In this paper the zeroes of the polynomial F(x, z) = 2x 4 −z 3in Gaussian integers Z[i] are determined, a problem equivalent to finding the solutions of the Diophatine equation x 4 + y4 = z3 in Z[i], with a focus on the case x = y. We start by using an analytical method that examines thereal and imaginary parts of the equation F(x, z) = 0. This analysis sheds light on the general algebraic behavior of the polynomial F(x, z) itself and its zeroes. This in turn allows us a deeper understanding of the different cases and conditions that give rise to trivial and non-trivial solutions to F(x, z) = 0, and those that lead to inconsistencies. This paper concludes with a general formulation of the solutions to F(x, z) = 0 in Gaussian integers. Results obtained in this work show the existence of infinitely many non-trivial zeroes for F(x, z) = 2x 4 −z 3 under the general form x = (1 + i)η 3and c = −2η 4 for η ∈ Z[i].

Radiology is a vital diagnostic tool for multiple disorders that plays an essential role in the healthcare sector. Nurses are majorly involved in a healthcare setting by accompanying patients during the examination. 'us, nurses tend to be exposed during inward X-ray examination, requiring them to keep up with radiation use safety. However, nurses’ competence in radiation is still a concept that has not been well studied in Malaysia. 'e study aimed to define the level of usage understanding and radiation protection among Malaysian nurses. In this research, a cross-sectional survey was conducted among 395 nurses working in hospitals, clinics, and other healthcare sectors in Malaysia. 'e survey is based on the developed Healthcare Professional Knowledge of Radiation Protection (HPKRP) scale, distributed via the online Google Forms. SPSS version 25.0 (IBM Corporation) was used to analyze the data in this study. Malaysian nurses reported the highest knowledge level in radiation protection with a mean of 6.03 ± 2.59. 'e second highest is safe ionizing radiation guidelines with 5.83 ± 2.77, but low knowledge levels in radiation physics and radiation usage principle (4.69 ± 2.49). 'erefore, healthcare facilities should strengthen the training standards for all nurses working with or exposed to radiation.

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications