Search Results (13)

This study examined the anthropometric, lower-limb flexibility, power, and kinematic correlates of 5 m and 7.5 m post-entry passive underwater glide performance during forward-weighted (FW) and rear-weighted (RW) swim starts in adolescent female swimmers. Twenty-three trained female swimmers aged 14–16 years completed FW and RW starts in a randomized within-subject repeated-measures design. Anthropometry, ankle dorsiflexion range of motion, lower-limb muscle extensibility, vertical jump performance, and start kinematics were assessed. FW starts produced shorter block exit time (mean difference = −0.06 s; p < 0.001; Cohen’s dz = −1.63), shorter water-entry time (mean difference = −0.07 s; p < 0.001; Cohen’s dz = −1.65), and higher average water-entry velocity (mean difference = 0.17 m·s⁻¹; p < 0.001; Cohen’s dz = 1.36) compared with RW starts. FW also yielded a faster 5 m post-entry passive underwater glide completion time (mean difference = −0.04 s, approximately 40 ms; p = 0.005; Cohen’s dz = −0.66), whereas 7.5 m post-entry passive underwater glide completion time did not differ between techniques (p = 0.725). Exploratory regression models accounted for 29.0–64.4% of the adjusted variance across outcomes, but these models were not externally validated and should be interpreted as exploratory, hypothesis-generating associations. Technique-related differences were specific to the block exit and early post-entry passive glide phases; selected physical characteristics may complement kinematic assessment in this population but should not be used as stand-alone criteria for start-technique selection. Full article

Road traffic accidents at intersections pose a persistent challenge in Riyadh, Saudi Arabia, contributing significantly to public health burdens and economic losses. Traditional statistical approaches often fail to capture the complex, non-linear interactions among geometric design, traffic parameters, and accident severity. This study develops a multi-methodological machine learning framework to predict intersection accident severity using the Equivalent Property Damage Only (EPDO) metric. Historical data (2017–2023) from Riyadh Municipality for 150 high-risk intersections were analyzed, incorporating predictors such as service road distance (SRD), U-turn distance (UTD), median width (MW), peak hour volume (PHV), heavy vehicle percentage (HV%), and injury/frequency counts. Six algorithms, i.e., Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Linear Regression, and Artificial Neural Network, were compared using a 70/30 train–test split and k-fold cross-validation in this study. The Gradient Boosting model achieved superior performance (R² = 0.89 with MSE = 63.43 and RMSE = 7.96) and was selected for final deployment. SHAP feature importance analysis revealed minor injuries (MIs), serious injuries (SRIs), and fatalities (FAs) as the most important dominant predictors, with geometric factors (UTD, MW) and traffic composition (HV%) providing actionable infrastructure insights. The model ranked intersections and identified the “Jeddah Road with Taif Road” (predicted EPDO = 137.22) as the highest-risk location. Evidence-based recommendations include enforcing the minimum 300 m U-turn buffers with staggering service road exits ≥150 m and restricting heavy vehicles during peak hours. The scalable framework developed in this study supports the data-driven prioritization of safety interventions and aligns with sustainable urban mobility goals and offers transferability to other metropolitan contexts worldwide. Full article

Background: The liver has a region called the hepatic hilum (HH) where structures enter and exit: anteriorly, the left and right hepatic ducts; posteriorly, the portal vein; and between these, the left and right hepatic arteries. The objective of this review is to know how variants in structures of the hepatic hilum are associated with clinical alterations of the liver. Methods: The databases Medline, Scopus, Web of Science, Google Scholar, CINAHL, and LILACS were researched until January 2024. The methodological quality was evaluated with an assurance tool for anatomical studies (AQUA). The pooled prevalence was estimated using a random effects model. Results: A total of six studies met the selection criteria established in this study for meta-analysis. The prevalence of hepatic hilus variants was 9% (CI = 5% to 13%), and the heterogeneity was 83%. The other studies were analyzed descriptively and with their respective clinical considerations in the presence of the variant, such as the high incidence of the Michels type III variant; among the portal vein variants, the type III variant of the Cheng classification stands out and in biliary anatomy, and the IIIa variant stands out according to the Choi classification. Conclusions: This review allowed us to know in detail the anatomical variants of HH; the structure with which the greatest care should be taken is the hepatic artery because of the probability of metastatic processes due to increased blood distribution in the hepatic lobules. Finally, we believe that new anatomical and clinical studies are needed to improve our knowledge of the relationship between HH variants and liver alterations or surgeries. Full article

Background: Childhood vitamin A deficiency leads to increased morbidity and mortality. Human milk is the only source of vitamin A for exclusively breastfed infants. Dried Moringa oleifera leaf powder (moringa) is a good food source of provitamin A and other carotenoids. Its effect during lactation on human milk vitamin A and carotenoid content is unclear. Objectives: Our objective was to investigate the effect of maternal moringa consumption on human milk retinol and carotenoid concentrations and maternal and infant vitamin A status. Methods: We conducted a 3-month pilot single-blinded cluster-randomized controlled trial in breastfeeding mother–infant pairs (n = 50) in Kenya. Mothers received corn porridge with (20 g/d) or without moringa with complete breast expressions and maternal and infant serum collected at enrollment (infant <30 days old) and 3 months. Milk was analyzed for retinol and selected carotenoids; maternal/infant serum was analyzed for retinol binding protein (RBP). Results: 88% (n = 44) pairs completed milk and serum samples. Four mothers (9%) had vitamin A deficiency (RBP <0.07 µmol/L); 11 (25%) were vitamin A insufficient (VAI; RBP <1.05 µmol/L). Alpha-carotene concentration in milk was higher in the moringa than the control group at baseline (p = 0.024) and at exit (least squares means, LSM, 95%CI µg/mL 0.003, 0.003–0.004 moringa vs. 0.002, 0.001–0.003 control, n = 22/cluster; p = 0.014). In mothers with VAI, alpha-carotene was higher in the moringa group than controls at exit (LSM, 95%CI µg/mL 0.005, 0.003–0.009 moringa, n = 3, vs. 0.002, 0.000–0.004 control, n = 8, p = 0.027) with no difference at baseline. Milk carotenoids did not correlate with vitamin A status (serum RBP) in infants or mothers. Conclusions: Maternal moringa consumption did not impact concentration of milk vitamin A and resulted in limited increase in milk carotenoids in this cohort. Full article

Erosion damage can occur in fans and blowers during industrial processes, cooling, and mine ventilation. This study focuses on investigating erosion caused by particulate air flows in a centrifugal fan with forward-inclined blades. This type of fan is particularly vulnerable to erosion due to its radial flow component and flow recirculation. The flow field was solved separately, and the data transferred to the particle trajectory and erosion code. This in-house code implements the Lagrangian approach and the random walk algorithm, including statistical descriptions of particle sizes, release positions, and restitution factors. The study involved two types of dust particles, with a concentration between 100 and 500 μg/m³: The first type is the Saharan (North Africa) dust, which has a finer size between 0.1 and 100 microns. The second type is the Coarse Arizona Road Dust, also known as AC-coarse dust, which has a larger size ranging from 1 to 200 microns. The complex flow conditions within the impeller and scroll, as well as the concentration and size distribution of particles, are shown to affect the paths, impact conditions, and erosion patterns. The outer wall of the scroll is most heavily eroded due to high-impact velocities by particles exiting the impeller. Erosion is more pronounced on the pressure side of the full blades compared to the splitters and casing plate. The large non-uniformities of erosion patterns indicate a strong dependence with the blade position around the scroll. Therefore, the computed eroded mass is cumulated and averaged for all the surfaces of components. These results provide useful insights for monitoring erosion wear in centrifugal fans and selecting appropriate coatings to extend the lifespan. Full article

With the development of remote sensing technology, a number of fine-resolution (30-m) global/national land cover (LC) products have been developed. However, accuracy assessments for the developed LC products are commonly conducted at global and national scales. Due to the limited availability of representative validation observations and reference data, knowledge relating to the accuracy and applicability of existing LC products on a regional scale is limited. Since Xinjiang, China, exhibits diverse surface cover and fragmented urban landscapes, existing LC products generally have high classification uncertainty in this region. This makes Xinjiang suitable for assessing the accuracy and consistency of exiting fine-resolution land cover products. In order to improve knowledge of the accuracy of existing fine-resolution LC products at the regional scale, Xinjiang province was selected as the case area. First, we employed an equal-area stratified random sampling approach with climate, population density, and landscape heterogeneity information as constraints, along with the hexagonal discrete global grid system (HDGGS) as basic sampling grids to develop a high-density land cover validation dataset for Xinjiang (HDLV-XJ) in 2020. This is the first publicly available regionally high-density validation dataset that can support analysis at a regional scale, comprising a total of 20,932 validation samples. Then, based on the generated HDLV-XJ dataset, the accuracies and consistency among three widely used 30-m LC products, GLC_FCS30, GlobeLand30, and CLCD, were quantitatively evaluated. The results indicated that the CLC_FCS30 exhibited the highest overall accuracy (88.10%) in Xinjiang, followed by GlobeLand30 (with an overall accuracy of 83.58%) and CLCD (81.57%). Moreover, through a comprehensive analysis of the relationship between different environmental conditions and land cover product performance, we found that GlobeLand30 performed best in regions with high landscape fragmentation, while GLC_FCS30 stood out as the most outstanding product in areas with uneven proportions of land cover types. Our study provides a novel insight into the suitability of these three widely-used LC products under various environmental conditions. The findings and dataset can provide valuable insights for the application of existing LC products in different environment conditions, offering insights into their accuracies and limitations. Full article

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially ‘black-box’ in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings. Full article

(1) Background: There have been many attempts to predict the weight-bearing line (WBL) ratio using simple knee radiographs. Using a convolutional neural network (CNN), we focused on predicting the WBL ratio quantitatively. (2) Methods: From March 2003 to December 2021, 2410 patients with 4790 knee AP radiographs were randomly selected using stratified random sampling. Our dataset was cropped by four points annotated by a specialist with a 10-pixel margin. The model predicted our interest points, which were both plateau points, i.e., starting WBL point and exit WBL point. The resulting value of the model was analyzed in two ways: pixel units and WBL error values. (3) Results: The mean accuracy (MA) was increased from around 0.5 using a 2-pixel unit to around 0.8 using 6 pixels in both the validation and the test sets. When the tibial plateau length was taken as 100%, the MA was increased from approximately 0.1, using 1%, to approximately 0.5, using 5% in both the validation and the test sets. (4) Conclusions: The DL-based key-point detection algorithm for predicting lower limb alignment through labeling using simple knee AP radiographs demonstrated comparable accuracy to that of the direct measurement using whole leg radiographs. Using this algorithm, the WBL ratio prediction with simple knee AP radiographs could be useful to diagnose lower limb alignment in osteoarthritis patients in primary care. Full article

This study evaluates the drivers of the adoption and dis-adoption of Integrated Pest Management (IPM) practices in the suppression of mango fruit-fly infestation in Embu County, Kenya. It employs a Correlated Random Effects Probit Model and a Discrete-time Proportional Hazard Model on two-wave panel data of 149 mango farmers selected using a cluster sampling technique. The descriptive results show that 59% and 17% of the respondents were adopters and dis-adopters of mango fruit fly IPM practices, respectively. Empirical findings reveal that the cost of IPM and training on IPM positively and significantly influenced adoption, while the unavailability of the technology had a negative and significant effect on adoption. For dis-adoption, the results indicate that farm size and the quality of IPM positively influenced the hazard of exit from IPM use, and hence, enhanced the sustained adoption of IPM. The study recommends capacity building for mango farmers through training and increased access to extension services to enhance the adoption of this technology and prevent dis-adoption. Full article

Bending control is one of the main methods of shape control for the hot rolled plate. However, the existing bending force setting models based on traditional mathematical methods are complex and have low control accuracy, which leads to poor strip exit shapes. Aiming at the problem of complex bending force setting of the traditional algorithm, an improved whale swarm optimization algorithm and twin support vector machine-based bending force model for hot rolled strip steel (LWOA-TSVR) is proposed. Based on the hot rolling field production data of a steel plant, the research group established the bending force prediction model by using the nonlinear approximation ability of the twin support vector machine. The introduction of the Levy flight improvement algorithm improves the generalization ability, prediction accuracy, and convergence speed of the whale swarm optimization algorithm with the help of the convergence of coefficient vectors, solves the problem of a random selection of the parameters of the traditional whale swarm optimization algorithm and optimizes the ability of the whale swarm algorithm to jump out of the local optimum. Based on the actual rolling database, the hit rate of the proposed method reaches 91% (from −5 to 5 KN), which fully meets the requirements of the detection accuracy on the actual production line. The model is not only able to overcome the local search to obtain the global optimal solution, but also has the advantages of fast convergence and higher prediction accuracy. A comparison of the model with twin support vector machines and traditional whale swarm algorithms shows that the prediction accuracy is higher. The experimental results also show that this model has advantages over existing bending force prediction models in terms of improving the accuracy of the strip shape control and providing theoretical guidance for practical bending force settings. Full article

In intercity expressway traffic, the multiplicity of available routes leads to randomness in exit selection. Random exit selection by drivers is hard to observe, and thus it is a challenge to model intercity expressway traffic sufficiently. In this paper, we developed a Random Quantum Traffic Model (RQTM), which modeled the stochastic traffic fluctuation caused by random exit selection and the residual regularity fluctuation with the quantum walk and autoregressive moving average model (ARMA), respectively. The RQTM considered the random exit selection of a driver as a quantum stochastic process with a dynamic probability function. A quantum walk was applied to update the probability function, which simulated when and where a driver will leave the expressway. We validated our model with hourly traffic data from seven exits from the Nanjing–Changzhou expressway in eastern China. For the seven exits, the coefficients of determination of the RQTM ranged from 0.5 to 0.85. Compared with the classical random walk and the ARMA model, the coefficients of determination were increased by 21.28% to 104.98%, and the relative mean square error decreased by 11.61% to 32.92%. We conclude that the RQTM provides new potential for modeling traffic dynamics with consideration of unobservable random driver decision making. Full article

Ribosome biogenesis is essential for plants to successfully acclimate to low temperature. Without dedicated steps supervising the 60S large subunits (LSUs) maturation in the cytosol, e.g., Rei-like (REIL) factors, plants fail to accumulate dry weight and fail to grow at suboptimal low temperatures. Around REIL, the final 60S cytosolic maturation steps include proofreading and assembly of functional ribosomal centers such as the polypeptide exit tunnel and the P-Stalk, respectively. In consequence, these ribosomal substructures and their assembly, especially during low temperatures, might be changed and provoke the need for dedicated quality controls. To test this, we blocked ribosome maturation during cold acclimation using two independent reil double mutant genotypes and tested changes in their ribosomal proteomes. Additionally, we normalized our mutant datasets using as a blank the cold responsiveness of a wild-type Arabidopsis genotype. This allowed us to neglect any reil-specific effects that may happen due to the presence or absence of the factor during LSU cytosolic maturation, thus allowing us to test for cold-induced changes that happen in the early nucleolar biogenesis. As a result, we report that cold acclimation triggers a reprogramming in the structural ribosomal proteome. The reprogramming alters the abundance of specific RP families and/or paralogs in non-translational LSU and translational polysome fractions, a phenomenon known as substoichiometry. Next, we tested whether the cold-substoichiometry was spatially confined to specific regions of the complex. In terms of RP proteoforms, we report that remodeling of ribosomes after a cold stimulus is significantly constrained to the polypeptide exit tunnel (PET), i.e., REIL factor binding and functional site. In terms of RP transcripts, cold acclimation induces changes in RP families or paralogs that are significantly constrained to the P-Stalk and the ribosomal head. The three modulated substructures represent possible targets of mechanisms that may constrain translation by controlled ribosome heterogeneity. We propose that non-random ribosome heterogeneity controlled by specialized biogenesis mechanisms may contribute to a preferential or ultimately even rigorous selection of transcripts needed for rapid proteome shifts and successful acclimation. Full article

Despite the extensive evidence on the benefits of physical activity (PA) in older adults, including reduced risk of disease, mortality, falls, and cognitive and functional decline, most do not attain sufficient PA levels. Theoretical work suggests that behavioral change interventions are most effective during life transitions, and as such, a theory-based, online intervention tailored for recently retired and empty nest individuals could lend support for increasing levels of PA. The aim of this study is to examine the feasibility of the intervention and study procedures for a future controlled trial. This study has a randomized controlled trial design with an embedded qualitative and quantitative process evaluation. Participants are randomized at 1:1 between the intervention and waitlist controls. Potential participants are within six months of their final child leaving the familial home or within six months of retiring (self-defined), currently not meeting the Canadian PA guidelines, have no serious contraindications to exercise, and are residing in Victoria, British Columbia, Canada. Participants are recruited by online and print flyers as well as in-person at community events. The study aims to recruit 40 empty nest and 40 retired participants; half of each group received the intervention during the study period. The internet-delivered intervention is delivered over a 10-week period, comprising 10 modules addressing behavior change techniques associated with PA. Primary outcomes relate to recruitment, attrition, data collection, intervention delivery, and acceptability. Secondary behavioral outcomes are measured at baseline and post-treatment (10 weeks). Intervention-selected participants are invited to an optional qualitative exit interview. The results of this feasibility study will inform the planning of a randomized effectiveness trial, that will examine the behavior change, health-related fitness, and well-being outcomes by exploring how reflexive processes of habit and identity may bridge adoption and maintenance in behavioral adherence. Full article