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.

As a key factor in the macroeconomic process, the interaction between public confidence and the commodity market, especially its impact on commodity facilitation returns and macroeconomic linkages, is worth exploring in depth. This study adopts the TVP-SV-VAR model to analyze the causal linkages, dynamic characteristics, and mechanisms of the interaction, and reveals the following core findings: (1) The economic background and information shocks contribute to the variations in the effects and orientations of the economic variables, which highlight the time-varying nature of the economic interactions. (2) Consumer and investor confidence exert heterogeneous influence on the macroeconomy, and their different responses to the negative effect of interest rates and convenience gains are particularly significant in the post-crisis recovery period. (3) In the short-term perspective, the influence of public confidence on monetary policy and inflation exceeds that in the medium and long term, highlighting the immediate sensitivity of individual economic behavior. (4) Since 2015, accommodative monetary policy has accelerated market capital flows, delaying the interaction between confidence indices and inflation, revealing policy time lag effects. (5) Convenience gains exhibit complex time-varying interactions with key economic parameters (interest rates, commodity prices, and inflation), with 2011 and 2014 displaying particular patterns, mapping differences between short- and long-term mechanisms, respectively. The study highlights the central role of consumer and investor confidence in the precise tailoring of macroeconomic policies, providing a scientific basis for policy forecasting and economic regulation, and contributing to economic stability. Meanwhile, the dynamic evolution of consumer confidence deepens market trend foresight, enhances the precision of market participants’ decision-making, and reinforces the resilience and predictability of economic operations.

This study investigates the potential of developing a maritime tourism project within the blue economy of Pakistan and explores the factors influencing blue growth and maritime tourism. A quantitative research approach has been adopted. The research gathered primary data from diverse experts and stakeholders within the maritime sector and related industries. The study’s target population comprised on various entities involved in these sectors. A sample of around 250 individuals was selected using a convenient sampling technique. The collected data underwent analysis using the Statistical Package for the Social Sciences (SPSS) and the Partial Least Square (PLS) method. This approach was chosen to explore and understand the intricate relationships between variables in the context of the maritime industry. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) techniques were then employed to scrutinize the data further, allowing for a comprehensive examination of the interconnections among the variables identified in the study. This robust methodological approach enhances the study’s credibility and provides valuable insights into the dynamics of the maritime sector and its associated industries. The findings indicate that a balanced approach, valuing business sustainability, top management support, and enabling innovation structures positively impact blue growth. Additionally, uncertainty avoidance and promoting short-term goals have an appositive impact on the blue economy. Moreover, two potential barriers, Functional strategy, and weak competency, do not significantly affect the blue economy. This study lays the foundation for further exploration and implementation of strategies that promote sustainable growth and development in Pakistan’s blue economy. By integrating the insights gained from this study into policy and decision-making processes, stakeholders can work together to create a vibrant and sustainable maritime tourism sector that benefits both local communities and the environment.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).

Competition in the telecommunications market has significant benefits and impacts in various fields of society such as education, health and the economy. Therefore, it is key not only to monitor the behavior of the concentration of the telecommunications market but also to forecast it to guarantee an adequate level of competition. This work aims to forecast the Linda index of the telecommunications market based on an ARIMA time series model. To achieve this, we obtain data on traffic, revenue, and access from companies in the telecommunications market over a decade and use them to construct the Linda index. The Linda index allows us to measure the possible existence of oligopoly and the inequality between different market shares. The data is modeled through an ARIMA time series to finally predict the future values of the Linda index. The results show that the Colombian telecommunications market has a slight concentration that can affect the level of competition.