The present paper discusses the case of the Madrid Nuevo Norte Project (MNNP) in order to examine the relation of this mega-project with the city’s sustainable development. For this reason, the study used a qualitative approach using semi-structural interviews with experts (Madrid’s town hall, Madrid State, and the program management office and other external) that relayed strongly with MNNP. The expert panel requirements are split in six expertise areas: sustainability, urban development, urban planning, government or public affairs, project management or Madrid Nuevo Norte (MNN) key stakeholders. The study highlighted the vital importance of MNNP as a flagship sustainable project for the rest of Europe, that meets sustainability criteria for contributing substantially in the improvement of the quality of life of final users and for the community in general. For instance, it contributes to the regeneration of the city’s degraded area, to the interconnection of an isolated part of the city and public transportation connection, improving the external image of Madrid. Despite of it, there are some challenges that should be carefully managed such as applying sustainable solutions from other cities not properly tailored to Madrid, housing pricing accessibility increase due to the lack of terrain in Madrid and the politization of the project as discussion topic between local parties. In this context, local authorities should give particular emphasis in complying with the principles of sustainability for improving the overall performance of MNNP, ensuring social justice and prosperity for the people of Madrid.

Climate change plays a vital role in shaping the knowledge construction of farmers for managing their agricultural land. Therefore, this study aims to analyze the coffee farmers’ knowledge construction process regarding climate change. This research utilizes qualitative methods. This research approach uses the grounded theory, which can help researchers uncover the relationship between the coffee farmers’ knowledge construction and climate change. The data were collected through semi-structured interviews and analyzed using constant comparative methods. The transcription of the field notes was analyzed using NVivo version 12, a program for analyzing qualitative data. There were 33 informants in the study. This study found that the conditions and situations of wind speed and uncertain whether strongly influence the farmers’ construction of climate knowledge. Coffee farmers are looking for new ways to respond to climate change, such as increasing the intensity of the care they give to their coffee plants, gradually harvesting according to the ripeness of the coffee fruits, finding alternative ways to dry the coffee beans, and reducing the use of fertilizer. However, coffee farmers are also starting to adapt old knowledge from their parents to the latest perceived climate phenomena, so that they can look for alternative sources of livelihood outside their farms. This knowledge construction process serves as a form of adaptation by the coffee farmers to climate change, and reflects the dynamic between traditional knowledge and current experience. Understanding this knowledge construction helps coffee farmers to cope with climate change and to design appropriate policy strategies to support the sustainability of coffee farming in an era of climate change. Further research is needed at the regional level.

Industrial heritage is a legacy from the past that we live with today and pass on to future generations. The economic value of this heritage can be defined as the amount of welfare that it generates for society, and this value should not be ignored. However, current research based on economic analysis has mostly focused on qualitative statements instead of quantitative assessment. This study proposes an innovative methodology combining qualitative (field research) and quantitative (willingness to pay and contingent valuation) methods to assess the economic value of industrial heritage. The industrial heritage of Tangshan, China, was chosen as a case study, and the research found that museums and cultural creative parks are effective ways to conserve industrial heritage. The entrance fee can be used to represent the economic value of the heritage site. There was a positive correlation between the influence of economic value and the entrance fees residents would prefer to pay. The results indicate the locals would prefer lower entrance fees for the transformed heritage museums (The average current cost: $2.23). Locals were most concerned about the entrance fees for the Kailuan Coal Mine and Qixin Cement Plant Museums, which have both been renewed as urban landmarks for city tourism. Renewal methods have been applied to six industrial heritage sites in Tangshan; these sites have their own conservation and renewal practices based on city-level development or industrial attributes. Thus, when residents recognize the economic value of a heritage site, they are willing to pay a higher entrance fee. This research demonstrates the economic value of industrial heritage using a mixed methods approach and provides a basis for assessing the value of cultural heritage for urban tourism analysis.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.

Green manufacturing is increasingly becoming popular, especially in lubricant manufacturing, as more environmentally friendly substitutes for mineral base oil and synthetic additives are being found among plant extracts and progress in methodologies for extraction and synthesis is being made. It has been observed that some of the important performance characteristics need enhancement, of which nanoparticle addition has been noted as one of the effective solutions. However, the concentration of the addictive that would optimised the performance characteristics of interest remains a contending area of research. The research was out to find how the concentration of green synthesized aluminum oxide nanoparticles in nano lubricants formed from selected vegetable oils influences friction and wear. A bottom-up green synthesis approach was adopted to synthesize aluminum oxide (Al₂O₃) from aluminum nitrate (Al(NO₃)₃) precursor in the presence of a plant-based reducing agent—Ipomoea pes-caprae. The synthesized Al₂O₃ nanoparticles were characterized using TEM and XRD and found to be mostly of spherical shape of sizes 44.73 nm. Al₂O₃ nanoparticles at different concentrations—0.1 wt%, 0.3 wt%, 0.5 wt%, 0.7 wt%, and 1.0 wt%—were used as additives to castor, jatropha, and palm kernel oils to formulate nano lubricants and tested alternately on a ball-on-aluminum (SAE 332) and low-carbon steel Disc Tribometer. All the vegetable-based oil nano lubricants showed a significant decrease in the coefficient of friction (CoF) and wear rate with Ball-on-(aluminum SAE 332) disc tribometer up to 0.5wt% of the nanoparticle: the best performances (e_COF = 92.29; e_WR = 79.53) came from Al₂O₃-castor oil nano lubricant and Al₂O₃-palm kernel oil; afterwards, they started to increase. However, the performance indices displayed irregular behaviour for both COF and Wear Rate (WR) when tested on a ball-on-low-carbon steel Disc Tribometer.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.