The sustainable development of Madeira Island necessitates the implementation of more precise and targeted planning strategies to address its regional challenges. Given the urgency of this issue within the context of sustainability, planning approaches must be grounded in and reinforced by a comprehensive array of thematic studies to fully grasp the complexities involved. This research leverages Geographic Information Systems (GIS) to analyze land use and occupancy patterns and their evolution within the municipality of Machico on Madeira Island. The study provides a nuanced perspective on the urban structure’s stagnation in the region, while concurrently highlighting the dynamic shifts in agricultural practices. Furthermore, it elucidates the transformation of predominant native vegetation within the municipality from 1990 to 2018. Notably, the research underscores the alarming decline in native vegetation due to anthropogenic activities, emphasizing the need for more rigorous monitoring by regional authorities to safeguard and preserve these valuable landscapes, habitats, and ecosystems.

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.

This study introduces a model designed to improve the strategic readiness of private hospitals in Amman by incorporating strategic competencies as an independent variable and using a healthcare information system as a mediator. Targeting private hospitals with over 140 beds, the research included a population of 3263 employees across various managerial levels. Data collection methods involved interviews and electronic questionnaires, resulting in a sample size of 344. Statistical analyses comprised exploratory and confirmatory factor analysis, structural equation modeling, and hypothesis testing with SMART PLS 3.3.3 software. The results indicated medium levels of both strategic competencies and healthcare information systems, while strategic readiness was found to be low. Nevertheless, the proposed model showed a direct positive effect of strategic competencies on strategic readiness, with the healthcare information system acting as a significant partial mediator. Evaluation metrics included the arithmetic mean, standard deviation, and path analysis. This model surpasses traditional methods by effectively linking strategic competencies and information systems to enhance strategic readiness, providing a strong framework for improving hospital responses to crises and dynamic changes. The study suggests focusing on enhancing and developing strategic competencies and integrating a comprehensive healthcare information system to optimize hospital operations and increase readiness.

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system’s architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

The implementation of data interoperability in healthcare relies heavily on policy frameworks. However, many hospitals across South Africa are struggling to integrate data interoperability between systems, due to insufficient policy frameworks. There is a notable awareness that existing policies do not provide clear actionable direction for interoperability implementation in hospitals. This study aims to develop a policy framework for integrating data interoperability in public hospitals in Gauteng Province, South Africa. The study employed a conceptual framework grounded in institutional theory, which provided a lens to understand policies for interoperability. This study employed a convergence mixed method research design. Data were collected through an online questionnaire and semi-structured interviews. The study comprised 144 clinical and administrative personnel and 16 managers. Data were analyzed through descriptive and thematic analysis. The results show evidence of coercive isomorphism that public hospitals lack cohesive policies that facilitate data interoperability. Key barriers to establishing policy framework include inadequate funding, ambiguous guidelines, weak governance, and conflicting interests among stakeholders. The study developed a policy to facilitate the integration of data interoperability in hospitals. This study underscores the critical need for the South African government, legislators, practitioners, and policymakers to consult and involve external stakeholders in the policy-making processes.

The rapid advancement of information and communication technology has greatly facilitated access to information across various sectors, including healthcare services. This digital transformation demands enhanced knowledge and skills among healthcare providers, particularly in comprehensive midwifery care. However, midwives in rural areas face numerous challenges such as limited resources, cultural factors, knowledge disparities, geographic conditions, and technological adoption. This research aims to evaluate the impact of AI utilization on midwives’ knowledge and behavior to optimize the implementation of healthcare services in accordance with Delima Midwife Service standards in rural settings. The analysis encompasses competencies, characteristics, information systems, learning processes, and health examinations conducted by midwives in adopting AI. The research methodology employs a cross-sectional approach involving 413 rural midwives selected proportionally. Results from Partial Least Squares Structural Equation Modeling indicate that all reflective evaluation variables meet the required criteria. Fornell-Larcker criterion demonstrates that the square root of AVE is greater than other variables. The primary findings reveal that information systems (0.029) and midwives’ competencies (0.033) significantly influence AI utilization. Furthermore, midwives’ competencies (0.002), characteristics (0.031), and AI utilization (0.011) also significantly impact midwives’ knowledge and behavior. Midwives’ characteristics also significantly affect their competencies (0.000), while midwives’ learning influences health examinations (0.000). Midwives’ knowledge and behavior affect the transformation of healthcare services in rural midwifery (0.022). The model fit results in a value of 0.097, empirically supporting the explanation of relationships among variables in the model and meeting the established linearity test.