Search Results (3,319)

Wildfires represent a major environmental hazard with significant impacts on ecosystems, climate, biodiversity, and human activities. The increasing frequency and intensity of wildfire events have highlighted the need for reliable and timely detection techniques based on satellite remote sensing. This study investigates the application of Proper Orthogonal Decomposition (POD) to thermal observations acquired from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite for wildfire anomaly detection. A wildfire event that occurred on 8 August 2021 in Calabria, Southern Italy, was selected as the primary case study. To assess the consistency of the POD response beyond the primary case, the analysis was further extended to two additional wildfire events, Viggianello–Abate and Pazzano–Montestella, using the 15 × 15 pixel extraction window. Middle Infrared (MIR, 3.9 $\mathsf{\mu }$m) observations collected at 15 min intervals over a complete day were analyzed using four different spatial extraction windows (3 × 3, 15 × 15, 30 × 30, and 45 × 45 pixels). POD was employed to separate dominant background thermal variability from localized fire-induced anomalies. The analysis focused on higher-order POD modes, particularly the 6th, 7th, and 8th modes, which exhibited enhanced sensitivity to wildfire activity. Results showed that POD successfully identified thermal anomalies corresponding to wildfire occurrence times independently detected by the RST-FIRES methodology. The comparison of extraction window sizes revealed that the 15 × 15 pixel window provided the best balance between anomaly enhancement, spatial localization, and noise reduction. Larger windows introduced excessive spatial smoothing and reduced localization capability, whereas the smallest window was more affected by noise. The findings demonstrate the potential of POD as an effective complementary approach for wildfire detection and monitoring using geostationary satellite observations. Full article

The increasing integration of renewable energy sources (RESs) into modern power grids has reduced the rotational inertia traditionally provided by synchronous generators. This reduction poses significant challenges for transient stability, frequency regulation, and the dynamic resilience of power systems. The problem is more pronounced in renewable-dominated hybrid microgrids, where inverter-based resources are progressively replacing conventional generation units. In this context, sudden disturbances can produce large frequency deviations, high RoCoF values, oscillatory behavior, and even loss of stability. This paper proposes an energy-aware adaptive virtual synchronous generator (VSG) control strategy coordinated with a model predictive control (MPC)-based energy-management layer for a battery energy-storage system (BESS), in order to enhance the transient stability of a hybrid microgrid with high renewable penetration. Unlike conventional fixed-parameter VSGs, the proposed MPC–VSG–BESS framework dynamically updates active-power references and adjusts the virtual inertia J and damping D parameters in real time. In this way, the framework seeks to reduce RoCoF, improve the frequency nadir, and preserve the BESS operating constraints, including its energy availability. The methodology is evaluated using detailed EMT simulations in MATLAB–Simulink^® R2023b under severe contingencies, including three-phase faults and sudden loss of conventional generation, with renewable-penetration levels ranging from 70% to 100%. Under the most severe 100% RES condition, the proposed MPC–VSG–BESS framework limits the frequency nadir to 59.26 Hz compared with 56.78 Hz for the conventional control case, corresponding to a 77.0% reduction in the magnitude of the frequency nadir deviation. The proposed controller also extends the critical clearing time from 0 ms to 185 ms under the considered severe fault condition, while maintaining a maximum observed RoCoF of approximately 0.32 Hz/s. During the transient response, the virtual inertia and damping reach maximum observed values of 48.5 kg·m² and 38.2 N·m·s/rad, respectively. Under a low initial SoC of 22%, the energy-aware constraint limits the admissible virtual inertia to 12.4 kg·m². The MPC implementation requires an average computation time of approximately 2.8 ms and a maximum of 6.5 ms for a 10 ms control interval, demonstrating computational feasibility within the adopted simulation framework. Full article

Bounded continuous data arise in many areas of applied probability and statistics, particularly when the underlying variable is restricted to a finite interval and the density is expected to vanish at the endpoints. This paper studies a parsimonious fixed-shape submodel of the Kummer–beta-G family, referred to as the asymmetric quadratic-exponential bounded generator model, abbreviated as AQEB-G. The model is obtained by fixing the two beta shape parameters at $a=b=2$, which yields the unit quadratic-exponential kernel $w_{\beta }\left(u\right)=u(1-u)exp(-\beta u)$, $0<u<1$, where $\beta \in \mathbb{R}$ is a dimensionless tilting parameter. Composing its normalized distribution function with an absolutely continuous baseline cdf G produces the corresponding fixed-shape Kummer–beta-G specialization on the support inherited from G. The bounded AQEB distribution on $(0,\alpha )$ is obtained by using the uniform baseline $G(x;\alpha )=x/\alpha$ and the endpoint-scale parametrization $\beta =\lambda \alpha$. We derive the cumulative distribution, density, survival, hazard and reversed hazard functions. We also show that, after standardization, the bounded AQEB model is exactly the natural exponential tilt of a beta$(2,2)$ distribution, and, hence, its version on $(0,\alpha )$ is a scaled exponentially tilted beta$(2,2)$ model. Several mathematical properties are obtained, including ordinary and incomplete moments, generating functions, entropy measures, Lorenz and Bonferroni curves, shape properties, stochastic representations and ordering results. Likelihood-based inference is also discussed with attention to the support-dependent endpoint parameter. The finite-sample behavior of the estimators is examined through a Monte Carlo simulation study, and two empirical applications illustrate the practical use of the AQEB model. In the examples considered, the AQEB model performs competitively relative to the evaluated alternatives according to the reported likelihood-based criteria, goodness-of-fit statistics, and residual diagnostics. Full article

Background: Pain is among the most frequent reasons for seeking medical care and presenting to the Emergency Department (ED). In Italy, Law No. 38/2010 establishes the right to pain therapy and requires pain intensity and its evolution during treatment to be assessed and documented. This process may be challenging in patients with language barriers because conventional verbal or numerical instruments require a degree of linguistic or numerical comprehension. Emoji-based visual tools may provide an additional means of communicating pain intensity in linguistically diverse clinical settings. Methods: We conducted a single-center, prospective, observational pilot study involving adult patients presenting to the ED with acute pain and a clinically relevant language barrier. Pain was assessed before and after analgesic treatment using a six-category Emoji-Based Pain Scale, scored 0, 2, 4, 6, 8, and 10, and the 11-point Numeric Rating Scale (NRS). Feasibility, independent completion, monotonic association, and agreement between the instruments were evaluated. Spearman’s rank correlation, exploratory Bland–Altman analysis, and quadratic-weighted Cohen’s kappa were calculated separately before and after treatment. A post hoc ancillary rank-ordering exercise was subsequently conducted in 50 adult volunteers using the same unlabelled and randomly shuffled emoji cards. Results: Eighty-seven ED patients were enrolled. The Emoji-Based Pain Scale was completed independently by 78 patients (89.7%), compared with 61 patients (70.1%) for the NRS. Mean pre-treatment scores were 4.90 ± 1.77 for the Emoji-Based Pain Scale and 5.15 ± 1.65 for the NRS; post-treatment scores were 2.16 ± 1.62 and 2.46 ± 1.73, respectively. Strong positive associations were observed before treatment (Spearman’s ρ = 0.860, p < 0.001) and after treatment (ρ = 0.805, p < 0.001). Mean paired differences were −0.25 points before treatment and −0.30 points after treatment. Quadratic-weighted kappa was 0.747 before treatment (95% bootstrap confidence interval [CI], 0.661–0.818) and 0.720 after treatment (95% bootstrap CI, 0.609–0.806). Exact categorical agreement was observed in 62.1% and 58.6% of assessments, respectively. In the ancillary exercise, all 50 volunteers reproduced the prespecified emoji sequence, with no inversion of the two upper anchors. Conclusions: The Emoji-Based Pain Scale was feasible and more frequently completed without assistance than the NRS in this selected population of adult ED patients with language barriers. Strong monotonic association, good ordinal agreement, and limited mean differences were observed between the instruments. These findings support further evaluation of the scale but do not establish formal validity, reliability, or interchangeability. Direct psychometric comparison with established visual pain scales remains necessary before routine clinical integration. Full article

This study presents a computational framework for determining the complete set of stabilizing PID controller parameters for high-order systems using the singular frequency decoupling method. By fixing the proportional gain K_p, the stability boundaries in the (K_i, K_d) plane are derived analytically, enabling automatic identification of stabilizing polygons without grid-based searches. The methodology is extended to uncertain plants through a vertex plant approach: stabilizing K_p intervals are computed for each vertex and intersected to yield a common solution, and the corresponding (K_i, K_d) polygons are intersected to guarantee robust stability. Results are visualized as 2D robust polygons and 3D stabilizing solids, providing designers with an efficient and reliable tool for robust PID synthesis. Full article

National estimates of the bioenergy potential of livestock manure still rest on static inventories that apply conversion yields to the gross mass of manure, ignoring the fraction that is actually recoverable under each production system and the substrate-specific nature of anaerobic co-digestion. This study develops a category-level predictive model of the technical biogas potential of livestock manure in Peru, projected to 2030, resolving seven livestock categories: dairy and non-dairy cattle, broilers and layers, swine, guinea pigs (Cavia porcellus) and goats. Recoverability is formulated as a time-dependent factor tracking the progressive confinement of production systems; the technical potential is bounded between a mono-digestion floor and a co-digestion ceiling constrained by a co-location criterion that admits synergy only when the co-substrate originates within the same production system; and a greenhouse-gas layer quantifies avoided emissions from methane conversion factors. All coefficients are reported as intervals and propagated jointly by Monte Carlo simulation. Two yield hypotheses are carried throughout: experimental biochemical methane potential and the IPCC-consistent digester ceiling. The national net technical potential is 1975 ${\mathrm{GWh}}_{\mathrm{p}}$${\mathrm{yr}}^{-1}$ under mono-digestion (731 ${\mathrm{GWh}}_{\mathrm{e}}$; 95% credible interval 1628–2404 ${\mathrm{GWh}}_{\mathrm{p}}$, Monte Carlo $N=\mathrm{100,000}$) and 2093 ${\mathrm{GWh}}_{\mathrm{p}}$${\mathrm{yr}}^{-1}$ under co-digestion. The gross approach exceeds the recoverable resource by a factor of 3.94, equivalently a contraction of 74.6% (95% CI 68.9–79.1%), and a symmetric Shapley decomposition attributes 76.1% of that contraction to recoverability rather than to conversion yield—a share that is stable across both yield hypotheses. The greenhouse-gas layer yields a result that reverses the expected policy ordering: broilers rank first by recoverable energy but last by avoided methane, whereas swine rank first by mitigation because liquid slurry storage is the only baseline system emitting enough methane for anaerobic digestion to displace. Below a digester leakage of 4.6%, methane abatement is negative at the national scale. Full article

Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LiFePO₄) batteries is challenging because the voltage feedback used for correction does not provide constant SOC-related information under different operating conditions. Conventional extended Kalman filters (EKFs) usually apply measurement correction based on predefined statistical assumptions, while overlooking variations in voltage-domain observability and innovation reliability. This paper proposes a hybrid estimation framework, termed observability- and innovation–reliability-regulated EKF with reliability-scaled residual learning (OIR-EKF-RSRL). The proposed method retains a first-order RC model and EKF as the physical estimation backbone, while regulating voltage correction according to local OCV-SOC sensitivity and normalized innovation reliability. A reliability-scaled residual learning module is further introduced after physical filtering to compensate for remaining SOC deviations rather than directly predicting SOC. The learned residual correction is modulated by a reliability-dependent scaling coefficient before fusion with the OIR-EKF estimate, after which the final SOC estimate is constrained to the physical interval [0, 1]. The framework is evaluated using the CALCE A123 LiFePO₄ dataset under a frozen temperature-disjoint train–validation–holdout protocol. On the independent holdout set, OIR-EKF-RSRL reduces the RMSE from 3.331 percentage points for the conventional EKF to 2.942 percentage points. The results demonstrate that reliability-aware measurement utilization and reliability-scaled residual compensation provide an interpretable solution for LiFePO₄ SOC estimation under varying voltage-information quality. Full article

Expert–public selection systems often reveal expert scores, institutional rules, and discrete elimination outcomes while concealing public-vote information. This paper develops a constrained Bayesian inverse-inference framework for inferring rule-compatible latent public-preference distributions. Public preferences are represented as simplex-valued latent variables, and rank-based, percentage-based, and judges’ save mechanisms are encoded through rule-induced likelihood constraints. We formally characterize the rule-compatible identified-set geometry: the percentage-rule set is a compact convex polytope, whereas the rank-based and judges’ save sets are finite unions of polyhedral rank cells. Prior-free sharp coordinate bounds are obtained by exact linear programming for all 170 percentage-rule weeks and by exact weak-order enumeration in representative rank and judges’ save cases. Posterior distributions are approximated using a mixed Metropolis–Hastings sampler whose transition kernel preserves the target posterior; the positive-probability global Dirichlet component provides irreducibility across separated positive-posterior-mass regions. In the primary 1800-case factorial synthetic study spanning two truth-generating mechanisms, three contestant-set sizes, and three institutional rules, marginal 95% credible-interval coverage was similar for the rule-constrained posterior (0.9400–0.9625) and a newly added prior-only benchmark (0.9475–0.9617), indicating that near-nominal coverage primarily reflects interval calibration rather than recovery accuracy. Conditioning on the observed elimination reduced the overall mean total-variation distance from 0.4613 to 0.4353 and increased the mean Spearman correlation from approximately zero (−0.0206) to 0.2147, although gains in Top-1 and Top-3 identification were not uniform under logistic-normal misspecification. A 54-case validation showed close agreement between vectorized rejection sampling and mixed Metropolis–Hastings sampling. In an empirical application to 34 seasons of Dancing with the Stars, multi-chain diagnostics provided no material evidence of non-convergence; posterior mean shares were stable under moderate specification changes, whereas rankings and uncertainty widths were more sensitive. Across 224 rule-constrained weeks, the Logistic Dynamic Weighting System agreed with percentage-based decisions in 86.66% of same-draw comparisons. The framework is therefore uncertainty-aware and institutionally transparent, rather than a method for exact vote recovery or a universally superior aggregation rule. Full article

Non-contact blood pressure (BP) estimation from remote photoplethysmography (rPPG) can yield unstable window-level predictions. This study combines a pulse-feature long short-term memory (LSTM) estimator with temporal variance-based prediction filtering. Fifty-two features from the rPPG waveform and its first and second derivatives were arranged into sliding-window sequences for systolic BP (SBP) and diastolic BP (DBP) estimation. The filter selects locally stable predictions from temporally ordered prediction sequences without using ground-truth BP values. Performance was first assessed in 10 repeated subject-dependent randomized-block experiments. Filtering reduced the SBP mean absolute error (MAE) from $7.04\pm 1.52$ to $5.34\pm 1.62$ mmHg and the DBP MAE from $4.63\pm 1.32$ to $3.07\pm 0.97$ mmHg while retaining $76.39\pm 7.59\%$ of predictions. A subject-specific mean predictor was also competitive under the subject-dependent setting and achieved lower DBP MAE at full coverage than the filtered LSTM. Chronological rolling-origin and leave-one-subject-out (LOSO) evaluations further showed that the improvement persisted for later intervals from the same subjects but not for unseen subjects. Because the filter operates only on model outputs, it requires neither additional trainable parameters nor model retraining. These findings show a low-overhead accuracy–coverage trade-off for within-subject rPPG-based BP estimation, while subject-independent generalization remains limited. Full article

Background: Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, fatigue, sleep disturbances, and cognitive and affective symptoms. Objectives: This study aimed to examine the acute effects of Resistance Training (RT) and Pilates on pain, functional impact, flexibility, and health-related quality of life in individuals with FM. Methods: In this randomized crossover pilot study, eight adults (7 females, 1 male; mean age: 48.5 ± 8.2 years) with a clinical diagnosis of FM participated. Each participant performed both RT and Pilates sessions in a randomized order. Outcomes were assessed at baseline and 24 h post-intervention using the Fibromyalgia Impact Questionnaire (FIQ), the 36-Item Short Form Health Survey (SF-36), the Visual Analog Scale (VAS) for pain, and the Wells sit-and-reach test (WELLS). Data were analyzed using two-way repeated-measures ANOVA and paired t-tests, with effect sizes (η²p and Cohen’s d) and 95% confidence intervals (95% CI). Statistical significance was set at p < 0.05. No formal sample size calculation was performed. Results: Participants presented a mean body mass index (BMI) of 27.4 ± 4.1 kg/m² and time since diagnosis of 6.2 ± 3.1 years. Within-subject variations were observed following both interventions. FIQ scores decreased at 24 h after both RT and Pilates sessions (RT: 52.21 ± 18.61; Pilates: 45.50 ± 18.63; p = 0.038, η²p = 0.42, 95% CI [−12.5, −1.2]). Conclusions: Both Resistance Training and Pilates acutely reduced overall disease impact in individuals with fibromyalgia, with Pilates promoting superior acute gains in posterior chain flexibility. Full article

Stabilisation of autonomous vehicle platoons becomes challenging in the presence of uncertain delays in actuator dynamics and vehicle-to-vehicle communication. This paper presents a distributed fractional-order proportional derivative (FOPD) control protocol for a platoon operating under various communication topologies, with bounded uncertainties in actuator lag and communication delay, respectively. It builds on an auxiliary function-based robust stability approach that provides a geometric interpretation of the delay-uncertain characteristic family and eliminates the need for exhaustive parameter gridding. This is followed by a numerical design procedure that evaluates the derived robust stability conditions on explicitly stated frequency grids. Numerical analysis of heterogeneous platoons indicates that the FOPD strategy produces bounded stable responses for the sampled uncertainty realisations and exhibits improved tracking behaviour compared with the integer-order PD baseline using the same numerical values of $K_P$ and $K_D$. It also provides better damping of oscillations and reduced tracking errors under uncertain operating conditions. Full article

Bearing damage caused by electric discharge machining (EDM) is a concern in electric ship propulsion drives, particularly during low-speed operations such as maneuvering and slow steaming. In a modular multilevel converter (MMC) operated with nearest-level modulation (NLM), rounding of the three-phase submodule insertion numbers produces a residual imbalance that appears as common-mode voltage (CMV) and charges the bearing film capacitance. The peak bearing voltage rises from 6.4 V at 60 Hz to 20.0 V at 10 Hz, while the thinning lubricant film lowers the dielectric breakdown threshold. Always-on CMV reduction approaches apply a corrected switching candidate in every control period, including intervals where the bearing voltage stays well below the threshold. This paper proposes a selective NLM correction driven by predicted bearing voltage risk: a reduced-order RC model predicts the bearing voltage the conventional NLM candidate would produce, and a hysteretic controller applies a zero-CMV candidate only when that prediction approaches the insulation threshold. Using a worst-case discharge criterion and thresholds of 5.9–29 V derived from elastohydrodynamic film thickness estimates, simulations at 10 Hz show that the method eliminates EDM events over the full evaluated threshold range. It achieves the same zero-EDM outcome as always-on correction while reducing the correction mode activation ratio from 100% to at most 30.8%, and remains inactive where conventional NLM is already safe. Full article

Background/Objectives: Human physiological evidence for functional clothing is limited, and garment changes may reflect ordinary material or measurement effects. We described surface-temperature and heart rate variability (HRV) observations before and after wearing black-silica-containing clothing and quantified paired changes and between-participant dispersion. Methods: Ten adults enrolled as healthy volunteers completed this single-center, non-randomized, unblinded, uncontrolled, fixed-order, single-group before–after pilot protocol. No physically matched control textile was used. No directional hypothesis or single primary outcome was prospectively specified. Exploratory domains comprised abdominal and leg surface temperature and eight RR interval (RRI)-derived HRV indices. All participants were analyzed; a post hoc n = 9 quality-control sensitivity analysis excluded one participant with a short post-wearing RRI segment. Effect estimates, 95% confidence intervals (CIs), and Holm-adjusted p values were reported. Between-participant dispersion was secondary and exploratory. Results: Leg surface temperature showed a modest increase of 0.668 °C (95% CI −0.001 to 1.336; raw p = 0.050; Holm p = 0.100); abdominal temperature changed by 0.007 °C (95% CI −0.447 to 0.462). No paired HRV outcome retained support after correction (all Holm p ≥ 0.797). In the secondary dispersion analysis, total power had an after/before log-scale SD ratio of 0.612 (bootstrap 95% CI 0.347 to 0.861; Holm p = 0.031); no dispersion outcome retained support in the n = 9 sensitivity analysis. Conclusions: This small uncontrolled pilot provides hypothesis-generating observations but cannot isolate an effect attributable specifically to black silica or demonstrate autonomic benefit, therapeutic action, or product efficacy. Confirmation requires an adequately powered, randomized, participant-blinded crossover study using physically matched garments and standardized measurement conditions. Full article

Integrated Water Vapor (IWV) is a key indicator of atmospheric moisture, and its accurate quantification requires precise characterization of the errors inherent to each observing system, a task complicated by the absence of a true, error-free atmospheric reference. This study characterizes the absolute error structures of IWV retrievals from Global Navigation Satellite Systems (GNSS), Microwave Radiometers (MWR), and Radiosonde using a temporally collocated dataset of 326 trivariate samples acquired in Nicosia, Cyprus over a complete annual cycle (March 2025–March 2026). Triple Collocation Analysis (TCA) and the Three-Cornered Hat (TCH) method were applied without assuming a ground truth, with non-parametric bootstrap resampling (10,000 iterations) used to derive 95% confidence intervals and flag statistically unstable estimates. During the dry season, the recovered error ordering was radiosonde (0.38 kg m⁻²) below MWR (0.60 kg m⁻²) and GNSS (1.69 kg m⁻²), which is consistent with the a priori budget. The MWR error rose sharply to 5.79 kg m⁻² in the wet season and to 8.91 kg m⁻² in the highest moisture bin, consistent with degradation of the K-band retrieval in the presence of liquid water. The wet season value is a lower bound, since any positive error covariance between the radiometer and the radiosonde acts to increase it, whereas the GNSS error approximately doubled to 3.53 kg m⁻², indicating comparatively greater resilience across weather regimes. The radiosonde error, lowest of the three sensors in dry conditions (0.38 kg m⁻²), could not be reliably resolved in the wet season (98.8% truncation), the lowest moisture bin (71.4%), or the highest moisture bin (94.5%), and it is reported as unstable in each. These results indicate a substantial degradation in radiometric retrieval accuracy and identify the specific regimes in which trivariate error decomposition itself becomes statistically unreliable. Full article

Deep metal mines commonly contain vertically stacked goafs whose geometry and rock mass properties are incompletely documented. This study evaluates a field-constrained screening framework for high-displacement material scenarios at the Lehong Pb-Zn mine. The framework combines a Latin hypercube sampling (LHS)-FLAC3D response library with static Bayesian inference. Evidence comprised 93 goaf records, 186 Mathews exposed-surface assessments, laboratory constraints, and 40 archived numerical scenarios, whose maximum downward displacement ranged from 4.48 to 31.75 cm. Friction angle φ showed the strongest marginal Pearson correlation with displacement (r = −0.81), followed by cohesion (r = −0.55) and elastic modulus (r = −0.22). At the response library Q75 threshold of 15.04 cm, the Laplace-smoothed probability increased from 0.262 (95% credible interval, 0.142–0.403) across all scenarios to 0.600 (0.352–0.824) under joint cohesion–friction angle degradation. However, the archived design was not an ideal 40-point LHS, and bootstrap resampling retained the scenario ordering in only 45.8–54.8% of replicates. All 93 inventory identifiers matched the Mathews stability table, enabling reproducible site-level triage when Bayesian network results are combined with treatment and stability evidence. The framework is an exploratory screening tool rather than an absolute failure probability model, collapse propagation model, or dynamic early warning system. Full article