Search Results (1,697)

Background/Objectives: The goal of this study was to characterize changes in autofluorescence of epithelial cells obtained from saliva stains that occur with time and investigate the potential for these changes to serve as time-since-deposition (TSD) signatures for this sample type. Methods: Saliva from 50 individuals was used to create 208 deposits that were aged between one day and nine months. Autofluorescence profiles of individual cells were obtained from each sample using imaging flow cytometry (IFC) and analyzed across nine different emission channels ranging between 435 nm and 800 nm. Results: Results showed strong evidence for linear increases in autofluorescence intensity when epithelial cells from a single donor deposit were measured over time (12 of 14 donors r ≥ 0.9). When autofluorescence profiles from all 50 donors were combined into a single time series, variation in autofluorescence intensity was observed between individual deposits with the same TSD. This inter-contributor variation decreased the overall strength of the linear relationship (r = 0.83) and yielded residual errors of ~8 days for samples that were actually 1 day old and ~82 days for samples that were over 180 days old using a linear regression model. Although this approach may not currently be amenable to estimating TSD to the day with high accuracy, clear, non-overlapping differences in autofluorescence intensity were still observed between certain time intervals, e.g., saliva deposits that were aged for 1 day compared to saliva deposits that were aged for more than 120 days. Conclusions: This suggests that cellular autofluorescence signatures have the potential to be probative when hypotheses for sample deposition involve disparate time intervals or as a screening tool for identifying which samples are most likely relevant to the crime in question based on their deposition time. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Ozone monitoring in laboratory environments is essential for ensuring personnel safety and maintaining stable experimental conditions, particularly in enclosed facilities where ozone may accumulate during high-energy radiation operations. This study investigates the short-term prediction of ozone concentration using data obtained from a sensor-based ozone monitoring system deployed at the National Synchrotron Radiation Research Center (NSRRC). Ozone concentration measurements were collected using a UV absorption-based ozone analyzer and analyzed as a time-series dataset under controlled experimental conditions. Three forecasting models—Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and linear regression—were evaluated for short-term ozone concentration prediction. Experimental results indicate that the ARIMA model provides superior predictive performance for the small-sample dataset used in this study. In the Right direction, ARIMA achieved R² values of 89.5%, 86.3%, and 81.1% at distances of 5 cm, 10 cm, and 15 cm, respectively, while also demonstrating stable performance in the Up direction. The results highlight the effectiveness of classical time-series models for sensor data analysis in environments with limited sensing data. The proposed framework demonstrates the potential of integrating sensing devices with predictive data analytics to support real-time environmental monitoring and safety management in laboratory facilities. Full article

Dam monitoring data are nonlinear and nonstationary time series. Most existing data-driven dam displacement models are developed independently for each measuring point, disregarding the fact that a dam is a complex structure composed of various interconnected elements that form a unified whole. Regardless of the dam type, all points on the dam are exposed to the same external environmental influences. To account for the correlation between displacement time series at different points, this paper proposes a novel fine-tuned deep-learning nonlinear autoregressive (NAR) model based on a Long Short-Term Memory (LSTM) network for predicting dam tangential displacement, and a new method for generating source data to train the base model. The models for three measuring points were developed and tested on experimental data collected over a period of slightly more than twelve years. Compared with the model without fine-tuning, the proposed approach achieves an average mean square error (MSE) reduction of 80.68% on the training set and 65.79% on the test set, as well as an average mean absolute error (MAE) reduction of 51.05% and 52.62%, respectively. Furthermore, the proposed model outperforms Random Forest (RF), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) models for dam displacement prediction. Full article

Background/Objectives: The clinical utility of summarizing long-term C-reactive protein (CRP) trends with a single mean remains unclear. We systematically characterized annual changes in CRP test volume and CRP level distributions using large-scale laboratory data collected at Dankook University Hospital (2008–2023) across the coronavirus 2019 pandemic period. Methods: Overall, 1,845,258 CRP values were analyzed; annual arithmetic, harmonic, and geometric means were calculated; long-term trends were assessed using weighted least squares (WLS) regression weighted by annual test volume; and temporal changes around the pandemic period were examined using a WLS-based interrupted time-series (ITS) segmented model with a prespecified 2020 break. Results: The annual test volume rose from 2008 to 2013 and 2019, dropped in 2020, increased in 2022, and declined in 2023. The arithmetic mean showed no long-term trend, whereas the harmonic and geometric means declined. ITS models exhibited no statistically significant immediate level-change term in 2020; however, post-2020 slope changes indicated a decline in the arithmetic mean and attenuation of the prior decline in the harmonic mean. As only four annual observations were available after 2020, these post-2020 trend estimates should be interpreted cautiously. Conclusions: Within this single-center tertiary-care dataset, different CRP summary measures showed different long-term patterns and post-2020 trend changes, without evidence of an abrupt shift in 2020, suggesting stratum-specific shifts that may be invisible to arithmetic mean-based surveillance. These findings are best interpreted as institution-specific and hypothesis-generating, and broader interpretive or operational implications require validation in multicenter settings with differing case-mix and care structures. Full article

Digital tax reforms are implemented through staged, publicly announced milestones, yet policymakers rarely have timely indicators of whether these signals mobilize information-seeking and whether such demand can be anticipated for operational planning. We analyze monthly Google Trends series for Greece’s myDATA/e-invoicing rollout (2016–present) using preregistered event study models that separate step changes from post-event trend shifts with HAC-robust inference, and we evaluate 1–3-month predictive performance via rolling-origin cross-validation against a seasonal-naïve benchmark. Search-based attention shifts appeared most clearly in application-related queries: invoicing app terms spike around visible rollout phases (≈+34 to +38 index points over six months) and decline around VAT–myDATA alignment (≈−34 to −43). Ecosystem attention (the “Electronic invoicing” topic) exhibits large, opposite-signed movements (≈−53 around public-sector expansion; ≈+46 around VAT alignment), whereas platform terms show smaller and less regular responses; a back-office milestone produces no detectable change. In out-of-sample tests, event-aware regressions improve short-horizon accuracy for platform terms (≈40–50% MAE reduction at one month; ≈18–32% at two to three months), with series- and horizon-dependent results elsewhere. Overall, the evidence supports using search activity as an intermediate planning signal—informative about when and where guidance demand concentrates but not evidence of compliance. Full article

Background: Cardiometabolic diseases are a leading cause of premature mortality globally, yet longitudinal national mortality patterns remain insufficiently characterised in Gulf Cooperation Council settings. This study examines national trends in cardiometabolic mortality alongside health system financing, capacity, and utilization in Kuwait between 2010 and 2022. Methods: A national ecological time-series analysis used Ministry of Health administrative data covering mortality, cardiac care unit (CCU) capacity and discharges, cardiovascular procedural volumes, and MOH expenditure. Cause-specific outcomes included circulatory disease, ischaemic heart disease (IHD), cerebrovascular disease, hypertensive disease, and diabetes mellitus. Ordinary least squares regression estimated annual trends; pre-COVID restricted models (2010–2019) separated secular from pandemic-period effects. Results: All-cause deaths rose significantly from 5448 (2010) to 8041 (2022; β = +373.5/year; p = 0.001), peaking at 10,938 in 2021. Circulatory disease mortality rates increased over the full series but not pre-COVID, indicating pandemic-era acceleration. IHD death counts rose significantly in both models (β = +68.4 and +67.0/year; p < 0.01); IHD rates showed no significant trend, implicating demographic growth. Diabetes demonstrated the strongest signal: significant increases in death counts (β = +36.5/year; p < 0.001) and mortality rates (β = +0.689/100,000/year; p = 0.002), rising progressively across all time blocks. Hypertensive mortality declined significantly (β = −0.113/year; p = 0.002). MOH expenditure, CCU capacity, and CCU discharges increased significantly, demonstrating sustained structural expansion of cardiovascular services. Conclusions: Rising cardiometabolic mortality—driven prominently by diabetes—occurred alongside sustained health system expansion in Kuwait, indicating that tertiary capacity growth alone is insufficient to offset underlying epidemiological pressures. These findings underscore the urgency of strengthening upstream cardiometabolic prevention, integrated diabetes surveillance, and long-term metabolic risk control as central pillars of sustainable NCD policy. Full article

The anomalous logging responses caused by complex geological and downhole engineering conditions, which can be the expansion of a borehole, the formation of fractures, and the mud intrusion, usually result in the absence of some important curves and undermine the accuracy of the reservoir evaluation. The strong nonlinearity and non-stationarity of the log curves remain problematic to conventional interpolation and statistical techniques; the traditional models do not take into account any sequential relationship between points along the depth axis, whereas the deep sequence models can only regress on the points, which limits their capability of ensuring the overall geological consistency. In order to resolve these difficulties, this paper introduces a Geology-Balanced Time Series Conditional Generative Adversarial Network (GC-TSGAN) in which the lithological data is converted into an initial state in the form of prior conditions and is input into both the generator and the discriminator. The model uses LSTM to learn depth-sequential dependencies and a BCE GAN-based adversarial loss to achieve distributional consistency and local morphological fidelity. Hyperparameter tuning is used with the help of random search and Bayesian optimization. The logging data of 41 wells in the B Basin, Chad, are experimented using GC-TSGAN alongside baseline models such as RF, XGBoost, LSTM and ANN; GC-TSGAN is proven to be much better than baseline models in terms of the RMSE, MAE, and squares of predicate and value. The findings confirm that the proposed model can effectively reconstruct log curves with high precision even in a complicated geological environment, thereby providing quality data for performing geological modeling and evaluating the reservoirs. Full article

A neural network simulation–regression method was developed to assess the impact of sanctions on the level of administrative offences under fragmented, noisy, and short administrative time series. The study addresses the problem of quantifying and predicting changes at the offence level as a sanction size function, using detection probability, prior violation level, compliance costs, and auxiliary contextual factors. The proposed framework combines a hybrid MLP–LSTM neural network, double machine learning-based orthogonal causal estimation, the simulation-based generation of counterfactual scenarios through domain randomization, multiple imputation for missing data, debiasing procedures, and ensemble uncertainty estimation. The contribution to administrative law consists of a quantitative tool creation for substantiating and optimising sanction policy, assessing heterogeneous effects, and supporting evidence-based rulemaking and law enforcement decisions. In comparative experiments, the developed method achieved an RMSE of 8…12%, a prediction accuracy of 93…96%, an overall accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 93.5%, thereby outperforming contemporary econometric, simulation, causal machine learning, and predictive machine learning approaches used for sanction effect modelling. Additional verification through nonparametric statistical testing confirmed that the proposed model’s superiority over the compared algorithms is statistically significant across the main quality metrics, which strengthens the evidence for its robustness and practical value in sanction policy analysis under fragmented administrative data conditions. Full article

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

In the context of the coordinated development of high-proportion renewable energy integration and alternating current/direct current (AC/DC) hybrid transmission, the sending-end power grid faces challenges such as decreased system strength, contracted stability boundaries, and difficulties in covering high-risk operating conditions. This paper proposes a new renewable energy scale impact analysis method that integrates “typical scenario construction-scale ladder comparison–prediction-driven time series injection” in response to the operational constraints of AC/DC transmission. In terms of method implementation, firstly, a two-layer typical scenario system is constructed under unified transmission constraints and fixed grid boundaries: A regular benchmark scenario covers the main operating range, and a set of high-risk scenarios near the boundaries is obtained through multi-objective intelligent search, which is then refined through clustering to form a computable stress-test scenario library. Here, the boundary scenarios are generated by a multi-objective search that simultaneously drives multiple key section load rates towards their limits, subject to AC power-flow feasibility and operational constraints, and the resulting Pareto candidates are reduced into a compact stress-test library by clustering. Secondly, a ladder scenario with increasing renewable energy scale is constructed, and cross-scale comparisons are carried out within the same scenario system to extract the scale effect and critical laws of key safety indicators. Finally, data resampling and Gated Recurrent Unit multi-step prediction are introduced to generate wind power output time series, enabling the temporal mapping of prediction results to scenario injection quantities, and constructing a closed-loop input interface of “prediction–scenario–grid indicators”. The results demonstrate that the proposed hierarchical framework, under unified AC/DC export constraints, can effectively construct a compact stress-test scenario library with enhanced boundary-risk coverage and can reveal how transient voltage security evolves across renewable expansion scales. By coupling boundary-oriented scenario construction, cross-scale comparable assessment, and forecasting-driven time series injection, the framework improves engineering interpretability and practical applicability compared with conventional scenario sampling/reduction workflows. For the forecasting module, the Gated Recurrent Unit (GRU) model achieves MAPE = 8.58% and RMSE = 104.32 kW on the test set, outperforming Linear Regression (LR)/Random Forest (RF)/Support Vector Regression (SVR) in multi-step ahead prediction. Full article

Lago Argentino and Lago Viedma are large lakes fed by glaciers in Southern Patagonia, characterized by extraordinarily strong, persistent westerly winds and sharp gradients in regional relief, climate, and gravity field. We present operational models of spatio-temporal lake-level variations that represent instantaneous ellipsoidal lake-surface height as the superposition of three components: (i) a time-averaged lake-level topography derived from geoid modeling and ICESat-2 residuals, (ii) temporally varying water-volume changes in the lake estimated from tide gauge time series corrected for atmospherically driven perturbations, and (iii) a static hydrodynamic response to wind stress and air-pressure forcing. The atmospheric response is parametrized through empirically derived transfer functions obtained by regressing instantaneous lake-level anomalies against ERA5 wind and pressure fields, capturing wind-driven tilting. Standard deviations of ICESat-2 ATL13 elevations amount to 106 cm and 70 cm over Lago Argentino and Lago Viedma, respectively. The subtraction of our models reduces these standard deviations to 8 cm (Argentino) and 14 cm (Viedma). Surface waves incompletely averaged out within ICESat-2’s narrow footprint are identified as a principal source for the residual variability. A standard deviation of ATL13 elevations below 2 cm on calm days demonstrates ICESat-2’s unprecedented capability of monitoring water resources from space in a region of sparse hydrological infrastructure. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

Forecasting crop pest outbreaks under conditions of increasing agroclimatic variability is a critical task for intelligent decision support systems in agriculture. Traditional statistical and empirical models typically have limited transferability and insufficient accuracy when describing nonlinear and multiscale relationships between climatic factors and pest population dynamics. This paper proposes a hybrid algorithm combining wavelet analysis and deep learning methods for forecasting agroclimatic pest infestation levels. The algorithm is based on multiscale decomposition of time series using a discrete wavelet transform, after which the extracted components are used as input features for a deep neural network implementing a nonlinear mapping between climatic parameters and infestation indicators. The developed computational framework includes the stages of data preprocessing, feature space formation, model training, and forecast generation in a single, reproducible pipeline. An experimental evaluation using long-term agroclimatic and phytosanitary data showed that the proposed algorithm outperforms classical regression and individual neural network models in terms of RMSE, MAE, and the coefficient of determination. The results confirm the effectiveness of integrating wavelet analysis and deep learning for developing phytosanitary risk forecasting algorithms and demonstrate the potential of the proposed approach for implementation in intelligent precision farming systems. Full article