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Simultaneous measurement of linear and angular displacement sensor using POF based on macro-bend loss
Comparative sedative effects of intravenous etomidate/fentanyl/midazolam versus propofol/fentanyl/midazolam combination for dental treatment of uncooperative children: a randomized clinical trial
Abstract This study compared the sedative effects of intravenous etomidate/fentanyl/midazolam versus propofol/fentanyl/midazolam combination, with a primary focus on behavioral outcomes assessed by the Houpt scale and hemodynamic safety, for dental treatment of uncooperative children between 3 and 10 years. This patient- and assessor-blinded, crossover clinical trial was conducted on 40 children (27 females, 13 males; mean age, 4.67 ± 1.46 years; mean weight, 15.93 ± 4.2 kg) with negative and very negative behavior, as assessed by the Frankl scale. The children were randomly assigned to 2 groups ( n = 20). Group 1 received etomidate (0.2 mg/kg), fentanyl (1 µg/kg), and midazolam (0.2 mg/kg) combination in the first treatment session, and propofol (1 mg/kg), fentanyl (1 µg/kg), and midazolam (0.2 mg/kg) in the second treatment session. This order was reversed in group 2. Hemodynamic indices, including the heart rate (HR) and arterial oxygen saturation (SPO2), were monitored during the procedure, and the behavior of children was recorded using the Houpt behavioral rating scale. Data were analyzed using the Mann-Whitney and t tests (α = 0.05). The hemodynamic indices were within the safe range, with no significant difference between the two groups ( P > 0.05). At the time of injection, a statistically significant but clinically modest difference in SPO2 was observed favoring the etomidate group (99.75% vs. 98.85%, P = 0.003); however, mean SPO2 remained above 97% in both groups throughout all procedures. The mean HR showed greater stability with less fluctuation from baseline in the etomidate group compared to propofol (e.g., smaller magnitude of change at injection in the second session, P < 0.005 overall for key time points). The two groups were similar regarding the Houpt scale, except during recovery, where the etomidate group had a significantly higher behavior score (7.45 vs. 6.60, P = 0.007). Both combinations are highly effective and safe for the intravenous sedation of uncooperative children, providing comparable intra-procedural sedation. However, the etomidate/fentanyl/midazolam combination demonstrated specific advantages regarding intra-procedural heart rate stability and more favorable behavioral scores during the recovery period. Trial registration IRCT20230515058193N1, registeredretrospectively on 22 November 2024 in the Iranian Registry of Clinical Trials ( https://www.irct.ir/trial/77589 ).
Residual threshold validation enables lightweight intrusion detection with a two-stage BiGRU autoencoder
Smokers’ knowledge, attitude and practices toward cigarette butt disposal: a Bayesian network analysis of a cross-sectional study in Iran
Clinical outcomes of the new generation VISUMAX 800 platform in small incision lenticule extraction for Myopia in Chinese patients
Assessment of biological individuality in periodontitis patients using a specific algorithm and the Salus method
Non-canonical ceRNA-independent mechanism of the lncRNA-H19/miR-212-5p axis targeting KLF4 in Hcy-triggered VSMCs dysfunction and atherogenesis
DRID: a spatiotemporal relational framework for robust IoT device identification in smart grids
Abstract Accurate device-type identification (DI) is critical for ensuring the security and stability of large-scale Internet of Things (IoT) deployments in smart grids. However, existing traffic-based DI methods often struggle in dynamic environments, as they fail to capture the temporal evolution of device behaviors, overlook complex inter-device dependencies, and lack robustness to the sparse or incomplete data common in practice. To address these challenges, we propose DRID, a novel Spatiotemporal Dynamic Relational Framework for Robust IoT Device Identification. DRID jointly captures structural communication patterns and multi-scale temporal dynamics via a structure–time interaction mechanism and multi-scale temporal modeling, while leveraging a differentiation-aware adaptive learning strategy to selectively enhance discriminative features under sparse or noisy traffic conditions. Extensive evaluations on two public IoT traffic datasets demonstrate that DRID consistently outperforms state-of-the-art baselines across diverse sampling scenarios. By effectively fusing structural and temporal information while maintaining robustness under data scarcity, DRID provides a scalable and accurate solution for IoT device identification, advancing secure and intelligent management of critical smart grid infrastructures.
Biochar–vermicompost–inorganic N/P₂O₅ integration improves maize yield and soil chemical properties in acidic soils
Abstract Improving soil chemical quality while enhancing maize yield is critical for sustainable crop production in acidic soils. We conducted a two-year field experiment on acidic Nitisols in the Burie district, Ethiopia, to evaluate the combined effects of maize cob biochar (BC), vermicompost (VC), and inorganic N/P₂O₅ rates on soil chemical properties and maize productivity. The experiment used a randomized complete block design with a 3 × 3 × 3 factorial arrangement, testing three levels of inorganic N/P₂O₅ (0/0, 120/69, 240/138 kg ha −1 ), BC (0, 4, 8 t ha −1 ), and VC (0, 5.02, 10.04 t ha −1 ). Grain yield was recorded annually, and soil samples were collected before and after the experiment to assess changes in chemical properties. Data were analyzed using factorial ANOVA, and treatment means were separated using Tukey’s HSD at 5% significance. Economic feasibility was evaluated via partial budget analysis. Under the conditions of this experiment, integrated application of BC, VC, and reduced N/P₂O₅, alongside lime, significantly improved soil pH (4.87 to 5.82), soil organic carbon, total nitrogen, and available phosphorus, while reducing exchangeable acidity, hydrogen, and aluminum. Maize grain yield increased markedly, with the highest yield (12.13 t ha −1 ) observed under 120/69 kg N/P₂O₅ + 8 t BC + 10.04 t VC, a 175.77% increase over the control (4.40 t ha −1 ). The combination 120/69 kg N/P₂O₅ ha −1 + 4 t BC ha −1 + 5 t VC ha −1 achieved consistently high yields (12.09 t ha −1 ) and the greatest net benefit (1,861 USD ha −1 ; 289,124 Ethiopian Birr ha −1 ) with a marginal rate of return of 149.1%. Yield was strongly positively correlated with improved soil chemical properties. Among predictive models, Linear Support Vector Machine (LSVM) provided the highest accuracy (R 2 = 0.923) for estimating maize yield under integrated nutrient management. These results indicate that integrating BC and VC with reduced inorganic N/P₂O₅, in combination with lime, can enhance soil chemical quality and maize productivity in acidic Nitisols. These findings suggest potential for sustainable intensification in the study area and similar agro-ecologies, although multi-location and longer-term validation is needed to confirm broader applicability. Further research is needed across diverse environments and longer timeframes.
circ-E2F3 drives ovarian cancer progression via the miR-1305/STAT3 axis through modulation of ferroptosis
A multi-objective optimization framework for planning electric vehicle charging infrastructure incorporating traffic demand, cost, and equity considerations
Differential roles of DTI and EEG in predicting cognitive function after left basal ganglia stroke: a proof-of-concept study
Robust model predictive load frequency control for DFIG based wind-integrated power systems with nonlinear physical constraints
Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana
Abstract Road traffic fatalities constitute a critical and preventable public health burden in Ghana, yet granular subnational analyses of monthly mortality patterns across the country’s sixteen administrative regions remain sparse. This study uses a Bayesian spatio-temporal negative binomial (NB) model to analyse monthly road traffic death counts across sixteen Ghanaian regions from January 2019 to December 2022, disaggregated by vehicle type (commercial, private, and cycle). The model decomposes the expected death count into a global intercept, fixed effects for vehicle type, accident counts entered as a log-transformed covariate rather than an offset, and temporal trend, together with region-specific spatial random effects (u i ) and year-level temporal random effects (v t ). Overdispersion (variance-to-mean ratio = 18.6) is explicitly accommodated by the NB distribution. Posterior inference was conducted using the no-U-turn sampler (NUTS) implemented in brms (version 2.21) under R 4.5, with four chains of 2000 iterations each (1000 warm-up), target acceptance rate δ = 0.90, and random seed 2024. Spatial random effects were specified as independent hierarchical Normal priors, not CAR/BYM priors, with a HalfNormal(0, 1.5) hyperprior on the regional standard deviation. The Bayesian NB model achieved superior fit (LOO-CV ELPD = − 2381.4; AIC = 4646; BIC = 4739 reported for comparison) relative to the competing count-data specifications. The posterior mean for the global intercept was β 0 = 2.070 (SD = 0.339; 94% HDI: 1.458, 2.677), corresponding to a baseline expected monthly death count of approximately 7.9. Substantial spatial heterogeneity was confirmed (σ_region = 1.139; 94% HDI: 0.786, 1.574), with Greater Accra and Ashanti exhibiting the largest positive spatial random effects (both u i = 1.699). A statistically significant negative monthly trend (β m = − 0.084; 94% HDI: − 0.160, − 0.013) was identified, while the annual temporal trend remained uncertain (β t = 0.043; 94% HDI: − 0.325, 0.361; HDI includes zero). Year-level random effects were small, and their credible intervals broadly included zero, indicating modest inter-annual variation beyond the linear trend. No divergent transitions were detected; all $${\hat{\text{R}}}$$ values were ≤ 1.01, and bulk ESS was > 350. Marked regional heterogeneity and vehicle-type-specific mortality patterns underscore the need for spatially differentiated road safety interventions.