Search Results (64)

Hydrodynamic slamming loads during water landing are one of the main concerns for the structural design and wave resistance performance of large amphibious aircraft. However, current existing sensors are not used for full-scale hydrodynamic load flight tests on complex models due to their large size, fragility, intrusiveness, limited range, frequency response limitations, accuracy issues, and low sampling frequency. Fibre-optic sensors’ small size, immunity to electromagnetic interference, and reduced susceptibility to environmental disturbances have led to their progressive development in maritime and aeronautic fields. This research proposes a novel hydrodynamic profile encapsulation method using ultra-thin surface-mounted micro-electromechanical system (MEMS) fibre-optic Fabry–Pérot pressure sensors (total thickness of 1 mm). The proposed sensor exhibits an exceptional linear response and low-temperature sensitivity in hydrostatic calibration tests and shows superior response and detection accuracy in water-entry tests of wedge-shaped bodies. This work exhibits significant potential for the in situ monitoring of hydrodynamic loads during water landing, contributing to the research of large amphibious aircraft. Furthermore, this research demonstrates, for the first time, the proposed surface-mounted pressure sensor in conjunction with a high-speed acquisition system for the in situ monitoring of hydrodynamic pressure on the hull of a large amphibious prototype. Following flight tests, the sensors remained intact throughout multiple high-speed hydrodynamic taxiing events and 12 full water landings, successfully acquiring the complete dataset. The flight test results show that this proposed pressure sensor exhibits superior robustness in extreme environments compared to traditional invasive electrical sensors and can be used for full-scale hydrodynamic load flight tests. Full article

Erosion poses significant threats to infrastructures and ecosystems, exacerbated by climate change-driven sea-level rise and intensified wave actions. Microbially induced calcium carbonate precipitation (MICP) has emerged as a promising, sustainable, and eco-friendly solution for erosion mitigation. This review synthesizes recent advancements in optimizing biomineralization efficiency, multi-scale erosion control, and field-scale MICP implementations in marine dynamic conditions. Key findings include the following: (1) Kinetic analysis of Ca²⁺ conversion confirmed complete ion utilization within 24 h under optimized PA concentration (3%), resulting in a compressive strength of 2.76 MPa after five treatment cycles in ISO-standard sand. (2) Field validations in Ahoskie and Sanya demonstrated the efficacy of MICP in coastal erosion control through tailored delivery systems and environmental adaptations. Sanya’s studies highlighted seawater-compatible MICP solutions, achieving maximum 1743 kPa penetration resistance in the atmospheric zone and layered “M-shaped” CaCO₃ precipitation in tidal regions. (3) Experimental studies revealed that MICP treatments (2–4 cycles) reduced maximum scour depth by 84–100% under unidirectional currents (0.3 m/s) with the maximum surface CaCO₃ content reaching 3.8%. (4) Numerical simulations revealed MICP enhanced seabed stability by increasing vertical effective stress and reducing pore pressure. Comparative analysis demonstrates that while the destabilization depth of untreated seabed exhibits a linear correlation with wave height increments, MICP-treated seabed formations maintain exceptional stability through cohesion-enhancing properties, even when subjected to progressively intensified wave forces. This review supports the use of biomineralization as a sustainable alternative for shoreline protection, seabed stabilization, and offshore foundation integrity. Full article

Background and Objectives: Maternal dyslipidaemia and low-grade inflammation are recognised drivers of in utero vascular remodelling, yet composite dynamic markers that integrate lipid–glycaemic, inflammatory and endothelial signals have not been evaluated. We investigated whether eight-week trajectories in the triglyceride–glucose index (TyG), interleukin-6 (IL-6) and flow-mediated dilation (FMD) outperform single-timepoint lipids for predicting fetal aortic remodelling. Materials and Methods: In a prospective repeated-measures study, 90 singleton pregnancies were examined at 24–26 weeks (Visit-1) and 32–34 weeks (Visit-2). At each visit, we obtained fasting lipids, TyG index, hsCRP, IL-6, oxidative-stress markers (MDA, NOx), brachial flow-mediated dilation (FMD), carotid IMT and uterine-artery Doppler, together with advanced fetal ultrasonography (abdominal-aorta IMT, ventricular strain, Tei-index, fetal pulse-wave velocity). Mothers were grouped by k-means clustering of the visit-to-visit change (Δ) in TG, TyG, hsCRP, IL-6 and FMD into three Metabolic-Inflammatory Response Phenotypes (MIRP-1/2/3). Linear mixed-effects models and extreme-gradient-boosting quantified associations and predictive performance. Results: Mean gestational TG rose from 138.6 ± 14.1 mg/dL to 166.9 ± 15.2 mg/dL, TyG by 0.21 ± 0.07 units and FMD fell by 1.86 ± 0.45%. MIRP-3 (“Metabolic + Inflammatory”; n = 31) showed the largest change (Δ) Δ-hsCRP (+0.69 mg/L) and Δ-FMD (–2.8%) and displayed a fetal IMT increase of +0.17 ± 0.05 mm versus +0.07 ± 0.03 mm in MIRP-1 (p < 0.001). Mixed-effects modelling identified Δ-TyG (β = +0.054 mm per unit), Δ-IL-6 (β = +0.009 mm) and Δ-FMD (β = –0.007 mm per %) as independent determinants of fetal IMT progression. An XGBoost model incorporating these Δ-variables predicted high fetal IMT (≥90th percentile) with AUROC 0.88, outperforming logistic regression (AUROC 0.74). Conclusions: A short-term surge in maternal TyG, IL-6 and endothelial dysfunction delineates a high-risk phenotype that doubles fetal aortic wall thickening and impairs myocardial performance. Composite dynamic indices demonstrated superior predictive value compared with individual lipid markers. Full article

Arterial diseases are a leading cause of morbidity worldwide, necessitating the development of robust simulation tools to understand their progression mechanisms. In this study, we present a finite volume solver based on the incompressible lattice Boltzmann method (iLBM) to model complex cardiovascular flows. Standard LBM suffers from compressibility errors and is constrained to uniform Cartesian meshes, limiting its applicability to realistic vascular geometries. To address these issues, we developed an incompressible LBM scheme that recovers the incompressible Navier–Stokes equations (NSEs) and integrated it into a finite volume (FV) framework to handle unstructured meshes while retaining the simplicity of the LBM algorithm. The FV-iLBM model with linear reconstruction (LR) scheme was then validated against benchmark cases, including Taylor–Green vortex flow, shear wave attenuation, Womersley flow, and lid-driven cavity flow, demonstrating improved accuracy in reducing compressibility errors. In simulating flow over National Advisory Committee for Aeronautics (NACA) 0012 airfoil, the FV-iLBM model accurately captured vortex shedding and aerodynamic forces. After validating the FV-iLBM solver for simulating non-Newtonian flows, pulsatile blood flow through an artery afflicted with multiple stenoses was simulated, accurately predicting wall shear stress and flow separation. The results establish FV-iLBM as an efficient and accurate method for modeling cardiovascular flows. Full article

S-wave seismic data are unaffected by natural gas trapped in strata, making them a valuable tool to study evaporite facies comparing to P-wave data. S-wave seismic data were utilized to construct an isochronous framework and analyze evaporite facies by seismic sedimentology methods in the Quaternary biogenic gas-bearing Taidong area, Sanhu Depression, Qaidam Basin, NW China, with calibration from wireline logs, geochemical evidences, and modern analogs. Techniques of phase rotation, frequency decomposition, R (Red), G (Green), B (Blue) fusion, and stratal slices were integrated to reconstruct seismic geomorphological features. Linear and sub-circular morphologies, resembling those observed in modern saline pans such as Lake Chad, were identified. Observations from Upper Pleistocene outcrops of anhydrite and halite at Yanshan (east of the Taidong area), along with lithological and paleo-environmental records from boreholes SG-5, SG-1, and SG-1b (northwest of the Taidong area), support the seismic findings. The slices generated from the S-wave seismic data indicate a progressive increase in the occurrence of evaporite features from the K2 standard zone upwards. The vertical occurrence of evaporite facies in the Taidong area increases, which coincides with the contemporary regional and global arid paleo-environmental changes. The interpretation of Quaternary stratal slices reveals a transition from a freshwater lake to brackish, saline, and finally, a dry saline pan, overlaid by silt. This analysis provides valuable insights into locating evaporites as cap rocks for biogenic gas accumulation and also into mining the evaporite mineral resources in shallow layers of the Taidong area. Full article

The emergence of 5G technology promises remarkable advancements in wireless communication, particularly in the realm of mmWave (millimeter-wave) massive multiple input multiple output (m-MIMO) systems. However, the realization of its full potential is hindered by the challenge of pilot overhead, which compromises system efficiency. The efficient usage of pilot signals is crucial for precise channel estimation and interference reduction to maintain data integrity. Nevertheless, this requirement brings up the challenge of pilot overhead, which utilizes precious spectrum space, thus reducing spectral efficiency (SE). To address this obstacle, researchers have progressively turned to artificial intelligence (AI) and machine learning (ML) methods to design hybrid beam-forming systems that enhance SE while reducing changes to the bit error rate (BER). This study addresses the challenge of pilot overhead in hybrid beamforming for 5G mmWave m-MIMO systems by leveraging advanced artificial intelligence (AI) techniques. We propose a framework integrating k-clustering, linear regression, random forest regression, and neural networks with singular value decomposition (NN-SVD) to optimize pilot placement and hybrid beamforming strategies. The results demonstrate an 82% reduction in pilot overhead, a 250% improvement in spectral efficiency, and a tenfold enhancement in bit error rate at low SNR conditions, surpassing state-of-the-art methods. These findings validate the efficacy of the proposed system in advancing next-generation wireless networks. Full article

In this paper, we investigate the effect of singular viscosity on the stability of a thin film of Oldroyd-B viscoelastic fluid flowing along a porous inclined surface under the influence of a normal electric field. First, we derive the governing equations and boundary conditions for the flow of the film and assume that the film satisfies the Beavers–Joseph sliding boundary condition when it flows on a porous inclined surface. Second, through the long-wave approximation, we derive the nonlinear interfacial evolution equation. Then, linear and nonlinear stability analyses are performed for the interfacial evolution equation. The stability analyses show that the singular viscosity has a stabilizing effect on the flow of the film, while the strain delay time of the Oldroyd-B fluid, the electric field, and the parameters of the porous medium all have an unsteady effect on the flow of the film. Interestingly, in the linear stability analysis, the parameters of the porous medium have an unsteady effect on the flow of the film after a certain value is reached and a stabilizing effect before that value is reached. In order to verify these results, we performed numerical simulations of the nonlinear evolution equations using the Fourier spectral method, and the conclusions obtained are in agreement with the results of the linear stability analysis, i.e., the amplitude of the free surface decreases progressively with time in the stable region, whereas it increases progressively with time in the unstable region Full article

Dynamical symmetries, time-dependent operators that almost commute with the Hamiltonian, extend the role of ordinary symmetries. Motivated by progress in quantum technologies, we illustrate a practical algebraic approach to computing such time-dependent operators. Explicitly we expand them as a linear combination of time-independent operators with time-dependent coefficients. There are possible applications to the dynamics of systems of coupled coherent two-state systems, such as qubits, pumped by optical excitation and other addressing inputs. Thereby, the interaction of the system with the excitation is bilinear in the coherence between the two states and in the strength of the time-dependent excitation. The total Hamiltonian is a sum of such bilinear terms and of terms linear in the populations. The terms in the Hamiltonian form a basis for Lie algebra, which can be represented as coupled individual two-state systems, each using the population and the coherence between two states. Using the factorization approach of Wei and Norman, we construct a unitary quantum mechanical evolution operator that is a factored contribution of individual two-state systems. By that one can accurately propagate both the wave function and the density matrix with special relevance to quantum computing based on qubit architecture. Explicit examples are derived for the electronic dynamics in coupled semi-conducting nanoparticles that can be used as hardware for quantum technologies. Full article

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry. Full article

The quest to reveal the physical essence of the infinitely many symmetries and/or conservation laws that are intrinsic to integrable systems has historically posed a significant challenge at the confluence of physics and mathematics. This scholarly investigation delves into five open problems related to these boundless symmetries within integrable systems by scrutinizing their multi-wave solutions, employing a fresh analytical methodology. For a specified integrable system, there exist various categories of n-wave solutions, such as the n-soliton solutions, multiple breathers, complexitons, and the n-periodic wave solutions (the algebro-geometric solutions with genus n), wherein n denotes an arbitrary integer that can potentially approach infinity. Each subwave comprising the n-wave solution may possess free parameters, including center parameters $c_i$, width parameters (wave number) $k_i$, and periodic parameters (the Riemann parameters) $m_i$. It is evident that these solutions are translation invariant with respect to all these free parameters. We postulate that the entirety of the recognized infinitely many symmetries merely constitute linear combinations of these finite wave parameter translation symmetries. This conjecture appears to hold true for all integrable systems with n-wave solutions. The conjecture intimates that the currently known infinitely many symmetries is not exhaustive, and an indeterminate number of symmetries remain to be discovered. This conjecture further indicates that by imposing an infinite array of symmetry constraints, it becomes feasible to derive exact multi-wave solutions. By considering the renowned Korteweg–de Vries (KdV) equation and the Burgers equation as simple examples, the conjecture is substantiated for the n-soliton solutions. It is unequivocal that any linear combination of the wave parameter translation symmetries retains its status as a symmetry associated with the particular solution. This observation suggests that by introducing a ren-variable and a ren-symmetric derivative, which serve as generalizations of the Grassmann variable and the super derivative, it may be feasible to unify classical integrable systems, supersymmetric integrable systems, and ren-symmetric integrable systems within a cohesive hierarchical framework. Notably, a ren-symmetric integrable Burgers hierarchy is explicitly derived. Both the supersymmetric and the classical integrable hierarchies are encompassed within the ren-symmetric integrable hierarchy. The results of this paper will make further progresses in nonlinear science: to find more infinitely many symmetries, to establish novel methods to solve nonlinear systems via symmetries, to find more novel exact solutions and new physics, and to open novel integrable theories such as the ren-symmetric integrable systems and the possible relations to fractional integrable systems. Full article

Background: Sodium disturbances are observed in one-third of patients with COVID-19 and result from multifaceted mechanisms. Notably, hyponatremia is associated with disease progression and mortality. Aim: We aimed to analyze the impact of hyponatremia on COVID-19 outcomes and its correlation with clinical and laboratory parameters during the first wave. Methods: We evaluated the sodium levels of 558 patients with COVID-19 between 21 March 2020, and 31 July 2020, at a single center. We performed linear regression analyses to explore the correlation of sodium levels with COVID-19-related outcomes, demographic data, signs and symptoms, and laboratory parameters. Next, we conducted Pearson correlation analyses. A p-value < 0.05 was considered significant. Results: Hyponatremia was found in 35.3% of hospitalized patients with COVID-19. This was associated with the need for intensive care transfer (B = −1.210, p = 0.009) and invasive mechanical ventilation (B = −1.063, p = 0.032). Hyponatremia was frequently found in oncologic patients (p = 0.002) and solid organ transplant recipients (p < 0.001). Sodium was positively associated with diastolic blood pressure (p = 0.041) and productive cough (p = 0.022) and negatively associated with dry cough (p = 0.032), anorexia (p = 0.004), and nausea/vomiting (p = 0.007). Regarding the correlation of sodium levels with other laboratory parameters, we observed a positive correlation with hematocrit (p = 0.011), lymphocytes (p = 0.010), pCO₂ (p < 0.0001), bicarbonate (p = 0.0001), and base excess (p = 0.008) and a negative correlation with the neutrophil-to-lymphocyte ratio (p = 0.009), the platelet-to-lymphocyte ratio (p = 0.033), and arterial blood glucose (p = 0.016). Conclusions: Hyponatremia is a risk factor for adverse outcomes in COVID-19 patients. It is associated with demographic data and clinical and laboratory parameters. Therefore, hyponatremia is an important tool for risk stratification in COVID-19 patients. Full article

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided. Full article

Recent years have witnessed growing research interest in dual-wavelength mode-locked fiber lasers for their pivotal role in diverse applications and the exploration of nonlinear dynamics. Despite notable progress in their development, achieving reliable mode-locked dual-wavelength operation typically necessitates intricate manual adjustments of the cavity’s polarization components. In this article, we present the realization of automatic mode-locking in a dual-wavelength soliton fiber laser. To provide guidance for the algorithm design, we systematically investigated the impact of polarization configurations and initial states on the laser’s operation through numerical simulations and linear scan experiments. The results indicate that operational regimes can be finely adjusted around the wave plate position supporting the mode-locked dual-wavelength solution. Furthermore, the laser exhibits multiple stable states at the mode-locked dual-wavelength point, with critical dependence on the initial conditions. Accordingly, we developed a two-stage genetic algorithm that was demonstrated to be effective for realizing automatic dual-wavelength mode-locking. To further improve the performance of the algorithm, a feedforward neural network was trained and integrated into the algorithm, enabling accurate identification of the dual-wavelength states. This study provides valuable insights into understanding how polarization configurations and initial conditions impact the operational regimes of dual-wavelength mode-locked fiber lasers. The algorithm developed can be extended to optimize other systems with multiple stable states supported at the same parameter point. Full article

l-Kynurenine (l-Kyn) is an endogenous metabolite produced in the catabolic route of l-Tryptophan (l-Trp), and it is a potential biomarker of several immunological disorders. Thus, the development of a fast and cheap technology for the specific detection of l-Kyn in biological fluids is of great relevance, especially considering its recent correlation with SARS-CoV-2 disease progression. Herein, a disposable screen-printed electrode based on a molecularly imprinted polymer (MIP) has been constructed: the o-Phenylenediamine monomer, in the presence of l-Kyn as a template with a molar ratio of monomer/template of 1/4, has been electropolymerized on the surface of a screen-printed carbon electrode (SPCE). The optimized kyn-MIP-SPCE has been characterized via cyclic voltammetry (CV), using [Fe(CN)₆)]^3−/4− as a redox probe and a scanning electron microscopy (SEM) technique. After the optimization of various experimental parameters, such as the number of CV electropolymerization cycles, urine pretreatment, electrochemical measurement method and incubation period, l-Kyn has been detected in standard solutions via square wave voltammetry (SWV) with a linear range between 10 and 100 μM (R² = 0.9924). The MIP-SPCE device allowed l-Kyn detection in human urine in a linear range of 10–1000 μM (R² = 0.9902) with LOD and LOQ values of 1.5 and 5 µM, respectively. Finally, a high selectivity factor α (5.1) was calculated for l-Kyn toward l-Trp. Moreover, the Imprinting Factor obtained for l-Kyn was about seventeen times higher than the IF calculated for l-Trp. The developed disposable sensing system demonstrated its potential application in the biomedical field. Full article

Introduction: The benefits of exclusive breastfeeding (EBF) are widely reported. However, it is crucial to examine potential disparities in EBF practices across different regions of a country. Our study uses Tanzania demographic and health survey data to report on the trends of EBF across regions from 1999 to 2016, the patterns of the practice based on geographical location and socioeconomic status, and explores its determinants across the years. Methods: Descriptive statistics were used to establish the trends of EBF by geographical location and wealth quintile. A generalized linear mixed model was developed to incorporate both infant and maternal attributes as fixed covariates while considering enumeration areas and regions as clusters. The fitted model facilitated the estimation of EBF proportions at a regional level and identified key determinants influencing EBF practices across the survey periods. Moreover, we designed breastfeeding maps, visually depicting the performance of different regions throughout the surveys. Results: Across the various survey rounds, a notable regional variation in EBF practices was observed, with coastal regions generally exhibiting lower adherence to the practice. There was a linear trend between EBF and geographical residence (p < 0.05) and socioeconomic standing (p < 0.05) across the survey periods. Rural-dwelling women and those from the least affluent backgrounds consistently showcased a higher proportion of EBF. The prevalence of EBF declined as infants aged (p < 0.001), a trend consistent across all survey waves. The associations between maternal attributes and EBF practices displayed temporal variations. Furthermore, a correlation between exclusive breastfeeding and attributes linked to both regional disparities and enumeration areas was observed. The intra-cluster correlation ranged from 18% to 41.5% at the regional level and from 40% to 58.5% at the enumeration area level. Conclusions: While Tanzania’s progress in EBF practices is laudable, regional disparities persist, demanding targeted interventions. Sustaining achievements while addressing wealth-based disparities and the decline in EBF with infant age is vital. The study highlights the need for broad national strategies and localized investigations to understand and enhance EBF practices across different regions and socioeconomic contexts. Full article