Numerical solution of the onset of Buoyancy‐driven nanofluid convective motion in an anisotropic porous medium layer with variable gravity and internal heating

The onset of buoyancy‐driven convective motion in a nanofluid saturated anisotropic porous medium layer is examined numerically in the occurrence of uniform internal heat source under the variable gravity field. Three kinds of gravity force variation functions: (a) G(z) = ?z (linear), (b) G(z) = ?z² (parabolic), and (c) G(z) = ?z³ (cubic) are considered. Wide‐range governing parameters impacts are inspected on the beginning of convective motion under the zero nanoparticle flux situation at the boundaries using the higher term Galerkin technique. It is established that the thermal anisotropy parameter η and the gravity variation parameter λ delay the arrival of convective motion, while the mechanical anisotropy parameter ξ, the internal heating parameter Hs, the nanoparticle Rayleigh‐Darcy number R_np, the modified diffusivity ratio NA_nf, and the modified nanofluid Lewis number Le_nf rapid the start of convective motion. The size of the convective cells reduces on raising the internal heating parameter Hs, while the gravity variation parameter λ, the mechanical anisotropy parameter ξ, the thermal anisotropy parameter η, the nanoparticle Rayleigh‐Darcy number R_np, the modified diffusivity ratio NA_nf, and the modified nanofluid Lewis number Le_nf amplify the dimension of the convective cells. It is also detected that the arrangement is more unstable for case (c), while it is more stable for case (a).     

Ergonomic assessment for designing manual material handling tasks at a food warehouse in India: A case study

Manual handling of heavy grain bags is a commonplace activity across agriculture produce supply chain. In the present research, a manual material handling involving lifting, lowering, and carrying grain bags along the paths of variable characteristics is critically analyzed to explore the risk associated with it. The humans engaged in this activity are found to be facing discomfort, suffering from pain and musculoskeletal disorders, and undergoing medication for the same. This study proposes and demonstrates a structured ergonomic evaluation methodology to not only assess the level of discomfort/pain but also evaluate risk factors such as load handled, method of handling, frequency of handling, awkward postures, path characteristics, and so on. It employs a systematic multimethod approach consisting of Nordic Musculoskeletal Questionnaire and Borg Rating of Perceived Exertion, Ovako Working posture Assessment System, NIOSH Lifting Equation, Rapid Entire Body Assessment, and Key Indicator Method. The study reveals that about 94% of the sample population suffered from moderate to severe discomfort in ankle, knee, and lower back. Eight basic activities responsible for risky postures are identified. Frequency of handling, weight being handled, back bending/twisting, poor coupling, and walking surface are observed as major contributors to musculoskeletal discomfort. Carrying on shoulder is found to be riskier than carrying on back; however, the risk is found to be significantly lowered by carrying on backpack. The present study identifies the need for task redesign, proper gripping arrangements, auxiliary devices for lifting/carrying, and improvement in the path characteristics.     

Efficient bulk heterjunction solar cells based on a low-bandgap polyfluorene copolymers and fullerene derivatives

A low-bandgap polymer (PF-PThCVPTZ) consisted of fluorene and phenothiazine was designed and synthesized. With the donor–acceptor segment, the partial charge transfer can be built in the polymer backbone leading to a wide absorbance. The absorption spectrum of PF-PThCVPTZ exhibits a peak at 510 nm and an absorption onset at 645 nm in the visible range. As blended with [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) as an electron acceptor, narrow bandgap PF-PThCVPTZ as electron donor shows significant solar cell performance. Under AM 1.5 G, 100 mA/cm² illumination, a power conversion efficiency (PCE) of 1.85% was recorded, with a short circuit current (J_SC) of 5.37 mA/cm², an open circuit voltage (V_OC) of 0.80 V, and a fill factor (FF) of 43.0%.     

Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing

We investigate the problem of scheduling a sequence of cars to be placed on an assembly line. Stations, along the assembly line install options (e.g. air conditioning), but have limited capacities, and hence cars requiring the same options need to be distributed far enough apart. The desired separation is not always feasible, leading to an optimisation problem that minimises the violation of the ideal separation requirements. In order to solve the problem, we use a large neighbourhood search (LNS) based on mixed integer programming (MIP). The search is implemented as a sliding window, by selecting overlapping subsequences of manageable sizes, which can be solved efficiently. Our experiments show that, with LNS, substantial improvements in solution quality can be found.     

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node-selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy-adjustment factor are introduced to optimize the node-selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node-selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data-receiving rate in sufficient time.     

Classification of complex environments using pixel level fusion of satellite data

The present study reports classification and analysis of composite land features using fusion images obtained by fusing two original hyperspectral and multispectral datasets. The high spatial-spectral resolution, multi-instrument and multi-period satellite images were used for fusion. Three pixel level fusion based techniques, Color Normalized Spectral Sharpening (CNSS), Principal Component Spectral Sharpening Transform (PCSST) and Gram-Schmidt Transform (GST), were implemented on the datasets. Performance evaluations of three fusion algorithms were done using classification results. The Support Vector Machine (SVM) and Gaussian Maximum Likelihood Classification (MLC) were used for classification using five types of images, viz. hyperspectral, multispectral and three fused images. Number of classes considered was eight. Sufficient number of ground field data for each class has also been acquired which was needed for supervise based classification. The accuracy was improved from 74.44 to 97.65% when the fused images were considered with SVM classifier. Similarly, the results were improved from 69.25 to 94.61% with original and fused data using MLC classifier. The fusion image technique was found to be superior to the single original image and the SVM is better than the MLC method.

     

Characterization of bread breakdown during mastication by image texture analysis

The methods currently used to characterise food breakdown during mastication are not easily applicable to cohesive and heterogeneous products such as white bread (baguettes). During this study, we investigated the applicability of image texture analysis to characterising the kinetics of bread bolus formation during chewing. Food boluses were collected from five subjects chewing four different breads after 10, 20 and 30 chewing cycles or at swallowing. Images were acquired and analysed using the Grey level co-occurrence matrix (GLCM) method. Food boluses were successfully discriminated: 60–73% of the images were correctly classified into their respective chewing cycles. Incorrect classifications arose from overlapping between subsequent cycles. Among the texture features the contrast was identified as being the best marker of food degradation. Different kinetics of bolus formation were observed between breads. This method revealed specific patterns of food degradation between subjects, which could be explained by their mastication behaviour and chewing efficiency.