The rise in the cost of essentials affects every nation around the world, but it has become a major concern for developing nations. It is getting increasingly difficult to keep up with rising prices for everyday items in these countries, where the majority of the population is from the middle class or lower middle class. Inflation, pandemics, wars, and other important variables all contribute to price increases. There may be another significant factor at play, which is supply-chain corruption. The supply chain's unreliable, chaotic, and opaque nature is to blame for this corruption. We are concentrating on the agri-food supply chain in our study. Because many of the current agri-food supply chains are intricate and challenging to monitor, dishonest parties can exploit the situation. Therefore, we suggested a thorough blockchain-based agri-food supply chain to identify the source of price increases. The private Ethereum blockchain was used in the suggested system. Since the private Ethereum blockchain is more efficient, safe, and fast, it was chosen. Smart contracts were created to describe the system and its underlying rules and laws. Furthermore, in order to showcase the usefulness of our smart contracts, we exhibited a sample decentralized application to support our hypothesis. We also gave the system a complete security and vulnerability assessment to make sure it is operating properly and is protected from threats and attacks. Due to the use of blockchain, the system is immutable, transparent, and simple to track and monitor. The proposed system has demonstrated greater transparency, traceability, reliability, speed, security, and cost-efficiency compared to conventional systems. It effectively traces the origin of corruption in the supply chain, providing a more straightforward means to tackle concerns related to price hikes.

Artificial Intelligence (AI)-enabled systems now appear in settings where failures can affect software quality, safety, and security, including autonomous driving, software engineering tools, and large language model services. Fuzzing offers a practical way to stress these systems by generating or mutating test inputs, but the resulting research is scattered across different targets, techniques, oracle designs, and failure definitions. This survey synthesizes 125 primary studies and organizes the literature using a taxonomy of testing targets, system layers, technique families, input-generation and mutation strategies, oracles, and failure types. Our analysis shows rapid but uneven growth, strong target-dependence in technique design, persistent oracle-construction challenges, failures beyond crashes, and the need for reproducible benchmarks and cross-layer evaluation.

We determined the sensitivity, specificity, positive and negative predictive values of physical examination and fine needle aspiration cytology (FNAC) for diagnosis of tumor’s, considering histopathology as standard that included fifty suspected tumor patients. Following the completion of interview for clinical history and physical examination, FNAC and histopathology were performed. Lump was present in 44% cases, weight loss was observed in 42% cases as complaint signs and pain occurred at the site of tumor in 44% cases. Physical examination indicated that 36% cases were malignant and 64% were benign tumors. Histopathological diagnosis of the tumor confirmed that 46% cases were malignant and 54% were benign. Thus, physical examination was sensitive in 52.11%, specific in 77.78%, accurate in 76% and the negative and positive predictive values were 34.37% and 66.57%, respectively. However, FNAC showed 72% neoplasm and 28% non-neoplasm, while histopathology revealed 86% neoplasm and 14% non neoplasm among the patients. So, the sensitivity, specificity, accuracy, false positive and false negative results , positive and negative predictive values of FNAC were 62.50%, 88.46%, 76%, 16.67%, 28.12%, 83.33% and 71.88%, respectively. Particularly in 28% non-neoplasm’s, FNAC showed equal accuracy and sensitivity and that was 50%. In correlation with 27 benign cases and 23 malignant tumors from histopathology, FNAC showed accuracy of 92.60% and 43.48%, respectively. Though FNAC is less sensitive than histopathology in making unequivocal diagnosis of cancer but insignificantly different from histopathology in interpreting atypical or malignant cases of tumors.