Search Results (23,247)

Accurate classification of physical activity (PA) intensity is essential for exercise prescription, rehabilitation monitoring, and evaluation of guideline adherence. However, widely used wrist-worn accelerometer cut-points may substantially misclassify physiological intensity. This study evaluated absolute accelerometer thresholds during a maximal 2400 m run in military office workers and examined whether individualized cut-points improve agreement with physiological intensity. Seventy-four military office workers completed the test while wearing a wrist-worn ActiGraph GT9X Link and a chest-worn Zephyr BioHarness. Participants achieved near-maximal physiological effort, with peak heart rate averaging 187 ± 11 bpm (95 ± 4.2% age-predicted HRmax). Despite this high intensity, absolute wrist-worn cut-points classified only 34.5% of participants as performing vigorous activity for most of the test. Individualized cut-points, derived from each participant’s individual reference intensity, calculated as the three highest consecutive one-minute epochs during the 2400 m test, substantially improved agreement between accelerometer-derived classifications and physiological intensity. Agreement with %HRmax increased from fair (κ = 0.31), using absolute thresholds, to good (κ = 0.74), using individualized thresholds, and intraclass correlation increased from 0.52 to 0.81. These findings demonstrate that absolute cut-points markedly underestimate high-intensity activity, potentially leading to inaccurate exercise load monitoring and misinterpretation of training intensity. Individualized calibration during a standardized maximal running test provides a feasible strategy to improve the validity of intensity assessment using wearables. Although the study population consisted of military office workers, the approach may be applicable to other active populations. However, further validation in independent samples is needed. Full article

Understanding the psychological mechanisms underlying consumer loyalty behavior constitutes a central challenge for the behavioral sciences. Despite growing research on experiential marketing, limited attention has been directed toward understanding the conditional cognitive mechanisms that determine when and how consumption experiences translate into stable loyalty patterns, particularly in emerging market contexts where consumer behavior dynamics differ substantially from those in mature economies. The present study examines how experiential processing influences the formation of behavioral loyalty patterns, considering the moderating role of cognitive value evaluation. A quantitative, correlational, cross-sectional design was employed with a sample of 500 consumers from retail businesses in Pueblo Nuevo, Peru. The instruments demonstrated adequate psychometric properties (α > 0.88; AVE > 0.50). The results of the moderation analysis using PROCESS Model 1 revealed that the model explains 79.9% of the variance in loyalty behavior (R² = 0.799, p < 0.001). The interaction effect was significant (B = 0.10, p < 0.001), confirming that cognitive value evaluation moderates the relationship between experiential processing and behavioral loyalty. Simple slopes analysis showed that the effect of experiential processing on loyalty intensifies as perceived value increases, ranging from B = 0.56 at low levels to B = 0.77 at high levels. The Johnson–Neyman criterion identified the transition point at 14.80. These findings contribute to consumer behavior theory by demonstrating that consumption experiences require a favorable cognitive evaluation to translate into stable behavioral loyalty patterns, with implications for Sustainable Development Goal 8 concerning sustainable economic growth. These results advance consumer behavior theory by providing an integrative moderating framework applicable beyond the Peruvian context, and offer retail managers a diagnostic tool for calibrating experiential strategies based on consumer value perception thresholds. Full article

Multi-line-scan camera systems provide high-frequency sampling and wide field-of-view coverage, making them valuable for three-dimensional measurement and dynamic reconstruction. However, their one-dimensional projection property introduces scale ambiguity and strong parameter coupling during calibration, which limits the consistency and stability of local optimization in multi-camera systems. To address this issue, this paper proposes a global calibration method based on physical constraints and hierarchical optimization. A unified imaging and motion model is constructed by incorporating physical scale constraints and structural priors, and geometric scale information is introduced into the joint optimization to reduce scale ambiguity and parameter coupling. Parameter normalization and staged optimization are further adopted to improve numerical stability for variables of different magnitudes and enable consistent estimation of multi-camera parameters within a unified framework. Simulation and experimental results show that the method achieves stable convergence under focal-length initialization perturbation, baseline deviation, and noise interference, with a three-dimensional reconstruction error below 0.67 mm and a convergence probability of at least 99.7%. These results indicate that the proposed method effectively reduces calibration uncertainty in multi-line-scan camera systems and supports high-precision online measurement and dynamic three-dimensional perception. Full article

This paper presents a 2-GS/s voltage–time hybrid pipelined analog-to-digital converter (ADC) with a 14-bit digital output, implemented in a 28-nm CMOS process. To alleviate the gain–bandwidth–power trade-off in deeply scaled technologies, the proposed architecture employs a SHA-less front-end and a low-gain inverter-based push–pull RA for energy-efficient coarse quantization. The residue is then transferred to the time domain via a highly linear constant-current voltage-to-time converter (CC-VTC) and digitized by a four-channel time-interleaved gated-ring-oscillator (GRO) TDC. To recover dynamic linearity degraded by low-gain amplification and interleaving mismatches, a multiplier-less digital background calibration engine is implemented. Leveraging mean absolute value (MAV) statistics and dither-injected least-mean-squares (LMS) algorithms, it effectively compensates for inter-channel and interstage errors with minimal hardware overhead. The prototype occupies an active area of 0.16 mm². At 2 GS/s, the ADC achieves a Nyquist SNDR of 63.42 dB and an SFDR of 73.71 dB, corresponding to an ENOB of 10.24 bits. Consuming 86.9 mW from a 1-V supply, it achieves a Walden FoM of 35.9 fJ/conv.-step. Measurement results from multiple chips under a wide range of operating conditions verify the robustness of the proposed ADC. Full article

Short-chain fatty acids (SCFAs) are key biomarkers of gut microbiota activity; however, routine quantification in fecal samples relies largely on chromatography, which is instrument-intensive and throughput-limited chromatography techniques. Herein, we present a rapid machine-learning-assisted electroanalysis platform for SCFAs profiling that integrates a disposable three-electrode planar gold chip with voltammetric fingerprinting and artificial neural network (ANN)-based signal decoupling. To generate orthogonal chemical information and improve the discrimination of structurally similar species, a dual pretreatment strategy combining acid-catalyzed esterification and alkaline dissociation was employed prior to electrochemical analyses. Differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were employed to acquire high-dimensional fingerprints, from which current-, potential-, and area-based descriptors were extracted using a cross-information feature strategy. A hierarchical modeling framework improved total SCFAs prediction by incorporating ANN-predicted propionate and butyrate concentrations as auxiliary inputs. While linear calibration was achievable in standard mixtures, direct linear models performed poorly in real fecal matrices due to strong sample-dependent matrix interference. In contrast, the ANN captured nonlinear relationships among multifeature inputs and suppressed matrix effects. Validation against gas chromatography–mass spectrometry in an independent fecal test cohort (n = 30) demonstrated excellent agreement and low prediction errors, with mean absolute error/root mean square error values of 0.063/0.072 mM (propionic acid), 0.029/0.034 mM (butyric acid), and 0.135/0.202 mM (total SCFAs). The DPV/CV acquisition requires only minutes per sample, whereas pretreatment takes 1~3 h depending on the target route but can be performed in parallel for batch processing; thus, overall throughput is determined mainly by batch pretreatment rather than per-sample instrument time. This electrochemical–ANN workflow provides a portable, high-throughput alternative to chromatography for fecal SCFAs profiling in clinical screening and microbiome research. Full article

Water age reflects water sources, storage, and pathways, and regulates the solute retention and dissolution associated with biogeochemical processes, highlighting its hydrological and ecological importance. However, accurate water age estimation in tracer-aided models depends heavily on the quality and spatio-temporal resolution of precipitation isotopic signals. This study investigates how distributed rainfall δ²H signals affect the simulation of young water fraction (F_yw) via the Storage Age Selection (SAS) model in topographically complex subtropical mountain catchments. Eight precipitation δ²H scenarios were generated using two temporal approaches (stepwise and sinewave) and four spatial interpolation methods: (1) raw data, (2) reversed effective recharge elevation method (rERE), (3) linear regression with elevation (ER), and (4) regression-kriging (RK). Later on, the time-variant SAS model was calibrated against observed stream water δ²H collected from the year 2022 to the year 2024. Results show that the SAS model consistently produced similar F_yw estimates for catchments (8%~40%) across all eight scenarios, demonstrating strong robustness to input uncertainty and validating the dominant role of catchment characteristics in regulating water age. The combined stepwise temporal and rERE spatial approach provided better agreement with observed stream δ²H, particularly in the eastern, steeper catchments, yielding superior model efficiency along with better constrained uncertainty. This study highlights the sensitivity of age-tracking models to precipitation isotopic inputs and provides practical guidance for selecting an interpolation strategy in data-limited mountainous environments. Full article

Background: Surface roughness in turning operations is still verified predominantly after machining, which limits the possibility of timely corrective intervention. Methods: This study examined whether normal-direction peak-to-peak vibration displacement can serve as a practical low-frequency indicator of surface roughness during finish turning of EN AW-2011 aluminum alloy. The analysis was based on 190 synchronized displacement-roughness observation pairs obtained in one controlled experimental campaign on a CQ6230 conventional precision lathe, using a VB-8206SD displacement logger mounted radially on the tool holder and contact profilometry measurements reported as Ra and Rz. The analytical workflow included explicit quality-control safeguards for malformed rows, missing values, and obvious artefacts; in the present dataset, these checks did not indicate a failure state that would invalidate the main calculations. The workflow combined descriptive statistics, moving-average trend inspection, low-frequency FFT and STFT descriptors, Pearson correlation analysis, and ordinary least squares regression. Results: The displacement signal exhibited a mean value of 0.0446 mm with a standard deviation of 0.0256 mm and showed strong within-dataset linear relations with roughness parameters: Ra = 14.204 + 24.191 V (R² = 0.9929, RMSE = 0.052 µm) and Rz = 63.207 + 105.253 V (R² = 0.9905, RMSE = 0.264 µm). Conclusions: The results support setup-specific roughness-related process-state assessment using low-rate normal-direction displacement measurements. However, because the 190 records represent a time-ordered synchronized sequence rather than 190 independent cutting trials, and because no separate validation set was available, the fitted equations should be interpreted as descriptive within-setup calibration rather than as universally validated predictive models. Full article

This paper presents a practical implementation of relative primary radiation thermometry (RPRT) together with MultiFixRadSoft, an open-source software package developed in accordance with the Mise-en-Pratique for the kelvin (MeP-K) for realization of the thermodynamic temperature scale and uncertainty evaluation under the new definition of the kelvin. The software enables realization of temperature scales using ITS-90 metal fixed points as well as metal–carbon and metal–carbide–carbon eutectic high-temperature fixed points (HTFPs) for both radiation thermometers and radiometers. It incorporates automated routines for melting plateau analysis, including determination of the point of inflection, liquidus point, and melting range, together with correction modules for size-of-source effect, detector nonlinearity, emissivity, and temperature drop. Validation is demonstrated through experimental realization using six fixed points (Cu, Fe–C, Co–C, Pd–C, Ru–C, and WC–C) and a linear radiation thermometer. The software also supports ITS-90 extrapolation procedures and flexible calibration schemes (n = 1 to n ≥ 3), with automated Sakuma–Hattori fitting and full uncertainty propagation compliant with MeP-K requirements. The results show excellent agreement with manual analyses and published data, confirming the correctness of the implemented algorithms. By integrating data processing, scale realization, and uncertainty analysis within a unified and transparent framework, MultiFixRadSoft provides a robust and accessible tool for traceable radiometric thermometry, supporting emerging NMIs and industrial laboratories while promoting the wider adoption of primary thermodynamic temperature realization methods. Full article

Non-invasive temperature estimation during online operation is a critical challenge in enclosed micro-reaction systems, particularly when the thermal mass of the working fluid varies dynamically or is uncertain. Conventional model-based approaches typically rely on fixed thermal parameters, leading to significant estimation errors when the actual reagent volume deviates from nominal conditions. To address this limitation, this study proposes a volume-adaptive temperature estimation framework applied to an ultrafast quantitative polymerase chain reaction (qPCR) system. By modeling the heat-transfer pathways via a simplified resistance–capacitance (RC) network, a nonlinear least squares (NLS) algorithm within an output-error (OE) framework is employed to identify key thermal parameters online. The framework separates the estimation into an offline calibration stage—where a thermocouple-equipped chip provides ground-truth data—and an online deployment stage that relies solely on non-invasive external measurements. This approach allows the system to explicitly compensate for volume-induced variations in thermal inertia. Validation experiments on an ultrafast qPCR platform with reagent volumes ranging from 100 to 250 $\mathsf{\mu }\mathrm{L}$ and heating rates exceeding 20 °C/s demonstrate that the method achieves robust performance, maintaining a mean absolute error (MAE) of reagent temperature at 0.24 ℃ and restricting the average volume estimation error to within 1.37 $\mathsf{\mu }\mathrm{L}$. DNA gel electrophoresis results further confirm the biological reliability of the temperature prediction strategy by verifying amplification specificity. This work provides a generalised solution for precise thermal management in micro-systems subject to variable thermal loads. Full article

Previous approaches to flexible-lined channel design typically isolate least-cost cross-section optimization from parameter uncertainty, or restrict reliability analysis to specific cases, limited failure modes, and proprietary codes. This paper presents VegyRap, an open-source QGIS-based plugin with an intuitive graphical user interface that unites these traditionally disjointed, sequential tasks into a single computational framework. The tool guides designers sequentially through: (i) terrain-driven longitudinal profile optimization using dynamic programming; (ii) least-cost cross-sectional optimization for riprap and vegetated linings; and (iii) multi-mode probabilistic reliability analysis coupled with dual risk–cost Pareto optimization. To seamlessly handle the stochastic behavior of uncertain variables, the framework features built-in statistical distributions and allows users to flexibly evaluate up to four distinct failure modes: overtopping, erosion, sedimentation, and near-critical flow oscillation. The framework’s capabilities are demonstrated through nine diverse design examples, incorporating benchmark validations against published studies and a comprehensive real-world case study in Wadi Al-Arja, Saudi Arabia. Results highlight that for vegetated channels, a hierarchical two-phase design logic is essential to satisfy both establishment-phase stability (Class E) and long-term conveyance (Class B). While benchmark comparisons show VegyRap achieves consistent cost reductions of 10–15% over traditional methods, the case study demonstrates that deterministic least-cost solutions can carry non-negligible failure probabilities. By utilizing marginal efficiency analysis to identify cost-effective enhancements, the integrated Pareto-based dual optimization produces transparent trade-off surfaces, empowering practitioners to transition from a single least-cost solution to a defensible, risk-calibrated preferred alternative. Full article

This paper presents a vision-based framework for detecting humans and estimating head surface temperature from aerial thermal imagery acquired by Unmanned Aerial Systems (UAS). A comparative evaluation of recent object detection architectures was conducted to identify the most stable and reliable model for thermal human detection under varying flight altitudes. The selected framework integrates two head localization strategies, namely, segmentation-based mask slicing and pose-assisted keypoint localization, to extract head regions and compute per-pixel temperature values from radiometric metadata. The results show that cross-domain inference using pre-trained YOLOv11 models achieves reliable human detection across controlled outdoor environments. Between the two pipelines, the pose-assisted method produced temperature estimates closer to the expected human physiological range (36–38 ${}^{\circ }$C), whereas the segmentation-based approach exhibited higher values attributable to mask boundary contamination and solar surface heating. In the absence of ground-truth validation from medical-grade sensors, these findings are characterized as relative comparisons rather than absolute accuracy claims. This study establishes a methodological foundation for future UAS-based thermal assessment systems and identifies critical calibration and validation requirements for field deployment. Full article

Entropic soft-min operators are widely used to obtain smooth approximations of minimum and argmin mechanisms in optimization, machine learning, and reinforcement learning. The quality of this approximation is controlled by an inverse temperature parameter that governs the trade-off between smoothness and fidelity, yet its selection is usually based on global heuristics or worst-case bounds that do not account for the geometry of the candidate cost vector. This study investigates the calibration of the inverse temperature parameter from a geometry-aware perspective, with explicit guarantees on the approximation error between the entropic soft-min and the exact minimum value. After establishing the structural properties of the relaxation error, including monotonicity with respect to the inverse temperature and its dependence on the geometry of the near-optimal set, we introduce a conformal calibration rule that selects the smallest inverse temperature, ensuring that a prescribed upper quantile of the approximation error remains below a target tolerance with distribution-free finite-sample validity. The resulting selector adapts to the geometry distribution represented in the calibration population and provides a principled alternative to mean-based and worst-case tuning rules. Numerical experiments, including geometry-controlled benchmarks and a contextual bandit setting illustrating the impact of geometry-aware calibration on decision-making under estimated action values, show that the proposed method accurately tracks oracle calibration temperatures, preserves the desired operator-level coverage, and makes explicit how geometric heterogeneity governs the effective sharpness required by the soft-min approximation. Additional shifted evaluations illustrate the role of exchangeability in the validity guarantee and the consequences of transferring temperatures across populations with different near-optimal geometries. Full article

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

Laser Powder Bed Fusion (PBF-LB) is currently one of the most versatile and adopted additive manufacturing technologies for printing metals. To take new PBF-LB machines into service, a thorough characterization and calibration is often necessary to get the desired output. This is commonly achieved empirically; however, data-driven methods have become more and more available over the last few years. This research explores the use of transfer learning (TL) to transfer process knowledge from an already-established source machine (Nikon SLM 500) to a target machine (Trumpf TruPrint 5000) with different hardware specifications. To predict the tensile properties of AlSi10Mg0.5 utilizing a minimal data set of merely 25 training samples, eight TL model variants, determined by their degrees of training freedom, were investigated. The results showed that TL is effective in transferring machine learning (ML)-based process models. High prediction accuracy was achieved on the target machine, with coefficient of determination ($R^2$) values reaching 75.5% for yield strength, 82.1% for ultimate tensile strength, and up to 92.0% for elongation at break in testing. Additionally, a weighted mean model ensemble of all eight single models was developed, including all eight TL variants, to enable higher prediction robustness. Validation trials for three different use cases confirmed the capability of the approach to optimize processing conditions, like increasing hatch scan speed by 167% to 292% while maintaining high mechanical performance. Additional microstructure analysis was given to support the findings. The results demonstrate a time- and resource-efficient approach for rapid industrialization of PBF-LB machines, combining ML-based process modeling with machine-specific data. Full article

Additive manufacturing (AM) requires process-comparison tools that remain practical when sample generation and testing are costly. We propose a univariate, nonparametric workflow for comparing a candidate AM process to a stable reference process by testing distributional equivalency for a single response variable. The method discretizes the reference distribution into empirical percentile-defined bins and combines this representation with a sequential sampling protocol designed to reduce unnecessary sampling when evidence for equivalency or non-equivalency becomes sufficient. Simulation studies were used to evaluate operating characteristics across experimental settings, and a validation case study based on geometric measurements of laser based powder bed fusion plate scans correctly classified a candidate process expected to be equivalent to the reference while identifying a non-equivalent process at the first sampling step. The workflow is most appropriate for low-sample, high-cost, or throughput-constrained settings, and is best viewed as a tool for process comparability, change control, calibration, and requalification support rather than as a standalone replacement for qualification standards. The full workflow is implemented in the open-source AMEquivalency package to support reproducible analysis. Full article