This study develops an optimisation model to facilitate inter-facility medicine sharing in response to anticipated medicine shortages. These facilities include hospitals and medical representatives. We adopt the concept of collective response proposed in our study literature. The optimisation model is developed according to the real-world practices of inter-facility medicine sharing. We utilise case studies of particular healthcare networks to demonstrate the efficacy of the developed model. The efficacy encompasses the model’s application to real-world case studies, as well as its validity and reliability within a specific system. The results show that the developed model is able to determine which facilities should share the requested amount of medicines; and to reduce total lead times by at least one day compared to the ones obtained in the current practice. The model can be used as a decision-support tool for healthcare practitioners when responding to shortages. The study presents the managerial implications of medicine sharing at the network level and supports the development of collaboration amongst facilities in response to medicine shortages.

The objective of this paper is to assess the influence of various types of crises, including the Subprime, COVID-19, and political crises, on corporate governance attributes, regulations, and the association with bank risk. The consecutive occurrences of crises have significantly impacted the global economy, causing substantial disruptions across various facets of the international banking system. Our hypothesis posits that these crises not only influence governance characteristics and regulations but also impact their correlation with the risk and financial distress experienced by banks. Our study is conducted within the Tunisian context spanning from 2000 to 2021, utilizing a GMM regression on a dataset comprising 221 bank-year observations. Our findings indicate that crises have a discernible effect on the relationship between corporate governance and bank risk, as well as between regulation and bank risk. Our results are strong in a range of sensitivity checks, including the use of alternative proxies to measure the bank risks and corporate governance metrics.

Indonesia has ratified United Nations Convention on the Law of the Sea 1982 (UNCLOS 1982) through Law No. 17 of 1985 concerning the ratification of the 1982 Law of the Sea Convention, thus binding Indonesia to the rights and obligations to implement the provisions of the 1982 convention, including the establishment of the three Northern-Southern Indonesia’s Archipelagic Sea Lane (ALKI). The existence of the three ALKI routes, including ALKI II, has led to various potential threats. These violations not only cause material losses but, if left unchecked and unresolved, can also affect maritime security stability, both nationally and regionally. The maritime security and resilience challenges in ALKI II have increased with the relocation of the capital, which has become the center of gravity, to East Kalimantan. The research in this article aims to identify and analyze the factors influencing the success of maritime security and resilience strategies in ALKI II. The factors used in this research include conceptual components, physical components, moral components, command and control center capabilities, operational effectiveness, command and control effectiveness, and the moderating variables of resource multiplier management and risk management to achieve maritime security and resilience. This study employed a mixed-method research approach. The factors are modeled using Structural Equation Modeling (SEM) with WarpPLS 8.0 software. Qualitative data analysis used the Soft System Methodology (SSM). The results of the study indicate that the aforementioned factors significantly influence the success of achieving maritime security and resilience in ALKI II.

In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.

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.

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).