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Coyote family activity in a landscape of fear
Optimization of centrifugal fan for oolong tea shaking machine based on Kriging model and multi-objective particle swarm algorithm
Abstract This study optimized the air volume and efficiency of the centrifugal fan in the shaking machine for oolong tea, aiming to enhance its aerodynamic performance and efficiency. Fitting blade profiles by constraining control points of a third-order Bézier curve through key geometric parameters of the blade, and forty-five sample points were generated in the design space by combining Computational Fluid Dynamics (CFD) simulation and Optimal Latin Hypercube Sampling (OLHS). The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was used to obtain the optimal parameter combination. Flow field analysis showed that after optimization, the recirculation phenomenon in the flow channel between the blades was improved; the strong turbulent kinetic energy at the inlet and outlet of the fan and the leading edge of the impeller tongue was effectively suppressed, the internal flow field was more stable, and the efficiency was improved. the air volume increased by 176.19 m³/h. These results demonstrate the significance of the proposed blade optimization in the aerodynamic performance of the shaking machine. This study addresses the research gap in the multi-parameter collaborative optimization for centrifugal fans used in oolong tea processing equipment, which traditionally relies on single-parameter empirical designs.
Immunisation decision-making and barriers to vaccine uptake among children under-5 in limited-resource settings
Abstract Immunisation coverage remains a fundamental challenge in sub-Saharan Africa, which still has the highest under-five mortality rate in the world, due to wide-ranging drivers that have complicated interventions. Unless the knowledge gaps affecting childhood vaccination decision-making are identified and effectively addressed, low immunisation coverage will persist in the region. This study aims to assess the factors influencing immunisation decision-making among caregivers of children Under-5 years old, and to understand the behaviours that shape these influences. The study employed qualitative methods, such as focus group discussions. Participants were caregivers of children Under-5 years old in Nigeria. A simplified cluster sampling approach was used to select caregivers from four geographical clusters. A minimum of seven caregivers from each cluster were purposively included. The data were analysed deductively using a meta-aggregation approach. The findings show that caregivers’ immunisation decision-making are mainly motivated by: inadequate knowledge about childhood immunisation, especially the conflict between vaccine-preventable and non-vaccine-preventable diseases; masculinity (attitudes of fathers or men can help or hinder immunisation); the child’s gender (the perception of stronger versus weaker sex); misinformation about immunisation (especially the perception that it is a form of family planning). Other influences include the exploitation of caregivers by healthcare workers; incessant stock-outs of vaccines leading to complacent behaviour associated with vaccine hesitancy; religious beliefs; poor attitudes of healthcare workers; among other factors. The factors influencing immunisation decision-making in limited-resource settings and the motivations shaping these behaviours are largely psychological, socio-cultural, behavioural, health system and structural. Interventions designed to address the root causes of gender inequity must start with men’s attitudes and the socio-cultural practices that enable them. Furthermore, the sandwich model for addressing vaccine misinformation seems promising in countering myths and conspiracies about vaccines.
Fast and accurate visual acuity prediction based on optical aberrations and machine learning
Abstract In this work, we propose three machine learning-based methods for predicting visual acuity (VA). Two methods utilize regression trees (LSBoost and XGBoost), and the third employs a neural network that classifies simulated aberrated optotypes as “recognized” or “unrecognized”. The overall VA is estimated by replicating the clinical procedure in which the subject reads optotypes and the VA is determined based on their responses. Here, the neural network acts as a substitute for the subject. Data were collected from a clinical trial involving 135 subjects providing for each sample 36 Zernike coefficients, amplitudes of accommodation, age, and VA values. Evaluation of the regression tree models demonstrates that LSBoost outperformes XGBoost in prediction accuracy, especially when incorporating amplitudes of accommodation. However, XGBoost is faster in computation time, making it more suitable for large datasets and design of visual compensations. The neural network, while achieving high optotype recognition accuracy, is less accurate in VA prediction due to its reliance on synthetic data and complex simulation processes, which requires large processing times. Overall, LSBoost offers the best performance in terms of accuracy, while XGBoost provides faster computation. These findings highlight the suitability of regression tree-based models for VA prediction using tabulated data.
“Therapeutic potential of Acalypha indica L. leaf fractions against foodborne pathogens: an in vitro and in silico study”
Abstract This study investigates the inhibition effects of Acalypha indica L. leaf extract obtained using various solvents, viz. petroleum ether, chloroform and ethanol. Among the extracts, the ethanolic extract showed the strongest antioxidant activity, with 84.36% scavenging potential in 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 69% in the Ferric ion Reducing Antioxidant Power (FRAP) assay. Similarly, in antimicrobial activity, the ethanolic extract showed the highest inhibition zone of 24.0 mm against S. aureus and 29.3 mm against E. coli. Response Surface Methodology (RSM) utilize a Box-Behnken design (BBD) analysed with optimize conditions for enhancing antioxidant activity. Gas Chromatography-Mass Spectrometry (GC-MS) identified eleven major phytocompounds, which were further evaluated through molecular docking and ADMET studies against S. aureus’s DNA gyrase B and E. coli’s Dihydrofolate reductase. Docking result shows highest binding affinity towards two compounds such as 2(5 H)-Furanone,3-chloro-5-((dimethylamino)methyl)-4,5-dimethyl- and N-(2,2-Dichloro-1-hydroxyethyl)-2,2-dimethylpropanamide with docking scores of -5.21 and − 8.38 kcal/mol for S. aureus and − 4.39 and − 8.75 kcal/mol for E. coli . These two hits were selected for molecular dynamic simulation studies to evaluate protein-ligand complex stability. Overall, the ethanolic extract exhibited strong antioxidant and antimicrobial properties, suggesting as potential candidate for application in food packaging.
Statistical analysis for heat transfer optimization of magnetohydrodynamics trihybrid nanofluid over a convectively heated Riga surface
Abstract The Riga plate is arrangement of electrodes and permanent magnets allows for efficient regulation of fluid flow. The Riga surface leverages Lorentz forces to control boundary layers (BL) and improve cooling purposes for effective electromagnetic flow control in nuclear and aeronautical engineering systems. Furthermore, by utilizing synergistic interactions of different nanoparticles, heat transfer rat can be optimized in industrial setup. The primary focus of this work is to investigate the unsteady BL flow of water-based tri-hybrid nanolfuid (tri-HNF) flow over a Riga plate senor under the influence of activation energy, cross-diffusion, and convective heating. Three different nanoparticles $$A{l}_{2}{O}_{3}$$ , $$CuO$$ and $$Ti{O}_{2}$$ are dispersed in a pure water. The model equations are constructed using BL theory and transformed into ordinary differential equations using an appropriate similarity rule. The Runge–Kutta fourth-order (RK-4) method, along with shooting approach, is used to address the problem numerically. The skin friction and Nusselt and Sherwood numbers are assessed using optimized statistical Response Surface Methodology (RSM) technique. The Gharesim model viscosity and Hamilton-Crosser thermal conductivity models are deployed in the governing model. A mathematical model is designed and developed using RSM to obtain an optimal skin friction, heat and mass transfer rate. Sensitivity analysis (SA) is performed to investigate the response of input on these coefficients. SA shows that in narrow BL, the skin friction rises with nanoparticle concentration. Velocity of tri-HNF boost with the Hartman number and the electrode-magnet distance parameter. The Soret number, and activation energy increases the concentration profile. Higher Nusselt number indicates improved heat transfer with increased nanoparticle load. Activation energy uplift the mass transfer rates, but dwindle with nanoparticle concentration.
Intraperitoneal immune microenvironment and efficacy of intraperitoneal chemotherapy in patients with gastric cancer and peritoneal metastasis
Adaptive virtual impedance control strategy based on IWOA-fuzzy PID and its application to reactive power sharing in islanded microgrids
Abstract Accurate reactive power sharing in islanded microgrids is often compromised by resistive line impedances and parameter mismatches, causing power coupling and uneven distribution. This paper proposes an adaptive virtual impedance control strategy that integrates a fuzzy PID controller with the Improved Whale Optimization Algorithm (IWOA). The fuzzy PID ensures nonlinear adaptability, while IWOA globally optimizes fuzzy rules, membership functions, and PID gains for robust self-tuning. By dynamically adjusting virtual resistance and reactance, the strategy reshapes inverter output impedance, decouples active and reactive power, and enables proportional reactive power sharing under diverse network conditions. Simulation studies in MATLAB/Simulink with multiple DG units, varying loads, and unequal capacities verify its effectiveness. Results show improved sharing accuracy, faster response (<0.1 s), and reduced steady-state error compared with conventional droop control, fixed virtual impedance, and PSO-fuzzy-PID methods. The proposed IWOA-fuzzy-PID approach thus offers a practical and scalable solution to enhance stability and reliability in islanded microgrids.