In the last several decades, cardiovascular diseases (CVDs) have emerged as a major hazard to human life and health. Conventional formulations for the treatment of CVD are available, but they are far from ideal because of poor water solubility, limited biological activity, non-targeting, and drug resistance. With the advancement of nanotechnology, a novel drug delivery approach for the treatment of CVDs has emerged: nano-drug delivery systems (NDDSs). NDDSs have shown significant advantages in tackling the difficulties listed above. Cytotoxicity is a difficulty with the use of non-destructive DNA sequences. NDDS categories and targeted tactics were outlined, as well as current research advancements in the diagnosis and treatment of CVDs. It’s possible that gene therapy might be included into nano-carriers in the delivery of cardiovascular medications in the future. In addition, the evaluation addressed the drug’s safety.

The H3N2 influenza virus is spiking dramatically, which is a major concern worldwide and in India. The multifunctional hetero-trimer influenza virus RNA-dependent RNA polymerase (RdRP) is involved in the generation of viral mRNA and is crucial for viral infectivity, which is directly related to the virus’s ability to survive. The goal of the current work was to use molecular docking to determine how the RdRP protein might be affected by powerful bioactive chemicals found in Calotropis gigantia latex. By applying CB-dock 2 analysis and 2D interactions, an in-silico docking study was conducted using a GC-FID (gas chromatography with flame-ionization detection) based composition profile. Tocospiro A (15%), Amyrin (7%), and Gombasterol A were found by GC-FID to be the main phytocompounds in the latex of Calotropis gigantia. The docking result showed that ligands were effectively bound to RdRP. According to interaction studies, RdRP/ligand complexes create hydrogen bonds, van der Waals forces, pi-alkyl bonds, alkyl bonds, and pi-Sigma bonds. Therefore, it was suggested that Calotropis gigantia latex may represent a possible herbal remedy to attenuate H3N2 infections based on the above findings of the fragrance profile and docking.

This paper, with its focus on national legislative regulations that have come into force and governments developed policies designed to clear away numerous problems regarding women’s employment has a threefold contribution to the existing literature. First, it summarizes the salient features of the new legislation and administrative measures adopted by the government of Turkyie, with special reference to Bursa Yıldırım Municipality. Second, we draw attention to the increasing recognition of the valuable potential of females in the workplace. Over recent decades and the implications for the central administration but also the private sector, local administration and voluntary agencies. Third, policy syndromes about livelihoods, and hardship alleviation policies, are examined and policy implications are discussed. This paper does not aim to provide definitive answers, yet intends to scrutinize the data and re-examine the trends in the light of key drivers such as economics, demographics, and urbanization. This was done mainly by reviewing the literature government reports and statistical data but was augmented by our fieldwork. There is an attempt to reach a conclusion about recent developments and make suggestions about countermeasures that could be implemented.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.