Search Results (1,573)

Atmospheric aerosol absorption is a key parameter for assessing aerosol effects on air quality and climate, yet the comparability of absorption measurements obtained with attenuation-based and offline filter-based techniques remains uncertain under real-world conditions. This issue is particularly relevant in complex urban coastal environments, where aerosols are influenced by traffic, shipping, industrial emissions, and marine air masses. Here, we investigate this methodological comparability through a multi-wavelength field intercomparison of the AE33 Aethalometer with two independent filter-based techniques, the Multi-Wavelength Absorption Analyzer (MWAA) and the Broadband Light Analyzer of Complex Aerosols (BLAnCA), at the Multedo urban coastal site in Genoa, Italy. The three techniques showed strong correlations across the ultraviolet, visible, and near-infrared spectral regions (R² = 0.93–0.99), with the closest agreement observed between MWAA and BLAnCA. AE33 reproduced the temporal variability in aerosol absorption well but systematically reported higher absorption coefficients than the two offline techniques, with differences of approximately 12–22% depending on wavelength. This systematic offset indicates that the default AE33 multiple-scattering correction may not fully represent the optical characteristics of the aerosol population sampled at this site. Absorption Ångström Exponent values derived independently from the three techniques remained close to unity, consistently suggesting a substantial influence of primary combustion emissions during the investigated campaign. Overall, the combined comparison of real-time and filter-based multi-wavelength techniques provides field-based evidence of their relative consistency and identifies a systematic AE33 bias that is relevant for improving the harmonization of aerosol absorption measurements in urban coastal monitoring environments. Full article

Over the past several decades, numerous methods have been developed to improve the signal-to-noise ratio, perceptual quality, and intelligibility of speech. In practice, no single method is universally optimal, as each category exhibits distinct strengths and limitations under specific acoustic conditions. The proposed taxonomy classifies speech enhancement methods according to their dominant signal modeling and enhancement mechanism, ranging from the classical signal processing approaches to modern machine learning techniques and advanced hybrid frameworks. The mathematical formulation illustrates the theoretical foundations of the different approaches, providing a clear understanding of their underlying principles and the evolution of performance across successive generations of speech enhancement methods. The comparative analysis demonstrates that statistical and subspace-based approaches offer low operational complexity but show limitations under highly nonstationary acoustic conditions. Adaptive filtering, transform-domain, and speech model-based methods exploit temporal, spectral, and speech production characteristics to achieve improved noise suppression, while perceptual methods improve subjective listening quality by incorporating psychoacoustic principles, thereby providing a more natural and intelligible listening experience. Machine learning-based techniques achieve excellent performance; however, they incur higher computational complexity and substantial training requirements. More recently, hybrid approaches have integrated complementary techniques from multiple paradigms, achieving robust performance under adverse acoustic environments. Overall, this review provides a unified perspective on speech enhancement techniques, identifies their strengths, and highlights emerging research opportunities for the development of robust, efficient, and intelligent speech enhancement systems. Full article

Predictive maintenance of railway wheelsets requires remaining useful life (RUL) estimates and treatment of model limitations. This study develops a Gamma-process-informed XGBoost surrogate for an ONCF fleet of 444 vehicles, 3552 candidate wheel positions, and 14 inspection campaigns. Flange width (Fw), flange height (Fh), and flange steepness (qR) were measured using a CALIPRI C42 gauge. After date normalisation and series-level IQR filtering, 3296 candidate series remained per indicator; Gamma eligibility retained 2366 Fw, 1628 Fh, and 2445 qR series. RUL quantiles were evaluated from the discrete Gamma first-passage distribution, with Monte Carlo used only for convergence checking. Vehicle-level median RUL was 13 months for Fw, 3 months for Fh, and 7 months for qR. RUL below six months occurred for 25/402, 283/344, and 100/404 vehicles, respectively. Under vehicle-grouped five-fold cross-validation, surrogate-label reconstruction yielded Pearson r values of 0.968, 0.759, and 0.964, with MAEs of 4.16, 1.67, and 2.82 months. Ablation confirmed strong target-feature coupling, while calibration and bootstrap diagnostics revealed substantial uncertainty. The framework supports model-conditional fleet screening, not validated real-world RUL forecasting or automatic maintenance decisions, until prospective temporal validation links scores to maintenance outcomes. Full article

Background: Modern lifestyles characterized by artificial light exposure, screen use, and altered sleep routines may be related to eating behaviors and dietary patterns relevant to the gut microbial environment. Although these factors have been investigated individually, limited evidence has examined self-reported light-exposure behaviors, sleep, eating behavior, and gut microbiota-related dietary patterns simultaneously within a single analytical framework. Objective: The aim of this study was to examine associations between five self-reported light-exposure behaviors and the global PSQI score, eating-behavior domains, and FFQ-GM-derived gut microbiota-related dietary pattern scores among adults in the recruited sample. Methods: A cross-sectional study was conducted among 518 adults aged 18–64 years recruited in Amman, Jordan, between April and May 2026 using convenience sampling. Participants completed the LEBA, the Arabic PSQI, the Arabic TFEQ-R18, and an Arabic-adapted gut microbiota-focused food frequency questionnaire (FFQ-GM). Associations were examined using Spearman’s rho and multivariable linear regression; multiplicity across the 25 LEBA factor–outcome coefficient tests was addressed using the Benjamini–Hochberg false discovery rate. Results: In the analyzed sample, 87.3% of participants had a global PSQI score > 5. Several LEBA factor–outcome coefficients had nominal p values < 0.05. Blue-light-filter use was positively associated with the global PSQI score (β = 0.29, p = 0.020), whereas phone use in bed (β = −0.43, p = 0.002) and light exposure before bedtime (β = −0.51, p = 0.009) were negatively associated with the global PSQI score. After false-discovery-rate adjustment across the 25 individual coefficient tests, most individual associations did not remain below the 0.05 threshold; therefore, these coefficient-level findings are interpreted as exploratory. Conclusions: Self-reported light-exposure behaviors were associated with questionnaire-derived sleep scores, eating-behavior domains, and FFQ-GM-derived gut microbiota-related dietary pattern scores. The cross-sectional design does not establish temporal direction, causality, or biological mechanism. Full article

Air quality modeling of sulfur dioxide (SO₂) concentrations remains challenging due to the high variability of both natural and anthropogenic emission sources, as well as the complexities associated with its multiphase chemistry. The data assimilation (DA) of satellite observations is a promising technique for constraining model uncertainties by combining the strengths of high-resolution and dense satellite retrievals with physical consistent model outputs. However, existing SO₂ DA applications have primarily focused on volcanic events while this study addresses them together with other emissions. The implementation of an SO₂ DA framework within the MINNI regional chemical transport model using an Ensemble Adjusted Kalman Filter (EAKF) via the DART framework is presented. The performances of the DA assimilation framework were tested using Sentinel-5P/TROPOMI SO₂-COBRA total column retrievals over continental Europe for August 2023. The filter constrained the ensemble variance to capture plumes from power plants and volcanic activity. The ensemble considered 20 members and perturbations of emissions and boundary conditions. On a monthly basis, the mean correction for the total column averaged over the domain was 2 × 10⁻⁵ mol m⁻², with localized maximum adjustments reaching 3.3 × 10⁻⁴ mol m⁻². At the surface level, domain-averaged corrections of concentrations reached up to 2.6 µg m⁻³. Despite current limitations related to ensemble size, static vertical localization, and the typical temporal fading of initial condition corrections, validation against in situ data confirmed the system’s ability to transfer column information to near-surface levels. These results demonstrate the feasibility and added value of integrating mixed-source SO₂ satellite retrievals into regional air quality simulations, contributing to more accurate, observation-driven atmospheric monitoring. Full article

Thailand has become a prominent destination for contemporary Chinese mobility through international education, employment, entrepreneurship, family relocation, retirement, and lifestyle-oriented movement. However, these pathways are usually studied separately, obscuring how education, work, legal status, digital infrastructures, everyday adaptation, and belonging interact over time. This article reports a structured integrative review of 53 empirical, theoretical, methodological, and policy sources identified through targeted searches of publisher platforms, scholarly discovery services, Thai journal repositories, and institutional sources, supplemented by citation chaining. The synthesis develops a framework of layered belonging under negotiated mobility. Mobility begins with aspirations and capabilities situated within a regional opportunity structure; educational and other entry pathways provide uneven access to linguistic, social, and professional resources; and the conversion of these resources is mediated by language, digital and interpersonal networks, institutional support, and everyday competence while being filtered through visa rules, work authorization, employer dependence, occupational access, and public narratives. Belonging is therefore differentiated across six interdependent dimensions: affective-place, relational-social, functional-everyday, professional-economic, legal-institutional, and future-temporal. This framework explains why migrants may feel emotionally at home and socially connected in Thailand while remaining legally temporary, professionally dependent, or uncertain about long-term residence. The review clarifies the added value of the framework relative to place-belongingness, integration domains, social anchoring, and differentiated embedding; proposes six empirically testable propositions; and identifies implications for universities, employers, public agencies, and migrant-serving organizations. Full article

Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these downstream functions are not evaluated directly here. This study reformulates UAV position prediction as a flight-wise, multi-horizon, three-dimensional forecasting problem and tests whether position, velocity, gravity-resolved acceleration, and quaternion-orientation histories improve predictive accuracy while measuring model-level edge-inference cost rather than end-to-end system latency. The dataset contains 3100 AirSim flights with high-rate kinematic, inertial, attitude, pressure, and magnetic-field measurements under variable horizontal wind. The reported generalization is flight-disjoint within one AirSim domain; route/scenario disjointness and transfer to physical UAVs are not established. Signals are converted to a common navigation frame, gravity-resolved, low-pass filtered, resampled to 50 Hz, and partitioned by flight identifier before normalization and window construction. Each learned model receives 2 s of history and predicts the complete next 1 s trajectory, with errors evaluated at 0.1, 0.5, and 1.0 s. The sensor-enriched LSTM (LSTM-PVAQ) is compared under matched conditions with persistence, constant-velocity, constant-acceleration, extended Kalman filter, reduced-feature LSTM, GRU, temporal convolutional network (TCN), and compact Transformer baselines. LSTM-PVAQ achieved 3D RMSE values of 0.043, 0.168, and 0.371 m at 0.1, 0.5, and 1.0 s, respectively. At 1 s, its RMSE was 21.7% lower than LSTM-PV, 13.1% lower than GRU-PVAQ, 9.3% lower than TCN-PVAQ, and 16.8% lower than Transformer-PVAQ. Its one-second ADE and FDE were 0.216 and 0.339 m. On a Raspberry Pi 5 CPU using one FP32 thread and batch size one, median neural forward-pass latency was 0.88 ms, well below the 20 ms model-update interval. The results show that gravity-resolved inertial and orientation histories improve multi-horizon prediction, while TCN-PVAQ remains an attractive lower-latency alternative. Full article

To address the coexistence of random measurement delays and intermittent data loss in multi-UAV cooperative navigation under weak communication scenarios, an adaptive cooperative localization method based on online diagnosis is proposed. A timestamp-driven diagnosis mechanism first identifies the physical reachability and temporal validity of cooperative measurements and classifies the channel state as normal, delayed, or lost. The estimator then adaptively switches between timestamp-diagnosed multi-step augmented filtering for delayed packets and Gray Wolf Optimizer (GWO)-optimized Gated Recurrent Unit (GRU) virtual measurement reconstruction for complete dropouts. In a four-UAV hybrid-anomaly simulation with continuous random delays and a 10 s communication blackout, the proposed method reduces the mean east and north RMSE by 69.1% and 75.2%, respectively, and lowers the mean horizontal-channel RMSE from 2.200 m to 0.608 m. Ablation experiments isolate the contribution of each module (diagnosis and multi-step delayed filtering and GWO-GRU virtual reconstruction), and 20 paired Monte Carlo runs with paired significance tests confirm the improvement. Relative to the strongest delay-aware baseline, the full-trajectory mean accuracy is comparable, and the main advantage of the proposed method is its robustness during complete communication outages and fast post-outage recovery. The simulation scenarios are driven by flight data collected with the DJI Matrice 350 RTK platform; the communication anomalies are included in the simulation, and all evaluations are performed offline. These results indicate that the proposed diagnosis-driven scheduling strategy can improve cooperative localization robustness when delay and data loss occur simultaneously. Full article

Persistent late-time variation can remain in physics-informed neural network (PINN) solutions after the governing transient has effectively decayed, while conventional error norms do not reveal whether this variation has a systematic temporal–frequency structure. This study develops an offline temporal–spectral diagnostic and postprocessing workflow for a one-dimensional advection–diffusion benchmark. A high-accuracy analytical reference and three fixed-resolution finite-difference baselines are used to assess a PINN whose architecture is selected by a fully supervised neural architecture search and whose parameters are trained with progressive temporal windowing. Candidate late-time intervals are selected without using the reference solution by applying the Bayesian information criterion (BIC) to a breakpoint model for the inter-reconstruction sensitivity; the selected field is subsequently reconstructed by retaining a prescribed fraction of its temporal spectral energy and is evaluated independently through reference-error and physics-consistency measures. For $[t_{\mathrm{cut}},t_{\max }]=[1.8,5]$, the zero-frequency component contains $0.9999996$ of the raw-field energy, so the $q=0.95$ reconstruction retains only the temporal mean. This projection reduces the final-time spatial error norm from $2.70\times {10}^{-3}$ to $1.06\times {10}^{-3}$, a factor of approximately $2.5$, while changing the discrete governing-equation residual by less than $0.3\%$ over the filtered window. Mean-removed tests for $q=0.90,0.95,0.99$ show that the discarded fluctuation is dominated by low-frequency approximation error rather than high-frequency noise. The result supports the proposed selection–validation workflow for this controlled benchmark but does not establish a universally transferable filter. Full article

Continuous monitoring of permafrost dynamics remains challenging in remote high-mountain environments due to logistical constraints, harsh climatic conditions, and the limited availability of spatially distributed observations. In addition to direct temperature observations in boreholes, Autonomous Electrical Resistivity Tomography (A-ERT) offers significant potential for long-term monitoring by providing high temporal resolution observations of subsurface electrical properties, which are highly sensitive to freeze/thaw processes. This study presents the field validation of a low-power A-ERT system designed for long-term autonomous operation in extreme environments. The system was deployed at a high-altitude permafrost site near the Kumtor gold mine in the Central Tien Shan, Kyrgyzstan, representing the first application of continuous A-ERT monitoring in the Central Asian mountain ranges. The system operated continuously under harsh environmental conditions with air temperatures as low as −30 °C. Data quality remained consistently high throughout the monitoring period, with less than 1% of measurements removed during filtering, and inversion results with root-mean-square errors generally ranging between 3% and 4%. Time-lapse resistivity observations revealed strong seasonal freeze–thaw dynamics within the active layer and continued seasonal resistivity variations within the underlying permafrost despite permanently frozen conditions. Analysis of depth-dependent resistivity–temperature relationships revealed increasingly pronounced hysteresis behavior below the active layer, indicating that subsurface electrical properties were not controlled solely by temperature. This behavior likely reflects variations in unfrozen water content and pore connectivity within the fine-grained permafrost, where liquid water can persist at sub-zero temperatures. The results demonstrate the capability of the A-ERT system for reliable long-term autonomous monitoring in remote permafrost environments and investigation of coupled thermal and hydrological processes in permafrost systems. Full article

Dryland shrub restoration is often assessed by vegetation recovery, yet belowground microbial organization remains less resolved. We analyzed bacterial 16S rRNA and fungal ITS amplicon sequence variants (ASVs) from four Caragana korshinskii plantations established 5, 10, 20 and 30 years before sampling. Within each plantation, five shrubs were sampled as within-plantation subsamples. Fine roots and rhizosphere samples were collected at 25–30 cm soil depth, while non-rhizosphere soils were collected at the same depth but 10 or 30 cm horizontally from the sampled root system. A soil-chemistry-matched subset defined a PCA-based edaphic axis (PC1 = 48.4%) that ordered the four sampled plantations and was associated with higher soil water content, lower electrical conductivity and lower total nitrogen and soil organic matter in the older plantations. ASV pools contracted from the soil to the rhizosphere and root compartments, indicating hierarchical filtering. Sample-level Bray–Curtis analyses showed strong microhabitat structuring, particularly for bacteria, while null-model analysis assigned most bacterial turnover to homogeneous selection (91.9%). Fungal turnover more often fell into the dispersal-limitation category (79.4%), but this ITS-based result should be interpreted cautiously. Rhizosphere candidate indicators and exploratory association-network summaries suggested differences among plantation age/site classes, including an apparent increase in retained associations in the 10-year plantation; however, these network comparisons are treated as hypothesis-generating because the network for each plantation age/site class was based on only five samples. Because each plantation age/site class was represented by a single plantation, plantation age is confounded with site, and all age-related patterns are interpreted descriptively within this chronosequence rather than as replicated temporal effects. Full article

The study introduces the Hybrid Anomaly-Filtered Spatio-Temporal Representation Network (HASTRN) to achieve accurate forecasting of short-term and day-ahead energy demand by utilizing smart-meter, solar PV, and weather-integrated data. Energy demand forecasting in residential buildings has become increasingly difficult due to varying loading patterns, solar irregularities, and the complexity of nonlinear relationships that exist between energy consumption and weather parameters. Conventional statistical and AI-based models have demonstrated a low capacity for managing anomalies, missing values, and temporal dependencies in the long run. To overcome these issues, the present paper creates a new hybrid approach to the problem, which combines multi-stage anomaly detection (Z-score, IQR, sliding-window), the Hybrid Correlation SVM Feature-Optimization Technique (HCSFOT) to perform advanced feature engineering, and a unified Deep Adaptive Spatio-Temporal Learning Network (DASTLN) network, which a combined Deep Neural Network (DNN), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) architecture to predict the energy demand. The data acquisition, preprocessing, feature optimization, development of hybrid models, and multi-horizon forecasting are the workflow components. The hierarchical nonlinear interactions, instantaneous load variations, and long-range temporal trends are well represented by the proposed HASTRN model. From the experiments’ outcomes, one can clearly see the superiority of the forecasting approach with respect to RMSE, MAE, MAPE, R² and solar variance metrics, indicating the effectiveness of the suggested HASTRN algorithm. The anticipated results are an increased stability of forecasting in the case of intermittency due to the sun, increased robustness in the presence of missing data, and an improved ability to manage the grid. The results make the HASTRN a good candidate for the next generations of smart energy-management systems. Full article

LiDAR–inertial odometry (LIO) is accurate in structurally rich environments but can become weakly observable in tunnels, stairways, and open terrain. This study introduces a degeneracy-aware, intensity-assisted LIO method that uses LiDAR reflectivity as an internal sensing modality without requiring a camera. Raw returns are projected onto a normalized panoramic intensity image, and image patches are selected according to their ability to complement the uninformative directions identified from the geometric information matrix. Point-to-plane, photometric, and inertial residuals are then fused in an iterated extended Kalman filter. Unlike fixed-scale intensity fusion, the proposed strategy adjusts the photometric residual scale according to the number of weak geometric directions and smooths this scale temporally. Experiments on the Newer College, ENWIDE, and GEODE datasets show that adaptive scaling reduces translational ATE RMSE, defined as the sequence-level root-mean-square of pose-wise translational absolute pose errors, by 11–40% on four ablation sequences. On GEODE-Stairs, the method obtains an ATE RMSE of 0.26 m, which is 46.9% lower than that of COIN-LIO. It also achieves the lowest ATE RMSE on each of the four tunnel sequences, with values ranging from 0.30 to 0.33 m. Mean processing times range from 15 to 25 ms per scan, corresponding to an average throughput of 40.0–66.7 scans/s on the evaluated platform. These results indicate that degeneracy-conditioned intensity fusion improves LIO robustness while maintaining average throughput compatible with real-time operation. Full article

Reliable near-coastal localization is difficult because LiDAR and scanning radar fail in complementary regimes: LiDAR provides precise local geometry but loses support over open water, whereas radar preserves long-range observations but suffers from clutter, multipath, and low spatial resolution. We present RACIF-SLAM, a reliability-aware framework that unifies continuous localization and hierarchical occupancy mapping. RACIF-SLAM transforms sensor-native motion increments into a common vessel frame, infers temporally filtered reliability from registration evidence, and applies an innovation gate to suppress the less credible modality under cross-sensor conflict before fusion on $\mathrm{SE}\left(2\right)$. When LiDAR tracking collapses, a radar bridge sustains motion estimation and re-anchors the recovered LiDAR segment without trajectory discontinuity. For mapping, LiDAR retains local occupied/free-space authority, while spatially broadened radar evidence extends support beyond the effective LiDAR range. In the four evaluated near-coastal segments, every reported pose was supported by at least one accepted sensor increment; the dead-reckoning fallback was not invoked. Against synchronized dual-antenna GNSS, RACIF-SLAM achieves a mean ATE of 0.700 m and a 10-frame translational RPE of 0.183 m, reductions of 66.3% and 65.1%, respectively, over the next-best evaluated method. These segment-specific results indicate that evidence-dependent authority transfer can improve localization continuity while retaining fine local map structure. Full article

A gain–phase-calibrated four-channel in-phase/quadrature (I/Q) fusion and recursive beat-frequency estimation framework is developed for a square He–Ne ring laser gyroscope. Calibration-derived channel parameters enable ordinary least-squares (OLS) fusion, while residual covariance estimation provides a generalized least-squares (GLS) extension. Known-ground-truth simulations verify the theoretical fusion gain, covariance propagation, and consistency of the recursive estimation framework. A chronologically partitioned experimental record is used for calibration, parameter tuning, and held-out validation. The proposed fusion improves spectral signal-to-noise ratio compared with individual channels, and the independently tuned extended and unscented Kalman filters achieve millihertz-level frequency estimation accuracy. Innovation analysis further reveals remaining carrier-synchronous temporal correlations, indicating the importance of more complete residual modeling for future stochastic refinement. Full article