Search Results (389)

This paper investigates the impact of rotor–nacelle assembly (RNA) structural models on the dynamic response of a 15 MW monopile-supported offshore wind turbine (MOWT). Three RNA models, distributed parameter (DPM), multi-particle (MPM), and concentrated point mass (CPM), were established in ADINA. Model reliability was confirmed through verification against BModes and OpenFAST, covering natural frequencies, mode shapes, and responses under normal environmental loads. The analyses reveal the following: (1) RNA modeling significantly impacts higher-order modal frequencies, with the MPM/CPM exhibiting substantial errors (up to −20.3% and 9.5% for second-order tower mode) and failing to capture blade deformation modes; (2) under low-frequency dominated wave loads, the MPM/CPM predict peak responses within ±10% tolerance; (3) for seismic loads, the discrepancy in three models is governed by input motion spectral characteristics, showing smaller errors under far-field motions (fundamental mode dominated) but significant errors under near-field motions (higher-mode excited). These findings collectively provide theoretical guidance for RNA model selection in MOWTs. Full article

Continuous intraocular pressure (IOP) monitoring is crucial for glaucoma management. Currently, traditional static IOP measurements often fail to detect circadian fluctuations, leading to a clinical dilemma where “normal IOP” is observed despite persistent visual field deterioration. This study presents a wireless, passive localized surface plasmon resonance (LSPR) sensing platform integrated into flexible silicone hydrogel contact lenses. Gold nanoparticles (AuNPs), synthesized via the sodium citrate reduction method, were incorporated into the lens periphery using a “swelling-induced nano-doping” technique to transduce IOP-induced corneal strain into detectable spectral shifts. Ex vivo porcine eye investigations established a physical mapping model, confirming significant LSPR peak wavelength response trends in correlation with IOP variations (10–50 mmHg) and corneal curvature changes. Subsequent 21-day in vivo rabbit studies demonstrated excellent ocular surface biocompatibility; quantitative histopathological analysis (HE, PAS, and Ki67 staining) revealed no significant adverse alterations in corneal endothelial cell density or conjunctival goblet cell function compared to control groups (p > 0.05). Furthermore, the platform maintained high structural integrity and anterior segment tolerance under transient high-IOP conditions. While currently a proof-of-concept, these results indicate that the LSPR-active hybrid system effectively captures dynamic IOP fluctuation patterns as an optical response to acute interventions, providing a foundational engineering path for next-generation, battery-free wearable diagnostics in personalized glaucoma care without the need for built-in electronics. Full article

Botulinum toxin A (BoNT-A) is well established for the esthetic treatment of upper-face dynamic lines, and long-term adult studies suggest that repeated treatment may remain effective and acceptable over time in many patients. However, wrinkle efficacy and patient satisfaction do not by themselves determine whether repeated treatment remains acceptable in broader morphologic, dynamic–expressive, and longitudinal terms. At the same time, the toxin’s pharmacology entails finite presynaptic blockade followed by active synaptic and architectural recovery, and imaging, histological, and neurophysiological studies indicate that repeated chemodenervation cannot be assumed to be biologically neutral at the muscle level. The resulting problem is not whether BoNT-A works, but whether current outcome frameworks are sufficient to judge repeated-treatment compatibility in full. Here, structural tolerance is proposed as a provisional clinical lens for an underdescribed boundary: whether repeated-treatment compatibility is fully captured by wrinkle reduction and patient acceptability alone. The paper organizes this problem across morphologic, dynamic–expressive, and longitudinal domains, outlines candidate warning signs, and develops a pragmatic tiered approach to assessment spanning routine care, structured clinical follow-up, and research-oriented evaluation. The aim is to support more complete longitudinal thinking in upper-face aesthetic BoNT-A, to clarify what current outcome frameworks remain unaddressed, and to identify priorities for future empirical study. Full article

Object detection is a core component of industrial vision systems in manufacturing, infrastructure monitoring, and safety-critical sensing. While the mean average precision (mAP) averages the performance over all confidence thresholds, real-world deployment demands committing to a single operating threshold under score imprecision, distribution shifts, and asymmetric—often only approximately known—error costs. From a soft-computing perspective, deployment should explicitly manage this uncertainty rather than rely on a static validation optimum. We propose domain-specific and robust localization recall precision (DSR-LRP), a three-phase decision-support framework. The framework elicits soft domain preferences—such as asymmetric error costs, tolerable localization imprecision, and expected perturbations—from practitioner knowledge and encodes them as three quantitative parameters (k, ${\alpha }_{\mathrm{IoU}}$, $\beta$). A cost-sensitive, threshold-local objective aggregates the performance within a robustness band around each candidate threshold, jointly capturing the accuracy and local stability. Finally, it yields an interpretable recommendation package comprising the operating threshold, its DSR-LRP score, and visual evidence. Experiments on four practical datasets (blood cell screening, wildfire smoke monitoring, pothole detection, and semiconductor sensor inspection) showed that DSR-LRP consistently selected operating thresholds that were robust and cost-aligned. For example, in pothole detection, an LRP-optimal threshold degraded by 15.6% under simulated shifts, while the DSR-LRP recommendation changed by only 1.8%. DSR-LRP complements global metrics such as the mAP and provides a soft-computing-oriented tool for reliable, evidence-driven deployment of industrial object detectors. Full article

Background/Objectives: Functional near-infrared spectroscopy (fNIRS) has emerged as a non-invasive, implant-compatible imaging modality capable of capturing cortical hemodynamics during ecologically valid auditory and linguistic tasks. Its silent operation and tolerance to electrical artifacts make it particularly well suited to the study of hearing-impaired individuals, including cochlear implant (CI) users. However, evidence on the application of fNIRS to investigate speech perception, cognitive performance, and proxy of cortical activation patterns in patients with hearing loss (HL) remains fragmented. This systematic review aims to provide a structured, population-stratified description of current fNIRS literature on auditory and cognitive processing in adults with age-related hearing loss (ARHL) and CI users. Methods: A systematic search on PubMed Central, Web of Science and Scopus, based on PRISMA (2020) guidelines, was conducted to identify original studies that evaluate speech perception by means of fNIRS to assess auditory and cognitive process in hearing-impaired populations. Results: Across studies, fNIRS consistently detected activation of superior temporal and frontal cortices during speech-related tasks. In ARHL, increased dorsolateral prefrontal cortex (DLPFC) recruitment during speech-in-noise indicated compensatory yet inefficient processing. Longitudinal auditory training led to reduced prefrontal overactivation and enhanced temporal–frontal connectivity. In CI users, cortical responses to phonological and comprehension tasks show partially overlapping activation patterns with normal hearing (NH) peers, although arising within different neurobiological contexts, and are modulated by device experience and residual hearing (AV) speech, and stimulus-level effects further shape cortical responses. When interpreted in light of developmental evidence, these findings may be contextualized as reflecting distinct trajectories of cortical reorganization, rather than a common mechanism. Conclusions: fNIRS provides a tool to investigate auditory and cognitive responses in distinct hearing-impaired populations under ecologically valid conditions. It detects maladaptive frontal inefficiency in ARHL, tracks neuroplastic changes after rehabilitation, and captures population-specific cortical recruitment patterns in CI users. These findings are descriptive and context-dependent, and do not support cross-population mechanistic generalizations. Standardized protocols and longitudinal pediatric studies are needed to clarify the potential clinical relevance of fNIRS-derived cortical measures. Full article

Carbon capture and utilization (CCU) is imperative for industrial decarbonization. However, current life cycle assessment (LCA) methodologies often apply a static, one-size-fits-all approach, assuming a 99% CO₂ purity standard for all utilization pathways. This ignores the thermodynamic limits of capture technologies and the tolerance of certain endpoints for coarse gas, leading to severe over-purification energy penalties. To bridge this gap, we developed a quality-matched dynamic LCA framework targeting steam methane reforming (SMR) tail gas in industrial parks. A superstructure matrix was constructed, coupling 16 capture configurations (spanning chemical absorption to cryogenic separation across 85–99% purities) with five utilization pathways, under a dynamic grid decarbonization model (2024–2060). The baseline scenario shows that methanol is the most carbon-intensive pathway at 16.88 kg CO₂-eq per kg CO₂ utilized, whereas mineralization and concrete curing remain near break-even at 0.221 and 0.010 kg CO₂-eq, respectively. When low-purity demand is matched with PSA capture at 85–90% purity, the net GWP of mineralization and concrete curing decreases to 0.134 and 0.005 kg CO₂-eq, corresponding to capture-stage penalty reductions exceeding 60% relative to unnecessary 99% purification. Under the dynamic electricity scenario, concrete curing reaches the net-zero tipping point around 2031, and the coupled mineralization substitution strategy ultimately achieves −0.046 kg CO₂-eq per kg CO₂ utilized. These findings provide a compelling scientific basis for policymakers to design dual-grade CO₂ pipeline networks and prioritize low-purity, high-circularity building materials over carbon-intensive chemical synthesis in near-term industrial transitions. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

Background: Mechanical trauma during pregnancy from motor vehicle accidents, falls, and maternal seizures poses significant risks to fetal development. The fetus is protected by multiple hierarchical layers including the uterine wall, amniotic fluid, and cerebrospinal fluid surrounding the brain. Despite the clinical significance of maternal trauma occurring in approximately six to eight percent of pregnancies, previous computational studies have focused primarily on amniotic fluid protection while treating the fetus as a homogeneous structure, without examining the nested protective architecture comprising both amniotic fluid and cerebrospinal fluid as an integrated system. Methods: This investigation implements a nested fluid–structure interaction framework simultaneously capturing three hierarchically organized systems: the uterine wall interacting with amniotic fluid, amniotic fluid interacting with the fetal body, and the cranial system comprising skull, cerebrospinal fluid, and brain tissue. The computational architecture employs smoothed particle hydrodynamics for fluid domains coupled with finite element methods for solid structures. Boundary conditions representing traumatic forces were obtained through experimental protocols using an instrumented medical simulation mannequin performing seizure movements. Results: Computational simulations predicted that amniotic fluid absorbed the majority of impact forces through hydraulic cushioning, while cerebrospinal fluid provided additional stress reduction through pressure redistribution, with model predictions suggesting total stress reduction exceeding ninety percent. Peak fetal brain stress values predicted by the model were below injury thresholds reported in adult neural tissue literature, though direct applicability of these thresholds to fetal tissue remains uncertain. The fetal brain exhibited minimal movement relative to the skull despite complex force cascades. Stress distributions showed elevated values in the frontal lobe and brainstem, though magnitudes remained within ranges that the model suggests may be tolerable. Conclusions: Computational modeling suggests that the nested fluid protection architecture operates as an integrated hierarchical system providing potential mechanical protection through sequential energy dissipation. These findings represent model predictions requiring experimental and clinical validation before translation to clinical practice. Full article

Top-heavy industrial towers, which carry large, concentrated masses of equipment at upper levels and feature open lower stories, are vertically irregular by design and tend to amplify seismic displacement and acceleration demands near the tower top. Although tuned mass dampers (TMDs) have been studied extensively for buildings, bridges and chimneys, their application to this particular class of slender industrial towers—where production-equipment vibration tolerance, retrofit accessibility and limited downtime drive the design—has received little dedicated attention. This paper reports a focused numerical investigation of seismic response mitigation for a 101.2 m molten-asphalt granulation tower retrofitted with a single pendulum-type TMD. A three-dimensional coupled finite element (FE) model was constructed in ABAQUS using C3D8R solid elements for the reinforced-concrete shaft and T3D2 truss elements for the embedded reinforcement; modal analysis returned a fundamental frequency of 0.912 Hz and a torsional-to-translational period ratio of 0.65, indicating a translational-mode-dominated response. Elastic time-history analyses under the El Centro and Taft records together with a code-spectrum-compatible synthetic accelerogram show that a pendulum TMD with mass ratio μ = 2.5%, tuning frequency offset Δf = 5% and damping ratio ξ = 10%—installed at the uppermost equipment level guided by the modal-displacement criterion—reduces the peak top displacement, peak top acceleration and peak base shear by roughly 23%, 23% and 22%, respectively, in both principal directions. The controlled top acceleration falls comfortably below the 2.94 m/s² operational tolerance of the on-tower melting equipment. To address the rationality of the chosen TMD parameters, a single-variable parametric sensitivity study spanning μ ∈ [1%, 5%], ξ ∈ [5%, 15%] and Δf ∈ [0%, 10%] is performed on an equivalent reduced model that captures the qualitative parameter-response trends; the chosen baseline values lie inside a stable performance plateau and are shown to be a balanced compromise among the three response measures. The principal contribution of the work is, therefore, (i) a complete TMD retrofit framework—modal-based placement, parameter design, coupled FE assembly and multi-record verification—adapted to top-heavy industrial towers, and (ii) qualitative evidence, supported by a sensitivity scan, with a robust proposed parameter set for small-to-moderate detuning. The study is restricted to elastic time-history analyses under frequent-earthquake-level excitation, three ground-motion records and a fixed-base assumption; nonlinear response, larger record sets and soil–structure interaction effects are explicitly identified as scope limitations and are left for follow-up work. Full article

Perovskite solar cells in aerospace applications are promising due to their high power output, radiation tolerance, and ability to extend spacecraft operational lifetimes. Numerical modelling is widely used to optimize solar cells as it can predict the real-world behavior of a device. In this work, we present a numerical simulation of CsMAFA-based perovskite solar cells with monolayer graphene as the front electrode. The model is implemented in the COMSOL Multiphysics^® finite-element environment. Graphene is modelled using the Kubo formula to account for its frequency-dependent surface conductivity, and the electromagnetic wavs interface is coupled with the semiconductor module to capture optical–electrical interactions. The influence of absorber layer thickness on the current density is also examined by sweeping the perovskite absorber thickness (300–450 nm). The current voltage characteristic demonstrates higher current density (27 mA/cm²) at an absorber thickness of ~450 nm. Shockley–Read–Hall recombination (SRH) is studied inside the model and maximum recombination was found to be centred in the absorber layer. The graphene/HTL side shows an SRH recombination of 2 × 10²⁰ cm⁻³ s⁻¹, which is much lower than what is typically seen at ITO-based HTL interfaces. Full article

The continuous increase in atmospheric CO₂ concentration exacerbates global climate change, making carbon reduction an urgent global priority. Carbonic anhydrase (CA), a highly efficient biocatalyst that converts CO₂ into bicarbonate, demonstrates significant potential for carbon capture and resource utilization. However, the stability and catalytic efficiency of native CA in industrial environments are limited, particularly its poor thermal tolerance under flue gas conditions and its sensitivity to impurities, hindering its direct large-scale application. This review systematically summarizes recent advances in modifying microbial CA through protein engineering (e.g., directed evolution, rational design) and immobilization techniques, which have markedly enhanced its thermal stability, adaptability, and reusability. Among these, the integration of machine learning with high-throughput experimentation has emerged as a transformative strategy for CA engineering. Furthermore, we outline CA-driven pathways for CO₂ conversion into high-value chemicals and bioenergy. Finally, future prospects are discussed, including interdisciplinary integration, computational modeling coupled with experimental validation, and comprehensive life-cycle and techno-economic assessments, to facilitate the scaled application of engineered microbial CA in carbon neutrality pathways. Collectively, this review highlights the critical role of engineered CA in bridging biocatalysis with industrial carbon management, offering a viable and sustainable pathway toward carbon neutrality. Full article

Desiccation tolerance is a critical adaptive trait that enables plants to survive extreme water loss, yet its physiological basis in tomato and its wild relatives remains poorly understood. In this study, chlorophyll a fluorescence imaging was used as a reliable tool to evaluate photosystem II (PSII) response to progressive desiccation. The analysis was conducted in cultivated tomato (Solanum lycopersicum) and five wild relatives (Solanum chilense, Solanum habrochaites, Solanum peruvianum, Solanum pimpinellifolium, and Solanum pennellii). Detached leaves were subjected to controlled desiccation for up to 50 h. During this period, tissue moisture content (TMC), relative water content (RWC), PSII photochemical efficiency [Fv/Fm; maximum quantum yield (QY_max)], minimal fluorescence (F0), maximal fluorescence (Fm), and variable fluorescence (Fv) were monitored to assess changes in photosynthetic performance. Desiccation caused a significant, moisture-dependent decline in PSII efficiency across all species, with QY_max showing a strong linear relationship with RWC (R² = 0.80–0.90). Interspecific variation was evident as S. chilense, S. habrochaites, S. peruvianum, and S. pimpinellifolium exhibited rapid PSII impairment, while S. lycopersicum showed moderate tolerance. In contrast, S. pennellii maintained higher PSII stability, with 50% loss of efficiency occurring only at lower RWC (30–35%). Overall, chlorophyll fluorescence imaging effectively captured functional diversity in desiccation tolerance, highlighting S. pennellii as a valuable genetic resource for improving drought resilience in tomato. Full article

Background/Objectives: The prognostic and predictive role of BMI in patients with HR+/HER2− MBC remains controversial, particularly in the era of CDK4/6 inhibitors. This study aimed to evaluate the association between baseline BMI and clinical outcomes in patients treated with palbociclib in a real-world setting. Methods: We conducted a multicenter retrospective observational cohort study including 326 patients with HR+/HER2− MBC treated with palbociclib in combination with endocrine therapy across six oncology centers in Romania. Only patients who received palbociclib for at least three months were included. Patients were stratified according to BMI into <25 kg/m² and ≥25 kg/m² groups. PFS and OS were the primary endpoints, while ORR and CBR were secondary endpoints. Results: Among the 326 patients, 66.56% were classified as overweight or obese (BMI ≥ 25 kg/m²). Median PFS was 23.66 months in the BMI < 25 group and 26.78 months in the BMI ≥ 25 group, with no statistically significant difference (HR 0.86; 95% CI 0.62–1.20; p = 0.373). Median OS was not reached in the BMI < 25 group and was 43.73 months in the BMI ≥ 25 group, also without a significant difference (HR 0.82; 95% CI 0.52–1.30; p = 0.397). ORR (29.07% vs. 28.89%) and CBR (90.70% vs. 88.33%) were comparable between BMI groups. In multivariate analysis, liver metastases and brain metastases were independently associated with worse outcomes, whereas BMI was not an independent prognostic factor. Conclusions: In this selected real-world cohort of patients with HR+/HER2− MBC who tolerated at least three months of palbociclib, baseline BMI was not associated with treatment response, PFS, or OS. While clinically informative, these results should not be interpreted as definitive evidence that body weight has no influence on palbociclib efficacy, given the methodological constraints of the analysis. BMI alone may be insufficient to capture the complex interplay between body composition and treatment outcomes, highlighting the need for more refined biomarkers of body composition in this setting. Full article

We propose a monolithically integrated optical frequency comb (OFC) generation platform on thin-film lithium niobate (TFLN), featuring cascaded dual-drive Mach–Zehnder modulators (DDMZM) and a ${\mathrm{S}\mathrm{i}}_3{\mathrm{N}}_4$-assisted spot size converter (SSC). To capture microscopic mode mismatches and spatial phase accumulation often overlooked in idealized scalar simulations, we establish a multi-physics co-simulation framework integrating finite-difference time-domain (FDTD) analysis with macroscopic transmission modeling. Based on this framework, the cascaded modulator architecture generates 25 highly stable comb lines with a dense 2 GHz spacing and an envelope flatness within 2 dB. Tolerance analysis indicates that the comb generation is highly resilient to typical manufacturing and environmental variations, including thermal bias drift, RF phase mismatch, and half-wave voltage ($V_{\pi }$) dispersion. Furthermore, physical-layer modeling shows that the integrated SSC reduces fiber-to-chip coupling loss to 0.55 dB per facet, preserving the necessary optical power budget. To validate the platform’s viability as a multi-wavelength continuous-wave source for spatial-division multiplexed (SDM) interconnects, a parallel transmission over a 20 km standard single-mode fiber is modeled. Using a digital signal processing (DSP)-free 10 Gb/s non-return-to-zero (NRZ) scheme, the 25-channel system maintains a worst-case bit error rate strictly below the forward error correction (FEC) threshold. This work offers a practical, physics-based evaluation framework for high-density co-packaged optics (CPO). Full article

Accurate prediction of food consumption is essential for strengthening regional food security planning, particularly in areas experiencing increasing food demand and environmental uncertainty. This study aims to predict food consumption patterns in Sukabumi Regency, West Java, Indonesia, using an integrated artificial intelligence approach. The research combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting food consumption trends with three machine learning classification algorithms—Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR)—to classify food consumption levels. Historical rice consumption data from 2014 to 2024 were used to train the forecasting model and generate projections up to 2030. The ANFIS training process was conducted with 100 epochs and an error tolerance of 0, resulting in a training error value of 0.182, indicating strong model learning capability. The comparison between predicted and actual consumption values showed a prediction accuracy of 95.2%, demonstrating the reliability of the model in capturing consumption patterns. Furthermore, food consumption levels were classified into three categories: low, medium, and high. The classification results revealed that Random Forest achieved the most consistent performance across cross-validation folds, while SVM and Logistic Regression experienced misclassification in the medium consumption category. In several evaluation scenarios, machine learning models achieved accuracy levels up to 99.75%, precision 99.76%, recall 99.75%, and F1-score 99.75%. The integration of ANFIS forecasting and machine learning classification provides a robust analytical framework for understanding food consumption dynamics and supports data-driven policy formulation aimed at strengthening regional food security in Sukabumi Regency. Full article