The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

E-commerce is increasingly developing as a platform large and small companies use to carry out online transactions with consumers. However, the development of e-commerce is also fraught with new environmental problems. Various problems occur, such as data leaks and fraud in buying and selling transactions. This research aims to deepen the study of the e-commerce environment from the perspective of local policymakers, and this research completes a study on the analysis of e-commerce problems and solutions from the perspective of policy actors. The development of e-commerce still has an unsafe environment; the potential for fraud and data leakage is still significant, and a government response is needed, such as creating new regulations or revising existing regulations. This research uses qualitative analysis with a content analysis approach and national online news media as research data and information sources. Nvivo 12 Plus software was used to identify problems and solutions offered by actors in their narratives in the media. The results of this research show that data leaks and e-commerce have the potential to threaten the country’s resilience, conventional businesses are threatened with closure, and policymakers are shifting responsibility for overcoming e-commerce problems. Current regulations still need to be made appropriately to overcome e-commerce problems.

This study explores the marginalization of a poor fishing community in Gwadar, Pakistan. The study provides an insight into how different levels of power, such as hidden, visible/pluralist, and invisible ideological powers, are used in policy arenas to hinder fishers’ access to participatory spaces, decision-making, and resource use. By employing Gaventa’s power cubes analytical model, we analyze fishers’ experiences and prevailing scenarios. Qualitative research methods were used to collect data, including in-depth interviews and participant observation. The finding shows that the interests of the fishing community in fishery policies and ongoing development projects are excluded both with intention and unintentionally. The exclusion of the local fisher community from key spaces brings interruptions and transformations that influence their lives. Due to this, they are induced to join insurgent groups to confront exclusion-based policies in Gwadar, Pakistan.

Global warming is a problem that affects humanity; hence, crisis management in the face of natural events is necessary. The aim of the research was to analyze the passage of Hurricane Otis through Acapulco from the theoretical perspective of crisis management, to understand the socio-environmental, economic, and decision-making challenges. For data collection, content analysis and hemerographic review proved useful, complemented by theoretical contrastation. Findings revealed failures in communication by various government actors; the unprecedented growth of Hurricane Otis led to a flawed crisis management. Among the physical, economic, environmental, and social impacts, the latter stands out due to the humanitarian crisis overflow. It is the first time that Acapulco, despite having a tradition in risk management against hydrometeorological events, faces a hurricane of magnitude five on the Saffir-Simpson scale. Ultimately, the city was unprepared to face a category five hydrometeorological event; institutional responses were overwhelmed by the complexity of the crisis, and the community came together to improve its environment and make it habitable again.

National governments and academic higher education institutions continue to realign human resource development (HRD) strategies to address the gaps in HRD mandate. This study will investigate new and recalibrated skills that higher institutions (HEIs) professionals and the labor force produce to reconfigure curriculum development in tertiary education. The study extracts narrative from 6 curriculum developers, 3 HRD heads and h3 manpower organizations on the labor landscapes from different local and multinational industries from entry-level to mid-career ranges through case scenario-based interviews and focus group discussions to determine the skills around motivation, innovativeness, and adaptability and subsequently integrate strategic initiatives to reconfigure the compatibility of these skills from higher education institutions to post-pandemic industries. The findings reveal skills that can be managed at the individual level, e.g., self-motivation and adaptability as well as the need to emerge from the technological pressures by adapting to organizational and clientele demands. These human resource traits become the mantra of surviving and progressing in a landscape shaped by the pre- and post-pandemic setting and become the basis of HEI programs to match the needs of the labor force and the industries.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.