Perspective Chapter: Airborne Pollution (PM2.5) Forecasting Using Long Short-Term Memory Deep Recurrent Neural Network Optimized by Gaussian Process

Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning, which is useful to treat temporary data such as pollution levels, leaving aside the physical and chemical characteristics of the particles and only focusing on predicting the next levels of contamination. This work seeks to contribute to society by presenting a useful way to optimize recurrent neural networks of the short and long-term memory type through a statistical process (Gaussian processes) for the correct optimization of the processes

Analysis of Key Features of Non-Linear Behavior Using Recurrence Plots. Case Study: Urban Pollution at Mexico City

ABSTRACT The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type non-linear problem. Two different approaches have been used to tackle this problem. The first approach is to show results according to monitoring network. The second approach is to show the results by particle type. This analysis shows the feasibility of using Recurrence Analysis for pollution monitoring and control

A Linear Criterion to sort Color Components in Images

The color and its representation play a basic role in Image Analysis process. Several methods can be beneficial whenever they have a correct representation of wave-length variations used to represent scenes with a camera. A wide variety of spaces and color representations is founded in specialized literature. Each one is useful in concrete circumstances and others may offer redundant color information (for instance, all RGB components are high correlated). This work deals with the task of identifying and sorting which component from several color representations offers the majority of information about the scene. This approach is based on analyzing linear dependences among each color component, by the implementation of a new sorting algorithm based on entropy. The proposal is tested in several outdoor/indoor scenes with different light conditions. Repeatability and stability are tested in order to guarantee its use in several image analysis applications. Finally, the results of this work have been used to enhance an external algorithm to compensate the camera random vibrations.El color y su representación juegan un papel fundamental en el proceso de análisis de imagen. Varios métodos pueden ser beneficiosos siempre que tengan una representación correcta de las variaciones de longitud de onda usadas para representar la escena. Una amplia variedad de espacios y representaciones de color se basa en la literatura especializada. Cada uno de ellos es útil en circunstancias concretas y puede ofrecer información de color redundante (por ejemplo, todos los componentes RGB están altamente correlacionados). En este trabajo se identifica y clasifica cuál componente ofrece la mayor cantidad de información acerca de la escena, a partir de varias representaciones de color. Este enfoque se basa en el análisis de las dependencias lineales entre cada canal y la implementación de un nuevo algoritmo para clasificar los componentes en base a la entropía. La propuesta se pone a prueba en varias escenas al aire libre y en interiores con diferentes condiciones de luz. La repetitividad y la estabilidad son probadas para garantizar su uso en aplicaciones de análisis de imágenes. Finalmente, los resultados de este trabajo son usados para mejorar un algoritmo externo para la compensación de vibraciones

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries