The purpose of this study is to investigate customer satisfaction with quality of service known as SERVQUAL improvement or service quality competitiveness in emerging markets. Using Indonesian government medical care as an example the author examines the satisfaction of patients. Information and data were collected through a survey of 399 BPJS users in Indonesia. All data were analyzed using Smart PLS. This study demonstrates that there is a negative value associated with the five-dimensional gap. As a result, the care provided to BPJS patients is below par. Specifically, the sensitivity dimension has the largest disparity at 0.15, while the physical evidence dimension has the smallest at 0.49. In order to raise the level of service provided, it may be necessary to take direct measures or examine tangible evidence. This study develops the relationship between different quality service models. There appears to be a substantial increase in the body of literature in the area of service quality, allowing for constant updates and the incorporation of the lessons learned from the experiences of the departed. These revised guidelines are intended to aid SERVQUAL study participants. The study gives practical support to academics and practitioners in directing service quality improvement through the use of data collected from large-scale surveys of patients and medical professionals as doctors in Indonesia.

The boom in nanotechnology over the last three decades is undeniable. Responsible for this interest in nanomaterials are mainly the nanostructured forms of carbon, since historically they were the ones that inaugurated the study of nanomaterials with the discovery of fullerenes in 1985 and carbon nanotubes in 1991. Although a variety of techniques exist to produce these materials, chemical vapor deposition (CVD) is particularly valuable as it allows the production of a wide variety of carbon nanostructures, is versatile, scalable, easy to implement and relatively low cost. This review article highlights the importance of CVD and details its principles, operating conditions and parameters, as well as its main variants. A description of the technique used to produce fullerenes, nano-ceramics, carbon nanotubes, nanospheres, graphene and others is made, emphasizing the specific parameters for each synthesis.

This study investigates the impact of toll road construction on 59 micro, small, and medium enterprises in Kampar, Pekanbaru, and Dumai cities. The research aims to analyze the economic and environmental effects of infrastructure expansion on businesses’ profitability and sustainability, providing insights for policymakers and stakeholders to develop mitigation strategies to support MSMEs amidst ongoing infrastructure development. Structural equation modeling, spatial environmental impact analysis, and qualitative data analysis using five-level qualitative data analysis (FL-QDA) were all used together in a mixed-methods approach. Data collection involved observations, interviews, questionnaires, and geospatial analysis, including the use of a Geo-Information System (GIS) supported by drone reconnaissance to map affected areas. The study revealed that the toll roads significantly enhanced connectivity and economic growth but also negatively impacted local economies (β = 0.32, R² = 0.60, P-value ≤ 0.05). and the environment (β = 0.34, P-value ≤ 0.05), as 49% of respondents experienced a 50% decrease in profitability. To mitigate the risk of impact, policymakers should prioritize the principle of prudence to evaluate the significance of mitigation policy implementation (β = 0.144, P-value ≥ 0.05). In a nutshell, toll road construction significantly impacts MSMEs’ business continuity, necessitating an innovative strategy involving monitoring and participatory approaches to mitigate risk.

Natural Protected Areas (NPAs) are critical for biodiversity conservation and ecological balance. These areas are not only refuges for wildlife but also pivotal in promoting sustainable tourism. Geoparks, a unique subset of NPAs, emphasize geological heritage, offering distinctive educational and recreational opportunities. This article explores the significance of Geoparks in Portugal for geotourism and assesses the accessible digital communication strategies of Portuguese Geoparks, emphasizing the analysis of pedagogical concerns. The study highlights the importance of online engagement in enhancing visitor experiences and promoting sustainable tourism practices.

Our study aims to investigate the impact of management control on the performance of Moroccan companies. Through an in-depth literature review and a survey conducted among companies from various sectors in Morocco, the crucial role played by tools such as cost accounting methods, budgetary control, and balanced scorecard in ensuring effective management were identified and highlighted. These tools enable accurate cost assessment, sound financial planning, and significant improvement in organizational performance. In light of these findings, the adoption and effective utilization of these tools as a means to enhance the competitiveness and sustainability of Moroccan companies were recommended.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.