Browse Articles

Vehicle speed measurement method using monocular cameras

Abstract This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed. By leveraging the characteristics of the speed measurement problem, the complex three-dimensional positioning problem is simplified into a two-dimensional model, reducing the overall computational complexity of the positioning problem. Finally, the algorithm is combined with You Only Look Once version 7 (YOLOv7) and Deep Simple Online and Realtime Tracking (DeepSORT) algorithms, integrating multiple model structures to optimize the network, achieving precise multi-target speed measurement. Experiments show that under frame-by-frame measurement conditions, the minimum and average accuracies of this method reach 95.1% and 97.6%, respectively. Compared with other methods, it has significant advantages in speed measurement accuracy and computational efficiency. Therefore, this research outcome is expected to play an important role in intelligent transportation systems and road safety management.

Evaluation of ecological geological environment carrying capacity and analysis of driving mechanisms based on normal cloud model and geodetector model

The link between tinnitus and menstrual cycle disorders in premenopausal women

Identification of prognostic biomarkers of sepsis and construction of ceRNA regulatory networks

Identifying key genes in cancer networks using persistent homology

Abstract Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ( $$\beta _2$$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.

Comparison of two plates and screw osteosynthesis configurations in a rat model of critical sized femoral defects to reduce implant related failures

Evaluating cutinase from Fusarium oxysporum as a biocatalyst for the degradation of nine synthetic polymer

Effects of HIV exposure on anemia and vitamin D nutritional status in children aged 6–24 months: a hospital-based cross-sectional study

Association between a body shape index and bone mineral density in US adults based on NHANES data

Comparative analysis of correlation and causality inference in water quality problems with emphasis on TDS Karkheh River in Iran

Abstract Water quality management is a critical aspect of environmental sustainability, particularly in arid and semi-arid regions such as Iran where water scarcity is compounded by quality degradation. This study delves into the causal relationships influencing water quality, focusing on Total Dissolved Solids (TDS) as a primary indicator in the Karkheh River, southwest Iran. Utilizing a comprehensive dataset spanning 50 years (1968–2018), this research integrates Machine Learning (ML) techniques to examine correlations and infer causality among multiple parameters, including flow rate (Q), Sodium (Na+), Magnesium (Mg2+), Calcium (Ca2+), Chloride (Cl−), Sulfate (SO4 2−), Bicarbonates (HCO3 −), and pH. For modeling the causation, the “Back door linear regression” approach has been considered which establishes a stable and interpretable framework in causal inference by focusing on clear assumptions. Predictive modeling was used to show the difference between correlation and causation along with interpretability modeling to make the predictive model transparent. Predictive modeling does not report the causality among the variables as it showed Mg is not contributing to the target (TDS) while the findings reveal that TDS is predominantly positive influenced by Mg, Na, Cl, Ca and SO4, with HCO3 and pH exerting negative (inverse) effects. Unlike correlations, causal relationships demonstrate directional and often unequal influences, highlighting Mg as a critical driver of TDS levels. This novel application of ML-based causal inference in water quality research provides a cost-effective and time-efficient alternative to traditional experimental methods. The results underscore the potential of ML-driven causal analysis to guide water resource management and policy-making. By identifying the key drivers of TDS, this study proposes targeted interventions to mitigate water quality deterioration. Moreover, the insights gained lay the foundation for developing early warning systems, ensuring proactive and sustainable water quality management in similar hydrological contexts.

From node to network: weaving a global perspective on efficacy and costs of non-pharmaceutical interventions

A gamma-ray spectrometer based on MAPD-3NM-2 and LaBr3(ce) and LSO scintillators for hydrogen detection on planetary surfaces

Abstract The presented work is dedicated to the detection of hydrogen, using detectors based on a MAPD (Micropixel Avalanche Photodiode) array based on new MAPD-3NM-2 type photodiodes and two different scintillators (LaBr3(Ce) and LSO(Ce)). The physical parameters of the MAPD photodiode used in the study and the intrinsic background of the scintillators were investigated. For the 2.223 MeV energy gamma-ray indicating the presence of hydrogen, the energy resolution was 6.89% with the MAPD array and LSO scintillator-based detector, and the number of events corresponding to this energy was 4817. With the MAPD array and LaBr3(Ce) scintillator, the energy resolution for the 2.223 MeV gamma-ray was 3.55%, and the number of events corresponding to this energy was 3868. The LSO scintillator-based detector allowed for the detection of 24.5% more 2.223 MeV energy gamma-rays compared to the LaBr3(Ce) scintillator. For the 2.223 MeV gamma-ray associated with hydrogen, the energy resolution with the LaBr3(Ce) scintillator was 48.5% better than with the LSO scintillator. The lower energy resolution compared to the LSO is due to the higher light output of LaBr3(Ce). The obtained results experimentally demonstrate that it is possible to obtain information about the presence of hydrogen in the target using both detectors.

Electrochemical deposition of bimetallic sulfides on novel BDD electrode for bifunctional alkaline seawater electrolysis

Publisher Correction: Predicting high sensitivity C-reactive protein levels and their associations in a large population using decision tree and linear regression

Enhancing social functioning using multi-user, immersive virtual reality

Association of domain-specific physical activity with nocturia: a population-based study

Comprehensive RNA-seq analysis of benign prostatic hyperplasia (BPH) in rats exposed to testosterone and estradiol

Hydrogenotrophic methanogenesis at 7–12 mbar by Methanosarcina barkeri under simulated martian atmospheric conditions

Analysis of retinal sublayers in patients with systemic COVID-19 illness with varying degrees of severity

Growth-rate distributions of gut microbiota time series

Abstract Logarithmic growth-rates are fundamental observables for describing ecological systems and the characterization of their distributions with analytical techniques can greatly improve their comprehension. Here a neutral model based on a stochastic differential equation with demographic noise, which presents a closed form for these distributions, is used to describe the population dynamics of microbiota. Results show that this model can successfully reproduce the log-growth rate distribution of the considered abundance time-series. More significantly, it predicts its temporal dependence, by reproducing its kurtosis evolution when the time lag $$\tau$$ is increased. Furthermore, its typical shape for large $$\tau$$ is assessed, verifying that the distribution variance does not diverge with $$\tau$$ . The simulated processes generated by the calibrated stochastic equation and the analysis of each time-series, taken one by one, provided additional support for our approach. Alternatively, we tried to describe our dataset by using a logistic neutral model with an environmental stochastic term. Analytical and numerical results show that this model is not suited for describing the leptokurtic log-growth rates distribution found in our data. These results support an effective neutral model with demographic stochasticity for describing the considered microbiota.