Financially Feasible and Sustainable - Reviewing Academic Literature on Sustainability related Investment Studies | Book Publisher International

 Sustainable investing is defined as an investment philosophy that prioritises sustainability in investment decisions. Climate change and environmental destruction/protection are examples of sustainability-related issues. Companies are often required to update their operations and investments in a more sustainable direction in order to comply with legislative regulations or simply to stay in business. However, most environmentally beneficial investments are not made as a result of political and legal pressure. The notion of sustainable investing has long been established in professional environments such as insurance companies and institutional investors, but its overall image remains hazy. This ambiguity is especially apparent when it comes to private enterprises' long-term investments, and there is much of need for more research in this area. This research project offers an opportunity to study, investigate, and assess how private enterprises and governments can aggregate value and capitalise on economic prospects by implementing financially viable sustainable measures. A thorough evaluation of scientific literature was conducted, using the United Nations' Sustainable Development Goals as the baseline concept of sustainability. The findings of this search may be of interest to economists and researchers for further research, in addition to governments and private sector investors.

Author(S) Details

Ari Happonen
LUT School of Engineering Science, LUT University, Finland.

Irene Laplaza Osta
LUT School of Engineering Science, LUT University, Finland.

Apurva Potdar
LUT School of Engineering Science, LUT University, Finland.

Jorge Luis Garcia Alcaraz
Department of Industrial Engineering and Manufacturing, Institute of Engineering and Technology, Autonomous University of Ciudad Juarez, Mexico.

View Book:- https://stm.bookpi.org/FFSRALSIS/article/view/3906

How to Reduce Radiation Exposure in the Follow-up of Patients with Scoliosis | Chapter 14 | Highlights on Medicine and Medical Research Vol. 11

 During their development, scoliosis patients are subjected to several x-ray investigations. While the need for x-rays during routine check-ups when clinical measurements are taken can be minimised (ATR, Surface Topography), the sensitivity of children and adolescents with scoliosis cannot be prevented because indications and check-ups also depend on the measurement of the Cobb angle on a complete standing x-ray. As a result, it would make sense to greatly minimise this patient group's exposure to radiation. The reduction of exposure is one way to minimise radiation.

The reduction of the diagnostic area is the other choice. The aim of this study was to see whether a single 45 x 45 cm detector from a DR device could be used to diagnose and follow up on adolescent girls with scoliosis.

Materials and Methods: A total of 32 consecutive standing x-rays of girls aged 10 to 14 were examined to see whether enough information was provided for diagnosis and follow-up of the patients using a single 45 x 45 cm detector from a DR device.

Ground plate Th1 was clearly visible in 31% of the cases, ground plate Th2 in 56%, and ground plate Th3 in 88% of the cases. In 100% of those tested, major curvatures (thoracic and lumbar) were fully visible. In 100% of cases, Risser 3 or 4 would have been (or has been) found.

Conclusion: Adolescent girls with scoliosis can be followed and diagnosed using a DR device with a 45 x 45 cm detector for direct radiography with a dramatic reduction in the diagnostic area. The exposure to radiation of children and adolescents with scoliosis can be drastically reduced by reducing the diagnostic area (window) and the exposure period. Expanding the field size to a level higher than Th3 is rarely required for proper diagnosis and follow-up.

Author (s) Details

Hans-Rudolf Weiss
Schroth Best Practice Academy, Neu-Bamberg, Germany.

Sarah Seibel
Department of Radiology, Medizinisches Versorgungszentrum Mittelmosel, Zell-Barl, Germany.

View Book :- https://stm.bookpi.org/HMMR-V11/article/view/893

Analysis of Different Filtering Methods for Preprocessing and GLCM Feature Extraction Using Wavelet in Mammogram Images | Chapter 15 | Innovations in Medicine and Medical Research Vol. 3

Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologists, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. In mammogram images, micro-calcifications are one of the imperative signs for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. Early stage detection for any medical image analysis application like brain tumor detection, breast cancer detection is considered as an important step. Micro calcification is small calcium deposits in the breast region and mammogram images are of low contrast. Thus, in this work, different types of filtering techniques used for noise reduction and image enhancement for medical image processing are analyzed on mini-MIAS mammogram image databases. Anisotropic diffusion with wavelet filtering method shows best results for enhancement and noise removal of the image. This filtered image is segmented; region of interest (ROI) is extracted through global Thresholding technique with discrete wavelet transform (DWT). Gray level cooccurrence matrix is used to extract the important features. Here, seven features are extracted for different categories of micro calcified images like normal, benign, malignant. Results show that from extracted features the values of malignant and micro calcified images are same whereas normal and benign are same. This proposed methodology can help to categorize different classes of images. 

Author(s) Details

Neha N. Ganvir
Department of Electronics Engineering, Sinhgad Institute of Technology and Science (SITS), Narhe, Sinhgad Technical Education Society (STES), Pune, India.  

Dr. D. M. Yadav
Pune University, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/178