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Fog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity O(n). [ABSTRACT FROM AUTHOR]

Introduction: Nicotine is the principal alkaloid contained in tobacco and is produced by plants of nightshade family. Nicotine has relatively high toxicity with varied effects on our body. The liver of albino rats resembles human liver both microscopically as well as microscopically. Aims and objectives: The aim of study was to study gross and histopathological changes induced by administration of graded doses of nicotine hydrogen tartrate in albino rats over a period. Material & methods: A total of 36 male albino rats were included in our study and after a period of acclimatization, were divided in to three groups of 12 rats each with one group serving as control while as other two groups received nicotine in concentration of 5mg/10ml and 7.5mg/10ml.The rats were sacrificed later to study for changes in liver anatomy. Results and observation: The most common changes were progressive decrease in weight of albino rats and pentad of microscopic changes including central venous congestion, portal venous congestion, sinusoidal congestion, fatty changes of hepatocytes and inflammation of portal triads. Conclusion: The findings of our study clearly revealed that changes induced in microanatomy of liver in male albino rats were directly related with dosage and duration of nicotine administration and considering the similarity between human liver and liver of male albino rats, the therapeutic administration of nicotine in human population should be rigorously studied by more experimental studies. [ABSTRACT FROM AUTHOR]

Today's forensic science introduces a new research area for digital image analysis formultimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images. Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features, and finally, XGBOOST is used to classify both Copy- Move and splicing forgery. The proposed method classified three types of forgeries, i.e., splicing, Copy-Move, and healthy. Benchmarked (CASIA 2.0, MICCF200) and RCMFD datasets are used for training and testing. On average, the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation, which is far better than existing methods. [ABSTRACT FROM AUTHOR]