Automated temporal NDVI analysis over the Middle East for the period 1982 - 2010

The NDVI time-series consist of trend, season and noise. Changes in the season component are related to climate factors and they happen gradually over long period of time. The changes in the trend component are often due to human activities, fires and etc. This paper implements two algorithms (PolyTrend and DBEST) in R language, in order to examine the vegetation changes in the Middle East and to give more possibilities in the hands of the remote sensing communities. DBEST can analyse the gradual and the abrupt changes by decomposing the data, while PolyTrend classifies the inter-annual change between the picks of the green season. PolyTrend and DBEST were adapted for R language environment. Two additional algorithms were developed to apply both algorithms over NDVI3g data set of the Middle East. A third algorithm discovered the affected land-cover through an overlay analysis by the use of the UMD land-cover classification data set. PolyTrend showed linear (4%), quadratic (2%) and cubic (3%) trends. The different trend types were often found to be grouped in clusters. The largest clusters of trends were found near the south-eastern corner of the Arabian Peninsula and in the central regions of Saudi Arabia. More than 10% of all mixed forests were affected by these trends, most of which were in negative direction. DBEST showed that 1% of the vegetation experienced a higher magnitude of change. Clusters of these changes were mainly located in the south-eastern and the western part of Turkey, the northern regions of Iraq and Syria, as well as along the coastlines of the Black Sea and the Caspian Sea. The changes were mainly related to the cropland and the grassland and were more in positive directions. The study demonstrated the potential of PolyTrend and DBEST in R language for the remote sensing. It concludes that probably climatic factors affected the forests in Turkey and Iran. The high magnitude of changes of the cropland and grassland indicates that in some regions the agriculture expanded, while in others it declined.The constant Earth observations from space allow monitoring of the vegetation changes on a regional scale. The changes in vegetation can be long and gradual (e.g. due to climatic factors) or more sudden and abrupt (e.g. due to fires, diseases and etc.). In order to estimate the changes of the vegetation, researchers use algorithms that decompose the observed data to seasonal, trend and remainder (e.g. noise). The algorithms that can distinguish these changes are of limited number, often hard to be accessed and most of the existing ones could be applied only to specific situations. This paper implements two such algorithms (PolyTrend and DBEST) in R language, in order to give more possibilities in the hands of the remote sensing communities, and both are used to examine the vegetation changes in the Middle East. DBEST can analyse the gradual and the abrupt changes by decomposing the data, while PolyTrend classifies the inter-annual change between the picks of the green season. PolyTrend and DBEST were re-coded and adapted for R language environment. Two other algorithms were developed to apply both algorithms over imagery data of the Middle East for the period between 1982 and 2010. A third algorithm related the results to a specific class of vegetation by comparing the results from the last two and a land-cover data set. PolyTrend showed linear (4%), quadratic (2%) and cubic (3%) trends. The different trend types were often found to be grouped in clusters. The largest clusters of trends were found near the south-eastern corner of the Arabian Peninsula and in the central regions of Saudi Arabia. More than 10% of all mixed forests were affected by these trends, most of which were in negative direction. DBEST showed that 1% of the vegetation experienced a higher magnitude of change. Clusters of these changes were mainly located in the south-eastern and the western part of Turkey, the northern regions of Iraq and Syria, as well as along the coastlines of the Black Sea and the Caspian Sea. The changes were mainly related to the cropland and the grassland and were more in positive directions. The study demonstrated the potential of PolyTrend and DBEST in R language for the remote sensing. The obtained results showed that long gradual inter-annual changes affected the forests in Turkey and Iran. The reasons for these changes should be further investigated, but are probably related to climatic factors. The land-cover associated with high magnitude of more sudden changes was related to grassland and cropland. This leads to the suggestion that in some regions the agriculture expanded, while in others it declined

Diseño de una guía para análisis situacional y planteamiento de soluciones en seguridad biológica ambiental para ambientes de salud

En el Perú no existe regulación sobre Bioseguridad Medioambiental en edificaciones ligadas al sector salud. La existencia de un sistema de evaluación de Bioseguridad Medioambiental para este tipo de edificaciones generaría información útil para mejorar los criterios en los que se basen los métodos para el diseño, edificación y mantenimiento de una institución de salud. En el Perú existen establecimientos ligados al sector salud que fueron diseñados para otros fines y no cumplen con las recomendaciones internacionales en bioseguridad medioambiental. Esto aumenta la probabilidad de que exista contaminación de usuarios, trabajadores, muestras biológicas y equipos, pudiendo generarse errores de determinación de casos clínicos, contaminaciones cruzadas, epidemias; pudiendo presentarse un accidente, el cual se origina por la conjunción de tres factores: la condición medioambiental insegura, la actitud insegura y el riesgo. Un Programa de Manejo de Bioseguridad medioambiental requiere de un constante intercambio de información entre todas las áreas de la institución de salud, para así evitar accidentes Se propone una guía como herramienta de valoración de calidad de la bioseguridad medioambiental de un ambiente ligado al sector salud, basada en manuales y normas internacionales vigentes al momento de la redacción. La guía es de fácil entendimiento, teniendo como parámetros básicos valederos para la evaluación de la bioseguridad medioambiental: la temperatura, humedad, presión ambiental, suministro eléctrico, material particulado y valores máximos de sonido en el área, y encuestas de conocimiento sobre bioseguridad y seguridad del personal que labora en el área. As se brinda un listado de verificación y encuestas base, que promueve el ordenamiento y gráfico de datos colectados en hojas de cálculo, pudiendo con la guía proponer soluciones a posibles problemas detectados durante un análisis.Tesi

Reducing Waste in Healthcare: A Systemic Design Approach for Sustainable Disposables Manufacturers

Efficient waste management is crucial in the healthcare system due to the complex composition and associated risks to workers, patients, and the environment. While there is growing awareness of the need to change the system, there is still an urgent need for a sustainable healthcare system. Sustainability concepts can guide designers in taking action, from considering waste reduction in the choice of materials to optimizing management systems through information and staff training. Systemic design methods can help involve business teams in the development of sustainable practices and strategies for production, use, and disposal. Research aimed to identify the types of disposables produced according to their polymers and weight, and to detect associated problems. The quantification of results led to the development of strategies aimed at reducing the number of polymer types used in product manufacturing with A case study on the Neonatal Nasal Mask, a consumable device was conducted to apply sustainable design strategies

Pronosticando las dinámicas competitivas en la Industria Cervecera Argentina

La cerveza es una de las bebidas alcohólicas más consumidas en el mundo. Particularmente en Argentina, es la de mayor consumo junto con el vino. En una industria que creció más de un 10% en 2021 y en la que, cada vez más, proliferan nuevas variedades y sabores que buscan adaptarse a las necesidades de los consumidores, poder contar con proyecciones precisas respecto a volumen de venta se vuelve un desafío complejo para los principales jugadores del mercado. En Cervecería y Maltería Quilmes (CMQ), la compañía cervecera líder en el mercado argentino, la proyección de la demanda es un pilar fundamental en la operatoria y planificación de corto, mediano y largo plazo. Con un portfolio de más de 100 SKUs activos que se distribuyen a lo largo y ancho del territorio nacional, contar con un pronóstico lo más preciso posible para todos ellos es determinante para los resultados de la empresa, puesto que dichas proyecciones constituyen el input que se usa para realizar el plan de producción. Si las proyecciones son demasiado optimistas, resultando en una venta menor a la esperada, los productos quedan varados en las plantas y depósitos, lo cual deriva no solo en costos de almacenamiento de la mercadería, sino eventualmente también en costos de derrame por el vencimiento de los líquidos. Por otro lado, si las proyecciones son muy pesimistas, podría incurrirse en costos de stock-out. Por estas razones, la precisión del pronóstico debe ser alta no solo a nivel producto, sino también a nivel geográfico. A pesar de contar con varias herramientas y drivers internos para la construcción del forecast, tales como la venta histórica y diversos equipos regionales que proveen un análisis focalizado en sus respectivas plazas, existe la oportunidad de sumar de manera pertinente el volumen histórico de los demás jugadores del mercado, tal de poder incorporar las dinámicas competitivas a las proyecciones. El objetivo de este trabajo consiste en explorar la posibilidad de refinar el forecast de demanda actual de la compañía proyectando el volumen de todos los competidores, construyendo así un modelo de industria y participación de mercado que permita mejorar la toma de decisiones, no solo en el campo de la proyección de la demanda, sino también en el de la planificación estratégica. Se puede pensar en la industria como la conjunción de dos macro-variables: el segmento y el calibre de venta. El análisis de ambas puede darnos una idea de lo que el consumidor de cerveza está buscando a la hora de adquirir un producto y, por ende, donde debiera estar el foco de las empresas proveedoras de este. Teniendo en cuenta estas dos dimensiones competitivas del mercado, se construirá un ranking de prioridades a atacar, que estará dado por la detección de aquellas combinaciones segmento-calibre donde se encuentren las mayores oportunidades de captura de participación de mercado. La recomendación de negocio estará basada en la priorización de la asignación de recursos a aquellas combinaciones que se encuentren en la cima del ranking

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection

Dimension reduction methods for functional data have been avidly studied in recent years. However, existing methods are primarily based on summarizing the data by their projections into principal component subspaces, namely the functional principal component analysis (fPCA). While fPCA could be effective sometimes, in this thesis we show with both real and synthetic data examples some pitfalls of this approach, especially when the components of interest of the functional data are orthogonal to the leading principal components. In multivariate data analysis, a possible alternative, the projection pursuit technique, was proposed by Kruskal (1972) and Friedman and Tukey (1974). In this thesis, we extend the idea of projection pursuit to functional data analysis. We develop several new computational tools needed to implement the high-dimensional projection pursuit. We apply this functional projection pursuit technique to three problems: (i) normality test for functional data; (ii) forecasting the functional time series; and (iii) change point detection for functional data. For each problem, a simulation study and several data analyses are provided to show the advantages of our proposed method to existing methods in the literature that mostly based on principal component analysis

Evaluating and Forecasting the Operational Performance of Road Intersections

Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of the whole transport network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data or expert opinions. However, due to the advancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies, it is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic conditions and enabling situational awareness of vehicle drivers. Towards this end, we design, develop and evaluate a system for evaluating and forecasting the operational performance of road intersections by mining streams of V2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network abstraction. In addition, the system enables interactive analysis and exploration of the various MOEs. The system architecture and methods are general and can be used in various settings requiring continuous monitoring and/or forecasting of the road network state

A statistical model of internet traffic.

PhDWe present a method to extract a time series (Number of Active Requests (NAR)) from web cache logs which serves as a transport level measurement of internet traffic. This series also reflects the performance or Quality of Service of a web cache. Using time series modelling, we interpret the properties of this kind of internet traffic and its effect on the performance perceived by the cache user. Our preliminary analysis of NAR concludes that this dataset is suggestive of a long-memory self-similar process but is not heavy-tailed. Having carried out more in-depth analysis, we propose a three stage modelling process of the time series: (i) a power transformation to normalise the data, (ii) a polynomial fit to approximate the general trend and (iii) a modelling of the residuals from the polynomial fit. We analyse the polynomial and show that the residual dataset may be modelled as a FARIMA(p, d, q) process. Finally, we use Canonical Variate Analysis to determine the most significant defining properties of our measurements and draw conclusions to categorise the differences in traffic properties between the various caches studied. We show that the strongest illustration of differences between the caches is shown by the short memory parameters of the FARIMA fit. We compare the differences revealed between our studied caches and draw conclusions on them. Several programs have been written in Perl and S programming languages for this analysis including totalqd.pl for NAR calculation, fullanalysis for general statistical analysis of the data and armamodel for FARIMA modelling

Developing a web-based system to visualize vegetation trends by a nonlinear regression algorithm

Comparing with traditional linear regression methods that used to monitor vegetation trends, a nonlinear regression algorithm (PolyTrend) developed by Jamali et al. (2014) can provide more accurate information of vegetation trends by fitting a polynomial line with a degree of up to three for ASCII-formed time-series NDVI (Normalized Difference Vegetation Index) dataset of a single pixel. To extend the ability of the PolyTrend algorithm for processing time-series NDVI satellite imagery and to increase its accessibility, a web-based system for visualizing vegetation trends by the PolyTrend algorithm has been developed. The PolyTrend web-based system allows users to define the value of statistical significance of the PolyTrend algorithm, the nominal range of the input data, and the range of desired NDVI input to be processed. It applies the PolyTrend algorithm to each pixel of the uploaded time-series NDVI satellite imagery dataset. It returns the types of vegetation changes, the slope of the changes of NDVI values in the whole time span, and whether the net change of NDVI increases or decreases during this period, in the forms of ASCII files (i.e. text files) and binary files (i.e. images). By refining the existing PolyTrend algorithm written in MATLAB and embedding it in a web environment, the PolyTrend web-based system has proved its ability in monitoring global vegetation trends using raw time-series NDVI satellite imagery.An index called NDVI (Normalized Difference Vegetation Index) has been widely used to describe the reflectance characteristics of land features (Lillesand et al. 2008). The temporal dynamics of vegetation (i.e. vegetation trends) can be gained by monitoring the changes of NDVI. In all publications founded by the author, straight-line relationships have been used to describe vegetation trends. However, straight lines cannot fit to all real-world situations of vegetation growth. An algorithm (PolyTrend) developed by Jamali et al. (2014) solved this problem by assuming cubic-polynomial relationships exist in vegetation trends at the beginning. However, the original version of this algorithm could only accept time-series NDVI values of a single pixel stored in a text file. To enable the PolyTrend algorithm to process image-level information of NDVI and to disseminate this algorithm, an online system (i.e. the PolyTrend web-based system) that includes this algorithm was developed to allow users to upload raw time-series satellite imagery containing NDVI values that gained from the Internet. After the PolyTrend algorithm processes the imagery, the PolyTrend web-based system returns values of the range and the inclination of the net changes of NDVI and the types of vegetation trends classified by the algorithm. These results are downloadable in the forms of images and text files with explanation. The images map the temporal dynamics of vegetation directly while the text files can be imported to other software for generating other forms of data and revealing statistical results. The PolyTrend web-based system provides a convenient way of monitoring global vegetation trends through the Internet with an innovative algorithm