In this work, we have proposed a new formulation of a hybrid nanofertilizer (HNF) for slow and sustainable release of nutrients into soil and water. Urea-modified hydroxyapatite was synthesized, which is a rich source of nitrogen, calcium, and phosphate. Nanoparticles such as copper, iron, and zinc were incorporated into urea-modified hydroxyapatite to increase the efficiency of the proposed fertilizer. Different techniques including powder X-ray powder diffraction, Fourier-transform infrared spectroscopy, and scanning electron microscopy were used to get insight into the properties, morphology, and structure of the as-prepared fertilizer. The developed HNF was used in a field experiment on the ladies’ finger ( Abelmoschus esculentus ) plant. The slow release of HNF was observed during leaching studies and confirmed the availability of Ca 2+ , PO 4 3– , NO 2– , NO 3– , Cu 2+ , Fe 2+ , and Zn 2+ . Furthermore, the presence of Cu 2+ , Fe 2+ , and Zn 2+ nutrients in ladies’ finger was confirmed by the inductively coupled plasma-optical emission spectrometry (ICP-OES) experiment. A considerable increase in the physicochemical properties such as swelling ratio and water absorption and retention capacities of the proposed fertilizer was observed, which makes the fertilizer more attractive and beneficial compared with the commercial fertilizer. The composition of the proposed HNF was functionally valuable for slow and sustainable release of plant nutrients. The dose of prepared HNF applied was 50 mg/week, whereas the commercial fertilizer was applied at a dose of 5 g/week to A. esculentus . The obtained results showed a significant increase of Cu 2+ , Fe 2+ , and Zn 2+ nutrient uptake in A. esculentus as a result of slow release from HNF.

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Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.