Search Results (7,969)

Acute kidney injury (AKI) is a frequent complication in critically ill patients and is associated with high morbidity, mortality, and an increased risk of progression to chronic kidney disease (CKD). In this context, nutritional management represents a key component of supportive therapy, as AKI is commonly characterized by hypercatabolism, negative nitrogen balance, and protein-energy wasting. Current nutritional strategies primarily focus on the quantity of protein intake required to compensate for catabolic losses, particularly in patients undergoing renal replacement therapy (RRT). However, growing evidence suggests that the quality and metabolic effects of dietary protein sources may also influence renal physiology and recovery. Plant-based proteins have recently gained attention as a potentially advantageous nutritional strategy in kidney disease. Compared with animal-derived proteins, plant-based proteins are associated with a lower dietary acid load, reduced production of gut-derived uremic toxins, and beneficial effects on the intestinal microbiota. In addition, their amino acid profile may modulate oxidative stress, inflammatory pathways, and renal hemodynamics. These characteristics may contribute to a more favorable metabolic environment in patients with AKI, potentially supporting renal recovery and reducing the risk of AKI-to-CKD transition. This review examines the pathophysiological mechanisms linking protein metabolism, renal injury, and nutritional support in AKI. Particular attention is given to the role of plant-based proteins, their amino acid composition, and their potential nephroprotective effects. Understanding the interaction between dietary protein sources, metabolic pathways, and the gut–kidney axis may help guide future nutritional strategies aimed at improving outcomes in critically ill patients with AKI. Full article

The development of supersonic aircraft presents significant challenges in ensuring safety during early design stages, particularly for fuel tank systems exposed to extreme thermal and structural loads. Conventional document-based zonal safety analysis methods are limited in their ability to capture dynamic interactions between spatial subsystem configurations and functional system behavior during early conceptual design, leading to delayed hazard identification. This study proposes an integrated framework combining computer-aided design (CAD) and model-based systems engineering (MBSE) to support early-stage zonal hazard analysis. The framework links spatial subsystem modelling with functional system architecture to enable iterative hazard identification and mitigation. Applied to the SA-24 Phoenix conceptual supersonic aircraft, the approach identifies critical risks, including fuel vaporization, over-pressurization, and structural fatigue, and evaluates mitigation strategies such as thermal insulation and redundant venting. Functional hazard analysis and fault tree analysis are used to assess failure scenarios and ensure compliance with EASA CS-25 requirements. Results indicate an estimated reduction of up to 40% in risk priority number (RPN) values for key thermal hazard pathways and a 25% reduction in conceptual design iteration time compared with conventional approaches. The findings demonstrate that CAD–MBSE integration offers a scalable and efficient methodology for early hazard identification, contributing to safer and more reliable supersonic aircraft design. Full article

Industrial isolated power systems are highly sensitive to load disturbances due to their limited inertia and absence of large-grid support. This article analyzes the dynamic stability of an isolated system with a current available generation contribution of approximately 24 MW, evaluating the integration of a new production plant planned to be integrated in two construction phases of 2 MW each (total 4 MW). The system operates with local generation at 13.8 kV and distribution at 34.5 kV; therefore, demand expansion requires a detailed assessment to maintain safe operating conditions. In addition, the study verifies compliance with spinning reserve requirements for Phase 1 and Phase 2 in accordance with applicable industrial power system criteria, including IEEE 3007.1 and IEEE C37.106, as part of the N−1 security assessment. The developed stability analysis is based on time-domain dynamic simulations using IEEE AC8C excitation models and a UG-8 governor. The results show that, under severe contingencies, the frequency nadir can reach deviations close to 1.5 Hz and RoCoF values above 4 Hz/s. The results indicate that Phase 1 (2 MW) can be incorporated while maintaining acceptable spinning reserve margins, whereas the additional 2 MW corresponding to Phase 2 cannot be integrated under the current operating conditions without violating reserve criteria. However, the system remains stable when generators operate under automatic voltage control, while fixed power factor mode produces less robust responses. Based on this result, the dynamic analysis is focused on the Phase 1 condition under critical contingencies, particularly the sudden outage of the 5 MW and 8 MW generating units, with special emphasis on the outage of the largest generator, mitigated through spinning reserve support and a RoCoF-based load shedding scheme of approximately 4.4 MW. Likewise, the energization of the new plant through the 8 km line requires the evaluation of the available reactive compensation resources, including the use of capacitor banks/reactive support, to prevent underexcitation and maintain acceptable voltage conditions. Full article

Crimean–Congo hemorrhagic fever virus (CCHFV) and severe fever with thrombocytopenia syndrome virus (SFTSV) are tick-borne pathogens that cause severe illness and high mortality. Early diagnosis is critical, particularly in resource-limited settings, to enable timely intervention. Host gene expression profiling offers a promising approach to identify potential biomarkers for early detection, disease staging, and logical treatment decision-making. Using a transient IFN-α/β receptor-suppressed mouse model, we performed targeted transcriptomic analysis on blood samples collected at 2, 3, and 4 days after CCHFV or SFTSV challenge. A significant increase in viral load and changes in gene expression were observed as early as two days post-challenge. CCHFV induced a progressively evolving interferon-driven response, while SFTSV triggered rapid, sustained immune activation. Affected targets included interferon-stimulated genes, chemokines, cytokines, Toll-like receptors, and genes associated with viral evasion and innate immune response. Despite shared expression patterns, unique genes were identified as potential biomarkers to distinguish between CCHFV and SFTSV infections. Differential gene expression revealed distinct immune response dynamics, with suppression of critical immune regulatory genes suggesting transcriptional signatures associated with viral evasion mechanisms contributing to disease severity. These findings provide a comparative analysis of molecular pathways and gene expression changes, offering critical insights for biomarker discovery, effective triage, and evaluation of appropriate medical intervention. Full article

As floating offshore wind turbines (FOWTs) scale towards 10 MW+ capacities, suppressing wave-induced rotational resonance becomes critical for system survivability. This study introduces an optimized, highly symmetrical four-float semi-submersible platform, explicitly tailored to support the DTU 10 MW wind turbine and paired with an orthogonal four-point mooring system. Using three-dimensional linear potential flow theory via ANSYS AQWA, comprehensive frequency- and time-domain hydrodynamic evaluations were conducted. To address the inherent limitations of inviscid potential flow assumptions, an empirical added-damping method was implemented. Quantitative results demonstrate a drastic reduction in motion responses: the peak Response Amplitude Operator (RAO) for heave decreased by 68.6% (from 1.945 m/m to 0.610 m/m). Most notably, the peak RAOs for the critical rotational degrees of freedom—pitch and roll—were reduced by over 92% (from 2.080 °/m and 2.216 °/m to ~0.168 °/m, respectively). Ultimately, compared to traditional asymmetric three-float concepts, this novel symmetric omnidirectional layout provides a more uniform restoring stiffness. The resulting suppression of pitch and roll resonance results in a profound reduction in tower-base bending moments and gyroscopic loads, thereby significantly enhancing the dynamic stability, safety margins, and fatigue life of the 10 MW FOWT under extreme survival sea states. Full article

Shot peening is a critical surface treatment for improving the fatigue resistance of aircraft propeller blades operating under complex cyclic loads. While accurate coverage evaluation is essential for quality assurance, its development is severely hindered by a fundamental bottleneck: the extreme scarcity of annotated datasets in this niche aerospace domain, where data collection is costly and low-frequency, as each acquisition requires the actual peening of high-value components. Consequently, existing practices are restricted to subjective manual inspection or conventional segmentation methods that lack robustness under complex textures. To bridge this gap, this study develops an integrated automated surface evaluation framework, termed DT-ZSAM (Dual-Threshold Zero-shot Assessment Model), which circumvents the data-dependency bottleneck by leveraging the zero-shot capabilities of the Segment Anything Model (SAM) within a custom-designed prompt-generation pipeline. To ensure end-to-end automation without manual intervention, the framework identifies candidate regions via a dual-threshold scheme in grayscale and brightness domains and extracts representative prompt points through density-based analysis refined by DBSCAN clustering. Experimental results demonstrate that the proposed framework achieves precise segmentation without requiring any pixel-level annotated training data. Notably, the proposed framework yielded a coverage rate of 30.57%, aligning closely with the expert visual consensus (25–35%), whereas the standard commercial instrument (TCV-2A) significantly overestimated the coverage at 62.33% due to its sensitivity to surface textures and fixed calibration logic. This framework provides a robust and pragmatic solution for high-stakes industrial quality control, offering a reliable path for automating inspection in domains where large-scale data acquisition is practically unfeasible. Full article

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

Intermodal container terminals play a critical role in modern logistics systems, where operational efficiency and energy consumption are strongly influenced by container stacking strategies. Inefficient yard organization leads to increased reshuffling operations, which negatively affect handling time and resource utilization. Despite extensive research, the relationship between operational performance and energy consumption remains insufficiently explored under dynamic terminal conditions. This study applies a discrete-event simulation framework to evaluate the impact of alternative container stacking strategies on both operational efficiency and energy consumption. The model represents container arrivals, storage decisions, retrieval processes, and reshuffling operations over a multi-day simulation horizon. Three stacking strategies—FIFO, balanced distribution, and departure-time clustering—are analysed under identical and dynamically evolving conditions using performance indicators related to reshuffling intensity, handling efficiency, and energy consumption. The results show that stacking strategies significantly affect terminal performance, but their effectiveness depends on the structure of container flows. While FIFO achieves the lowest reshuffling intensity and energy consumption under high-load conditions, departure-time clustering improves performance in outbound-dominated scenarios. The findings also reveal a structural discrepancy between operational and energy-related performance, as non-productive movements account for a higher share of operations than of total energy consumption. The study demonstrates that container stacking should be treated as a multi-criteria decision problem, where minimizing reshuffles does not directly correspond to minimizing energy consumption. The proposed simulation-based framework provides a consistent environment for evaluating trade-offs between operational and energy-related performance under controlled dynamic conditions. Full article

Biodegradable metals represent a paradigm shift in orthopedic fixation by providing temporary mechanical support synchronized with bone healing while eliminating long-term complications associated with permanent implants. Conventional bioinert alloys, including stainless steels, Ti-based alloys, and Co-Cr alloys, exhibit high elastic moduli that induce stress shielding and often require secondary removal surgeries. In response, resorbable metallic systems based on Mg, Zn, and Fe have emerged as promising alternatives. Among these, Fe-Mn-C alloys stand out for load-bearing applications due to their exceptional strength-ductility balance governed by twinning-induced plasticity mechanisms, tunable degradation behavior, and intrinsic magnetic resonance imaging compatibility through austenitic phase stabilization. Focusing on Fe-Mn-C alloys, this review critically examines the metallurgical design principles underlying stacking fault energy optimization, phase stability, and Mn-controlled electrochemical behavior. Processing innovations, such as additive manufacturing, are discussed as tools to architecture porosity, refine microstructure, and accelerate degradation by graded designs while preserving mechanical structural support during healing. Hybrid metallic-bioactive systems, surface functionalization strategies, and functionally graded porous architectures were evaluated as advanced approaches to enhance osteointegration and modulate degradability. Despite these advances, significant barriers remain for clinical translation. Persistent discrepancies between in vitro and in vivo degradation rates, often attributed to biological encapsulation and degradation product accumulation, complicate lifetime prediction. Localized corrosion at microstructural heterogeneities such as twin boundaries and phase interfaces can undermine structural reliability under load-bearing conditions. Moreover, predictive multi-physics modeling frameworks capable of coupling electrochemical kinetics, mechanical loading, microstructural evolution, and bone remodeling remain underdeveloped, limiting reliable safety-margin estimation. Regulatory progress is further hindered by the absence of standardized testing protocols specifically tailored to Fe-based biodegradable alloys, including harmonized degradation rate windows, validated corrosion-mechanics coupling methodologies, and clinically defined Mn ion release thresholds. This review aims to discuss whether Fe-based alloys, especially Fe-Mn-C alloys, can transition from promising laboratory materials to clinically viable next-generation orthopedic implants capable of delivering patient-specific, mechanically compatible, and biologically synchronized temporary fixation. Full article

Hospital wastewater represents a critical hotspot for the dissemination of antibiotic resistance genes (ARGs), serving both as an environmental reservoir and a transmission pathway for multidrug-resistant bacteria into receiving ecosystems. The intense antibiotic selective pressure within healthcare facilities promotes the emergence, persistence and amplification of resistant strains, posing substantial risks to public health and environmental integrity. This study aimed to characterize Escherichia coli (E. coli) isolates recovered from hospital wastewater effluents in multiple cities across Gabon, with emphasis on bacterial loads, antimicrobial resistance patterns and associated genetic determinants. Wastewater samples were aseptically collected from sewer outlets of eleven healthcare facilities distributed across five provinces over a 12-week period, structured into two six-week sampling campaigns to capture temporal variability. A total of 158 bacterial isolates were obtained, among which 49 were confirmed as E. coli. Mean concentrations of presumptive E. coli ranged from 7.1 × 10³ to 1.49 × 10⁹ CFU/mL, indicating substantial microbial contamination of hospital effluents. Antimicrobial susceptibility testing using the Kirby–Bauer disk diffusion method against 19 antibiotics revealed that all isolates exhibited multidrug-resistant phenotypes. Resistance rates were particularly high to β-lactams and third-generation cephalosporins, reaching 90–100% in most facilities, reflecting strong selective pressure and widespread circulation of resistance mechanisms in urban aquatic environments. In contrast, carbapenems and amikacin remained comparatively effective, with resistance levels below 40%, suggesting partial preservation of last-resort therapeutic options. The values of the Multiple Antibiotic Resistance Index (MARI) ranged from 0.21 to 0.84, indicating selection pressure on different classes of antibiotics. Phylogenetic analysis showed a predominance of phylogroup A, traditionally considered commensal but increasingly associated with the spread of resistance. Groups B2, D/E and F proved to be the most resistant. These groups showed marked resistance to first-line antibiotics. The blaCTX-M-1 was the most prevalent resistance determinant (66.6%), occurring twice as frequently as blaSHV (33.3%), a finding that confirms the significant circulation of extended-spectrum β-lactamase-producing E. coli. Overall, these findings highlight hospital wastewater as a major reservoir and dissemination source of multidrug-resistant E. coli, underscoring the urgent need for improved wastewater treatment, strengthened antimicrobial stewardship and integrated One Health-based surveillance strategies. Full article

The prediction of metal fatigue life has evolved from classical empirical approaches to advanced, data-driven computational models. However, traditional methods struggle with large data scatter, complex variable-amplitude loading, and the cost of experimental testing. These limitations are particularly pronounced in additively manufactured (AM) components, which exhibit random porosity and are highly sensitive to process parameters. This review integrates classical fatigue mechanics with modern data-driven methodologies. It evaluates fatigue-life prediction for metallic alloys, welded assemblies, and AM materials. We review classical prediction tools, machine learning (ML) algorithms, deep learning architectures, and physics-informed neural networks (PINNs). ML models capture nonlinear degradation patterns but suffer from limited interpretability (“black-box” behavior) and are unable to extrapolate from small datasets. Embedding governing physical laws into PINNs helps mitigate these limitations. This approach enhances physical consistency, reduces training-data requirements, and strengthens extrapolation capability. In additively manufactured metals, defect location is often a more critical predictor of fatigue failure than defect size or morphology. To address data scarcity, we highlight the use of generative adversarial networks and transfer learning. Integrated models, combined with real-time structural health monitoring data, enable accurate, dynamic digital twins and preemptive fatigue prognosis. Full article

Recycling spent lithium-ion batteries (LIBs) is critical for resource sustainability and carbon neutrality. This work presents a green strategy in which NaA zeolite is used to preferentially recover lithium from leachate of spent NCM111 batteries, combined with temperature-regulated stepwise separation of transition metals. Benefiting from the distinct hydrated ionic radii and charge density between Li⁺ and divalent metal ions, NaA zeolite selectively adsorbs Ni²⁺, Co²⁺ and Mn²⁺, leaving Li⁺ in the raffinate. Under optimized conditions, two-stage adsorption achieves 95.6%, 96.7% and 99.7% removal of Ni²⁺, Co²⁺ and Mn²⁺, respectively, with 11% Li⁺ co-adsorption. Thermodynamic analysis reveals that the adsorption process is endothermic and thermodynamically spontaneous. The interaction strength between metal ions and NaA zeolite follows the order Ni²⁺ > Co²⁺ > Mn²⁺, and ion exchange is identified as the dominant mechanism. It is determined that 96.8% of Mn²⁺ can be recovered at 0 °C, followed by the desorption of 93.5% of Co²⁺ at 90 °C, and the sequential separation of Mn, Co and Ni is realized. Three consecutive adsorption–desorption cycles demonstrate the acceptable reusability of the Ni-loaded NaA adsorbent. High-purity Li₂CO₃ (purity 96.7%, yield 93.5%), MnO₂ (purity 99.3%, yield 98.4%) and Co₃O₄ (purity 98.8%, yield 97.6%) are obtained from the corresponding solutions. This approach provides a scalable closed-loop pathway for full-component recovery of valuable metals from spent LIBs. Full article

Background: Blood flow restriction training (BFRT) is a low-load resistance training modality capable of inducing muscle hypertrophy and strength adaptations that are comparable to traditional high-load resistance training. Beyond athletic performance settings, BFRT has growing relevance for musculoskeletal health, rehabilitation and populations unable to tolerate high mechanical loads. However, substantial inter-individual variability in adaptive responses has been reported. Genetic and molecular factors may partly contribute to this variability and inform more individualised exercise strategies. Other intrinsic and extrinsic factors, including age, sex, training status, nutrition, and protocol-related differences, may also influence adaptive responses. Objective: This scoping review aimed to map available evidence on molecular modulators of adaptation to BFRT and to identify gaps in the literature regarding genetic influences on BFRT responses. Methods: A structured search of PubMed, Web of Science and Google Scholar was conducted till 1 February 2026. Experimental and quasi-experimental studies examining BFRT in relation to genetic polymorphisms, gene expression, and molecular signalling pathways associated with strength and hypertrophy outcomes were included. Primary outcomes were genetic and molecular factors relevant to BFRT adaptation, including genetic polymorphisms, gene expression, and molecular signalling markers. Secondary outcomes included muscle strength, hypertrophy, vascular responses, and related functional outcomes where reported. Study selection and data extraction were conducted according to PRISMA-ScR guidelines. The methodological quality of randomised controlled trials was assessed using the PEDro scale. This scoping review was registered retrospectively in the Open Science Framework on 17 March 2026, after completion of the literature search. Results: From an initial 47 records, only three studies (n = 3) met the inclusion criteria. The included studies reported molecular responses associated with BFRT, including downregulation of proteolytic genes, suppression of myostatin expression, and upregulation of angiogenic markers. Notably, no studies directly examined genetic polymorphism or genotype–BFRT interactions, highlighting a clear need for these studies in this field. Conclusions: This scoping review therefore identifies a critical evidence gap, with genotype-informed BFRT prescription remaining unsupported by the current literature. Limited evidence supports the possible role of BFRT in molecular responses associated with muscle adaptation. Future research should prioritise well-designed studies integrating both genetic and molecular analyses to better understand inter-individual variability in BFRT adaptations. Full article

This study proposes a novel pneumatically driven mechanically prestressed rhombic bistable composite laminate with asymmetric cylindrical curvature, which exhibits two weakly-coupled cylindrical shapes where each shape is influenced by planform and geometry parameters. A reduced-order analytical model is developed to predict the laminate’s quasi-static equilibrium shapes and snap-through transitions of the laminate under pneumatic work loading, which is triggered by the internal pressure applied to the fluidic channels. A sensitivity study based on the model investigates the influence of key planform and geometric parameters (the internal angle α and aspect ratio E) on the laminate’s out-of-plane deflection and snap-through pressure. The results show that increasing α reduces the critical prestrain required to achieve bistability and amplifies the out-of-plane deflection, while excessive α may lead to monostable curvature. Variations in aspect ratio modify the coupling stiffness between orthogonal PEMC layers, thereby influencing the bistable domain and critical snap-through pressure. These findings provide methods for the design of bistable composite structures. Full article

Transformer fires within indoor substations constitute severe hydrocarbon fire scenarios characterized by rapid heat release rates and extreme peak temperatures, posing a critical threat to the structural integrity of steel frameworks and power grid stability. To rigorously assess structural safety under such conditions, this study employs a sequential thermal-mechanical coupled numerical methodology combining Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). Focusing on a 110 kV indoor substation, the research simulates the transient, non-uniform temperature fields induced by transformer oil combustion and analyzes the thermo-mechanical response of key steel components. Furthermore, the protective efficacy of two non-intumescent coatings (Material A and Material B) with distinct thermal conductivities is systematically evaluated. Computational results elucidate significant thermal stratification, with upper-level structures sustaining exposure to temperatures exceeding 1500 K. Unprotected steel components subjected to direct flame impingement exhibit severe stress concentrations and plastic deformation, reaching their load-bearing limit within 4825 s. The application of fire-retardant coatings markedly enhances fire resistance; a 5 mm layer of Material A (λ = 0.20 W/(m·K)) extends the time to failure to approximately 9390 s. Notably, increasing the thickness of Material A to 20 mm, or alternatively employing a 10 mm layer of Material B (λ = 0.10 W/(m·K)), effectively mitigates thermal stress concentrations. This ensures structural deformation remains within safe limits throughout a 3 h (10,800 s) fire duration. This study provides a theoretical basis and quantitative engineering references for the optimal fire protection design of substation steel structures. Full article