Chronic conditions like diabetes and hypertension are prevalent worldwide, impacting a large portion of the population. It’s imperative to develop more efficient healthcare models to manage the burden of chronic dise...
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Sexual harassment and gang rape in Egypt have garnered attention from both traditional and digital media. This study employed a volunteer HarassMap to analyse sexual harassment crimes (SHCs) across Egypt from a spatia...
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Sexual harassment and gang rape in Egypt have garnered attention from both traditional and digital media. This study employed a volunteer HarassMap to analyse sexual harassment crimes (SHCs) across Egypt from a spatial perspective. The specific aims were to apply the Hierarchical Density-Based Spatial Clustering of Applications with Noise (hdbscan) algorithm to locate clusters of reported SHCs, and to assess their spatial dependence on land use types. To accomplish this task, ring buffers of 100, 200, 300, 400, and 500 metres were established around each crime scene to determine which land use was mostly associated with the incidence of these SHCs. Local bivariate relationships were used to explore the associations between SHC and each land-use category. Results from the hdbscan algorithm revealed four crime clusters within the study domain, mainly located in Greater Cairo, Alexandria, and Behaira. Notably, commercial establishments and transit stations showed a significantly positive correlation with SHC. The study shows how land uses shape SHC and showed that it is possible to identify environmental risk factors for harassment. These risk factors can help policymakers, urban planners, and community stakeholders prevent and reduce sexual harassment and gender inequality, and promote just and inclusive societies. The study examined sexual harassment crimes from a geographical standpoint. It utilised the Egyptian Sexual Harassment Map based on survivors' comments. Commercial districts and transit stations are the land uses that have the strongest spatial associations with SHC and could be made ***
The application of modern machine learning methods in industrial settings is a relatively new challenge and remains in the early stages of development. Current computational power enables the processing of vast number...
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The application of modern machine learning methods in industrial settings is a relatively new challenge and remains in the early stages of development. Current computational power enables the processing of vast numbers of production parameters in real time. This article presents a practical analysis of the welding process in a robotic cell using the unsupervised hdbscan machine learning algorithm, highlighting its advantages over the classical k-means algorithm. This paper also addresses the problem of predicting and monitoring undesirable situations and proposes the use of the real-time graphical representation of noisy data as a particularly effective solution for managing such issues.
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