Search Results (7,477)

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

Impact craters are among the most abundant geological structures on solid planetary surfaces and provide valuable information about impact processes and surface evolution. However, the systematic characterization of crater morphology remains challenging due to dataset heterogeneity, measurement uncertainty, and gradual transitions between morphological classes. This study proposes FAS-XAI, an interpretable framework for the comparative analysis of planetary crater datasets that combines fuzzy clustering and explainable artificial intelligence (XAI). The methodology combines exploratory data analysis, measurement-uncertainty assessment, unsupervised learning, supervised consistency analysis, and interpretable machine learning to identify and characterize crater morphologies through a structured workflow. The framework is applied to the Moon Crater Database v1 and the Robbins Mars Crater Database, two large-scale crater catalogs sharing a common geometric parameterization of crater properties. Using the variables available in both datasets, Fuzzy C-Means identifies morphological crater groups, while XGBoost assesses how consistently the resulting dominant cluster labels can be reconstructed from the same descriptor space. XAI techniques are then used to explain the contribution of each variable to the identified groups. The results reveal distinct structural patterns in the organization of lunar and Martian crater populations. Full article

Aero-engine quality assessment under the RAMS framework faces two persistent challenges: the inherent epistemic and linguistic uncertainty in expert evaluation, and the systematic neglect of inter-dimensional coupling. This paper proposes an integrated assessment method that combines Interval Type-2 Fuzzy Sets (IT2FS)-based group decision-making with Partial Least Squares Structural Equation Modeling (PLS-SEM). At the measurement level, IT2FS encodes dual-layered uncertainty through the Footprint of Uncertainty (FOU); multi-expert judgments are aggregated via the fuzzy weighted geometric average operator and defuzzified using the Karnik–Mendel algorithm. At the structural level, a reflective second-order PLS-SEM model built on the RAMS framework enables parametric estimation and significance testing of inter-dimensional coupling. Validation on a 63-engine turbofan dataset confirms that all measurement model criteria are satisfied, the second-order model explains 82.4% of the variance in overall quality (R² = 0.824), and predictive relevance is strong (Q² = 0.567). Comparative experiments against three benchmark methods demonstrate consistent advantages in quality grade discrimination, information richness, sensitivity to technical improvements, and ranking robustness. These properties position the framework as a statistically rigorous, model-based complement to existing condition-monitoring and digital health management systems for complex propulsion systems, supporting quantitative decision-making within digital engineering programmes. Full article

State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH estimation method is proposed based on alternating electrical signals. First, an aging test was carried out using commercial 18650-type batteries. Considering the current uncertainty in practical applications, tests under different discharge conditions were conducted to obtain the capacity and wide frequency band impedance data of each battery throughout its life cycle. Then, important features at specific frequencies were extracted from the impedance data, and an interpretable analysis of the features was performed using the distribution of relaxation times (DRTs). Finally, the impedance features were combined with the Gaussian process regression algorithm in machine learning to estimate and validate the SOH. The results show that using fixed-frequency impedance features can achieve accurate estimation. The average value of the maximum absolute error of each battery under different working conditions can be controlled within 1.59%. With the development of embedded chips and online measurement technology, battery management systems can obtain important impedance features by applying alternating electrical signals within a certain frequency range, thus achieving online estimation of SOH. Full article

The rapid integration of distributed solar photovoltaic (PV) generation is reshaping low-voltage distribution grids (LVDGs), creating voltage rise, reverse power flow, and congestion challenges for distribution system operators (DSOs). Flexibility in generation and demand, broadly understood as the capability to adjust generation or consumption in response to variability and uncertainty in net load, is increasingly central to cost-effective grid operation under high PV penetration. This review examines flexibility and controllability options in LVDGs, focusing on voltage regulation methods, supply- and demand-side flexibility resources, and market-based coordination mechanisms. The Norwegian Regulation on Quality of Supply (FoL) provides the regulatory context: it enforces 1 min average voltage compliance, stricter than the 10 min averaging window of EN 50160, making short-duration voltage excursions operationally significant and directly influencing the trade-off between curtailment, grid reinforcement, and local flexibility measures. Inverter-based active–reactive power control emerges as the most cost-effective overvoltage mitigation option, complemented by local battery energy storage systems (BESS) and demand response for congestion relief and energy shifting. Key gaps include limited LV observability, insufficient application of quasi-static time series (QSTS) assessment in planning, and underdeveloped DSO-aggregator coordination frameworks. Combined inverter control, feeder-end storage, and demand-side flexibility can defer costly reinforcements, particularly in rural 230 V IT feeders where voltage constraints dominate. Full article

Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure. Full article

Choosing a future career represents a complex developmental task, often accompanied by multiple concerns and anxieties. The Social Cognitive Career Theory and Life Design paradigm emphasize the importance of supporting individuals in managing career-related challenges. However, global stressors—such as the COVID-19 pandemic, the war in Ukraine, and increasing awareness of the climate emergency—have introduced new and multifaceted sources of uncertainty that are not adequately captured by existing instruments. This gap highlights the need for a psychometrically sound measure to assess emerging career-related concerns in the contemporary context. Accordingly, the study aimed to develop and validate the Career Concerns related to Perceived Environmental Challenges (CC-PEC Scale). Four studies were conducted. Study 1 employed exploratory factor analysis, supporting a three-factor structure (Career-related COVID-19 pandemic concern, Career-related war concern, and Career-related climate emergency concern). Study 2 confirmed this structure using confirmatory factor analysis and demonstrated measurement invariance across gender, supporting a hierarchical factorial model. Study 3 provided evidence of concurrent and discriminant validity through associations with related constructs. Study 4 offered preliminary evidence of stability and predictive validity using life satisfaction and flourishing as outcome variables. Overall, the findings support the CC-PEC Scale as a reliable and valid instrument for assessing career-related concerns linked to global environmental challenges. These results have important implications for research and career guidance interventions aimed at supporting young people’s career development in increasingly uncertain contexts. Full article

A physics-informed neural network (PINN) is developed to estimate the spatially varying parameters of the time-dependent heat equation in one dimension. The proposed model incorporates both the forward and inverse problems to estimate the temperature and thermal properties of a laser-induced interaction with biological tissue. The network can detect the presence and location of a second layer of tissue, if it exists, and estimate the thermal coefficients of each substance. This ability to model nonhomogeneous properties in tissue subjected to laser irradiation has many important applications in medical procedures. An ensemble method is used to quantify the epistemic uncertainty of all estimates to identify weaknesses in the model. Aleotoric uncertainty is simulated through noise perturbations, demonstrating robust estimates in the presence of measurement error. The uncertainty associated with parameter estimation provides insight into the ill-posedness of the inverse problem. Full article

Municipal solid waste (MSW) landfill site selection is a complex multi-criteria decision-making (MCDM) problem involving uncertainty and conflicting criteria. Although spherical fuzzy extensions of the Technique for Order Preference by Similarity to Ideal Solution (SF-TOPSIS) are widely used, existing studies rely on conventional distance measures that do not fully capture the geometric structure of spherical fuzzy sets. To address this limitation, this study proposes an enhanced SF-TOPSIS framework incorporating a novel spherical distance measure to improve consistency, discrimination capability, and structural compatibility. The framework integrates Spherical Fuzzy Weighted Arithmetic Mean (SWAM) and Spherical Fuzzy Weighted Geometric Mean (SWGM) operators and evaluates robustness using Spearman rank correlation. Additionally, a coefficient of variation (CV)-based analysis is conducted to examine the dispersion of closeness coefficients. The applicability of the approach is demonstrated through a landfill site selection case; however, the main contribution lies in a generalized distance-based formulation applicable to various MCDM problems. Results show that the proposed distance improves agreement between aggregation operators, increasing correlation values from 0.905 to 0.976, while producing a more stable distribution of closeness coefficients. Overall, the study advances spherical fuzzy MCDM by introducing a geometrically consistent distance formulation. Full article

Vicarious calibration is a technique that makes use of radiometrically stable targets such as dry lakebeds, desert sites, and open grasslands for the post-launch calibration of a satellite sensor. Top-of-the-atmosphere radiances or reflectances are provided from those sites for the calibration of a sensor. The reflectance of a remote sensing vicarious calibration site is measured by ratioing the signal from a ground target to the signal from a reference target, often a white panel made of PTFE whose reflectance is known. When physically mapping a vicarious calibration site prior to a satellite sensor overflight, there can be elapsed times between the two measurements as great as 10 min. The solar illumination can vary on time scales relevant to the time between measurements of a ground target and a reference panel, impacting the variance in the measured reflectance. In this work, we explore the impact of a temporal delay between two measurements taken outdoors on the Type A uncertainties in their ratios. A factor of 3 reduction in the Coefficient of Variation of the ratio taken simultaneously versus sequentially with delays on the order of 10 min was realized. Implications for protocols employed to measure the surface reflectance at sites used for the vicarious calibration of aircraft and satellite sensors are discussed. Full article

Introduction: Placental malaria increases the risk of adverse birth outcomes. Current preventive measures are undermined by poor coverage, growing resistance to chemo-preventive and therapeutic drugs, and vector eliminating insecticides. Candidate placental malaria (PM) vaccines (PAMVAC and PRIMVAC) have shown safety and immunogenicity in Phase I trials, but empirical evidence on their potential population-level value is lacking. This study modelled the expected cost-effectiveness of a PM vaccine administered before pregnancy. Methods: A decision-analytic model compared two strategies from the provider’s perspective: vaccinating women of childbearing age versus no vaccination. The model incorporated gravidity-specific risks of PM, neonatal mortality and the malaria attributable fractions from the literature. Since the efficacy of a PM vaccine for malaria prevention is unknown, we assumed a 40% efficacy and varied this estimate widely in sensitivity analyses. Primary outcomes were incremental cost-effectiveness ratios (ICERs) per perinatal disability adjusted life years (DALYs) averted. Baseline, best-case, and worst-case scenarios were analysed. One-way and probabilistic sensitivity analyses were used to assess parameter uncertainty. Cost-effectiveness was defined as an ICER below half of sub- Saharan Africa’s 2025 GDP per capita ($1556). Results: The vaccine was most cost-effective among primigravidae. Under baseline assumptions (40% efficacy; 30% uptake; $5 dose price), the ICER was $321 per perinatal DALY averted for primigravidae versus $4444 for multigravidae. Best-case assumptions further improved cost-effectiveness ($225 vs. $3148). Sensitivity analyses showed robust cost-effectiveness for primigravidae across all plausible parameter ranges, while ICERs in multigravidae were highly sensitive to programme costs and vaccine efficacy. Cost-effectiveness acceptability curves demonstrated that vaccination becomes favourable for primigravidae at relatively low willingness-to-pay thresholds. Conclusions: A placental malaria vaccine delivered before pregnancy has high potential to be cost-effective in endemic areas when targeted to protect primigravidae. These findings support prioritised deployment strategies and highlight the value of early economic modelling to inform vaccine development and policy planning. Full article

Owing to multi-target tracking in scenarios with nonlinearity, uncertain measurement model and high clutter density, the Poisson multi-Bernoulli mixture (PMBM) recursion is prone to unstable covariance propagation under nonlinear dynamics as well as uncertainty in measurement-to-target association caused by mismatched gate that causes erroneous updates from clutters. In the prediction stage, the singular value decomposition (SVD) is used in place of Cholesky factorization to construct and propagate the square-root covariance factor in the square-root cubature Kalman filter (SCKF), yielding a numerically stable square-root implementation. Then, the resulting SVD-SCKF is incorporated into the PMBM prediction step and used to propagate the Gaussian-mixture components of both the Poisson point process (PPP) intensity and the Bernoulli component in the Multi-Bernoulli mixture (MBM), yielding predicted means and covariances under nonlinear dynamics. An adaptive fading factor is determined from innovation statistics, and covariance inflation is performed to improve robustness under target maneuvers and model mismatch. In the update stage, the unknown measurement function is regressed by Gaussian process (GP) using historical state–measurement samples, yielding an equivalent measurement mapping and state-dependent uncertainty. Furthermore, the predicted measurement distribution is generated from the GP-based conditional measurement distribution with state prior approximated by SVD-SCKF cubature points. An adaptive gate is determined from the GP-based conditional measurement distribution, which is approximated by an equivalent ellipsoidal gate via fitting for screening the current measurements and filtering out clutter. Residual in-gate clutter measurements are handled via Bayesian target discrimination, where the posterior probability of measurement originated from target is employed as a weight and incorporated into association weights and update likelihoods. Simulation results further confirm the effectiveness and stability of the proposed filter in complex scenarios. Full article

Energy transition includes the substitution of centralized energy systems with decentralized variable renewable energy sources (vRES), the growth of which brings drawbacks such as grid congestion and intermittency. These issues are increasingly troublesome in many local energy systems, including in The Netherlands. Biogas may provide options to provide backup renewable energy in times of energy supply uncertainty. In The Netherlands, the consideration of biogas in such functions is limited. Meanwhile, local energy initiatives (LEIs) are spearheading the adoption of vRES. Because of concern over local grid balancing, LEIs may want or need to innovate and diversify their activities. Such innovation could include bioenergy in general, and biogas specifically. However, only a small number of LEIs consider bioenergy, and Dutch LEIs seem hesitant to venture into biogas specifically. In this paper we explore the question of what hinders adoption of biogas in The Netherlands in general, and by LEIs specifically, deploying an approach based on the technological innovation systems (TIS) concept. In that approach, we take insights from current and expected policy in The Netherlands juxtaposed with insights from similar countries surrounding The Netherlands. We conclude that historic developments in biogas already created a moderately supportive platform for large-scale biogas development, but some essential factors remain inadequately developed. Key barriers to biogas innovation, especially for LEIs, are insufficient mobilization of financial and knowledge resources, and insufficient attention to alleviating preconceptions. Dependable support and attention for socio-economic factors in policymaking would improve conditions associated with resources, preconceptions and resistance, and the situation for LEIs to explore the potential of biogas. However, it remains uncertain whether such measures would be sufficient to improve the potential of local biogas utilization in The Netherlands in a way that opens a role for biogas in solving energy transition challenges such as energy system balancing. Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article

This study addresses the growing need for structured, measurable organizational development (OD) models amid digital transformation, geopolitical uncertainty, and increasing managerial complexity. Contemporary middle- and top-level managers are expected to ensure productivity, strategic clarity, resilience, and data-driven decision-making; however, existing leadership methodologies are often examined separately and lack integrated evaluation frameworks. The research analyses two prominent approaches: the American Action Science methodology and the Scandinavian (particularly Finnish) consensus-based leadership concept. While Action Science emphasizes explicit reasoning, double-loop learning, accountability, and measurable performance outcomes, the Finnish consensus model prioritizes trust, participation, and relational cohesion. The aim of the study is to develop and empirically test the original digital model LeadWinO (LEADership for WINning Organizations) for evaluating the organizational development activities of middle- and top-level managers. The model was empirically tested on managers in Lithuania. The novelty of the research lies in combining management and informatics perspectives by embedding organizational development evaluation into a digital, indicator-based, and potentially predictive framework. The type of study is quantitative research integrating questionnaire analysis in the case of multi-profile sections. Analytical tool used for data simulation is Feedforward Neural Network for constructing sufficient gapless sets of digitalized data. Research results showed that the American Action Science methodology is most effectively used by managers working in very small and small enterprises in the service and maintenance sectors. The findings are expected to contribute to the operationalization of leadership effectiveness under uncertainty and provide organizations with an auditable structure linking managerial behaviour, decision-making processes, and organizational performance outcomes. Full article