Abstract In this study, we investigate the capabilities of magnetohydrodynamic bioconvective micropolar nanofluids, considering impact Soret and Dufour effects using a non-similarity analysis. Our objective is to forecast complex heat mass transfer phenomena observed in both biological industrial systems. recent years, notable progress energy applications has spurred our inquiry exploration. To augment thermal conductivity explore potential biocompatibility, utilize blood as base fluid,...

Managing and controlling energy amidst rapid industrial technological advancements pose significant challenges. Enhanced heat transfer processes, like free convection in solar panels nuclear power plants, offer potential to reduce consumption improve system efficiency. Understanding elements such as magnetic fields, thermal radiation, nanoparticle morphology can optimize fluid dynamics across various domains including energy, aerospace, power. This study aims develop a two-dimensional...

Purpose In this study, we investigate the effects of an extended ternary hybrid Tiwari and Das nanofluid model on ethylene glycol flow, with a focus heat transfer. Using Cross non-Newtonian fluid model, explore transfer characteristics unique in various applications such as pharmaceutical solvents, vaccine preservatives, medical imaging techniques. Design/methodology/approach Our investigation reveals that flow follows laminar pattern, influenced by radiation occurring around stretched...

Silicon nanoparticles can be used as contrast agents in medical imaging procedures such computed tomography (CT), magnetic resonance (MRI), and fluorescence imaging. They aid the early identification diagnosis of illnesses by enhancing image sensitivity resolution through adjustable optical properties. Additionally, knowledge gathered from this research might help create cooling systems for electronics thermal management applications that are more effective. The focus current is on steady...

Detection of the Fusarium head blight (FHB) is crucial for wheat yield protection, with precise and rapid FHB detection increasing protecting agricultural ecological environment. tasks in production are currently handled by cloud servers utilize unmanned aerial vehicles (UAVs). Hence, this paper proposed a lightweight model ear based on UAV-enabled edge computing, aiming to achieve purpose intelligent prevention control disease. Our utilized You Only Look Once version 4 (YOLOv4) MobileNet...

In this research, we explore the potential of magnetohydrodynamic (MHD) bioconvective nanofluids with varying viscosity through non-similarity analysis, aiming to enhance thermal performance biological and industrial systems. Recent years have witnessed significant advancements in energy applications, motivating our investigation. To heat transfer, employ propylene glycol as base liquid, incorporating silicon dioxide molybdenum disulfide nanomaterials. This unique configuration enables...

The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information the ground, which can be coupled with emerging deep learning approaches that enable latent features hidden geographical patterns to extracted. This article marks first attempt cross-compare performances popular state-of-the-art models in estimating population distribution from images, investigate contribution neighboring effect, explore potential systematic estimation biases. We...

The wheat above-ground biomass (AGB) is an important index that shows the life activity of vegetation, which great significance for growth monitoring and yield prediction. Traditional estimation methods specifically include sample surveys harvesting statistics. Although these have high accuracy, they are time-consuming, destructive, difficult to implement monitor at a large scale. main objective this study optimize traditional remote sensing estimate AGB based on improved convolutional...

Abstract Wheat ( Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive real‐time methods for monitoring can help prevent control plant diseases in agricultural production. In this study, we obtained multispectral imagery of canopy acquired by an unmanned aerial vehicle, selected vegetation index using K‐means algorithm (KA) genetic (GA), established a model based on backpropagation neural network (BPNN) method. The results showed that R 2...

The thermal properties of nanofluids are crucial across various industries, particularly for efficient heat dissipation, which is key to optimising performance. This study examines the transfer characteristics a steady, two-dimensional radiative flow blood-containing carbon nanotube particles along curved, stretched surface. analysis emphasises behaviour magnetised micropolar nanofluid, favouring hybrid nanofluid over an extended, curved A mathematical formulation equations developed using...

Taro blight is a major disease affecting taro cultivation. Traditional methods for prevention rely on manual identification, which limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural fields, facilitating the efficient monitoring blight. Field survey data were integrated with these develop model severity. The back propagation neural network (BPNN) showed optimal performance during early middle stages formation when...

This study investigates the unsteady flow of a hybrid nanofluid (Al2O3+Cu/H2O) over spinning disk in porous medium under influence viscous diffusion, Coriolis force, and magnetohydrodynamics (MHD), following von Kármán swirling model. Slip velocity thermal radiation significantly affect dynamics heat transfer, increasing potential nanofluids for cooling turbine disks jet engines, where protective layer controls boundary through viscosity diffusivity. The novelty this work lies conducting...

An 8-week feeding trial was conducted to assess the effects of different dietary lipid levels on growth performance, feed utilization, and body composition juvenile kelp grouper Epinephelus moara. Six diets with varying 2.82%, 5.30%, 7.83%, 11.76%, 14.19%, 16.32% (designated as CL1 CL6) were carefully formulated. A cohort 324 fish (initial weight 5.87 ± 0.09 g fish−1) randomly divided into six groups three replicates in each group. The results showed that gain (WG) significantly improved...

Zhang, W., J. Shi, H. Huang, and T. Liu. 2017. The impact of disturbance from photographers on the Blue-crowned Laughingthrush (Garrulax courtoisi). Avian Conservation Ecology 12(1):15. https://doi.org/10.5751/ACE-01007-120115

As the aging of population aggravates, problem fall has aroused more attention. We have proposed a novel pre-impact detection algorithm and protection system. The real-time used thresholds vertical velocity displacement profile to detect events in switching periods fast motion. It can effectively solve false alarms jump jogging for daily-life applications. A second-order lag filtering method was utilized reduce drift with good performance. In addition, calculated by using estimation. got...

An eight-week feeding trial was conducted to evaluate the influence of dietary arachidonic acid (20:4n-6) on growth performance, fatty composition and antioxidant capacity postlarva mud crab (Scylla paramamosain). Five isonitrogenous isoenergetic diets with 0.34% (the control diet), 0.48%, 0.84%, 1.18% 1.81% ARA levels were formulated by adding rich oil in basal diet. There triplicate groups 28 postlarval crabs (initial weight 8.15 mg) for each diet treatment. The results indicated that fed...

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing its complex physical mechanism, enhancing underwater quality based on deep learning method well-developed recently. However, individual studies use different image datasets, leading generalization ability other water conditions. To solve this domain adaptation problem, paper proposes an enhancement scheme that combines...

Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection segmentation technologies mitigate their impact. To address challenges these technologies, development deep learning-based fire surveillance has significantly accelerated. Nevertheless, integration graph convolutional networks (GCNs) in remains relatively underexplored. In this context, we introduce a novel superpixel-based network (SCGCN) for image segmentation. Our proposed...

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos videos, underwater fish is challenging due variations light conditions, water turbidity levels, shape deformations, and similar appearances of fish. This article proposes a robust fish-school algorithm (FSTA). The FSTA based on tracking-by-detection paradigm. To solve problem low...