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BACKGROUND: Skin cancer shows geographic and ethnic variation. New Zealand-with a predominantly fair-skinned populations, high UV indices and outdoor lifestyles-has high rates of skin cancer. However, population prevalence data is lacking. This study aimed to determine the demographics and socioeconomic disease trends of non-melanoma skin cancer prevalence in New Zealand from a large targeted-screening study. METHODS: A targeted screening programme was conducted among 32,839 individuals, Fitzpatrick Skin Types I to IV in Auckland, New Zealand during the 2008-2022 period. This data was analyzed retrospectively. Linear regression models were used to assess statistical trends of skin cancer prevalence over time, along with associated factors that included demographics, disease trends and overall prevalence. RESULTS: A total of 32,839 individuals were screened and 11,625 skin cancers were detected. 16,784 individuals were females who had 4,378 skin cancers. 16,055 individuals were males who had 5,777 skin cancers. 54 males and 65 females had multiple skin cancers. The article presents detailed descriptions of tumour types and subtypes detected, age groups, demographic and socioeconomic information. regarding the non-melanoma skin cancers detected. CONCLUSION: Overall men have more non-melanoma skin cancer (NMSC) than females; however females develop more BCC on the lips. BCC is three times more common in the 31-50 age group, whereas SCC are significantly more prevalent after age 80. Prevalence of BCC has not changed over the 15-year timeframe of the study but SCC has increased. Older ages and higher incomes are associated with higher rates of NMSC in New Zealand.
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Introduction: The challenge of navigating a Mobile robot in dynamic environments has grasped significant attention in recent years. Despite the available techniques, there is still a need for efficient and reliable approaches that can address the challenges of real-time near optimal navigation and collision avoidance. Methods: This paper proposes a novel Log-concave Model Predictive Controller (MPC) algorithm that addresses these challenges by utilizing a unique formulation of cost functions and dynamic constraints, as well as a convergence criterion based on Lyapunov stability theory. The proposed approach is mapped onto a novel recurrent neural network (RNN) structure and compared with the CVXOPT optimization tool. The key contribution of this study is the combination of neural networks with model predictive controller to solve optimal control problems locally near the robot, which offers several advantages, including computational efficiency and the ability to handle nonlinear and complex systems. Results: The major findings of this study include the successful implementation and evaluation of the proposed algorithm, which outperforms other methods such as RRT, A-Star, and LQ-MPC in terms of reliability and speed. This approach has the potential to facilitate real-time navigation of mobile robots in dynamic environments and ensure a feasible solution for the proposed constrained-optimization problem.
