–BDGWO ensemble method was rigorously evaluated through an extensive simulation study under multiple high-dimensional scenarios with varying correlation structures. The ensemble method was further validated on several real datasets. Comparative analyses were conducted against single-stage feature selection methods, including BPSO, BGWO, MBPSO, and BDGWO, using evaluation metrics such as accuracy, the F1-score, the true positive rate (TPR), the false positive rate (FPR), the AUC, precision, and the Jaccard stability index. Simulation studies conducted under various dimensionality and correlation scenarios show that the proposed ensemble method achieves a low FPR, a high TPR/Precision/F1/AUC, and strong selection stability, clearly outperforming both classical and advanced single-stage methods, even as dimensionality and collinearity increase. In contrast, single-stage methods typically experience substantial performance degradation in high-correlation and high-dimensional settings, particularly BPSO and BGWO. Moreover, on the real datasets, the ensemble method outperformed all compared single-stage methods and produced consistently low MAD values across repetitions, indicating robustness and stability even in ultra-high-dimensional genomic datasets. Overall, the findings indicate that the proposed ensemble method demonstrates consistent performance across the evaluated scenarios and achieves higher selection stability compared with the single-stage methods. Full article

Melasma management is challenged by heterogeneity in patient presentation, particularly among individuals with darker skin tones. This study applied k-means clustering, an unsupervised machine learning algorithm that partitions data into k distinct clusters based on feature similarity, to identify patient subgroups that could provide a hypothesis-generating framework for future precision strategies. We analysed clinical and demographic data from 150 South African women with melasma using k-means clustering. The optimal number of clusters was determined using the Elbow Method and Bayesian Information Criterion (BIC), with t-distributed stochastic neighbour embedding (t-SNE) visualization for assessment. The k-Means algorithm identified seven exploratory patient clusters explaining 52.6% of the data variability (R² = 0.526), with model evaluation metrics including BIC = 951.630 indicating optimal model fit and a Silhouette Score of 0.200 suggesting limited separation between clusters consistent with overlapping clinical phenotypes, while the Calinski-Harabasz index of 26.422 confirmed relatively well-defined clusters that were characterized by distinct profiles including “The Moderately Sun Exposed Young Women”, “Elderly Women with Long-Term Melasma”, and “Younger Women with Severe Melasma”, with key differentiators being age distribution and menopausal status, melasma severity and duration patterns, sun exposure behaviours, and quality of life impact profiles that collectively define the unique clinical characteristics of each subgroup. This study demonstrates how machine learning can identify clinically relevant patient subgroups in melasma. Aligning interventions with the characteristics of specific clusters can potentially improve treatment efficacy. Full article

Urban forests and green spaces are increasingly promoted as Nature-Based Solutions (NbS) to mitigate climate risks, enhance human well-being, and support resilient and sustainable cities. Focusing on the theme of optimizing urban green space ecosystem services to foster resilient and sustainable cities, this study systematically analyzes 861 relevant publications indexed in the Web of Science Core Collection from 2005 to 2025. Using bibliometric analysis and scientific knowledge mapping methods, the research examines publication characteristics, spatial distribution patterns, collaboration networks, knowledge bases, research hotspots, and thematic evolution trajectories. The results reveal a rapid upward trend in this field over the past two decades, with the gradual formation of a multidisciplinary knowledge system centered on environmental science and urban research. China, the United States, and several European countries have emerged as key nodes in global knowledge production and collaboration networks. Keyword co-occurrence and cluster analyses indicate that research themes are mainly concentrated in four clusters: (1) ecological foundations and green process orientation, (2) nature-based solutions and blue–green infrastructure configuration, (3) social needs and environmental justice, and (4) macro-level policies and the sustainable development agenda. Overall, the field has evolved from a focus on ecological processes and individual service functions toward a comprehensive transition emphasizing climate resilience, human well-being, and multi-actor governance. Based on these findings, this study constructs a knowledge ecosystem framework encompassing knowledge base, knowledge structure, research hotspots, frontier trends, and future pathways. It further identifies prospective research directions, including climate change adaptation, integrated planning of blue–green infrastructure, refined monitoring driven by remote sensing and spatial big data, and the embedding of urban green space ecosystem services into the Sustainable Development Goals and multi-level governance systems. These insights provide data support and decision-making references for deepening theoretical understanding of Urban Green Space Ecosystem Services (UGSES), improving urban green infrastructure planning, and enhancing urban resilience governance capacity. Full article

Background: Diabetic nephropathy (DN) is largely driven by transforming growth factor-β1 (TGF-β1)-mediated fibrosis. Clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) ribonucleoprotein (RNP) complexes offer precise gene disruption, yet effective non-viral delivery remains a challenge. This study developed cationic lipid-based hybrid nanostructured lipid carriers (NLCs) for intracellular delivery of TGFB1-targeting RNP as an early-stage platform for DN gene modulation. Methods: A single-guide RNA (sgRNA) targeting human TGFB1 was assembled with Cas9 protein (1:1 and 1:2 molar ratios). Hybrid NLCs comprising squalene, glyceryl trimyristate, and the cationic lipid 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) were formulated via optimized emulsification–sonication to achieve sub-100 nm particles. Physicochemical properties, including polydispersity index (PDI), were assessed via dynamic light scattering (DLS), while silencing efficacy in HEK293T cells was quantified using quantitative reverse transcription PCR (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). Results: Optimized NLCs achieved hydrodynamic diameters of 65–99 nm (PDI < 0.5) with successful RNP complexation. The 1:2 Cas9:sgRNA formulation produced the strongest gene-editing response, reducing TGFB1 mRNA by 67% (p < 0.01) compared with 39% for the 1:1 ratio. This translated to a significant reduction in TGF-β1 protein (p < 0.05) within 24 h. Conclusions: DOTAP-based hybrid NLCs enable efficient delivery of CRISPR–Cas9 RNP and achieve significant suppression of TGFB1 expression at both transcriptional and protein levels. These findings establish a promising non-viral platform for upstream modulation of profibrotic signaling in DN and support further evaluation in kidney-derived cells and in vivo renal models. Full article

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

Background: Helmet therapy is considered to be a treatment for infants with positional plagiocephaly. Although some studies suggest that anterior fontanelle (AF) size may also affect treatment outcomes, evidence and influence remain unclear. The aim of this study is to assess the impact of anterior fontanelle size on the effectiveness of helmet therapy, with the goal of determining the optimal timing and patient criteria for treatment. Methods: We conducted a retrospective study of 94 infants treated with helmet therapy for positional plagiocephaly at Kwangju Christian Hospital between January 2020 and December 2021. Patients were divided into two age groups (≤6 months and >6 months) and three SAF quartiles (≤25%, 25–75%, ≥75%). Parameters reflecting the degree of cranial asymmetry correction, including cranial vault asymmetry (CVA) and cranial vault asymmetry index (CVAI), were recorded at the start and end of treatment. Results: Infants aged ≤6 months showed significantly greater improvements in cranial vault asymmetry (CVA) and cranial vault asymmetry index (CVAI) compared to older infants (CVA: 4.57 ± 2.30 mm vs. 7.04 ± 3.85 mm, p = 0.003; CVAI: 3.10 ± 1.55% vs. 4.45 ± 2.44%, p = 0.011). When analyzed by anterior fontanelle (AF) size quartiles (≤25%, 25–75%, ≥75%), no significant differences in treatment outcomes were observed at the end of therapy for CVA (p = 0.88) or CVAI (p = 0.91). In infants ≤6 months, SAF quartile analysis also showed no significant differences in CVA (p = 0.97) or CVAI (p = 0.98) improvements. Conclusions: Our findings indicate that anterior fontanelle size is not a predictor of helmet therapy outcomes in positional plagiocephaly. Early initiation of helmet therapy (≤6 months) remains the most critical factor for achieving optimal results. Full article

With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed profiles are two critical issues in ensuring the performance of automatic train operation systems. However, conventional model predictive control (MPC) methods are highly dependent on parameter settings and show limited adaptability, while heuristic optimization approaches such as the whale optimization algorithm (WOA) often suffer from premature convergence and insufficient robustness. To overcome these limitations, this study proposes an optimized model predictive controller using the multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control. In the improved optimization algorithm, a nonlinear convergence mechanism and the Tchebycheff decomposition method are introduced to enhance convergence accuracy and population diversity, which enables effective optimization of the initial parameters of the MPC. During real-time operation, the MPC is further enhanced by integrating a fuzzy satisfaction function that adaptively adjusts the softening factor. In addition, the control coefficients are corrected online according to the speed error and its rate of change, thereby improving adaptability of the control system. Taking the section from Lvshun New Port to Tieshan Town on Dalian Metro Line 12 as the study case, the proposed control algorithm was deployed on a TMS320F28335 embedded processor platform, and hardware-in-the-loop simulation experiments (HILSEs) were conducted under the same simulation environment, a unified train dynamic model, consistent operating conditions, and an identical evaluation index system. The results indicate that, compared with the Fuzzy-PID control method, the proposed control strategy reduces the integral of time-weighted absolute error nearly by 39.6% and decreases energy consumption nearly by 5.9%, while punctuality, stopping accuracy, and comfort are improved nearly by 33.2%, 12.4%, and 7.1%, respectively. These results not only verify the superior performance of the proposed MPC-MOWOA, but also demonstrate its capability for real-time implementation on embedded processors, thereby overcoming the limitations of purely MATLAB-based offline simulations and exhibiting strong potential for practical engineering applications in urban rail transit. Full article

(1) Background: School-based mental health interventions represent a promising approach to address the substantial treatment gap affecting adolescents, with only 20% of youth with diagnosable mental health conditions receiving adequate care. (2) Methods: This meta-analysis synthesized evidence from 18 randomized controlled trials to examine the effectiveness of school-based mental health interventions and potential moderators of outcomes. (3) Results: Using Hedges’ g as the effect size index and a random-effects model, the analysis revealed a statistically significant overall effect size of 0.068 (95% CI [0.019, 0.117], p = 0.006), indicating small but reliable improvements in adolescent academic, social, emotional, behavioral, and mental health outcomes. Heterogeneity across studies was minimal (I² = 15%), suggesting consistent effects across diverse intervention types and contexts. Meta-regression analyses examining eight potential moderators including intervention focus, grade level, provider type, delivery format, duration, study design, geographic location, and theoretical foundation did not reveal statistically significant moderation effects, likely due to limited statistical power. However, descriptive patterns suggested that targeted interventions, small-group formats, and interventions delivered by mental health professionals may produce larger effects than universal programs, classroom-based approaches, and teacher-delivered interventions. (4) Conclusions: These findings support continued investment in school-based mental health programming while highlighting the need for specialized focus to optimize outcomes for all adolescents. Full article

Background/Objectives: Lactate, traditionally considered a byproduct of anaerobic metabolism, is increasingly recognized as a biomarker of tissue perfusion and systemic stress. While hyperlactatemia is frequent after pediatric cardiac surgery, evidence regarding its prognostic role remains controversial. This study aimed to evaluate whether serial lactate measurements predict mortality in children undergoing surgery for congenital heart disease in Southeast Mexico. Methods: We conducted a retrospective cohort study including children aged 0–210 weeks with confirmed congenital heart disease who underwent first-time cardiac surgery between January 2022 and December 2024. Serum lactate was measured intraoperatively, at intensive care unit (ICU) admission, and at 12 and 24 h postoperatively using a Gem^® Premier™ 3500 analyzer. Sociodemographic, clinical, and surgical data were recorded. Associations between lactate levels and mortality were analyzed with Cox regression, adjusting for RACHS-2 category and intraoperative complications. Predictive performance was assessed with ROC curves and Harrell’s C-index. Results: 103 patients were included (median age 49.2 weeks; 60% female). Lactate levels overlapped intraoperatively but significantly discriminated against survivors from non-survivors thereafter. ICU admission lactate ≥ 4.2 mmol/L predicted mortality with 100% sensitivity and 60% specificity (AUC = 0.84). Hazard ratios confirmed that lactate at ICU admission (HR 2.17, 95% CI 1.16–4.06; p = 0.015), 12 h (HR 6.37, 95% CI 1.02–39.6; p = 0.047), and 24 h (HR 1.81, 95% CI 1.07–3.09; p = 0.028) were significant predictors of mortality. The model showed excellent discrimination (Harrell’s C = 0.986), though optimism due to the limited number of deaths should be considered. Conclusions: Serial lactate monitoring, particularly upon ICU admission, provides strong prognostic information for in-hospital mortality in pediatric cardiac surgery patients. Incorporating early postoperative lactate into routine monitoring may allow timely therapeutic adjustments. Preoperative lactate assessment warrants further evaluation as a potential risk stratification tool. Full article

Background/Objectives: Frailty, a syndrome characterized by diminished physiological reserves and increased vulnerability to stressors, is a strong predictor of adverse outcomes in heart failure. The MECKI (Metabolic Exercise Cardiac Kidney Index) score, derived from cardiopulmonary exercise testing and renal function parameters, has demonstrated prognostic value in HF patients. This study aimed to evaluate the prognostic value of physical frailty on mortality in patients with advanced heart failure and to compare it directly with the MECKI score. Methods: A total of 104 patients with advanced HF receiving optimized guideline-directed medical therapy were prospectively enrolled. At baseline, all patients underwent clinical, echocardiographic, and laboratory assessment and CPET for MECKI score calculation. Physical frailty was assessed using a modified Fried phenotype tailored for HF. The composite endpoint comprised all-cause mortality, urgent heart transplantation, or LVAD implantation. Results: Over a mean follow-up of 30.0 ± 15.3 months, there were 25 deaths, 5 urgent heart transplants, and 1 LVAD implantation. Patients who experienced the composite outcome had significantly worse NYHA class, higher NT-proBNP, lower VO₂max, higher VE/VCO₂ slope, higher frailty, and higher MECKI score (all p < 0.001). Frailty was significantly correlated with all MECKI score components, as demonstrated by Spearman’s rank correlation analysis. Both frailty (HR = 1.89; 95% CI 1.22–2.93; p = 0.005) and MECKI score (HR = 1.04; 95% CI 1.00–1.08; p = 0.037) independently predicted outcomes. ROC analysis showed high and comparable discriminative performance (AUC = 0.86 for frailty; AUC = 0.88 for MECKI). Conclusions: Physical frailty and MECKI scores independently predict mortality and adverse events in advanced HF. Physical frailty, despite its simplicity and low cost, provides prognostic insight comparable to the MECKI score and may represent a practical alternative when CPET is unavailable. Full article

Background/Objectives: Compensated advanced chronic liver disease (cACLD) affects millions and carries risk for portal hypertension, large varices, and clinical decompensation. The HepQuant DuO^® test provides a blood-based assessment of liver function and physiology, generating a disease severity index (DSI) validated for risk stratification. A retrospective, real-world, observational analysis was conducted to evaluate the utility of HepQuant DuO’s DSI cutpoint (18.3) in guiding endoscopy and clinical management decisions for patients with cACLD in the United States. Methods: De-identified data from 87 cases with cACLD were extracted from physician-provided Statements of Medical Necessity documenting the reasons for the HepQuant DuO test. The primary endpoint was concordance of endoscopy decisions with DSI ≤ 18.3 (avoid) or >18.3 (proceed). The secondary endpoint was concordance of clinical management intensity with the same cutpoint. Results: Among the 55 cases analyzed for endoscopy decisions, overall concordance with DSI 18.3 was 93% (p < 0.001 by Fisher’s exact test): 96% of cases with DSI ≤ 18.3 avoided endoscopy, and 90% with DSI > 18.3 underwent endoscopy. For the 45 cases assessing management intensity, overall concordance was 89% (p < 0.001): 90% of cases with DSI ≤ 18.3 had reduced follow-up, and 89% with DSI > 18.3 had intensified management. The cohort exhibited broad functional heterogeneity not captured by standard labs or elastography. Conclusions: HepQuant DuO’s DSI cutpoint 18.3 demonstrated strong concordance with real-world clinical decisions, supporting its utility for risk stratification, optimizing endoscopy use, and tailoring management in cACLD. Full article

In recent years, the increasing frequency of extreme weather events, the large-scale integration of distributed generation into distribution networks, and the widespread application of new power electronic devices have posed severe challenges to the security of power supply in distribution networks. To enhance the power supply reliability of the distribution network while considering its economic efficiency, this paper proposes a collaborative planning method for a flexible distribution network focused on power supply restoration and resilience enhancement In this method, a planning model for flexible distribution networks is established by optimally determining the siting and sizing of soft open point (SOP), with the objective of minimizing the annual comprehensive cost of the distribution network under multiple operational and planning constraints. Second-order cone programming (SOCP) relaxation and polyhedral approximation-based linearization techniques are employed to reformulate and solve the model, thereby obtaining the optimal siting and sizing Case for SOPs. Finally, simulations are conducted on a modified IEEE 33-bus test system to verify the effectiveness of the proposed method. The results show that, through appropriate siting and sizing of SOPs, outage loss costs can be significantly reduced, nodal voltage profiles can be improved, and load support can be provided to de-energized areas, leading to a reduction of more than 70% in the annual comprehensive cost of the distribution network and an improvement in the system reliability index from 99% to 99.999%, thus effectively enhancing both the economic efficiency and reliability of the distribution system. Full article