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Readily prepared cis-β-(α',α'-dimethyl)-4'-methindolylstyrenes undergo acid-catalyzed intramolecular hydroindolation to afford tetrahydrobenzo[ cd]indoles. Our experimental and computational investigations suggest that dispersive interactions between the indole and styrene preorganize substrates such that 6-membered ring formation is preferred, apparently via concerted protonation and C-C bond formation. When dispersion is attenuated (by a substituent or heteroatom), regioselectivity erodes and competing oligomerization predominates for cis substrates. Similarly, all trans-configured substrates that we evaluated failed to cyclize efficiently.

Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might be vulnerable to power outages, data misuse, privacy or security violations, and an audit trail. Hence, researchers have focused recently on cutting-edge technologies to develop secure HIE systems and ensure data privacy during transactions. Among such technologies, blockchain and artificial intelligence (AI) occupy a vital role in researchers' focuses and efforts to detect risky transactions in HIE systems, thus enhancing their security and privacy. While the blockchain allows HCFs to link to each other without requiring a central authority, AI models offer an additional security layer when sharing patient data between HCFs. This paper presents a survey about HIE systems, and the aim is two-fold: we first present the architecture of HIE systems along with their challenges; then, we categorize and classify the current state-the-art-techniques that show the potential of using blockchain and AI technologies in such systems. [ABSTRACT FROM AUTHOR]

Readily prepared cis-β-(α’,α’-dimethyl)-4’-methindolylstyrenes undergo Brønsted acid-catalyzed intramolecular hydroarylation to afford a variety of 3-aryl-5,5-dimethyl-1,3,4,5-tetrahydrobenzo[cd]indoles. Our experimental and computational investigations suggest that dispersive interactions between the indole and styrene preorganize substrates such that 6-membered ring formation occurs via a concerted hydroarylation step. When dispersability is attenuated (by a substituent or heteroatom), regioselectivity erodes and competing oligomerization predominates for cis substrates; all trans-configured substrates that we evaluated failed to cyclize efficiently.

Effective data fragmentation is essential in enhancing the performance of distributed database management systems (DDBMS) by strategically dividing extensive databases into smaller fragments distributed across multiple nodes. This study emphasizes horizontal fragmentation and introduces an advanced machine learning algorithm, Red Fox Optimization-based Support Vector Machine (RFO-SVM), designed for optimizing the data fragmentation process. The input database undergoes meticulous pre-processing to address missing data concerns, followed by analysis through RFO-SVM. This algorithm efficiently classifies features and target labels based on class labels. The RFO algorithm optimizes critical SVM parameters, including the kernel, kernel parameter, and boundary parameter, leveraging the accuracy metric. The resulting classified data serves as fragments for the fragmentation process. To ensure precision in fragmentation, a Genetic Algorithm (GA) allocates these fragments to diverse nodes within the DDBMS, optimizing the total allocation cost as the fitness function. The proposed model, implemented in Python, significantly contributes to the efficient fragmentation and allocation of databases in distributed systems, thereby enhancing overall performance and scalability. [ABSTRACT FROM AUTHOR]

In this article, we propose UPDEMIN, a novel approach for predicting UPcoming patient situation, DEcision Making and INtervention in wireless body sensor network. Inspired by the National Early Warning Score (NEWS) system, a new representation and usage of all the vital signs scores to create a diagnostic label (DL) representing the overall health situation. The formulation of the DL does not help only in accurate prediction of the upcoming health situation but provides a decoding mechanism to identify the reasons for deterioration and which vital signs are driving it. Simulations show high accuracy in predicting several upcoming situations ahead of time with 90% accuracy. Compared to other methods, UPDEMIN shows superior performance with less historical data or a smaller database, exceeding 91% prediction accuracy versus 66% for the closest method. The performance of the classification model has shown perfect precision and recall values, as demonstrated by the F1-score, which recorded 0.92. Moreover, the predicted situation can be decoded easily to identify which vital signs cause criticality, allowing the medical team to make the appropriate decision and prepare for the right intervention. The capability to decode the predicted health situation is unique and expected to have an insightful return on preventative intervention decisions. [ABSTRACT FROM AUTHOR]