Search Results (1,171)

Background: Critical illness is characterized by profound and rapidly evolving metabolic derangements driven by systemic inflammation, hypercatabolism, fluid shifts, and endocrine dysregulation. These dynamic changes markedly limit the accuracy of predictive equations, increasing the risk of both underfeeding and overfeeding. Indirect Calorimetry Energy represents the gold standard for measuring energy expenditure, while bioelectrical impedance vector analysis (BIVA) provides complementary insights into hydration status, cellular integrity, and body cell mass. In palliative care, AI-supported integration of indirect calorimetry and BIVA enables goal-concordant artificial nutrition by aligning energy delivery with real-time metabolic status while minimizing symptom burden. Artificial intelligence (AI) has emerged as a promising tool to integrate these heterogeneous data streams and support adaptive nutritional strategies. Methods: We conducted a structured narrative review of the literature published between 2000 and 2025 using PubMed, Scopus, Embase, and Web of Science. Artificial intelligence was not used to perform the literature search or study selection. Instead, AI was analyzed as a clinical and technological component within the included studies and explored as a future enabling strategy. Eligible publications involved adult critically ill patients and addressed indirect calorimetry, BIVA-derived parameters, or AI-based metabolic modeling applied to nutritional support. Given the heterogeneity of study designs and outcomes, findings were synthesized qualitatively. Results: Predictive equations showed substantial inaccuracy in unstable metabolic states, with errors frequently exceeding ±20–40%. Indirect calorimetry enabled individualized assessment of energy expenditure but remained limited by intermittent availability. Serial BIVA assessments consistently identified clinically relevant alterations in hydration status, body cell mass, and phase angle, the latter being strongly associated with adverse outcomes. Studies incorporating AI demonstrated improved integration of calorimetry, BIVA, and clinical variables, allowing identification of metabolic phenotypes, anticipation of metabolic shifts, and generation of adaptive nutritional recommendations. Conclusions: This narrative review highlights the complementary roles of Indirect Calorimetry and BIVA in characterizing metabolic needs in critical illness. Artificial intelligence does not replace these tools but enhances their clinical utility by integrating multidimensional data into dynamic, patient-specific nutritional strategies. The combined AI–IC–BIVA approach represents a promising framework for metabolic precision nutrition in the ICU, warranting prospective validation. Full article

This paper presents an absolute distance measurement system based on three-wavelength synchronous phase-shifting interferometry. A synthetic wavelength chain is established using three semiconductor lasers in an all-fiber Fizeau interferometer. By integrating a piezoelectric transducer (PZT)-driven sinusoidal phase modulation with multi-channel synchronous sampling for phase demodulation, and further combining it with a fractional multiplication method, the proposed system achieves high-precision absolute distance measurement over an extended range. Experimental results demonstrate an unambiguous measurement range of 240 μm, a static measurement precision better than 0.6 nm, and a dynamic displacement measurement accuracy superior to 2 nm in comparison with the reference device. The main error sources of the system, including synthetic wavelength uncertainty, phase measurement uncertainty, and air refractive index uncertainty, are systematically modeled and analyzed. In addition, the influence of dynamic factors, such as PZT nonlinearity, is discussed and compensated. The proposed method provides a robust and high-precision solution for absolute ranging and shows strong potential for applications in industrial precision inspection and optical sensing. Full article

Four perovskite manganite samples, La_0.80Ag_0.20MnO₃ (LA), La_0.78Eu_0.02Ag_0.20MnO₃ (LEA), La_0.80Pb_0.05Ag_0.15MnO₃ (LPA), and La_0.77Eu_0.03Pb_0.05Ag_0.15MnO₃ (LEPA), were prepared by the Pechini sol–gel method. The samples were characterized by X-ray diffraction, scanning electron microscopy, energy-dispersive spectroscopy, X-ray photoelectron spectroscopy, and a magnetic property measurement system. A systematic investigation was conducted into the individual effects of Eu and Pb doping, as well as their co-doping, on the microstructural, magnetic and magnetocaloric properties of the materials. The results show that all samples are mainly composed of a rhombohedral perovskite phase with the R$\overline{3}$c space group, accompanied by a trace amount of Ag. Addition of Eu³⁺ and Pb²⁺ induces lattice contraction and expansion, respectively. Under the same processing conditions, the average crystallite and particle sizes of the LEA sample (45.3 nm and 0.18 μm) are smaller than those of the other three samples (69.6~80.6 nm and 0.38~0.44 μm), indicating that the introduction of Eu alone suppresses crystallization ability, which can be avoided through Eu/Pb co-doping. All samples undergo a second-order ferromagnetic–paramagnetic transition, and the Curie temperature T_C shifts to either lower or higher temperatures upon the introduction of Eu or Pb alone (from 310.8 K to 298.0 K or 318.0 K, respectively), which is attributed to the variation of the Mn³⁺/Mn⁴⁺ double-exchange (DE) interaction resulting from the ionic size mismatch and lattice distortion. In the LPA sample, an additional contribution arises from the altered Mn³⁺/Mn⁴⁺ ratio and enhanced DE interaction caused by the substitution of Pb²⁺ for Ag⁺. By modifying the Eu/Pb ratio, the T_C of the LEPA sample was tuned to 299.3 K, and its maximum magnetic entropy change was enhanced to 3.90 J·kg⁻¹·K⁻¹ ($∆$H = 2 T). These results indicate that multicomponent synergistic regulation can improve the magnetocaloric performance of La-based perovskite manganites, providing a useful strategy for the development of room-temperature magnetic refrigeration materials. Full article

Mesenchymal stromal/stem cells (MSCs) are increasingly explored for immune-mediated diseases, yet standardized analytical readouts that capture coordinated immunomodulatory output across complementary secretory pathways remain limited. Here, we report the feasibility of an HPLC-based multi-analyte secretome characterization panel that quantifies two small-molecule outputs—adenosine and kynurenine—alongside two immunomodulatory proteins—interleukin-10 (IL-10) and transforming growth factor-beta (TGF-β)—in conditioned media from canine adipose-derived MSCs (cAD-MSCs). Canine immune-mediated hemolytic anemia (IMHA) was used as a disease context to motivate the selection of these analytes, given the pro-inflammatory cytokine environment characteristic of this condition. Three independent cAD-MSC lines were evaluated under baseline conditions and following cytokine stimulation with recombinant interferon-gamma (IFN-γ; 100 ng/mL) and tumor necrosis factor-alpha (TNF-α; 50 ng/mL), referred to herein as inflammatory priming or licensing. Conditioned media were collected at 72 h for metabolite analysis and 48 h for protein analysis, and quantified by HPLC using external calibration and peak integration. Across all three lines, licensing produced directionally consistent increases: mean adenosine increased 2.3-fold, mean kynurenine increased 3.1-fold, mean IL-10 increased 1.6-fold, and mean TGF-β increased 1.7-fold compared with unlicensed controls. Metabolite measurements for adenosine and kynurenine are reported with full chromatographic selectivity data; IL-10 and TGF-β measurements by reversed-phase HPLC with UV detection are presented as exploratory/semi-quantitative outputs and will require orthogonal confirmation (e.g., immunoassay) in future work. These findings are preliminary, derived from three independent donor lines with no comparator group, and are intended to support feasibility of the analytical framework rather than establish definitive performance specifications. Collectively, the data support the potential of a multi-analyte HPLC-based characterization panel to capture licensing-responsive secretory shifts across mechanistically complementary pathways, providing a foundation for expanded development and validation. Full article

Standard fringe-based structured-light processing estimates wrapped phase from phase-shifted sinusoidal images and commonly relies on phase unwrapping to obtain a globally consistent phase representation. In practical measurements, this approach may become unstable on reflective objects and under low or non-uniform illumination, where the recorded fringe signal is distorted and the recovered phase becomes unreliable. To address these limitations, we propose a correspondence extraction method based on subpixel peak localization performed directly on phase-domain images. The wrapped phase is transformed into absolute value phase profiles, $\Phi =|{\varphi }_w|$, whose local structure follows the projected fringe pattern and is less affected by object-dependent intensity variations. The proposed method reformulates correspondence extraction as a local signal-based estimation problem in the phase-domain, thereby reducing reliance on global phase-consistency constraints at the correspondence stage. A practical advantage observed in the evaluated examples is that the method remained usable in some regions where the phase became locally flat because of low modulation, saturation, or reflective surface effects. In such regions, conventional processing relies on sufficiently reliable phase gradients and subsequent unwrapping, whereas the proposed method uses local peak geometry in the transformed phase representation. In the implementation used here, Gray-code information is employed only for pixel-wise phase extension and reference indexing, not as a spatial phase-unwrapping mechanism. The method does not require machine learning models or training data and can be integrated as a correspondence analysis stage in practical structured-light systems. Full article

Driven by global carbon reduction targets, supersulfated cement has emerged as a promising low-carbon cementitious material. This study investigates the influence of a polycarboxylate superplasticizer (PCE) on the rheological behavior and early interfacial evolution of phosphogypsum-based supersulfated cement (PSSC). Rheological measurements, pore solution ion analysis, hydration heat analysis, X-ray diffraction (XRD), and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM–EDS) are employed to correlate early hydration processes with structural development. The results indicate that the incorporation of PCE significantly reduces the initial yield stress and moderates the structural build-up rate. At a PCE dosage of 0.3 wt.%, the initial static yield stress decreases from 1313 Pa to approximately 125 Pa, while the structural build-up index I_s,s reaches 10.19, indicating improved particle dispersion while maintaining progressive structural reconstruction during hydration. Phosphogypsum (PG) functions not only as a sulfate source but also as an active interfacial substrate that promotes the preferential nucleation of AFt on its surface. In the absence of PCE, continuous Ca–P-enriched layers form on PG particles, accompanied by localized AFt accumulation. After the incorporation of PCE, the primary crystalline phases remain unchanged; however, gypsum dissolution and AFt formation are delayed. Meanwhile, Ca–P enrichment shifts from continuous coverage to a more dispersed distribution, promoting the spatially separated growth of AFt crystals rather than dense localized aggregation. Overall, PCE influences the evolution of the structure and properties of the system by regulating early interfacial reactions and the spatial organization of hydration products. Full article

Background/Objectives: Essential oils (EOs) have multi-target antifungal activity, but their translation is limited by volatility and poor aqueous dispersibility. Randomly methylated β-cyclodextrin (RAMEB) inclusion may enhance effective exposure and thereby alter susceptibility, stress responses, and biofilm outcomes in a species-dependent manner. This study quantified species-specific planktonic and biofilm susceptibility to four EOs and their RAMEB complexes across clinically relevant Candida species. Methods: Lavender (L), lemon balm (B), peppermint (P), and thyme (T) oils and their RAMEB complexes (RL, RB, RP, and RT) were tested against C. albicans and non-albicans Candida. Susceptibility thresholds were used to derive phase plasticity metrics. Functional inhibition was assessed via planktonic metabolism/viability and established biofilm metabolism/viability/biomass. Mechanistic signatures were captured by ROS/RNS measurements and a qPCR analysis of antioxidant genes (CAT1, GPX1, and SOD1) was performed. Mixed-effects models and multivariate/unsupervised and interpretable classification approaches (k-means, PCA, and CRT) were used to integrate endpoints and stratify response phenotypes. Results: Susceptibility thresholds were strongly species-structured (lowest MIC₉₀/EC₁₀ for C. albicans; higher thresholds and broader sublethal windows in non-albicans species). RAMEB complexation produced formulation-dependent shifts in efficacy, with RT emerging as the most consistent broad-spectrum inhibitory condition across compartments. Biofilm biomass was comparatively insensitive even when viability was suppressed, indicating a decoupling of structural biomass from biocidal activity. Mechanistic signatures were broadly conserved across species and linked to antioxidant-program engagement, with CAT1-related rules contributing to responder/tolerant classification. Conclusions: Integrating MIC/EC plasticity with functional and mechanistic markers supports the rational selection of EO formulations; RAMEB complexation, particularly RT, prioritizes candidates for further pharmaceutical optimization while highlighting species-specific vulnerabilities. Full article

Achieving high-accuracy and high-speed 3D shape measurement remains a significant challenge. This paper presents a novel technique using phase-order lines (POLs), which eliminates the need for phase unwrapping in a binocular system. By combining phase-shifting for high resolution and speckle projection for robust features, our method extracts POLs directly from the wrapped phase. The speckle patterns are then used to establish robust POL correspondences between stereo images. These matched POLs serve as reliable seeds to guide dense, sub-pixel matching directly on the wrapped phase, thus bypassing the complex phase unwrapping process. This approach significantly reduces the number of required patterns. The experimental results demonstrate that our method achieves a root-mean-square (RMS) error of 0.058 mm using only five patterns, delivering accuracy comparable to a 12-pattern temporal phase unwrapping (TPU) method while being significantly faster. Full article

Multipath propagation presents a major challenge to acoustic communication, causing signal distortion, delay spread, and inter-symbol interference, which degrade data integrity. This study investigates the use of Orthogonal Frequency Division Multiplexing (OFDM) as a robust modulation strategy for communication in complex acoustic environments where radio frequency (RF) propagation is severely attenuated. Using a software-defined radio (SDR) platform implemented in GNU Radio, OFDM performance was experimentally evaluated against Binary Frequency Shift Keying (BFSK) and Binary Phase Shift Keying (BPSK) under simulated and real multipath conditions in materials including air, water, and steel. The results show that OFDM achieves consistently lower bit error rates (BERs) and greater resilience to multipath interference due to its sub-carrier orthogonality and cyclic-prefix structure. The research also highlights how the frequency selectivity and coherence bandwidth of acoustic channels influence modulation performance across different media. By implementing custom transducers and real-time baseband processing, the study demonstrates how software-defined acoustics can be adapted for highly reflective and frequency-dependent environments. The observed improvements in BER and signal stability validate OFDM’s effectiveness in maintaining data integrity despite time and frequency dispersion effects. These findings demonstrate that OFDM enables reliable acoustic data transmission across heterogeneous media and is well suited to sensor-network applications in RF-hostile environments such as railway infrastructure, sealed containers, and submerged systems. Future work will include quantitative channel characterisation—specifically measuring delay spread, coherence bandwidth, and impulse response profiles—to further optimise OFDM parameters and provide a generalisable framework for adaptive modulation in dynamic acoustic channels. Full article

In fringe projection profilometry, phase accuracy is a key factor in determining the ultimate measurement precision. However, errors stemming from the nonlinear response of the projector and camera are introduced into the phase map, manifesting as periodic artifacts that seriously compromise measurement fidelity. Although traditional phase filtering can effectively mitigate these artifacts, it often introduces edge blurring and detail loss. To address this, we first establish models for both the nonlinear error and its propagation and then propose a novel phase filtering algorithm based on low-pass guided filtering. This method effectively suppresses nonlinear artifacts while preserving edges, thereby improving calibration and measurement accuracy without requiring additional hardware. Our algorithm enhances the traditional four-step phase-shifting method: in simulations, it reduces calibration error by 52.2% (from 0.1490 mm to 0.0712 mm), and measurement error by over 36.8% (from 0.0855 mm to 0.0559 mm); in real experiments, these reductions are 54.1% (from 0.1180 mm to 0.0875 mm) and more than 36.7% (from 0.0954 mm to 0.0604 mm), respectively. Experimental results show that our method achieves accuracy comparable to the eight-step phase-shifting method while preserving the efficiency of the four-step method, highlighting its significant practical value. Full article

In vitro cell barrier models have been increasingly integrated into pharmaceutical and academic research pipelines to evaluate drug safety and drug delivery due to a shift towards New Approach Methodologies (NAMs) in research and regulatory safety assessment. Such models require reliable and interpretable functional readouts. Bioimpedance-based monitoring, particularly transepithelial/endothelial electrical resistance (TEER), is a widely adopted readout due to its non-invasive and real-time capabilities. However, substantial variability arises from differences in measurement settings, frequency selection, electrode configuration, impedance measuring techniques, and data analysis strategies. In numerous studies, TEER is approximated from single-frequency impedance magnitude measurements, which do not isolate the resistive component associated with tight junction-mediated paracellular transport but instead reflect the combined response of a coupled electrochemical system. This review clarifies impedance measuring techniques and systematically analyzes impedance-based measurement and analysis strategies for in vitro biological cell barrier integrity. We compare mono-frequency and broadband acquisition approaches, examine the influence of electrode–electrolyte interfaces, electrode geometry, and culture configuration, and evaluate equivalent circuit modeling and phase-resolved electrical impedance spectroscopy (EIS). Based on this comparison, we propose a three-level analytical hierarchy adapted to experimental objectives and instrumentation constraints. We conclude that phase-informed impedance analysis and harmonized reporting are essential to improve measurement reproducibility, inter-platform comparability, and integration of impedance-derived cell barrier assessment within NAMs-oriented research workflows. Full article

Currently, China boasts abundant shale gas resources. However, in the process of flowing production, there remain significant discrepancies in our understanding of the flow patterns of gas and water, and many challenges persist in gas–water measurement. Given the dense pore structure and complex micro-features of shale gas reservoirs, this study proposes a method to estimate the fractal dimension by utilizing shale mercury injection curves based on experimentally determined relative permeability curves, thereby enabling a more accurate fitting of these curves. Experimental results show that the two-phase co-infiltration zone in the shale is narrow overall, with bound water saturation exceeding 50%. The findings indicate that the experimentally measured relative permeability curves closely match those fitted using the fractal dimension approach. Moreover, the lower the permeability, the more the equal-permeability points of the fitted curves shift toward the lower-right quadrant. Overall, the fitting performance is satisfactory, providing additional research directions and insights for determining relative permeability curves of gas and water in shale gas reservoirs. Full article

As oil and gas reservoirs progress into the mid-to-late stages of development, produced fluids increasingly exhibit high water-cut and complex flow regimes. Conventional water-cut measurement techniques based on capacitance, conductance, and resistance often face challenges in terms of accuracy, stability, and adaptability. In this study, a novel non-contact broadband microwave system, based on a ridged-horn antenna microwave transmission sensor (RHAMTS), is proposed to achieve highly sensitive full-range (0–100%) water-cut monitoring. The RHAMTS consists of two identical ridged-horn antennas, whose geometries are optimized through analytical design calculations and full-wave finite-element simulations. Numerical simulations are first performed to elucidate the sensing mechanism. Subsequently, static and dynamic experiments are conducted under two representative conditions: emulsified oil-water mixtures and stratified oil-water layers. The results indicate that the broadband spectral signatures of the RHAMTS can effectively characterize water-cut in both emulsified mixtures and stratified oil-water layers. For emulsified mixtures, both amplitude attenuation and phase shift vary systematically with water-cut, and the RHAMTS can still effectively characterize water-cut under saline conditions. For stratified oil-water flow, results from both static and dynamic experiments demonstrate that amplitude attenuation provides more robust features for practical water-cut discrimination. Compared with conventional methods, the proposed RHAMTS offers non-contact operation, rich spectral information, and compatibility with various flow regimes, providing a feasible and efficient approach for water-cut monitoring under complex field conditions. Full article

Fungal decay fundamentally alters moisture transport in wood through complex bio-physical coupling mechanisms that remain poorly understood. Brown-rot fungi such as Coniophora puteana (Schumach.: Fr.) P. Karst. degrade wood through chelator-mediated Fenton (CMF) chemistry, producing hydroxyl radicals that depolymerise cellulose and hemicellulose before significant mass loss. This diffusion-dependent process requires elevated moisture content and leads to structural degradation. However, existing models fail to capture the interaction between boundary-driven fungal colonization, decay-induced property changes, and multi-phase multi-Fickian moisture redistribution, particularly the separate evolution of bound- and free-water phases during decay. Here, we present a transport-response bio-hygrothermal finite element model that couples boundary-driven Monod-type fungal colonization kinetics with multi-phase moisture transport (free water, bound water, vapor) in decaying wood. Although fungal biomass evolution is simulated via a reaction–diffusion equation, decay progression is not derived from biomass–substrate interaction but prescribed independently as an experimentally informed input. The model incorporates decay-modified sorption isotherms, permeability evolution, and boundary-driven biomass influx, along with associated moisture transport, into the governing equations. The model is validated against low-field nuclear magnetic resonance (LF-NMR) measurements of C. puteana decay in Scots pine over 35 days. The model successfully reproduces the experimentally observed moisture evolution: a peak free-water content of 50%–70% during weeks 1–2, followed by a progressive decline, while bound water remains remarkably constant despite advancing decay. Monte Carlo uncertainty quantification demonstrates hierarchical parameter control: bound water is governed solely by thermodynamic factors, while free water responds to interacting biological and physical processes. Time-resolved correlation analysis shows a fundamental transition from colonization-dominated (weeks 1–2) to transport-dominated (weeks 3–5) moisture control, quantitatively explaining the experimentally observed shift from accumulation to depletion. This transport-response framework for analyzing moisture behavior under externally defined decay progression establishes quantitative parameter hierarchies that may inform the development of future substrate-coupled bio-hygrothermal models. Full article

Background: Neuropsychiatric manifestations are a recognized complication of COVID-19, yet their temporal evolution across pandemic waves remains poorly characterized in hospitalized cohorts. This study examined whether their prevalence and composition changed across five successive waves. Methods: We conducted a retrospective observational study of 1471 hospitalized adults with confirmed Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection at Sibiu County Emergency Clinical Hospital, Romania (March 2020–January 2025), spanning ancestral through Omicron variants. A custom natural language processing pipeline extracted symptoms, medications, and International Classification of Diseases, 10th Revision (ICD-10) codes from electronic medical records. Nine hierarchical clinical clusters were defined; temporal trends were assessed using multivariable logistic regression with age-stratified replication. Results: Severe neurological presentations (stroke, seizures, hemiparesis) increased six-fold from 3.5% in Wave 1 to 20.1% in Wave 5, while psychiatric symptoms (anxiety, insomnia) declined from 13.3% to 4.3%. Overall, neuropsychiatric burden remained stable (~40–45%), revealing a compositional shift. This neurological trend persisted after multivariable adjustment (adjusted odds ratio 4.34, for Wave 5 vs. Wave 1) and within age-stratified subgroups, was inversely associated with respiratory severity and could not be attributed to vaccination status. The composite neurological severity index independently predicted mortality and intensive care unit admission. Conclusions: Neuropsychiatric manifestations in hospitalized Coronavirus disease of 2019 (COVID-19) patients underwent a compositional shift from psychiatric dominance in early waves to severe neurological dominance in later waves, consistent with a transition from reactive psychiatric presentations toward progressive neurological injury. This pattern, largely independent of measured confounders, underscores the need for sustained neurological surveillance beyond the acute respiratory phase. Full article