Search Results (15,957)

Background: Children with congenital heart disease (CHD) are at substantially increased risk for respiratory infections, which occur more frequently and with greater severity than in healthy peers. This heightened vulnerability stems from multifactorial immune impairment, including defects in innate and adaptive immunity, chronic inflammation related to abnormal hemodynamics and hypoxia, reduced thymic function, and genetic syndromes affecting both cardiac and immune development. Viral pathogens—particularly respiratory syncytial virus (RSV), influenza viruses, and SARS-CoV-2—account for most infections, although bacterial pathogens remain relevant, especially in postoperative settings. Methods: This narrative review summarizes current evidence on infection susceptibility in children with CHD, the epidemiology and clinical relevance of major respiratory pathogens, and the effectiveness of available preventive measures. Literature evaluating immunological mechanisms, infection burden, vaccine effectiveness, and passive immunization strategies was examined, along with existing national and international immunization guidelines. Results: Children with CHD consistently exhibit higher rates of hospitalization, intensive care unit admission, mechanical ventilation, and mortality following respiratory infections. RSV, influenza, and SARS-CoV-2 infections are particularly severe in this population, while bacterial infections, though less common, contribute substantially to postoperative morbidity. Preventive options—including routine childhood vaccines, pneumococcal and Haemophilus influenzae type b vaccines, influenza vaccines, COVID-19 mRNA vaccines, and RSV monoclonal antibodies—demonstrate strong protective effects. New long-acting RSV monoclonal antibodies and maternal vaccination markedly enhance prevention in early infancy. However, vaccine coverage remains insufficient due to parental hesitancy, provider uncertainty, delayed immunization, and limited CHD-specific evidence. Conclusions: Respiratory infections pose a significant and preventable health burden in children with CHD. Enhancing the use of both active and passive immunization is essential to reduce morbidity and mortality. Strengthening evidence-based guidelines, improving coordination between specialists and primary care providers, integrating immunization checks into routine CHD management, and providing clear, condition-specific counseling to families can substantially improve vaccine uptake and clinical outcomes in this vulnerable population. Full article

This paper presents a precision, wide-dynamic-range leakage current sensor tailored for in-situ monitoring of aging mechanisms such as Time-Dependent Dielectric Breakdown (TDDB) in both active and passive components. The proposed architecture supports high-voltage stress and is fully monolithic, integrating a current-to-voltage front-end, tunable-gain amplifier, and a successive approximation register (SAR) analog-to-digital converter (ADC). To validate the concept, a discrete-component prototype was implemented and evaluated across a leakage current range of 1 nA to 1 $\mathsf{\mu }$A. The sensor achieves 12-bit resolution with measured integral non-linearity (INL) and differential non-linearity (DNL) within $\pm 1.5$ LSB and $\pm 0.3$ LSB, respectively. Compared to prior monitors, the design enables linear current digitization and supports high-voltage stress, features essential for accurate and scalable TDDB characterization. Applications include embedded reliability monitoring in power converters, analog building blocks, and large-scale aging test arrays. Full article

Conventional polymer-based plugging materials often fail in deep-well environments due to passive thermal softening and network relaxation, which significantly compromise mechanical integrity and interfacial retention. To address this challenge, a novel smart Upper Critical Solution Temperature (UCST)-responsive hybrid microgel (SUPA) was synthesized for adaptive plugging in complex formations. The distinctive UCST responsiveness was conferred by incorporating N-(2-amino-2-oxoethyl)acrylamide (NAGA) and N-(2-hydroxypropyl) methacrylamide (HPMA) functional units into a robust dual-crosslinked network. Particle size analysis and oscillatory rheology in saline solution revealed the thermal activation mechanism: surpassing the critical temperature triggers the dissociation of intramolecular hydrogen bonds, driving polymer chain extension and volumetric expansion. This conformational transition induces dynamic network reinforcement, quantified by a significant ~7.5-fold increase in the storage modulus (G′). Consequently, the SUPA-enhanced fluid exhibited superior rheological performance, including a 4.4-fold increase in low-shear viscosity and rapid thixotropic recovery (ratio of 1.06). Crucially, lost circulation tests confirmed reliable and highly efficient sealing performance under harsh conditions of 150 °C and 5 MPa, even in fractured models. This study validates a design strategy centered on UCST-activated network reinforcement, offering a robust, mechanism-driven solution for severe lost circulation control in deep-well drilling. Full article

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) has become one of the most influential materials in neural engineering, offering high electrical conductivity, mechanical softness, and stable processing in complex aqueous media. Beyond these well-known merits, recent studies indicate that PEDOT:PSS can be regarded as a bio-solid electrolyte interphase (bio-SEI) that governs the interactions between neural probes and biological tissue. In this framework, PEDOT:PSS functions as a selective and adaptive interphase that mediates ion and electron transport, buffers mechanical mismatch, and mitigates chemical or biological degradation at the device-tissue boundary. This review critically summarizes the progress in molecular design, synthesis, and post-treatment strategies that enhance PEDOT:PSS stability and compatibility within physiological environments. Developments such as polydopamine-assisted adhesion, zwitterionic modification, and hybridization with soft hydrogels have expanded its role from a passive coating to an active, self-regulating interphase that prolongs implant performance. We further discuss how the hierarchical structure of PEDOT:PSS—from its molecular organization to device-level morphology—contributes to long-term electrochemical and biological stability. By treating PEDOT:PSS as an intrinsic bio-SEI rather than a simple conductive coating, this perspective highlights its central role in the development of durable, biocompatible, and intelligent neural interfaces for next-generation implantable electronics. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

Traditional diode-based rectifiers (TDRs) in railway traction substations (TSSs) are inefficient at handling bidirectional power flow and cannot recover regenerative braking energy (RBE). Replacing these conventional systems with reversible traction substations (RTSSs) requires detailed modeling, extensive simulations, and validation using real data. This paper presents a DT-oriented real-time modeling and Hardware-in-the-Loop (HIL) platform for the analysis and performance assessment of RTSSs in DC railway systems. The integration of interleaved PWM rectifiers enables bidirectional power flow, allowing efficient RBE recovery and its return to the main grid. Modeling railway networks with moving trains is complex due to nonlinear dynamics arising from continuously varying positions, speeds, and accelerations. The proposed approach introduces an innovative multi-train simulation method combined with low-level transient and power-quality analysis. The validated DT model, supported by HIL emulation using OPAL-RT, accurately reproduces real-world system behavior, enabling optimal component sizing and evaluation of key performance indicators such as voltage ripple, total harmonic distortion, passive-component stress, and current imbalance. The results demonstrate improved energy efficiency, enhanced system design, and reduced operational costs. Meanwhile, experimental validation on a small-scale RTSS prototype, based on data from the Italian 3 kV DC railway system, confirms the accuracy and applicability of the proposed DT-oriented framework. Full article

Grey cast iron brake discs are widely used in automotive applications due to their excellent thermal and mechanical properties. However, stricter environmental regulations such as Euro 7 demand improved surface durability to reduce particulate emissions and corrosion-related failures. This study evaluates multilayer coatings fabricated by Laser Metal Deposition (LMD) as a potential solution. Two multi-layer systems were investigated: 316L + (316L + WC) and 316L + (430L + TiC), which were primarily reinforced with ceramic additives to increase wear resistance, with their influence on corrosion being critically evaluated. Electrochemical tests in 5 wt.% NaCl solution (DIN 17475) revealed that the 316L + (316L + WC) coating exhibited the lowest corrosion current density and most stable passive behavior, consistent with the inherent passivation of the austenitic 316L matrix. In contrast, the 316L + (430L + TiC) system showed localized corrosion associated with micro-galvanic interactions, despite the chemical stability of TiC particles. Post-corrosion SEM and EDS confirmed chromium depletion and chloride accumulation at corroded sites, while WC particles exhibited partial dissolution. These findings highlight that ceramic reinforcements do not inherently improve corrosion resistance and may introduce localized degradation mechanisms. Nevertheless, LMD-fabricated multilayer coatings demonstrate potential for extending brake disc service life, provided that matrix–reinforcement interactions are carefully optimized. Full article

Objective: A modeling and simulation tool, OpenSim, was used to determine the optimal relationship between actuator stiffness and actuation timing of a passive ankle exoskeleton for reducing metabolic costs during walking. We hypothesized that the absolute minimum in total metabolic cost would exist at an actuation timing of 15% of stance and at a spring stiffness of 7.5 kN/m. We also hypothesized that a local minimum in total metabolic cost would exist at an actuation timing of 50% of stance. Methods: Bilateral kinematics and kinetics data were collected on a healthy male walking overground wearing his regular tennis shoe. The passive ankle exoskeleton geometry and the spring actuator were integrated into the OpenSim model. Simulations were performed for every combination of 25 spring stiffnesses ranging from 5.5 kN/m to 17.5 kN/m (increments of 0.5 kN/m) and 10 actuation timings ranging from 15% to 60% of stance (increments of 5%). Total energy expenditure was calculated as the sum of the energy expenditure of all the muscles in the model. Results: The greatest reduction in energy consumption (−2.67%) was observed at an actuation timing of approximately 15% of the stance phase with a spring stiffness of ~5.5 kN/m. A quadratic relationship between spring stiffness and energy consumption was identified (R² = 0.99), with an optimal stiffness of approximately 5.5 kN/m minimizing the energy cost. Conclusions: Our findings suggest that OpenSim effectively predicts optimal exoskeleton parameters, supporting personalized assistance to improve energy efficiency and rehabilitation outcomes. Full article

The digitization of historic architecture increasingly relies on dense point clouds, yet the conversion of these datasets into structured Historic Building Information Models (HBIM) remains slow, inconsistent, and heavily dependent on manual interpretation. This paper introduces a classification-driven, mesh-based semi-automated workflow designed to close this gap by providing a controlled, repeatable path from raw TLS data to BIM-ready geometry. The method combines three elements strategically integrated into a unified framework: (1) pre-classified point cloud groups that establish a structured starting point, (2) mesh simplification and slice-based geometric reconstruction executed through Rhino and Grasshopper, and (3) direct BIM integration using Rhino.Inside.Revit to generate categorized HBIM components rather than passive mesh imports. The workflow is validated on an irregular exterior stone column from the historic chapel in Sopronhorpács, Hungary, an element characterized by surface erosion, asymmetric profiles, and deviations from verticality. This type of geometry typically challenges both manual modeling and fully automated shape-fitting. The proposed method reconstructed the column as a Revit Structural Column element with a substantial reduction in modeling time compared to a manual Scan-to-BIM workflow. A deviations analysis confirmed that the reconstructed geometry remained within the millimeter-level accuracy required for conservation-grade documentation. The study demonstrates that combining element-based classification, mesh preprocessing, and controlled semi-automation can significantly improve both the speed and reliability of Scan-to-HBIM processes without requiring technical expertise yet delivers results that align with the precision expected in scientific documentation. By formalizing the Pre-Classified Modeling Logic (PCML), the approach provides a foundation for reconstructing a wide range of heritage elements and establishes a practical step forward toward more efficient, interpretable, and accessible digital preservation practices. Full article

Palladium, a non-toxic platinum-group metal, is widely used in catalysis, electronics, hydrogen storage, and chemical industries because of its excellent physical and chemical properties. However, given that the number of palladium-producing countries is limited, recycling is considered essential for ensuring a stable and sustainable global supply. Here, I describe a simple and efficient method for palladium recovery from electronic waste (e-waste) using Enterobacter oligotrophicus CCA6^T. To clarify biomineralization capacity, the role of electron donors in modulating biomineralization capacity was examined. Findings showed that formic acid was the most effective donor, enhancing the relative recovery rate to 44% compared to 23% without electron donors. Transmission electron microscopy analysis revealed palladium particles (1–10 nm) distributed across the cell wall, periplasmic space and cytoplasm, confirming active biomineralization rather than passive biosorption. Moreover, based on a comparison with the biomineralization mechanism of Escherichia coli, the biomineralization mechanism of E. oligotrophicus CCA6^T was estimated . Reaction parameters were then optimized by testing the effects of formic acid concentration, reaction temperature, and reaction pH. Under optimized conditions, the relative recovery rate exceeded 99% within 6 h using 40 mg/L palladium. When this method was applied to a metal dissolution solution prepared from e-waste , a recovery rate of 94% was achieved from trace concentrations (36 µg/L), and palladium loss from bacteria after the palladium recovery test was negligible (<0.01%). Taken together, these results demonstrate that biomineralization using E. oligotrophicus CCA6^T could potentially be applied to the recovery of palladium from e-waste, particularly for trace-level concentrations where conventional methods are ineffective. Full article

Aviation hydraulic systems operate under high pressure and large flow rates, which induce significant fluid pressure pulsations and hydraulic shocks in pipelines. These pulsations, exacerbated by complex external loads, can lead to excessive vibration stress, component damage, oil leakage, and compromised system safety. While existing methods—such as pump structure optimization, pipeline layout adjustment, and active control—can reduce pulsations to some extent, they are limited by cost, reliability, and adaptability, particularly under high-pressure and multi-excitation conditions. Passive control, using pressure pulsation damping devices, has proven to be more practical; however, conventional designs typically focus on low-load systems and have limited frequency adaptability. This paper proposes a multi-hole parallel pressure pulsation damping device that offers high vibration attenuation, broad adaptability, and easy installation. A combined simulation–experiment approach is employed to investigate its damping mechanism and performance. The results indicate that the damping device effectively reduces vibrations in the 200–500 Hz range, with minimal impact from changes in load pressure and rotational speed. Under a high pressure of 21 MPa and a speed of 1500 rpm, the maximum insertion loss can reach 15.82 dB, significantly reducing the pressure pulsation in the hydraulic pipeline. Full article

This study evaluates and compares the sustainability performance of selected historic, commercial, and institutional buildings in Istanbul to identify effective climate-responsive and energy-efficient design strategies. The objectives are to assess performance using LEED-based criteria, examine variations across building typologies, and outline implications for future sustainable design. Using an evaluation matrix, responses from 175 experts were analysed across key LEED categories for seven case study buildings. The comparative assessment reveals notable variations in sustainability performance across the seven evaluated buildings. ERKE Green Academy consistently achieved the highest mean scores (≈4.40–4.60), particularly in Sustainable Sites, Water Efficiency, Energy and Atmosphere, and Indoor Environmental Quality. This strong performance reflects its integration of advanced green technologies, optimised daylighting strategies, biophilic elements, and smart system controls. Modern commercial towers, such as the Allianz Tower and Sapphire Tower, recorded strong mean scores (≈4.20–4.50) across categories related to Integrative Design, Energy Efficiency, and Materials and Resources. Their performance is largely driven by intelligent façade systems, double-skin envelopes, automated shading, and high-performance mechanical systems that enhance operational efficiency. In contrast, heritage buildings including Hagia Sophia and Sultan Ahmed Mosque demonstrated moderate yet stable performance levels (≈4.00–4.40). Their strengths were most evident in Indoor Environmental Quality, where passive systems such as thermal mass, natural ventilation, and inherent spatial configurations contribute significantly to occupant comfort. Overall, the findings underscore the complementary value of combining traditional passive strategies with modern smart technologies to achieve resilient, low-energy, and user-responsive architecture. This study is novel as it uniquely demonstrates how traditional passive design strategies and modern smart technologies can be integrated to enhance climate-responsive and energy-efficient performance across diverse building typologies. The study recommends enhanced indoor air quality strategies, occupant education on system use, and stronger policy alignment with LEED standards. Full article

Generative AI has rapidly entered college writing classrooms, raising practical questions about how student texts are changing and what that means for instruction. This study analyzes 255 final-draft analytical essays written in first-year writing classes across three instructional contexts—pre-Gen-AI (Winter/Spring 2022), AI-prohibited, and AI-permitted with specified uses (Winter/Spring 2024). We combined holistic quality ratings of essays with Coh-Metrix indices of writing volume, lexicality, referential cohesion, and syntax. Analytically, we estimated a regression of essay quality on class type and demographics, and MANCOVAs (with essay score and demographics as covariates) for the four linguistic constructs. Essay quality did not differ by AI policy. However, compared to 2022, essays of AI-permitted classes were organized into fewer but shorter paragraphs; displayed greater lexical diversity and used less frequent, less familiar vocabulary; showed lower local and global anaphor overlap (other cohesion indices were stable); and exhibited lower verb-phrase, passive, and negation densities but higher gerund density. We interpret these as selective redistributions of linguistic resources rather than uniform gains or losses. For instructors, the actionable implication is two-fold: leverage AI-era gains in lexical precision while explicitly teaching referential continuity and clause-level strategies that sustain argumentative coherence. Full article

The Space–Air–Ground Integrated Network (SAGIN), a core architecture for 6G, faces formidable routing challenges stemming from its high-dynamic topological evolution and strong heterogeneous resource characteristics. Traditional protocols like OSPF suffer from excessive convergence latency due to frequent topology updates, while existing intelligent methods such as DQN remain confined to a passive reactive decision-making paradigm, failing to leverage spatiotemporal predictability of network dynamics. To address these gaps, this study proposes an adaptive routing algorithm (GCN-T-PPO) integrating a GCN-Transformer hybrid encoder, Particle Swarm Optimization (PSO), and Proximal Policy Optimization (PPO) with spatiotemporal attention. Specifically, the GCN-Transformer encoder captures spatial topological dependencies and long-term temporal traffic evolution, with PSO optimizing hyperparameters to enhance prediction accuracy. The PPO agent makes proactive routing decisions based on predicted network states (next K time steps) to adapt to both topological and traffic dynamics. Extensive simulations on real dataset-parameterized environments (CelesTrak TLE data, CAIDA 100G traffic statistics, CRAWDAD UAV mobility models) demonstrate that under 80% high load and bursty Pareto traffic, GCN-T-PPO reduces end-to-end latency by 42.4% and packet loss rate by 75.6%, while improving QoS satisfaction rate by 36.9% compared to DQN. It also outperforms SOTA baselines including OSPF, DDPG, D2-RMRL, and Graph-Mamba. Ablation studies validate the statistical significance (p < 0.05) of key components, confirming the synergistic gains from spatiotemporal joint modeling and proactive decision-making. This work advances SAGIN routing from passive response to active prediction, significantly enhancing network stability, resource utilization efficiency, and QoS guarantees, providing an innovative solution for 6G global seamless coverage and intelligent connectivity. Full article

In the context of privacy-preserving face recognition systems, entropy plays a crucial role in determining the efficiency and security of computational processes. However, existing schemes often encounter challenges such as inefficiency and high entropy in their computational models. To address these issues, we propose a privacy-preserving face recognition method based on the Face Feature Coding Method (FFCM) and symmetric homomorphic encryption, which reduces computational entropy while enhancing system efficiency and ensuring facial privacy protection. Specifically, to accelerate the matching speed during the authentication phase, we construct an N-ary feature tree using a neural network-based FFCM, significantly improving ciphertext search efficiency. Additionally, during authentication, the server computes the cosine similarity of the matched facial features in ciphertext form using lightweight symmetric homomorphic encryption, minimizing entropy in the computation process and reducing overall system complexity. Security analysis indicates that critical template information remains secure and resilient against both passive and active attacks. Experimental results demonstrate that the facial authentication efficiency with FFCM classification is 4% to 6% higher than recent state-of-the-art solutions. This method provides an efficient, secure, and entropy-aware approach for privacy-preserving face recognition, offering substantial improvements in large-scale applications. Full article