Search Results (1,490)

The structural safety of multi-span ultra-high-voltage (UHV) substation gantries is a cornerstone for the reliability and resilience of sustainable energy grids. The wind-resistant design of the structures is complicated by their complex modal behaviors and highly non-uniform wind load distributions. This study proposes a novel hybrid framework that integrates segmented high frequency force balance (HFFB) testing with a multi-modal stochastic vibration analysis, enabling the precise assessment of wind load distribution and dynamic response. Five representative segment models are tested to quantify both mean and dynamic wind loads, a strategy rigorously validated against whole-model HFFB tests. Key findings reveal significant aerodynamic disparities among structural segments. The long-span beam, Segment 5, exhibits markedly higher and direction-dependent responses. Its mean base shear coefficient reaches 4.34 at β = 75°, which is more than twice the values of 1.74 to 2.27 for typical tower segments. Furthermore, its RMS wind force coefficient peaks at 0.65 at β = 60°, a value 2.5 to 4 times higher than those of the tower segments, all of which remained below 0.26. Furthermore, a computational model incorporating structural modes, spatial coherence, and cross-modal contributions is developed to predict wind-induced responses, validated through aeroelastic model tests. The proposed framework accurately resolves spatial wind load distribution and dynamic wind-induced response, providing a reliable and efficient tool for the wind-resistant design of multi-span UHV lattice gantries. Full article

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu₂O, g-C₃N₄/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies. Full article

Image semantics, by revealing rich structural information, provides crucial guidance for image inpainting. However, current semantic-guided inpainting frameworks generally operate unidirectionally, relying on pre-trained segmentation networks without a feedback mechanism to adapt segmentation dynamically during inpainting. To address this limitation, we propose an innovative inpainting methodology that incorporates semantic segmentation feedback recorrection via semi-supervised learning. Specifically, the fundamental concept involves enabling the initial inpainting network to deliver feedback to the semantic segmentation model, which subsequently refines its predictions by leveraging cross-image semantic consistency. The iteratively corrected semantic segmentation maps serve to direct the inpainting neural network toward improved reconstruction quality, fostering a synergistic interaction that enhances both segmentation accuracy and inpainting performance. Furthermore, a semi-supervised learning strategy is implemented to reduce reliance on ground truth labels and improves generalization by utilizing both labeled and unlabeled datasets. We conduct our methodology on the CelebA-HQnd Cityscapes datasets, employing multiple quantitative metrics including LPIPS, PSNR, and SSIM. Results demonstrate that the proposed algorithm surpasses current methodologies: on CelebA-HQ dataset, it achieves a 5.89% reduction in LPIPS and a 0.52% increase in PSNR, with notable improvements in SSIM; on the Cityscapes dataset, LPIPS decreases by 6.15% and SSIM increases by 1.58%. Ablation studies confirm the effectiveness of the feedback recorrection mechanism. This research provides novel insights into synergistic interactions between segmentation and inpainting, demonstrating that fostering such interactions can substantially improve image processing performance. Full article

Introduction: Robotic splenectomy has emerged as a promising alternative to laparoscopic surgery, offering potential advantages in precision, ergonomics, and individualized surgical planning. In the context of personalized medicine, robotic technology may enable tailoring of surgical strategies to patient-specific anatomy, spleen size, and comorbid hematologic conditions. However, its clinical superiority remains uncertain due to limited and heterogeneous evidence. Methods: We performed a systematic review and meta-analysis following PRISMA guidelines, utilizing PubMed, CINAHL, Web of Science, and EMBASE databases to locate studies on robotic splenectomies in children. This review was prospectively registered in PROSPERO (CRD420251104285). Risk of bias was assessed using the ROBINS-I tool for non-randomized studies. Random-effects models were fitted using restricted maximum likelihood (REML), and confidence intervals were adjusted using either Knapp–Hartung (HKSJ) or modified Knapp–Hartung (mKH) methods when appropriate. 95% prediction intervals were calculated, and the certainty of evidence for each outcome was assessed using the GRADE approach. Results: This review included 272 pediatric patients from 16 studies conducted between 2003 and 2025, of which five were included in the meta-analysis. No statistically significant differences were observed between robotic and laparoscopic splenectomy for operative time, intraoperative blood loss, conversion to open surgery, blood transfusions, or complications. However, the direction of effect estimates consistently favored the robotic approach. A statistically significant reduction in hospitalization days (−0.93 days; 95% CI: −1.61 to −0.24; p = 0.01) was found, though this became marginally significant after HKSJ adjustment (p = 0.06). Intraoperative blood loss showed significance in the primary model (−63.88 mL; 95% CI: −120.38 to −7.38; p = 0.03), but not after mKH correction (p = 0.16). Heterogeneity was substantial-to-extreme for several outcomes and was only partially accounted for by leave-one-out sensitivity analyses. All findings were rated as very low certainty according to the GRADE framework. Conclusions: Robotic-assisted splenectomy in pediatric patients has been reported as technically feasible and performed safely in selected cases. However, the small number of studies, their retrospective design, substantial methodological heterogeneity, and the resulting very low certainty of the evidence according to GRADE preclude any firm conclusions about its comparative safety or efficacy versus laparoscopy. Well-designed prospective studies are needed to clarify its clinical benefits. Full article

Background: Cardiorenal syndrome (CRS) reflects bidirectional heart–kidney injury whose mechanisms extend far beyond hemodynamics. High-throughput genomics and multi-omics now illuminate the molecular circuits that couple cardiac and renal dysfunction. Methods: We narratively synthesize animal and human studies leveraging transcriptomics, proteomics, peptidomics, metabolomics, and non-coding RNA profiling to map convergent pathways in CRS and to highlight biomarker and therapeutic implications. Results: Across acute and chronic CRS models, omics consistently converge on extracellular matrix (ECM) remodeling and fibrosis (e.g., FN1, POSTN, collagens), immune–inflammatory activation (IL-6 axis, macrophage/complement signatures), renin–angiotensin–aldosterone system hyperactivity, oxidative stress, and metabolic/mitochondrial derangements in both organs. Single-nucleus and bulk transcriptomes reveal tubular dedifferentiation after cardiac arrest-induced AKI and myocardial reprogramming with early CKD, while quantitative renal proteomics in heart failure demonstrates marked upregulation of ACE/Ang II and pro-fibrotic matricellular proteins despite near-normal filtration. Human translational data corroborate these signals: urinary peptidomics detects CRS-specific collagen fragments and protease activity, and circulating FN1/POSTN and selected microRNAs (notably miR-21) show diagnostic potential. Epigenetic and microRNA networks appear to integrate these axes, nominating targets such as anti-miR-21 and anti-fibrotic strategies; pathway-directed repurposing exemplifies dual-organ benefit. Conclusions: Genomics and multi-omics recast CRS as a systems disease driven by intertwined fibrosis, inflammation, neurohormonal and metabolic programs. We propose a translational framework that advances (i) composite biomarker panels combining injury, fibrosis, and regulatory RNAs; (ii) precision, pathway-guided therapies; and (iii) integrated, longitudinal multi-omics of well-phenotyped CRS cohorts to enable prediction and personalized intervention. Full article

This study proposes a Transformer–NSGA-III multi-objective optimization framework for high-rise residential buildings in Haikou, a coastal city characterized by a hot summer and warm winter climate. The framework addresses four conflicting objectives: Annual Energy Demand (AED), Predicted Percentage of Dissatisfied (PPD), Global Cost (GC), and Life Cycle Carbon (LCC) emissions. A localized database of 11 design variables was constructed by incorporating envelope parameters and climate data from 79 surveyed buildings. A total of 5000 training samples were generated through EnergyPlus simulations, employing jEPlus and Latin Hypercube Sampling (LHS). A Transformer model was employed as a surrogate predictor, leveraging its self-attention mechanism to capture complex, long-range dependencies and achieving superior predictive accuracy (R² ≥ 0.998, MAPE ≤ 0.26%) over the benchmark CNN and MLP models. The NSGA-III algorithm subsequently conducted a global optimization of the four-objective space, with the Pareto-optimal solution identified using the TOPSIS multi-criteria decision-making method. The optimization resulted in significant reductions of 28.5% in the AED, 24.1% in the PPD, 20.6% in the GC, and 18.0% in the LCC compared to the base case. The synergistic control of the window solar heat gain coefficient and external sunshade length was identified as the central strategy for simultaneously reducing energy consumption, thermal discomfort, cost, and carbon emissions in this hot and humid climate. The TOPSIS-optimal solution (C = 0.647) effectively balanced low energy use, high thermal comfort, low cost, and low carbon emissions. By integrating the Energy Performance of Buildings Directive (EPBD) Global Cost methodology with Life Cycle Carbon accounting, this study provides a robust framework for dynamic economic–environmental trade-off analyses of ultra-low-energy buildings in humid regions. The work advances the synergy between the NSGA-III and Transformer models for high-dimensional building performance optimization. Full article

Background: Point mutations in mitochondrial DNA (mtDNA) cause a range of neurometabolic disorders that currently have no curative treatments. The m.8993T>G mutation in the Homo sapiens MT-ATP6 gene leads to neurogenic muscle weakness, ataxia, and retinitis pigmentosa (NARP) when heteroplasmy exceeds approximately 70%. Methods: We engineered a split DddA-derived cytosine base editor (DdCBE), each half fused to programmable TALE DNA-binding domains and a mitochondrial targeting sequence, to correct the m.8993T>G mutation in patient-derived induced pluripotent stem cells (iPSCs). Seven days after plasmid delivery, deep amplicon sequencing showed 35 ± 3% on-target C•G→T•A conversion at position 8993, reducing mutant heteroplasmy from 80 ± 2% to 45 ± 3% with less than 0.5% editing at ten predicted off-target loci. Results: Edited cells exhibited a 25% increase in basal oxygen consumption rate, a 50% improvement in ATP-linked respiration, and a 2.3-fold restoration of ATP synthase activity. Directed neural differentiation yielded 85 ± 2% Nestin-positive progenitors compared to 60 ± 2% in unedited controls. Conclusions: Edits remained stable over 30 days in culture. These results establish mitochondrial base editing as a precise and durable strategy to ameliorate biochemical and cellular defects in NARP patient cells. Full article

Stereotactic ablative radiotherapy (SABR) has revolutionized the management of patients with oligometastatic and selected primary cancers due to its ability to deliver highly conformal, high-dose radiation in few fractions with minimal toxicity. However, the biological heterogeneity among patients treated with SABR results in variable outcomes, emphasizing the need for predictive and prognostic biomarkers to guide patient selection and post-treatment management. This narrative review discusses the current landscape of biomarker development in the context of SABR across tumor types. Key classes include circulating tumor DNA (ctDNA), extracellular vesicles (EVs), radiomic features, and immunological markers. We highlight the role of each biomarker category in refining therapeutic approaches, their integration into ongoing clinical trials, and future directions for personalized SABR paradigms. Translating these promising biomarker strategies into clinical SABR workflows will require further standardisation, validation, and regulatory alignment. Full article

Industry 4.0 is steadily advancing a reality of deepening integration between humans and technology, a phenomenon aptly described by the metaphor of the “technologically augmented human”. This study identifies the digital and personal factors that predict a preference for the “digital centaur” strategy among adolescents and young adults. This strategy is defined as a model of human–AI collaboration designed to enhance personal capabilities. A sample of 1841 participants aged 14–39 completed measures assessing digital centaur preference and identification, emotional intelligence (EI), mindfulness, digital competence, technology attitudes, and AI usage, as well as AI-induced emotions and fears. The results indicate that 27.3% of respondents currently identify as digital centaurs, with an additional 41.3% aspiring to adopt this identity within the next decade. This aspiration was most prevalent among 18- to 23-year-olds. Hierarchical regression showed that interpersonal and intrapersonal EI and mindfulness are personal predictors of the digital centaur preference, while digital competence, technophilia, technopessimism (inversely), and daily internet use emerged as significant digital predictors. Notably, intrapersonal EI and mindfulness became non-significant when technology attitudes were included. Digital centaurs predominantly used AI functionally and reported positive emotions (curiosity, pleasure, trust, gratitude) but expressed concerns about human misuse of AI. These findings position the digital centaur as an adaptive and preadaptive strategy for the technologically augmented human. This has direct implications for education, highlighting the need to foster balanced human–AI collaboration. Full article

This study employed a combined computational and experimental approach to investigate the molecular recognition mechanism of 4-methylphenol by human olfactory receptor hOR9Q2. The strategy integrated molecular docking using BIOVIA Discovery Studio, structural modeling of hOR9Q2 based on the AlphaFold2-predicted, molecular dynamics simulations with GROMACS software employing the AMBER14SB force field, and systematic site-directed mutagenesis validation. Computational simulations revealed that the binding cavity formed by transmembrane domains TM3, TM5, and TM6 serves as the key interaction region, with van der Waals, hydrophobic, and Pi-sulfur interactions driving stable binding (ΔG = −40.173 ± 0.34 kJ/mol). Functional characterization identified six critical residues (Cys112, Val158, Met207, Phe251, Leu255, and Tyr259) as essential for receptor activation, while mutations at Ile71 and Ala108 resulted in partial functional impairment. This study reveals the structural basis for hOR9Q2’s selective response to 4-methylphenol, while establishing a computational–experimental framework for precisely locating functional sites on olfactory receptors. These findings elucidate the molecular mechanism of odorant recognition and provide insights for developing odorant prediction models and designing specific olfactory receptor modulators. Full article

The impingement of a molten droplet on a solid surface, forming a “splat,” is a fundamental phenomenon observed across numerous industrial surface engineering techniques. For example, thermal spray deposition is widely used to create metal, ceramic, polymer, and composite coatings that are vital for aerospace, biomedical, electronics, and energy applications. Significant progress has been made in understanding droplet impact behavior, largely driven by advancements in high-resolution and high-speed imaging techniques, as well as computational resources. Although droplet impact dynamics, splat morphology, and interfacial bonding mechanisms have been extensively reviewed, a comprehensive overview of the mechanical behaviors of single splats, which are crucial for coating performance, has not been reported. This review bridges that gap by offering an in-depth analysis of bonding strength and residual stress in single splats. The various experimental techniques used to characterize these properties are thoroughly discussed, and a detailed review of the analytical models and numerical simulations developed to predict and understand residual stress evolution is provided. Notably, the complex interplay between bonding strength and residual stress is then discussed, examining how these two critical mechanical attributes are interrelated and mutually influence each other. Subsequently, effective strategies for improving interfacial bonding are explored, and key factors that influence residual stress are identified. Furthermore, the fundamental roles of splat flattening and formation dynamics in determining the final mechanical properties are critically examined, highlighting the challenges in integrating fluid dynamics with mechanical analysis. Thermal spraying serves as the primary context, but other relevant applications are briefly considered. Cold spray splats are excluded because of their distinct bonding and stress generation mechanisms. Finally, promising future research directions are outlined to advance the understanding and control of the mechanical properties in single splats, ultimately supporting the development of more robust and reliable coating technologies. Full article

Adjoint-based mesh adaptation method serves as an effective approach to improve the predictive accuracy of aerodynamic characteristics. However, viscous boundary layer grids often encounter issues such as hanging nodes, negative volumes, and directional constraints during adaptation, significantly limiting their practical application. To address these challenges, this study proposes an innovative polyhedral conversion strategy. Cells containing hanging nodes resulting from refinement are converted into polyhedra, effectively eliminating topological constraints between adjacent mesh elements. This approach is combined with surface-conforming projection and distance function-based mesh deformation techniques to ensure precise geometric representation and high mesh quality after adaptation. Numerical experiments demonstrate that the proposed viscous boundary layer mesh adaptation strategy successfully handles both refinement and coarsening of boundary layer grids. In a typical high-angle-of-attack case for the NACA0012 airfoil, the adjoint-based mesh adaptation method reduced lift coefficient error from 4.21% to 0.30% after four adaptation cycles. For the CHN-F1 low-aspect-ratio flying wing configuration, the method reduced the lift discrepancy from 10.05% to 6.65% at 40° angle of attack. The polyhedral conversion approach effectively resolves common challenges in viscous boundary layer mesh adaptation, providing a robust solution for high-fidelity prediction of aerodynamic characteristics with significantly improved accuracy. Full article

This paper presents a novel approach to the controllability of nonlinear dynamic systems using recurrent neural networks (RNNs). We develop a comprehensive theoretical framework that integrates controllability analysis, stability verification via Lyapunov functions, and the derivation of optimal control laws based on Pontryagin’s Maximum Principle. Our methodology not only ensures theoretical soundness but also offers practical effectiveness. To demonstrate its applicability, we conduct simulations using real-world data from the AAPL stock database. The proposed RNN-based control framework significantly reduces the deviation between predicted system outputs and actual observations. We further enhance performance through two complementary strategies, a direct control method and a parameter optimization approach, both of which contribute to the accuracy and adaptability of the control system. These results confirm the potential of neural network-based control in managing complex nonlinear dynamics. Full article

Depression is among the most prevalent mental disorders, and its early detection is essential to improving therapeutic outcomes in psychotherapy. This systematic review and meta-analysis evaluated the accuracy, interpretability, and generalizability of supervised algorithms (SVM, Random Forest, XGBoost, and GCN) for clinical detection of depression using real-world data. Following PRISMA guidelines, 20 studies published between 2014 and 2025 were analyzed across major scientific databases. Extracted metrics included F1-Score, AUC-ROC, interpretability methods (SHAP/LIME), and cross-validation strategies, with statistical analyses using ANOVA and Pearson correlations. Results showed that XGBoost achieved the best average performance (F1-Score: 0.86; AUC-ROC: 0.84), although differences across algorithms were not statistically significant (p > 0.05), challenging claims of algorithmic superiority. SHAP was the predominant interpretability approach (70% of studies). Studies implementing combined SHAP+LIME showed higher F1-Score values (F(1,7) = 8.71, p = 0.021), although this association likely reflects greater overall methodological rigor rather than a direct causal effect of interpretability on predictive performance. Clinical surveys and electronic health records demonstrated the most stable predictive outputs across validation schemes, whereas neurophysiological data achieved the highest point estimates but with limited sample representation. F1-Score strongly correlated with AUC-ROC (r = 0.950, p < 0.001). Considerable heterogeneity was observed for both metrics (I² = 74.37% for F1; I² = 71.49% for AUC), and Egger’s test indicated a publication bias for AUC (p = 0.0048). Overall, findings suggest that algorithmic performance depends more on data quality, context, and interpretability than on the choice of model, with explainable approaches offering practical value for personalized and collaborative clinical decision-making. Full article

Prostate-specific membrane antigen (PSMA) targeting agents have been the cornerstone of advanced prostate cancer (PCa) management in theranostics due to their high sensitivity for detecting and treating metastatic disease. However, approximately one-third of metastatic castration-resistant PCa (mCRPC) lesions may exhibit low or absent PSMA expression due to tumor heterogeneity, prior androgen deprivation therapy, or loss of androgen receptor expression, subsequently altering their response to PSMA-targeted therapy. The molecular and biological mechanisms underlying PSMA downregulation remain elusive but may include neuroendocrine differentiation or epithelial-to-mesenchymal transition (EMT). This review addresses this knowledge gap by examining recent preclinical and clinical evidence on novel radiotracers with the potential to provide alternative strategies beyond PSMA for imaging and treating PCa. The diagnostic performance and therapeutic potential of three emerging radiotracer classes are discussed, including gastrin-releasing peptide receptor (GRPR) ligands, androgen receptor (AR) ligands, and amino acid analogs. This article further highlights the complementary roles of these radiotracers along with their utility in specific patient populations, such as those with low prostate-specific antigen (PSA), biochemical recurrence (BCR), or confirmed PSMA-negative disease. For instance, GRPR-targeted radiotracers have achieved sensitivity of up to 88% and specificity of up to 90% for detecting primary tumors in PCa. The radiolabeled androgen agonist, fluorine-18 (¹⁸F)-fluoro-5α-dihydrotestosterone (FDHT), has demonstrated 98% true-positive rate in predicting lesions on positron emission tomography (PET) scans of mCRPC patients. On the other hand, the synthetic amino acid analog ¹⁸F-fluciclovine demonstrated a lesion detection rate of 84% for PSA levels at or above 5, and 62.5% for PSA levels ranging from 0.7 to less than 1. This review concludes with future directions on the paradigm of multi-tracer and dual-targeting strategies, which can effectively address challenges associated with PCa tumor heterogeneity and facilitate personalized approaches in theranostics. Full article