Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

The rise of online gambling in Indonesia has emerged as a significant public health concern, driven by various psychological, social, and regulatory factors. Despite stringent laws prohibiting gambling, the accessibility and appeal of online platforms have led to increased participation, particularly among young adults. This phenomenon is characterized by a paradoxical sense of control that users feel while gambling online, which can lead to compulsive behaviors and addiction. The structural characteristics of online gambling platforms, including fast-paced games and easy accessibility, further exacerbate this issue. Social influences, particularly through social media and peer interactions, normalize gambling behaviors, making them more appealing to adolescents. Mental health issues, such as anxiety and depression, are closely linked to online gambling addiction, as individuals may use gambling as a coping mechanism. The COVID-19 pandemic has intensified these challenges, with many individuals turning to online gambling for entertainment during lockdowns. To address the growing prevalence of online gambling addiction, comprehensive regulatory frameworks are needed, alongside responsible gambling initiatives and public awareness campaigns. Collaboration among stakeholders, including government agencies, healthcare providers, and gambling operators, is crucial for effective intervention. Continuous monitoring and evaluation of online gambling trends will inform future policies and help identify emerging risks. By adopting a multifaceted approach, Indonesian policymakers and stakeholders can work towards minimizing the risks associated with online gambling and fostering a healthier environment for its citizens.

The fast-growing field of nanotheranostics is revolutionizing cancer treatment by allowing for precise diagnosis and targeted therapy at the cellular and molecular levels. These nanoscale platforms provide considerable benefits in oncology, including improved disease and therapy specificity, lower systemic toxicity, and real-time monitoring of therapeutic outcomes. However, nanoparticles' complicated interactions with biological systems, notably the immune system, present significant obstacles for clinical translation. While certain nanoparticles can elicit favorable anti-tumor immune responses, others cause immunotoxicity, including complement activation-related pseudoallergy (CARPA), cytokine storms, chronic inflammation, and organ damage. Traditional toxicity evaluation approaches are frequently time-consuming, expensive, and insufficient to capture these intricate nanoparticle-biological interactions. Artificial intelligence (AI) and machine learning (ML) have emerged as transformational solutions to these problems. This paper summarizes current achievements in nanotheranostics for cancer, delves into the causes of nanoparticle-induced immunotoxicity, and demonstrates how AI/ML may help anticipate and create safer nanoparticles. Integrating AI/ML with modern computational approaches allows for the detection of potentially dangerous nanoparticle qualities, guides the optimization of physicochemical features, and speeds up the development of immune-compatible nanotheranostics suited to individual patients. The combination of nanotechnology with AI/ML has the potential to completely realize the therapeutic promise of nanotheranostics while assuring patient safety in the age of precision medicine.

There has been a growing interest in studying dysfunctional personality traits in the workplace. In line with this trend, this study examines how the Dark Triad personalities (Machiavellianism, narcissism, and psychopathy) can predict innovative work behavior. Additionally, the study builds on Trait Activation Theory and proposes a moderating effect of training satisfaction on this relationship. The purpose of this study is to understand if the Dark Triad traits predict innovative behavior while simultaneously examining the role of training satisfaction in channeling these traits toward innovative behavior. A questionnaire-based survey was conducted on the five largest telecommunication companies in Pakistan. The data gathered was analyzed using structural equation modeling. Results established a positive relationship between each trait of the Dark Triad and innovative work behavior. Moreover, training satisfaction was found to moderate the relationship between the psychopathy trait and innovative work behavior. In light of these findings, the study contributes to personality-behavior research in organizations by demonstrating that the Dark Triad predicts innovative work behavior in managers and that the innovative behaviors associated with the psychopathy trait can be enhanced in the presence of training satisfaction.