As pharmaceutical manufacturing shifts towards continuous processing and increasingly potent, low-dose Active Pharmaceutical Ingredient (API) products, there is a growing need for more accurate and reliable Process Analytical Technology (PAT). This study presents a direct, side-by-side comparison of Near-Infrared (NIR) spectroscopy and Near-Infrared Spatially Resolved Spectroscopy (NIR-SRS) for API concentration monitoring within a Continuous Direct Compression (CDC) process, using a Feed Frame Simulator (FFSIM). Both probes simultaneously measured moving powder under dynamic conditions to assess their modelling performance. Instrument settings, including the number of spectral averages, were selected to ensure the consistent measurement of a unit-dose for both instruments. The resulting datasets were processed and Partial Least Squares (PLS) models were built. Root Mean Square Error (RMSE) values indicate that NIR-SRS performed comparably to NIR, resulting in similar error values and marginally higher concentration detection limits; however, its narrower wavelength range currently limits its suitability for drug loading quantification and modelling, particularly for low-dose formulations where broader spectral coverage is critical to enhance model robustness and sensitivity.

Accurate assessment of body composition is essential for understanding human physiology and health risks and developing personalized medical strategies. Traditional approaches, such as single-slice segmentation at the L3 vertebra, often fail to capture the complexities of whole-body tissue distribution, which is influenced by genetics, metabolism, environment, and physiology. To address these limitations, we present a novel methodology to estimate whole-body composition using sub-body anatomical region computed tomography (CT) scans. For this study, a cohort of 101 subjects with plasmacytoma cancer who underwent whole-body (head-to-toe) CT scans was utilized. Using in-house segmentation software, skeletal muscle (SKM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT) were quantified. Generally, sub-bodies were either individual regions, such as the chest (CHE), abdomen (ABD), and pelvis (PLV), or combined regions, including chest and abdomen (CHA) and abdomen and pelvis (ABP), as defined according to standard vertebral landmarks. A multivariate linear regression model incorporating demographic and CT-derived features is developed to predict whole-body tissue volumes. Tenfold cross-validation was used to validate the performance of the prediction models, and evaluation metrics included the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), 95% confidence intervals across folds, and the coefficient of determination (R[2]). For individual regions, SKM demonstrates strong performance with R[2] values from 0.83 to 0.91 and low MAE (e.g., chest 1.14 ± 0.83 L, R[2] = 0.913). SAT performs best in the PLV region (R[2] = 0.918, MAE = 1.58 ± 1.28 L), while VAT achieves its highest single-region performance in the ABD region (R[2] = 0.950, MAE = 0.37 ± 0.35 L). IMAT shows small MAE values (e.g., chest 0.26 ± 0.21 L) but relatively low R[2], with the highest predictability obtained in the CHE region (R[2] = 0.822). For combined regions, SKM performs best in the CHA region (R[2] = 0.924, MAE = 1.01 ± 0.84 L), while SAT improves in the ABP region (R[2] = 0.923, MAE = 1.45 ± 1.35 L). VAT shows consistently excellent performance across both combined regions, with similar R[2] values (CHA 0.977, ABP 0.981) and low MAE. Broader anatomical coverage improved predictions for all four tissues, with the largest gains observed for VAT (mean single-region R[2] 0.84 vs combined 0.98) and SAT (0.88 vs 0.92).

This paper analyzes the long-run associations between environmental, financial, macroeconomic, and healthcare factors on life expectancy in E7 countries over the period 1990-2023. Special emphasis is placed on PM2.5 air pollution exposure, which has become one of the key environmental factors influencing population health and longevity. The analysis examines the effects of domestic credit to the private sector (DCPS), foreign direct investment (FDI), inflation (INF), exposure to PM2.5 air pollution (PM2.5), immunization coverage (IMM), and hospital beds per 1,000 people (HBEDS) using life expectancy (LE) as the dependent variable. Second-generation panel unit root and cointegration tests are used in the empirical framework. FMOLS and DOLS estimators are then used. The CCEMG estimator and FE-DKSE are used to evaluate robustness. The findings show a stable long-run connection between the variables. Financial development and FDI are positively associated with LE, but inflation and PM2.5 exposure are negatively associated with LE. Immunization coverage is generally positively associated with life expectancy, while hospital beds are negatively associated with LE. The robustness estimations generally support the baseline findings. This study adds to existing research by examining how finances, economy, environment, and health care impact LE in the E7 countries. It takes into account the connections and differences between these nations. The findings highlight that financial growth, economic stability, clean environments, and preventive healthcare play crucial roles in long-term health outcomes for large emerging economies.

High and stable maize yields are central to ensuring global food security. However, canopy growth differentiation caused by traditional water and fertilizer management patterns remains a key bottleneck constraining maize productivity improvement. This study aimed to elucidate the regulatory effects of water-fertilizer integration on maize canopy uniformity and yield.

Background/Objectives: Root canal treatment (RCT) may be required after full-coverage crown placement in initially vital teeth due to biological and restorative factors affecting pulp vitality. However, the timing and distribution of clinical variables associated with teeth requiring RCT after crown placement remain insufficiently described. The aim of this study was to evaluate this timing and distribution using retrospective data collected between 2011 and 2024. Methods: This study included 588 vital teeth treated with metal-ceramic or zirconia crowns between 2011 and 2024. Recorded variables were age, gender, tooth type and location, crown material, bruxism, number of abutment teeth, and time to RCT. Statistical analyses were performed using SPSS v25.0, and as data were not normally distributed (Shapiro-Wilk test), Mann-Whitney U, Kruskal-Wallis, and chi-square or Fisher's exact tests were used (p < 0.05). Results: Of the 588 teeth, 36.1% were from male and 63.9% from female patients, and most crowns were metal-ceramic (80.8%), followed by zirconia (19.2%). Significant associations were observed between tooth group and jaw location (p < 0.001), number of abutment teeth (p < 0.001), and crown material type (p < 0.001). RCT was most frequently recorded during the fifth year after crown placement (52.6% of cases), with a mean time of 4.13 ± 1.79 years. Tooth extraction was observed in 5.3% of teeth. Conclusions: This study provides descriptive information regarding the distribution and timing of RCT in initially vital teeth following fixed prosthodontic restorations. The findings should be interpreted as a characterization of affected cases rather than as evidence of risk factors or predictors of RCT occurrence.

Ride-hailing global positioning system (GPS) mobile probe data provide flexible urban traffic observations, but their sparse and uneven coverage makes model evaluation difficult because observed targets, valid predictions, and historical input support do not always coincide. This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing.

This retrospective study evaluated the anatomical variations and morphological interrelationships of mandibular anterior, premolar, and first molar (MFM) teeth in a Turkish subpopulation using cone-beam computed tomography (CBCT), while categorizing and comparing root canal anatomies based on both the traditional Vertucci and contemporary Ahmed's classification systems. The study analyzed CBCT images of 9,504 mandibular teeth from 792 patients. Root canal configurations were classified utilizing both the Vertucci and Ahmed's systems. The study additionally evaluated root counts, distolingual root (DLR) presence in MFMs, bilateral agreement, and cross-group anatomical correlations. All anterior teeth were single-rooted but displayed significant internal morphological diversity. MFMs predominantly featured a two-rooted structure, with a DLR (Radix Entomolaris) observed in 5.3%-6.8% of cases. A significant positive correlation emerged between complex canal configurations in lateral incisors and the presence of an ipsilateral MFM DLR (p = 0.003, OR = 2.23). Males exhibited significantly higher anatomical complexity in premolars than females. Mandibular teeth demonstrate high morphological diversity and bilateral agreement. The statistically significant but weak correlation between lateral incisor canal complexity and MFM DLR presence may represent a preliminary indicator that requires further validation. Furthermore, the Ahmed's system provides broader descriptive coverage for for defining complex root canal anatomies compared to the traditional Vertucci classification.

Accurate environmental and climate modelling depends on the availability of large observational datasets, yet the generation of such data is often costly and logistically challenging for individual research groups. This limitation is particularly acute in New Zealand, where geographic isolation, complex topography, and strong climatic gradients require dense observational networks for robust national and regional scale modelling. Here we present New Zealand's national precipitation isotope database, compiled through a community data-collection effort. The database integrates historical and contemporary datasets with varied analytical methods, sampling strategies, and temporal coverage. To enable combined use of these data, we describe a standardisation framework, including weighting using gridded precipitation data, and alignment of sampling periods to calendar months. We then show how the database updates and improves the Precipitation Isotopes New Zealand (PINZ) machine-learning isoscapes. Incorporation of newly contributed data, particularly from inland and high-elevation regions, reduces overall root mean square error relative to earlier model versions and substantially expands the model's area of applicability in New Zealand. Our results highlight that further advances will benefit from the availability of submonthly precipitation isotope measurements and improved coverage of alpine environments. We show that community participation offers a practical pathway to strengthening national precipitation isotope products and their application across hydrology, climate science, and related environmental science disciplines.

Reliable and accurate farm-level forecasting of greenhouse gas concentrations from dairy cattle is important to the formulation of climate change mitigation strategies and policy planning in livestock systems. This study proposed an uncertainty-aware deep learning model that integrated data from multiple sources, including remote sensing and ground-based sensors, to forecast hourly concentrations of nitrous oxide, methane, and carbon dioxide in a controlled zero-grazing dairy system. The forecasting was implemented by formulating a causal multivariate time series using a 24-h lookback window with direct one-step-ahead prediction. A two-stage model evaluation protocol, model hyperparameter tuning via rolling cross-validation, and holdout Test Evaluation were implemented during model development. The model was evaluated using the coefficient of determination, root-mean-square error, and mean squared error. To evaluate model reliability, the study employed a dual-output gated recurrent unit to estimate the conditional mean and heteroscedastic variance, and Monte Carlo dropout and Gaussian Negative Log-Likelihood to quantify epistemic and aleatoric uncertainty. Results indicate stable generalisation across temporal folds and strong probabilistic calibration, with empirical 95% coverage ranging from 93.6 to 94.8% on the holdout test set. Feature selection indicates that rainfall, normalised difference vegetation index, humidity, temperature, trend, and season influence the prediction of concentration. The study shows that incorporating exogenous variables improves model performance. The proposed framework demonstrates proof of concept for controlled zero-grazing systems, with potential for broader application following multi-site validation.

Increasing emergency medical service (EMS) capacity within integrated delivery systems is essential for universal health coverage, particularly in low- and middle-income countries. However, in underdeveloped ethnic regions of western China, developing EMS capacity within county-level medical alliances (CLMAs) remains a critical challenge. Using classic grounded theory, we conducted semistructured interviews with 47 health care professionals from 11 CLMAs across Guangxi, a representative underdeveloped, multiethnic, mountainous region. Systematic three-level coding of 575 statements revealed 57 initial concepts, 15 categories, and four core dimensions: institutional deficiencies (root cause), inefficient coordination (key bottleneck), resource shortcomings (direct manifestation), and service efficacy constraints (final outcome). These dimensions form a closed-loop constraint model reinforced by reverse feedback. Crucially, service efficacy constraints-specifically, poor emergency care quality and collective public cognitive bias-do not merely represent outcomes but actively reinforce the institutional deficiencies that generated them, trapping the system in a low-level equilibrium. This finding explains why piecemeal interventions fail. Effective strengthening requires simultaneously targeting all four dimensions to disrupt the negative cycle, a transferable strategy for integrated health systems facing analogous constraints worldwide.