Engineering

Abstract: Background: Functional near-infrared spectroscopy (fNIRS) during cognitive tasks contains slow haemodynamic oscillations from neurovascular, superficial systemic and cardiorespiratory sources. We investigated whether output-only modal analysis can provide dynamic systems descriptors during a cognitive task in type 2 diabetes with cognitive impairment versus healthy controls. New method: Total haemoglobin (HbT) from our previously published Mini-Cog exercise-intervention dataset in older adults with type 2 diabetes and healthy controls was re-analysed using a harmonized modal phenotyping framework. Trigger-bounded INIT/LAST segments were processed by three estimators – multiverse Koopman dynamic mode decomposition (DMD), residual-validated Koopman DMD, and numerical algorithms for subspace state-space system identification/operational modal analysis (N4SID/OMA). Brain modes were classified using spatial, haemodynamic and short-separation evidence, cardiorespiratory modes were labelled by physiological bands and evaluated with internal automatic multiscale peak detection support. Results: The DMD-family estimators revealed a reproducible INIT to LAST increase in brain modal frequency and spatial structure across all groups. Multiverse DMD showed false discovery rate (FDR)-significant effects for all four primary brain metrics in all groups with Hedges’ (d_z=0.61-2.00), and residual-validated DMD reproduced the pattern. N4SID was conservative, yielding one sensitivity supported primary cell. Mixed models showed no FDR-significant univariate Group x Phase interaction. External fNIRS–ECG–respiration validation showed N4SID/OMA was most accurate for cardiac rate with mean absolute error 2.04–2.38 beats/min and (r=0.98) whereas respiration from prefrontal HbT was unreliable. Conclusions: Output only modal phenotyping provides a transferable, claim tiered approach for fNIRS dynamics. The data support a shared within-task brain-state transition, not a diabetes- or exercise-specific intervention effect.

Abstract: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease character-ized by parenchymal scarring, increased tissue stiffness, and impaired gas exchange. This study investigates the fluid dynamics and structural response of central airways in both healthy and fibrosis-inspired lungs under a 50% increased flow demand. A three-dimensional airway geometry was reconstructed from computed tomography (CT) scans up to the fifth bronchial generation using a hybrid modeling approach. Transient computational fluid dynamics (CFD) simulations of inhalation and exhalation were performed using ANSYS Fluent with the SST k-ω turbulence model. A complementary static structural analysis was conducted to assess deformation and stress under pleural pressure loading. Results indicate that fibrosis-inspired lungs required 92% higher inlet pressure losses compared to healthy lungs, highlighting the increased energetic cost of breathing. Flow patterns remained qualitatively similar. Structurally, fibrosis-inspired tissue exhibited 17% lower equivalent elastic strain under the same pressure load, confirming the impact of increased stiffness on bronchial dis-tensibility. Maximum principal stress concentrations of 22.1 kPa were identified at the left main bronchus bifurcation, indicating potential mechanical stress hotspots.

Abstract: Diabetes mellitus is a major global public health challenge, with prevalence rising due to aging populations, urbanization, sedentary lifestyles, and dietary changes. Effective glucose monitoring is essential for diagnosis, treatment, and long-term disease management, driving significant research into improved sensing technologies. Conventional invasive and minimally invasive glucose monitoring methods often cause discomfort and reduce patient compliance, motivating the development of non-invasive alternatives. Recent advances in photonics, biomedical engineering, nanotechnology, wearable devices, and artificial intelligence have accelerated the emergence of innovative glucose sensing approaches capable of improving comfort, safety, and monitoring frequency. This review presents a comprehensive overview of recent non-invasive glucose monitoring technologies reported in the literature, including optical, electromagnetic, nanotechnology-based, and physiological sensing methods evaluated through human studies, biological samples, or tissue-equivalent models. The underlying sensing principles, measurement sites, performance characteristics, and practical implementation challenges of these technologies are discussed. Particular attention is given to the integration of machine learning algorithms, which have demonstrated significant potential for enhancing glucose prediction accuracy and supporting real-time monitoring applications. The review also critically examines the advantages, limitations, clinical feasibility, and commercialization prospects of existing technologies, highlighting the key barriers that continue to impede widespread adoption. By consolidating recent developments across multiple scientific and engineering disciplines, this work provides researchers, clinicians, and technology developers with a concise assessment of the current state of non-invasive glucose sensing and identifies future research directions necessary for advancing reliable, accurate, and user-friendly next-generation diabetes management systems.

Abstract: Acquiring real-time biological data is essential for effective control of microalgae cultivation processes, yet routine monitoring still depended on laborious offline analyses that relied on time-consuming wet-chemical techniques. This study introduces a flow-through, multi-wavelength visible-light (VIS) sensor for real-time monitoring of biomass and pigment concentrations in microalgae cultivation processes. Based on 209 experimental data points, six machine-learning regression models were developed to estimate dry biomass, chlorophyll α, chlorophyll β, total chlorophyll, total carotenoids, and astaxanthin concentrations. Validation under realistic continuous operation with an independent dataset demonstrated that biomass and astaxanthin predictions were within ± 10% of offline reference measurements. The proposed low-cost and versatile multi-wavelength platform, together with machine-learning-based calibration, provides a practical soft-sensor concept for real-time monitoring of microalgal bioprocesses and offers a foundation for future integration of model-based and predictive control strategies.

Abstract: Vibration therapy — the use of controlled mechanical vibration as an exercise or rehabilitation intervention — is increasingly used in UK gyms, physiotherapy clinics and communal settings. Studies have shown that it can increase muscle strength and power in athletes, improve balance in older adults, and reduce some of the effects of conditions such as osteoporosis. However, despite its growing use, the neural mechanisms by which vibration produces these benefits have remained largely unknown. Without that knowledge, researchers and clinicians have no rational basis on which to choose vibration parameters or to identify which patients are most likely to benefit. My Churchill Fellowship was designed to begin to close that gap. I travelled to the Human Neurophysiology Laboratory at the University of Alberta in Canada, one of the world's leading laboratories for the study of the neural control of human movement, to acquire training in Transcranial Magnetic Stimulation (TMS), a non-invasive technique that allows scientists to measure how readily the brain talks to the muscles. By combining TMS with electrical reflex (H-reflex) techniques and our own purpose-built vibration device, I worked with my host laboratory to design and complete the first experimental study to ask, with the necessary precision, whether and how vibration applied to the upper limb acts on the brain, and on the spinal cord.

Abstract: Red blood cell (RBC) aggregation and viscosity-related flow resistance are important hemorheological parameters for assessing blood flow abnormalities, but their simultaneous measurement often requires multiple pumps or intermittent flow stoppage. In this study, we propose a single syringe pump microfluidic sensing method for simultaneous evaluation of RBC aggregation and transient flow response under continuous pulsatile blood delivery. The device consists of a single inlet, a main straight channel, a bifurcated test channel, and a big outlet. A programmed pulsatile-flow profile is applied by switching between high and low flow rates, and the transient velocity response is analyzed to extract the time constant (λ₁) as a viscosity-related indicator. After optimization, the selected flow profile provides stable and reproducible measurements of both λ₁ and the RBC aggregation index (AI) while reducing unnecessary blood consumption. The λ₁ shows a strong correlation with viscosity and is significantly affected by syringe air compliance. The proposed AI exhibits consistent trends when compared with conventional aggregation indices. Furthermore, it exhibits temporal stability under continuous blood flow. Finally, the method is adopted to detect time-dependent changes in blood during continuous blood infusion, which demonstrates its potential as a simple, sensitive, and practical microfluidic sensor for real-time hemorheological monitoring.

Abstract: Age-related changes in motor–cognitive function are associated with altered neural adaptability, particularly during tasks requiring integration of cognitive and motor processes. Dual-task paradigms are commonly used to assess such interactions; however, gait-based tasks may be unsuitable for older adults with balance impairments. This study used electroencephalography (EEG) to examine neural modulation during a controlled seated foot-tapping paradigm under dual-task and feedback conditions. Thirty-six cognitively healthy participants (18 younger adults aged 18–30 years; 18 older adults aged 65–90 years) completed three conditions: single-task tapping (ST), dual-task tapping with a flanker task (DT), and dual-task tapping with auditory biofeedback (DT+). Relative EEG spectral power across the alpha, beta, and high beta bands were analysed across left, right, and midline regions. An aggregate modulation index (AMI) was also computed to quantify overall signal changes across the three tested conditions. Results revealed a significant group difference in alpha-band modulation, with greater modulation in younger adults compared to older adults. In particular, younger adults had significant reductions in alpha activity in DT+ relative to DT across all brain regions. In contrast, older adults showed no significant alpha modulation across conditions but exhibited increased high beta activity from ST to DT. These findings suggest that alpha-band EEG activity reflects age-related differences in feedback-driven neural adaptability, supporting alpha-band modulation as a quantifiable biomarker of motor–cognitive adaptability.

Abstract: Aspartase from Escherichia coli (AspA) catalyzes the direct conversion of acrylic acid to β-alanine; however, its substrate specificity and low catalytic efficiency limit its broader application. We engineered an AspA mutant capable of efficiently catalyzing the amination of acrylic acid for β-alanine synthesis, using Rosetta Enzyme Design to computationally redesign the Cβ-binding region of the acrylic acid binding site in AspA. Based on energy scores, structural configurations, and hydrogen bonding networks, 51 candidate variants with penalty scores below 30 were selected for mutant construction and performance testing; >70% of these variants exhibited enhanced catalytic activity in acrylic acid’s hydrogen amination. Four mutants achieved over 3-fold improved activity. The optimal mutant, M1 (T190I-M324I-I327L-C329), demonstrated a 7.3-fold increased specific enzyme activity and a 13.0-fold improved k_cat/K_m compared with the wild type. Conformational changes in the S-loop and enhanced hydrophobic interactions near the active site contributed significantly to M1's enhanced activity. Upon reaction optimization, the conversion rate of β-alanine synthesis using M1 in whole-cell catalysis increased from 5% with the wild type to 90% with M1. This study provides a reference for the biocatalytic synthesis of β-alanine, significantly enhancing the conversion rate of acrylic acid and demonstrating the enzyme's potential for industrial applications.

Abstract: This paper presents a two-stage ensemble machine learning pipeline for breast cancer diagnosis from digital mammograms. A complete end-to-end platform was developed and trained using publicly available datasets. The proposed methodology includes a dedicated preprocessing stage followed by two classification stages. In the first classification stage, six convolutional neural networks (CNNs), each trained under different conditions and dataset configurations, are used to extract predictive information from mammographic images. In the second stage, the outputs of these CNNs are combined into feature vectors and used for final classification. Several machine learning approaches were evaluated for this stage, including multilayer perceptron (MLP), support vector machine (SVM), bagged trees (BT), gradient boosting (XGBoost), and a heuristic fusion method. Among them, an optimized two-layer MLP was selected due to its high sensitivity and suitability for continuous risk estimation, achieving an F1-score and recall exceeding 0.90. AUC is also computed showing a value over 0.97. Additionally, a demonstration application was developed to provide a quantitative breast cancer risk score directly from mammography images, supporting clinical decision-making and early diagnosis. Finally, the Grad-CAM technique is applied to provide explainability by highlighting the image regions that most strongly contribute to and support the final diagnostic decision.

Abstract: Efficient aerosol delivery to the maxillary sinuses remains challenging because narrow ostia limit sinus entry. This in vitro study evaluated whether low-frequency, large-amplitude pulsatile flow can deliver humidifier-generated water aerosols to the maxillary sinuses, compared retention with e-vapor under identical conditions, and identified setup modifications required for water aerosol transport. Experiments used three transparent anatomically realistic sinonasal models: two single-passage models with narrow-long (NL) and wide-short (WS) ostial geometries, and one dual-passage dual-maxillary-sinus model (RL). Water aerosols and e-vapor were delivered using a modified servo-actuated syringe generator under fixed conditions: 50 mL stroke volume, 0.33 Hz frequency, 1 L/min vacuum-induced flow, and 1.5 min delivery. Water aerosols were larger than e-vapor aerosols (D50 = 5.553 µm vs. 3.394 µm) and required setup modification because of greater wall interactions, condensation, coalescence, and transport losses. Pulsatile delivery achieved plume entry into all tested maxillary sinuses. E-vapor showed greater retained mass than water aerosols in NL (1.060 ± 0.152 vs. 0.540 ± 0.089 mg) and WS (0.800 ± 0.071 vs. 0.520 ± 0.110 mg). Water-sensitive Sar-Gel visualization confirmed bilateral water aerosol retention in RL. These findings support pulsatile delivery as a feasible strategy for water aerosol transport to the maxillary sinuses, but with a lower efficiency than e-vapor aerosols.

Abstract: The increasing demand for sustainable packaging materials has led to growing interest in biodegradable alternatives to petroleum-based plastics. Starch-based films are renewable and compostable, yet their application in food packaging is limited by poor mechanical and barrier properties. This review presents recent advances in the development of montmorillonite (MMT)-reinforced starch films, highlighting their improved structural, thermal, and functional performance. The mechanisms of reinforcement, intercalation, exfoliation, and network formation, are discussed in relation to polymer–clay interactions and film morphology. Special emphasis is placed on the role of these nanocomposites in food preservation. Case studies demonstrate their capacity to extend shelf life by reducing moisture loss, microbial spoilage, and oxidative degradation in perishable products such as strawberries, paneer, chicken, and litchi. Intelligent applications, such as spoilage detection through pH-induced color changes, are also explored. Comparative tables summarize mechanical, thermal, and barrier improvements across formulations. Finally, key challenges related to large-scale processing, nanoparticle dispersion, compostability, and cost-efficiency are addressed. Future research is encouraged to explore agro-industrial starch sources, green modification strategies, and hybrid systems to enable sustainable, high-performance packaging. MMT–starch films thus represent a promising platform for active food preservation aligned with circular economy and environmental goals.

Abstract: Voice-based approaches for screening and diagnostic applications, particularly in telemedicine, often rely on patient recordings collected outside clinical environments. Establishing normative baselines is essential to advance voice analytics and clinical utility. This pilot study examined acoustic parameters in 32 healthy young adults (ages 18–24) with no history of vocal pathology, neurological disorders, or speech impediments. Recordings focused on sustained vowels (/a/, /e/, /o/, /u/) for the primary study of voice characteristics, and an exploratory speech study and a standardized phonetically balanced phrase. Analyses focused on features including fundamental frequency, jitter, shimmer, harmonics-to-noise ratio, formants (F1–F3), speaking rate, intensity, and spectral measures. Preliminary results revealed significant differences between healthy controls and a reference dataset of laryngitis patients, suggesting acoustic features can serve as objective markers of vocal fold inflammation. However, pathology-specific biomarker identification was constrained by the quality of available laryngitis data. Simple statistical comparisons proved insufficient, emphasizing the value of multivariate analysis, cepstral peak prominence (CPP), and mel-frequency cepstral coefficients (MFCC). Challenges in non-clinical data collection highlight the need for standardized, detailed annotation of patient recordings to improve diagnostic accuracy and strengthen the predictive power of future biomarker studies.

Abstract: Intracellular dielectric parameters are promising biomarkers, especially for specific cancerous subpopulations such as cancer stem cells. While valuable, these parameters remain challenging to extract, typically remaining complex microfluidic setups combined with inverse fitting computations. This paper introduces a simplified methodology based on a dual-mode dielectrophoretic measurement approach, integrated with a machine learning-based predictive pipeline, to extract intracellular dielectric parameters of cancerous cells cultured under standard versus stemness-promoting conditions. These parameters are then used to compute the Clausius-Mossotti factor in the ultra-high frequency range, enabling a comparative analysis of the dielectric responses between the two cellular phenotypes.

Abstract: This study developed and validated a digital twin of patient flow and waiting list dynamics in a Sleep‑Disordered Breathing Unit, based on a discrete‑event simulation (DES) model in the context of growing diagnostic demand mainly driven by obstructive sleep apnoea. The stochastic model, implemented in MATLAB, reproduces six care stages (e-Consultation, face‑to‑face consultation, overnight pulse oximetry, respiratory polygraphy, polysomnography and follow‑up consultation), using operational data from year 2024 for parameterisation and data from year 2025 for correlation and validation. Key performance indicators included mean waiting time and mean queue length per stage, with model fidelity assessed via Mean Absolute Error, Root Mean Squared Error and Relative Percent Difference (RPD), both by stage and globally across the full patient pathway. The model accurately reproduced the aggregate system workload, with a weighted RPD of 3.6% for mean queue length and 34.6% for mean waiting time. Respiratory polygraphy showed the best agreement in terms of service load (RPD 2.7%), whereas the follow‑up consultation exhibited the largest discrepancies in waiting times (RPD 73.7%), likely related to prioritisation rules and organisational variability not explicitly modelled. Overall, this digital twin provides an operationally useful representation to support “what‑if” analyses of organisational scenarios aimed at reducing diagnostic delays, representing one of the first validated applications of DES model in a dedicated Sleep-Disordered Breathing Unit and provides a basis for future applications incorporating urgency stratification and prospective analysis.

Abstract: The linearity of a glucose sensor is one of the most important indicators for ensuring measurement accuracy, system stability and post-measurement data processing efficiency. As yet, this issue has not been seriously investigated in the field of glucose sensing. In this work, the use of a substrate-integrated-waveguide (SIW) together with a specially tailored membrane-supported resonator (CSRR) is proposed for realizing an ultra-linear glucose sensor. Method: An SIW waveguide with two coplanar ports were realized on an FR4 substrate. On one side of the SIW waveguide, a CSRR was fabricated at the central region, which also served as the sensing region. On the other side of the SIW waveguide, the substrate at the central sensing region was thinned down to a negligible thickness using a nail grinder, thereby forming a circular recess with a membrane-thick bottom for holding a glucose-loaded test solution. The glucose concentration was determined by obtaining the resonant frequency shift in the reflection coefficient (i.e. S11). Results: The proposed glucose sensor has exhibited a highly linear correlation between the glucose concentration and the resonant frequency, with a Pearson correlation coefficient (r) reaching 0.99, even though the magnitudes of S11 minima were subjected to ambient electromagnetic interference. The sensitivity was found to be 1.6 MHz/(mg/dL). The resonant frequency was very weakly dependent on the volume of the test solution. Conclusion: Overall, the proposed glucose sensor has exhibited not only a high sensitivity but also a highly linear characteristics even in the presence of external electromagnetic interference.

Abstract: Photonic crystal fiber (PCF)-based biosensors have emerged as highly efficient optical sensing devices because of their superior light confinement capability, structural flexibility, and tunable optical characteristics. This review presents a comprehensive overview of recent developments in PCF-assisted biosensors integrated with surface plasmon resonance (SPR) technology for biomedical applications. The influence of structural parameters such as pitch spacing, air-hole diameter, and plasmonic coating thickness on resonance conditions and sensing performance is critically analyzed. These biosensors have demonstrated remarkable capability in detecting glucose, serum proteins, pathogens, pH levels, cancerous cells, and biochemical analytes in blood, urine, saliva, food, water, and environmental samples. The paper discusses the operating principles, structural classifications, and sensing mechanisms of PCF biosensors while comparing their performance with conventional optical sensing systems. Different PCF configurations including dual-core, hollow-core, D-shaped, rectangular-core, octagonal-core, and hexagonal structures are reviewed based on sensitivity, confinement loss, and refractive index detection range. The findings indicate that SPR-assisted PCF biosensors possess strong potential for future biomedical diagnostics, environmental monitoring, and intelligent healthcare systems because of their compact size, enhanced sensitivity, and fabrication flexibility.

Abstract: Reliable monitoring of power system equipment is essential for ensuring operational stability, minimizing unexpected outages, and improving grid reliability. Among various condition monitoring techniques, optical sensing technologies have attracted significant attention due to their high sensitivity, electromagnetic interference immunity, and suitability for harsh electrical environments. This paper presents the design and application of a photonic crystal fiber (PCF)-based sensing system for real-time monitoring in power systems, with particular emphasis on dissolved gas detection in oil-immersed transformers. The proposed sensing approach employs hollow-core photonic crystal fiber (HC-PCF) as an optical absorption chamber, enabling enhanced light–gas interaction while maintaining a compact and flexible sensor configuration. Based on infrared absorption spectroscopy and Beer–Lambert theory, the system is designed to achieve high-sensitivity detection of characteristic fault gases generated during transformer insulation degradation. The diffusion characteristics of gases inside the HC-PCF are theoretically analyzed and experimentally verified to evaluate sensor response performance. Experimental investigations demonstrate that the proposed PCF-based sensing system provides excellent linearity, strong selectivity, and improved detection sensitivity for low-concentration acetylene monitoring. Allan variance analysis indicates that the optimal signal-to-noise ratio is achieved with a 29 s averaging time, resulting in a minimum detection limit of 4.5 ppm. Furthermore, the compact structure and extended optical interaction length offered by the HC-PCF significantly improve the practicality of online transformer condition monitoring. The results confirm that photonic crystal fiber-based sensing technology offers a promising solution for next-generation real-time power system monitoring applications. Owing to its high sensitivity, compactness, and capability for continuous online operation, the proposed system demonstrates strong potential for deployment in intelligent grid monitoring and predictive maintenance of high-voltage electrical equipment.

Abstract: Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurements, feedstock variability, and plant--model mismatch. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, remaining insoluble substrate, soluble sugars, and ethanol, was used to evaluate measurement packages ranging from ethanol-only sensing to full-proxy sensing. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state observability and parameter identifiability analysis, parameter-correlation diagnostics, and unscented Kalman filter soft-sensing reconstruction. The results show that ethanol-only sensing is insufficient for state-aware CBP digital twins. At 6 h sampling, the state-observability log-pseudo determinant increased from 4.20 with ethanol-only sensing to 8.56 after adding soluble sugar, and to 16.42 with full-proxy sensing. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol--sugar--biomass--enzyme package provided the best parameter identifiability across the sampling intervals. In the soft-sensing analysis, full-proxy sensing reduced the mean latent-state RMSE from 1.2080 to 0.5314 and gave the highest aggregate sensor value of 0.8144. The ethanol--sugar--biomass--enzyme package gave the best reduced sensor set, with a score of 0.7423 and lower measurement burden. Noise sensitivity analysis showed no ranking change under the tested noise levels, while cost-weight sensitivity analysis indicated ethanol--sugar--biomass as the preferred cost-sensitive package. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings.

Abstract: The inefficient management of agro-industrial residues, particularly cocoa pod husk and mucilage, represents a critical environmental and economic challenge in cocoa-producing regions such as Santander and Norte de Santander, Colombia. These by-products, constituting approximately 70% of the fruit’s total weight, are currently underutilized, generating pollution and wasting resources with high valorization potential. This article proposes the design and rigorous experimental validation of an empirical model based on artificial intelligence capable of predicting quantities of valuable compounds, including bioethanol, essential oils, paraffins, antioxidants, and pectins, obtained from cocoa residues. The model integrates critical variables such as cocoa variety, extraction methods, and process conditions, incorporating advanced machine learning techniques trained on a 100% empirical database of eighty-four (84) laboratory trials, combined with a post-inference sensitivity analysis via the Monte Carlo method with 10,000 simulations. Preliminary results demonstrate significant varietal differences; for instance, the CCN-51 variety achieves a mean bioethanol yield of 79.30 ± 4.96 mL/kg with a 95% confidence interval of (69.44–88.93) mL/kg, while the Criollo variety reaches 43.55 ± 2.72 mL/kg (38.14–48.84 mL/kg), both exhibiting identical coefficients of variation (6.25%). Furthermore, the integration of an optimized extraction sequence combined with neural networks allows for maximizing by-product yields while reducing final residue generation by 40%. This tool not only contributes to the circular economy and alignment with the Sustainable Development Goals (SDGs 9 and 12) but also offers a tangible pathway to improve the competitiveness of the Colombian cocoa industry through data-driven decision-making and sustainable technology adoption.

Abstract: Background: Maxillary hypoplasia and skeletal Class III malocclusion are deeply intertwined with upper airway constriction and paranasal sinus dysfunction. Conventional orthopedic interventions often struggle to achieve true 3D skeletal translation without inducing undesirable rotational side effects. The Right Angle Maxillary Protraction Appliance (RAMPA) therapy offers a biomimetic and mechanotherapeutic approach, focusing on anterosuperior protraction to restore both structural harmony and respiratory function. Methods: This feature paper systematically reviews the multi-disciplinary evidence supporting RAMPA therapy, synthesizing findings from recent computational and clinical studies. We examine Finite Element Method (FEM) simulations detailing sutural mechanotransduction and osteogenic "BMP-2 Trigger Zones", Computational Fluid Dynamics (CFD) utilizing shear-thinning rheological models for two-phase air-mucus interactions, and large-cohort CBCT and Coben analyses quantifying longitudinal growth. Results: FEM studies confirm that RAMPA, especially when combined with intraoral devices (e.g., gHu-1, VomPress, Hybrid), achieves predictable anterosuperior displacement and concentrates tensile stress to trigger molecular bone remodeling. CFD simulations reveal that this precise skeletal remodeling optimizes wall shear stress (WSS) and actively facilitates paranasal mucus clearance via enhanced suction and shear-thinning effects. Clinically, RAMPA induces a 1.2-fold acceleration in natural sinonasal growth velocity. Furthermore, volumetric gains are distinctively pronounced in patients with pre-existing empyema (61.2% increase) compared to those with clear sinuses (18% increase), indicating rapid pathophysiological obstruction relief. Conclusions: By integrating controlled biomechanical forces with fluid-dynamic airway optimization, RAMPA therapy acts as a mechanotherapeutic modulator. It bridges the gap between mechanical intervention, molecular signaling, and physiological homeostasis, offering a comprehensive paradigm for pediatric craniofacial and respiratory restoration.