Search Results (2,022)

Background: Population aging has increased the prevalence of cognitive impairment and dementia, highlighting the need for personalized non-pharmacological interventions. Although socially assistive robots have shown therapeutic benefits, most rely on predefined interactions with limited adaptability. This study proposes an AI-enabled framework integrating continuous self-monitoring and explainable multicriteria decision-making to personalize robot-assisted interventions. Methods: A 12-week longitudinal quasi-experimental study was conducted involving 78 older adults with mild-to-moderate cognitive impairment allocated to three groups: an adaptive AI-based robot (n = 26), a sensor-based robot (n = 26), and a control group receiving conventional care (n = 26). The proposed framework combined continous self-monitoring, AI-based emotional-state estimation, and an Analytic Hierarchy Process (AHP) model to adapt robot behaviour according to participants’ clinical and behavioural profiles. Results: The AI-based robot achieved the greatest improvements in emotional status, social interaction, and functional performance. Depressive symptoms decreased by 42.9%, anxiety decreased by 39.1%, social interaction increased by 60.7%, and functional independence improved by 20.1%. Although the sensor-based robot showed slightly higher adherence (97.2% vs. 95.6%), the AI-based intervention achieved the highest overall effectiveness (AHP global score = 0.90). Conclusions: Integrating continuous self-monitoring, AI-based emotional-state estimation, and explainable multicriteria decision-making enables personalized robot-assisted interventions that improve emotional well-being, social engagement, and functional independence. These findings support the potential of adaptive socially assistive robots as AI-driven clinical decision-support systems for dementia care. Full article

Background: Elite soccer performance emerges from the interplay of cognitive, emotional, psychophysiological, and tactical processes that operate in real time during matches. Advances in wearable sensors and artificial intelligence (AI) now allow continuous monitoring of physiological and psychological states. They also allow modeling of how these states relate to tactical and physical performance. Existing reviews have examined machine learning in soccer, heart rate variability (HRV) monitoring, and psychological determinants of performance separately. No scoping review has mapped the intersection of AI analytics, wearable psychophysiological monitoring, and psychological performance constructs as one integrated decision-support framework in elite soccer. Aim: The aim of this study was to map the available evidence on the integration of AI and machine learning with psychophysiological monitoring for performance modeling in elite soccer, to identify the psychological constructs already used as model inputs, to describe the wearable technologies and AI methods applied, and to set out the translational challenges and evidence gaps that need priority attention. Methods: The review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) and the updated Joanna Briggs Institute (JBI) methodology. The protocol was registered on the Open Science Framework (OSF). Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and PsycINFO) were searched from January 2000 to March 2026 using the Population–Concept–Context (PCC) framework. Two reviewers independently screened titles, abstracts, and full texts (Cohen’s kappa = 0.82). Results: Thirty-six sources met the eligibility criteria after screening of 3104 records. AI and machine learning have been applied widely to predict physical and tactical performance in soccer, yet they rarely include psychological constructs. Reported models (decision trees, gradient boosting, and artificial neural networks) reach high accuracy for physical outcomes in internal validation, for example, above 66% for injury risk. Multi-modal models that add physiological and psychological inputs report stronger prediction. These figures come mostly from internal validation, and external validation and overfitting controls are seldom reported, so they should be read as optimistic upper bounds. Psychological and psychophysiological inputs remain under-represented. Explainable AI (XAI) methods, in particular Shapley Addictive exPlanations (SHAP) values, are appearing, but validation with domain experts is scarce. HRV has been reviewed as a psychophysiological marker in soccer, yet its use within AI decision-support tools for real-time psychological readiness has not been mapped. Three translational challenges stand out: the ecological validity gap between laboratory cognitive tests and match-embedded psychophysiology; the interpretability problem of opaque AI in high-stakes decisions; and the data fragmentation problem created by disconnected physical, tactical, and psychological data streams. Conclusions: Integrating AI with wearable psychophysiological monitoring offers a credible route toward integrated performance modeling in elite soccer. Closing this gap calls for multi-modal frameworks that combine psychological constructs, physiological markers, and tactical data within explainable AI. Research priorities include ecologically valid psychophysiological assessment protocols, position-specific psychological profiling, and practitioner-validated tools that turn AI outputs into usable coaching recommendations. Full article

Advancements in pulmonary drug delivery have enabled effective treatment approaches for more severe disease conditions, such as chronic obstructive pulmonary diseases, asthma, cystic fibrosis, and other pulmonary disorders, via targeted, sustained, and immediate drug delivery routes with minimal systemic side effects. However, conventional delivery systems have many limitations, such as poor drug targeting, adherence, and deposition, which ultimately cause variations in drug profiles and therapeutic efficacy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of smart inhaler drug–device combination systems for personalized therapy using predictive formulation parameters, design variables, device performance, and inhalation pattern monitoring. Advanced AI techniques, such as artificial neural networks, deep learning, random forests, support vector machines, deep learning algorithms, and computational modeling, predict the mass median aerodynamic diameter (MMAD), fine-particle fraction (FPF), emitted dose, and regional lung deposition. Smart inhalation devices coupled with digital sensors and computing systems enable the real-time monitoring of inhalation profiles and adherence. Moreover, AI- and ML-enabled Quality by Design (QbD) and digital twin framework technologies enhance the optimization of manufacturing process parameters, consistency, robustness, and scale-up performance. Although several developments have been reported, there is still room for improvement in terms of data heterogeneity, algorithm transparency, interpretability, cybersecurity, regulations, and long-term clinical standardization. This review emphasizes the use of AI to improve the performance of pulmonary drug delivery through smart inhaler drug–device combination therapies, focusing on technological advancements, formulation optimizations, smart inhalers, regulatory issues, current limitations, and future perspectives of AI-based pulmonary drug delivery. Full article

Automated license plate readers (ALPRs) are often evaluated as discrete police tools, although their public power arises from cross-vendor socio-technical infrastructure. This article examines roadside and mobile sensors, vehicle-attribute classification, cloud archives, commercial databases, real-time crime center integration, interagency access, automated alerts, and police action. Flock Safety supplies the principal documentary case because unusually extensive public records permit system-level tracing; Axon/Fusus, Motorola Vigilant/VehicleManager, and federal access to commercial ALPR data establish the wider vendor-independent boundary. A structured documentary analysis of 59 sources triangulates official records, peer-reviewed research, vendor materials used only for stated functions, and record-based investigations. It integrates boundary critique, control-structure mapping, feedback analysis, constitutional doctrine, a STRIDE-informed threat model, and empirical research on policing effectiveness and surveillance effects through 3 August 2026. The analysis identifies four conditional mechanisms: infrastructure aggregation, authority diffusion, asymmetric feedback, and rights invisibility. The article reformulates the Rights Control Deficit (RCD) as a non-arithmetic profile relation between operational demands and effective governance capacity and applies it to three documented configurations and a clearly labeled normative benchmark. Seven falsifiable propositions specify variables, indicators, suitable methods, and disconfirming conditions for later empirical study. A rights-preserving hybrid-intelligence architecture combines bounded automation with judicial authorization, short retention, sensitive-location protections, immutable audit, availability safeguards, independent review, contestability, sanctions, and credible termination authority. The evidence identifies capabilities, activated pathways, and conditional risks; it does not estimate population prevalence or a universal ALPR-specific causal effect. Meaningful human oversight is an institutional control property, not merely an officer’s presence at an interface. Full article

A variety of measurement systems are available for monitoring existing concrete bridges with deficiencies in shear capacity. In addition to established systems, fiber optic sensors (FOS) offer significant potential for structural health monitoring. However, FOSs are often installed in the tension zone, where crack formation may occur even under service loads, increasing the risk of sensor failure and potentially resulting in a loss of measurement capability. A promising approach to significantly reduce this risk is the installation of FOSs in the compression zone. However, it remains unclear whether measurements obtained from FOSs in the compression zone can be used to assess the load-bearing and deformation behavior of reinforced concrete beams and how they relate to measurements obtained in the tension zone. To address this question, shear tests were carried out on reinforced concrete beams with shear reinforcement ratios commonly used in practice. The experimental results demonstrate close agreement between measurements obtained from FOSs installed in the compression and tension zones under service loads. Furthermore, the findings and the characteristic strain profiles associated with shear and flexural failure provide a basis for assessing existing structures monitored using FOSs. Full article

The development of indoor photovoltaic devices for powering Internet of Things (IoT) sensors requires low-cost and sustainable components, making dye-sensitized solar cells (DSSCs) an ideal candidate for artificial light harvesting. The counter electrode plays a critical role in transferring electrons and catalyzing the reduction in the redox electrolyte. However, the traditional use of expensive and scarce platinum (Pt) limits the cost-effective, large-scale commercialization of these devices. While carbon-based materials offer a highly porous, conductive, and abundant alternative, commercial carbon pastes frequently require energy-intensive high-temperature sintering. In this study, we propose a sustainable, low-temperature, and screen-printable carbon composite counter electrode (LoT-HPC) using bio-derived hydroxypropyl cellulose (HPC) as a highly effective binder. Rheological characterizations confirm that the formulated LoT-HPC ink possesses an ideal shear-thinning profile and rapid structural recovery, ensuring excellent printability and film homogeneity. By comparing the custom LoT-HPC composite against a commercial high-temperature screen-printed graphite paste (HT-Elco) and a standard sputtered Pt-FTO electrode, we demonstrate the structural and electrocatalytic advantages of this material. When integrated into full DSSC devices and evaluated under low indoor illumination (1000 lux), the LoT-HPC cell delivers a power conversion efficiency (PCE) of 14.8% and a high short-circuit current density of 103.9 µA cm⁻². Furthermore, the custom device demonstrated exceptional operational stability, retaining 98.6% of its initial efficiency (from 14.8% to 14.6%) after 200 h of continuous light-soaking and J-V cycling under 1000 lux. Ultimately, the successful implementation of the HPC binder enables the low-temperature fabrication of sustainable carbon counter electrodes without the need for energy-intensive thermal treatments, presenting a highly scalable pathway for indoor DSSC manufacturing. Full article

Thin film printed electronics, such as flexible circuits and sensor sheets, require non-contact inspection to detect defects and edge degradation. The present paper presents a laser-enhanced machine vision framework for detecting and analyzing the edges of printed conductive tracks using Canny edge detection and Otsu thresholding. Using a coherent laser source, Otsu’s method enhances contrast at the ink–substrate interface, enabling robust segmentation of edge lines. Canny operator is applied to thresholded images to extract precise edge profiles. Multiple printed tracks are analyzed to calculate four lateral edge roughness values (Ra). As a result, the values are 40.43 µm, 40.09 µm, 50.26 µm and 40.94 µm. The results show that the suggested method can detect and qualify variations in edge parameters. Printed electronics are produced using an inline inspection and quality control system based on non-contact, high-resolution, and scalable technologies. Full article

Formic acid (HCOOH), the most abundant carboxylic acid in the troposphere, modulates rainwater acidity, aerosol water uptake, and the oxidative capacity of remote atmospheres, yet its global budget remains poorly constrained. Herein, we present the first HCOOH total-column retrieval from thermal–infrared (TIR) measurements of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer-2 (TANSO-FTS-2) on board GOSAT-2, providing an early-afternoon observational perspective that complements existing morning low-Earth-orbit and geostationary HCOOH products. The Optimal Estimation retrieval sequentially fits the surface state, the atmospheric background (temperature, water vapor and ozone), and the HCOOH profile in a 1104–1109 cm⁻¹ microwindow centered on the ${\nu }_6$ Q-branch, with a radiance-ratio-scaled a priori that adapts to each scene. Averaging-kernel diagnostics concentrate the sensitivity in the 500–900 hPa layer with degrees of freedom for signal of approximately 1.05 under enhanced-emission conditions. For a 2019–2020 Australian bushfire case, including HCOOH in the state vector reduces the mean spectral residual from −0.327 K to 0.033 K. Independent evaluation against 113 time-coincident Toronto NDACC FTIR overpasses gives R = 0.95 and a zero-intercept slope of 2.12 for raw FTIR versus GOSAT-2. Applying the GOSAT-2 a priori and averaging kernel to the FTIR profiles changes the slope to 0.77 and reduces the RMSE to $0.23\times {10}^{16}$ molec cm⁻²; this one-sided smoothing is treated only as a sensitivity diagnostic. Monthly global maps for December 2019 and June 2020 show cross-sensor consistency with the IASI/MetOp-B ANNI-HCOOH product at R = 0.83 and 0.76. Over East Asia during April–June 2023, GOSAT-2 correlates with FY-4B/GIIRS at R = 0.90 (April) and R = 0.65 (June), with coherent three-sensor daily variability. These satellite comparisons are treated as cross-sensor consistency assessments rather than independent validation. GOSAT-2 consistently reports lower columns, a sensitivity-limited tendency consistent with a priori dominance under weak signals, limited information content, a narrow retrieval window, and differences among retrieval frameworks. The current product is a first demonstration for cloud-free daytime land scenes; this domain defines its sampling scope and representativeness but is not interpreted as a direct cause of the lower columns. The product offers a traceable GOSAT-2 TIR observational constraint on tropospheric HCOOH for future multi-platform synergy. Full article

Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer from poor generalization, unphysical behaviors, and limited interpretability when facing noisy measurements and unseen operating conditions. This paper introduces a knowledge-guided physics-informed hybrid learning framework that integrates recurrent neural networks with Unscented Kalman Filter (UKF) state estimation and embedded thermodynamic constraints within a unified uncertainty-aware architecture. The proposed PI-LSTM-UKF framework achieves competitive predictive accuracy and improved physical consistency relative to the residual-learning hybrids by tightly integrating physics-informed recurrent learning, thermodynamic constraints, and sequential UKF state estimation. While the UKF provides robust recursive correction under noisy measurements during closed-loop operation, the physics-informed Long Short-Term Memory (PI-LSTM) learns nonlinear corrections and long-term dynamics that cannot be captured by the linear model alone. The proposed framework is systematically benchmarked against a hierarchy of seven modeling approaches, including Dynamic Mode Decomposition with control (DMDc), Sparse Identification of Nonlinear Dynamics (SINDy), and residual-learning variants based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). High-fidelity Simscape simulations of a Rankine-cycle steam turbine system are used as a challenging simulation-based case study. Results show that the knowledge-guided hybrid approach achieves competitive predictive accuracy, improved physical consistency, and robust performance under an unseen load profile, severe thermodynamic degradation, valve hysteresis, and substantially elevated sensor noise. The framework provides a promising simulation-based foundation for uncertainty-aware digital twins of nonlinear thermal power systems. Validation using operational plant data remains necessary before its application to real-time monitoring and predictive maintenance. Full article

The stability and reliability of temperature sensors in industrial cooling systems are critical to process quality, energy efficiency, and operational safety. However, existing approaches lack systematic stability metrics and intelligent predictive capabilities. This study proposes an AI-driven framework integrating stability feature engineering with machine learning models for fault identification and early prediction of temperature sensors in power plant cooling systems. The framework introduces three physics-based stability indicators—rolling standard deviation (σ_roll), variation intensity index (VII), and short-term variation magnitude (ΔT_short)—to quantify sensor signal quality. These features, combined with operational parameters, are used to train support vector machine (SVM) and Long Short-Term Memory (LSTM) models for binary classification. The framework is validated using over 260,000 one-minute records per unit collected from three parallel steam-turbine generating units (Units 1, 2, and 3) of the same coastal thermal power plant. Each unit is served by an independent once-through seawater cooling loop instrumented with redundant Pt-100 temperature sensors at the inlet and outlet manifolds; the three units differ in their operating profile—Unit 1 operates under variable load with frequent cold-start events, Unit 2 under moderate variable load, and Unit 3 under stable high-load conditions—with data collected at 1 min intervals from January to June 2025. Under an explicitly anomaly-positive evaluation, with the full confusion matrix reported for every unit and model, classification performance is limited and strongly unit-dependent. In real-time identification, AUC-based ranking ability varies across units (SVM AUC = 0.65, 0.75, and 0.98 for Units 1–3; LSTM AUC = 0.66, 0.31, and 0.52), but under the extreme class imbalance (anomaly rate ≈ 0.07–0.13% in the test partitions), the calibrated operating-point precision and F1-scores remain low for all unit–model combinations (F1 ≤ 0.26, MCC ≤ 0.28). McNemar’s test indicates statistically significant paired differences for Units 1 and 2 but not for Unit 3. These results show that, on this dataset, neither model attains reliable anomaly classification, and that all reported metrics must be interpreted together with the disclosed confusion-matrix counts and severe class imbalance. The primary contribution of the framework is therefore methodological—physics-based stability indicators, redundant sensor cross-checking, and an operational false-alarm analysis—rather than high-accuracy prediction, and the study highlights the difficulty of learning-based prediction for rare, rule-defined thermal sensor anomalies. Full article

The lectin pathway (LP) of complement activation functions as a crucial effector of innate immunity and a homeostatic sensor operating at the intersection of systemic metabolism, nutritional status, and endothelial integrity. This review provides a comprehensive synthesis of current molecular, clinical, and epidemiological literature regarding the environmental and metabolic regulation of this pathway. First, we summarize the biophysical, structural, and stoichiometric requirements for divalent cations in fluid-phase activation and macromolecular assembly, integrating the contrasting roles of calcium (Ca²⁺) and zinc (Zn²⁺) into an explanatory Dual-Cation Dichotomy Framework. Second, we evaluate the transcriptomic mechanisms of nutrigenetic licensing, reviewing how fat-soluble vitamins sustain endoplasmic reticulum chaperone networks and mucosal barrier competence. We discuss how these micronutrient-driven axes interact with host genetic diversity, presenting a Nutrigenetic Rescue Framework to contextualize the environmental modulation of low-expressing MBL2 alleles. Third, the LP responds to metabolic and endocrine shifts, focusing on its biomarker value in gestational diabetes and its clinical patterns in type 1 diabetes. These connections to metabolic disease and related microvascular complications are further integrated into a broader Somatotropic-Gestational Sentinel Hypothesis. Fourth, we connect these metabolic profiles with public health challenges, reviewing LP hyperactivation in viral infections and discussing localized surface plasmon resonance (LSPR) biosensors, at present a conceptual, preclinical technology, as a candidate approach for future point-of-care population screening. In conclusion, bridging nutritional biochemistry with metabolic endocrinology and diagnostic technologies that remain largely preclinical outlines a potential shift from empiric management toward biomarker-driven, point-of-care stratification, which could help mitigate both infectious thromboinflammation and chronic microvascular failure once these technologies undergo further mechanistic and clinical validation. Full article

A UAV operating without GNSS faces unbounded inertial drift. A layered navigation architecture is evaluated in which terrain contour matching (TERCOM) provides periodic position corrections and satellite-image scene matching adds a condition-dependent precision layer. The architecture is examined through a single-trajectory simulation over a 1° × 1° ASTER GDEM V2 tile (N31E081, Tibetan Plateau, 4555–6468 m elevation, 16.1 mean slope) representing a one-hour flight (127 km, 35.2 m/s). The simulation models GNSS loss with idealised sensor behaviour: IMU error is described by a Gauss–Markov model without temperature dependence, and the radar altimeter is represented with additive Gaussian noise. Under these conditions, TERCOM reduced RMS position error from 1467 m to 317 m (78.4% reduction); with ideal noise-free scene-matching registration added, RMS further decreased to 103 m (a best-case estimate). The idealised Cramér–Rao lower bound already incorporates the 5 m radar-altimeter and 20 m DEM noise terms (it is therefore not a noise-free value) at the flight mean slope of 16.1°; averaging this local bound over the full trajectory—where near-flat segments inflate it—gives the tile-averaged CRLB of ≈150 m. The remaining gap between the realised TERCOM RMS (317 m) and this realistic bound is attributed to residual INS drift during profile collection, DEM interpolation error, and low-entropy terrain segments; a quantitative decomposition of these factors is provided in this paper. Results are based on a single noise realisation and a single trajectory; they characterise the specific simulation scenario rather than the architecture’s general performance. The altitude-error decomposition argument—that TERCOM’s sensitivity depends primarily on short-term dynamic altitude drift rather than the accumulated systematic error—is developed specifically for the normalised cross-correlation (NCC) metric and requires mean-centring of the terrain profile for generalisation to other correlation metrics. Full article

High penetration of distributed energy resources (DERs) is reshaping radial distribution networks, yet weather-dependent generation, variable demand, and feeder-level surplus–deficit imbalance can compromise voltage quality and coordinated operation. Existing planning approaches often optimize feeders independently and therefore provide limited insight into how local DER portfolios should support inter-feeder energy exchange under time-varying conditions. This study proposes a weather-driven two-stage Geese V-Formation Algorithm (GVFA) framework for planning DER integration and feeder coordination in a practical five-feeder 11 kV Tajeeyaat/North Baghdad system, with complementary validation on a five-instance IEEE 33-bus benchmark cluster. Stage 1 optimizes the siting and sizing of photovoltaic units, wind turbines, battery energy storage systems, capacitor banks, and feeder-specific auxiliary resources using backward/forward-sweep load flow. Stage 2 uses hourly surplus–deficit profiles to select tie-switch configurations and exchange capacities for feeder-to-feeder energy sharing. The framework is evaluated through convergence analysis, optimizer comparison, N-1 contingencies, and seasonal load-growth tests. For the practical system, 24 h aggregate losses decreased from 11,258.1571 to 3367.8481 kWh-eq, corresponding to a 70.0853% reduction. The minimum-voltage range improved from 0.9497–0.9898 to 0.9897–0.9997 p.u., while grid-import reduction reached 94.0270%. For the IEEE-33 cluster, 24 h aggregate losses decreased from 31,746.4712 to 6381.1403 kWh-eq, corresponding to a 79.8997% reduction. The minimum-voltage range improved from 0.8268–0.8632 to 0.9465–0.9683 p.u., while grid-import reduction reached 84.4596%. The framework provides a planning-oriented, sensor-ready decision-support basis for DER siting, voltage-support assessment, grid-import reduction, and candidate inter-feeder exchange corridors. Full article

Low-cost metal-oxide-semiconductor (MOS) electronic noses promise rapid, non-destructive meat freshness screening, and published classifiers frequently approach perfect accuracy. Such figures are rarely tested against the two conditions that most inflate them: a target-derived label among the inputs, and random splitting of the correlated samples. Beef freshness is benchmarked here on a public 11-sensor, 12-cut MOS dataset using leakage-free leave-one-cut-out cross-validation in order to predict freshness class and total viable count (TVC) with paired significance tests. A gradient-boosted-tree pipeline is the strongest model (accuracy $0.81\pm 0.10$, macro-F1 $0.68\pm 0.15$, TVC $R^2=0.77$), significantly outperforming a multi-scale attention convolutional network (macro-F1 $0.50\pm 0.15$; $p<0.001$). The advantage of this study lies in the representation, not the model family: a network given the same window summaries reaches $0.64\pm 0.17$, indistinguishable from the tree. Near-perfect accuracy returns only when TVC is supplied as a feature or samples are split at random (macro-F1 0.97). Under nested, per-fold selection, a five-sensor subset matches the full array. On a rich BME688 heater profile dataset, the network surpasses the tree, an advantage that vanishes as the profile shortens to one step. Evaluation and representation, not architecture, govern reported performance; a signal-richness criterion predicts when a deep temporal model is justified. Full article

Temperature–time profiles obtained through thermistor-based monitoring provide a rich but noise-sensitive source of information for estimating kinetic and thermal parameters in exothermic batch reactions. Conventional workflows typically combine deterministic smoothing with numerical differentiation, an approach that can amplify measurement noise and fail to propagate preprocessing uncertainty into the resulting reaction-rate and parameter estimates. To address these limitations, this study presents an uncertainty-aware Bayesian machine-learning framework that integrates scalable random-Fourier-feature Gaussian-process (RFF–GP) smoothing, analytical differentiation, temperature-derived apparent conversion, Bayesian parameter inference, posterior validation, predictive calibration, model comparison, ablation, sensitivity analysis, probabilistic benchmarking, simulation of thermal nonideality, and endpoint diagnostics. The framework was applied to $379,631$ cleaned thermistor observations. The production RFF–GP achieved a validation root-mean-square error of $0.04805\mathrm{K}$, yielding a stable latent temperature trajectory and an uncertainty-aware estimate of $dT/dt$. On a smaller matched subset, exact Gaussian-process regression achieved the highest predictive accuracy and the best probabilistic scores, whereas the RFF–GP reduced central-processing-unit runtime by approximately $4.1$-fold and remained applicable to the larger production fit. A Monte Carlo dropout neural comparator produced larger prediction errors and substantially wider predictive intervals. Six apparent thermokinetic structures were evaluated using mean-field variational inference, after which the nth-order and autocatalytic structures were validated using the No-U-Turn Sampler (NUTS). Under mean-field variational inference, the apparent autocatalytic structure achieved the lowest point estimate of the widely applicable information criterion (WAIC), the lowest derivative-domain error, and the lowest full-profile temperature-reconstruction root-mean-square error of $0.2920\mathrm{K}$. Its posterior obtained using NUTS yielded $E_a=40.98\mathrm{kJ}{\mathrm{mol}}^{-1}$, $k_{\mathrm{ref}}=0.005815{\min }^{-1}$, $\Delta T_{\mathrm{ad}}=56.11\mathrm{K}$, $m=0.1694$, and $n=1.0784$. The sampling diagnostics indicated satisfactory convergence, large effective sample sizes, and no divergent transitions. Although the MFVI posterior means and NUTS posterior medians were similar, variational inference produced narrower uncertainty intervals for several correlated parameters. Moving-block bootstrap intervals did not establish a decisive separation in WAIC among the leading structures. Expanded sensitivity, ablation, imperfect-insulation simulation, and endpoint-holdout analyses further showed that the apparent parameter estimates were sensitive to optimization, thermal nonideality, sensor response, and Gaussian-process boundary behavior. The autocatalytic formulation should therefore be interpreted as the best-performing apparent structure among the candidates tested rather than as evidence of a unique chemical mechanism. Overall, the framework extracted physically plausible apparent thermokinetic information from noisy temperature-only measurements while explicitly quantifying uncertainty arising from prediction, parameter estimation, model form, computation, thermal nonideality, and boundary behavior. Full article