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.

The rapidly growing construction industry often deals with complex and dynamic projects that pose significant safety risks. One of the state-owned companies in Indonesia is engaged in large-scale toll road construction projects with a high incidence of workplace accidents. This study aims to improve safety performance in toll road construction by implementing the Scrum framework. The study uses a System Dynamics approach to model interactions between the Scrum framework, project management, and work safety subsystems. Various scenarios were designed by modifying controlled variables and system structures, including introducing a punishment entity. These scenarios were evaluated based on their impact on reducing incidents and the incident rate over the project period. The results indicate that the combined scenario significantly reduces incidents and incident rates in different conditions. The study also finds a strong relationship between Scrum framework implementation and improved safety performance, demonstrating a reduction in incidents and incident rates by over 50% compared to existing conditions. This research underlines the effectiveness of the Scrum framework in enhancing safety in construction projects.

This study aims to assess the efficacy of speech-to-text (STT) technology in improving the writing abilities of special education pupils in Saudi Arabia. A deliberate sample of 150 special education college students was selected, with participants randomly allocated to either an experimental group employing STT technology or a control group using traditional writing methods. The study utilized a comprehensive approach, which included standardized writing assessments, questionnaires, and statistical analyses such as t-tests, correlation, regression, ANOVA, and ANCOVA. The results demonstrate a substantial enhancement in writing skills among the experimental group utilizing Speech-to-Text (STT) technology. The findings contribute to the discussion on assistive technology in special education and offer practical recommendations for educators and policymakers.

This study investigates the interaction between audit firms and key audit matters (KAMs) to measure their impact on financial reporting quality in Palestine, thereby enriching the discourse on financial reporting. A descriptive statistical method was used to analyze the audit reports of listed Palestinian firms from 2018 to 2022. A methodology that scrutinizes the clarity and informativeness of KAMs across different audit firms and KAM types, the research investigates how audit procedures and risk assessments contribute to the comprehensibility of KAM disclosures. The findings highlight a significant disparity in the readability of KAMs attributable to audit firm selection, with the non-Big Four firms exhibiting distinct approaches. This understanding, gathered through multivariate analysis, offers valuable contributions to the ongoing discourse on financial reporting quality, emphasizing the essential role of audit firms in shaping the effectiveness of audit reports and KAM disclosures.

Ticket revenues are crucial for the financial success of sports teams. To maximize these revenues, teams continuously explore effective ticket promotional strategies. One such strategy includes partial season plans, which mirror bundle offers common across various industries. Another widespread promotional strategy across industries is offering discounted credit (i.e., store credit purchased at a lower price than its face value). However, its application in sports (e.g., providing a $500 credit for tickets at $450) remains limited. Therefore, this study explores critical questions such as: “How effective is offering discounted credit compared to partial season plans?” and “What factors influence ticket promotion preferences?” Consequently, the study employed a 2 × 2 × 2 experimental designs, considering three independent variables: promotion type (discounted credit vs. partial season plans), promotion flexibility (predefined vs. customizable), and the consumer’s distance to the venue (near vs. distant). Results indicated that partial season plans generated significantly higher perceived value and purchase intentions  while presenting lower perceived risks than discounted credit . Promotion flexibility did not significantly influence the three dependent variables , but the distance to the venue did . Both practical and theoretical implications of these findings are discussed.

In this paper, we explore the static and dynamic effects of oil rent on competitiveness in Saudi Arabia’s economy during the period 1970–2022. In addition, we examined the short-run, strong and long-run relationships between exports and industry, inflation, energy use (oil rents) and agriculture using the Autoregressive Distributed Lag (ARDL) approach developed. The analysis showed that government spending will contribute to enhancing the competitive environment with a difference of one year. Moreover, the industry will contribute to increasing competitiveness for a positive relationship in the long term. The results stated that there is an insignificant relationship between competitiveness, inflation, and oil rents. The analysis also shows that inflation has a negative impact with statistical significance in the short term. In addition, the error correction model (ECM) coefficient is negative and has statistical significance at 0.76 at a 1% significant level, which indicates the existence of an error correction mechanism and thus the existence of a long-term relationship between the variables.