Search

The ecology and all of its components are suffering greatly as a result of the unchecked speed of development. At this rate, environmental degradation will have an impact on humanity and associated fields. In order to prevent the consequences of expansion from pushing the environment into a situation from which it is incapable of recovering, there should be ongoing, earnest efforts made towards sustainable development. The three pillars of ecodevelopment are the environment, humanity, and economy. A stable growth rate is necessary to attain a just balance between these pillars. Since agriculture employs the majority of the population, it also has an impact on the balance of the ecosystem. Because every unplanned step towards progress puts us back in the environmental front, we must thus be mindful of the boundaries of environmental challenges in order to achieve equitable economic growth. The hope for sustainable development lies in decreased deforestation, greater food security, conservative agricultural practices, the use of biopesticides, and prudent use of natural resources. To be effective, environmental policy probably needs to employ a variety of tools, each addressing a distinct aspect of the issue and attempting to minimise redundancies and pointless regulations. Appropriately pricing environmental inputs facilitates sustainable resource provision and use management. Long-term corporate investment in new technology and innovation is encouraged by a consistent and clear environmental policy, which also increases investment certainty. Environmental and economic success are interdependent. Economic activity and advancement depend on the natural environment because it provides the resources needed to produce goods and services and because it processes and absorbs waste and pollution, which are unwanted byproducts. This paper focuses on how environmental assets assist control risks associated with social and economic activities, flood risks, local climate regulation (temperature and air quality), and the availability of clean water and other resources.

The inclusion of several new models and methods had been taken into consideration by the educational institutes and organisations to battle the negative impact of the COVID-19 pandemic across the educational paradigm. The study looked into the research questions and the research objectives which were based on the core topic of the study. With the help of the section of the literature review, the examination of the past research was looked into. The provision of the themes and concepts was done through the integration of data from secondary sources and the qualitative research method was implemented. The future scope of the study and the limitations of the respective assignment was also jotted down.

Human resource management is included within an organisation for recruiting employees, guiding work procedures, providing training, and developing policies to motivate and satisfy employees for better productivity and profit. This study sheds light on the importance of human resource management to influence employee turnover and job satisfaction of the employees. The study provided an overview of the different factors which prove to have a dramatic influence on employee turnover and the job satisfaction of the workers. The strategies integrated by human resource management for the heightening such crucial aspects of employee turnover and job satisfaction was observed in the study.

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]