In this paper the zeroes of the polynomial F(x, z) = 2x 4 −z 3 in Gaussian integers Z[i] are determined, a problem equivalent to finding the solutions of the Diophatine equation x 4 + y 4 = z 3 in Z[i], with a focus on the case x = y. We start by using an analytical method that examines the real 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 3 and c = −2η 4 for η ∈ Z[i].

The spread of novel coronavirus disease (COVID-19) has resulted in chaos around the globe. The infected cases are still increasing, with many countries still showing a trend of growing daily cases. To forecast the trend of active cases, a mathematical model, namely the SIR model was used, to visualize the spread of COVID-19. For this article, the forecast of the spread of the virus in Malaysia has been made, assuming that all Malaysian will eventually be susceptible. With no vaccine and antiviral drug currently developed, the visualization of how the peak of infection (namely flattening the curve) can be reduced to minimize the effect of COVID-19 disease. For Malaysians, let’s ensure to follow the rules and obey the SOP to lower the R0 value from time to time, hoping that the virus will vanish one day.

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

A Heron triangle is a triangle that has three rational sides (a,b,c) and a rational area, whereas a perfect triangle is a Heron triangle that has three rational medians (k,l,m) . Finding a perfect triangle was stated as an open problem by Richard Guy [Unsolved Problems in Number Theory (Springer, New York, 1981)]. Heron triangles with two rational medians are parametrized by the eight curves C1,…,C8 mentioned in Buchholz and Rathbun [‘An infinite set of heron triangles with two rational medians’, Amer. Math. Monthly 104(2) (1997), 106–115; ‘Heron triangles and elliptic curves’, Bull. Aust. Math.Soc. 58 (1998), 411–421] and Bácskái et al. [Symmetries of triangles with two rational medians, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.65.6533, 2003]. In this paper, we reveal results on the curve C4 which has the property of satisfying conditions such that six of seven parameters given by three sides, two medians and area are rational. Our aim is to perform an extensive search to prove the nonexistence of a perfect triangle arising from this curve.