Cone beam computed tomography (CBCT) is the modern third dimension applied in the field of oral maxillofacial region. With lower radiation dose compared to conventional CT, its applications in dentistry has increased tremendously. Artefacts can seriously degrade the quality of computed tomographic (CBCT) images, sometimes to the point of making them diagnostically unusable. To optimize image quality, it is necessary to understand why artifacts occur and how they can be prevented or suppressed. CT artifacts originate from a range of sources; physical based, scanner based and patient based. This article highlights the causes of artefacts on CBCT images and methods to avoid them.

Aim: To develop imeglimin-inspired novel 1,3,5-triazine derivatives as antidiabetic agents. Materials & methods: These derivatives were synthesized and tested against DPP enzymes. Compound 8c was tested for in vivo antidiabetic activity in streptozotocin-induced diabetes in Wistar rats by estimating various biochemical parameters. Docking experiments were also performed. Results: Compound 8c was identified as a selective and potent DPP-4 inhibitor. It was proficiently docked into the catalytic triad of Ser 630, Asp 710 and His740 in S1 and S2 pockets of DPP-4. In experimental animals, it also showed dose-dependent improvement in blood glucose, blood insulin, bodyweight, lipid profile and kidney and liver antioxidant profiles. Conclusion: This study demonstrated the discovery of imeglimin-inspired novel 1,3,5-triazines as a potent antidiabetic agent.

The Modern agriculture industry is datacentred, precise and smarter than ever. Advanced development of Internet-of-Things (IoT) based systems redesigned "smart agriculture". This emergence in innovative farming systems is gradually enhancing the crop yield, reduces irrigation wastages and making it more profitable. Machine learning (ML) methods achieve the requirement of scaling the learning performance of the model. This paper introduces a hybrid ML model with IoT for yield prediction. This work involves three phases : preprocessing, feature selection(FS) and classification. Initially, the dataset is preprocessed and FS is done on the basis of Correlation based FS (CBFS) and the Variance Inflation Factor algorithm (VIF). Finally, a two-tier ML model is proposed for IoT based smart agriculture system. In the first tier, the Adaptive k-Nearest Centroid Neighbour Classifier (aKNCN) model is proposed to estimate the soil quality and classify the soil samples into different classes based on the input soil properties. In the second tier, the crop yield is predicted using the Extreme Learning Machine algorithm (ELM). In the optimized strategy, the weights are updated using modified Butterfly Optimization algorithm (mBOA) to improve the performance accuracy of ELM with minimum error values. PYTHON is the implementation tool for evaluating the proposed system. Soil dataset is utilized for performance evaluation of the proposed prediction model. Various metrics are considered for the performance evaluation such as accuracy, RMSE, R2, MSE, MedAE, MAE, MSLE, MAPE and Explained Variance Score (EVS).

Novel 1,3,5-triazine derivatives bearing oxazine have been synthesized and tested for their ability to inhibit a panel of dipeptidyl peptidase (DPP)-4, 8, and 9 enzymes. In a comparative inhibitory assay,...

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSNassisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.