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With the implementation by the European Union since the 1980s of strict measures to reduce emissions of air pollutants (PM10, PM2.5, NH3, CH4, O3, NOX, SOX, VOCs (volatile organic compounds), etc.), these emissions have fallen from a total of nearly 65 million tons in 1990 to around 20 million tons in 2021, according to data from the European Environment Agency. In the road transport sector, the implementation of the first European emissions standard in 1992 and the electrification of vehicles have made it possible to reduce exhaust emissions. As non-exhaust emissions increased, the European Commission introduced thresholds for braking systems for certain vehicle categories in the future Euro 7 standard. Car manufacturers are looking for solutions to reduce brake particle emissions, including the modification of the composition of the brake pads and discs. This literature review aims to present the state of the art of a set of parameters that can influence brake particle emissions. The parameters highlighted here include the raw materials and manufacturing process parameters of the brake pads, the composition of brake discs, some test parameters, and some characteristics of brake pads and discs. A brief analysis of the tribological mechanisms that could be involved in particle emissions is also described.
Edible seaweeds are a rich source of antioxidants, essential amino acids, polysaccharides, polyunsaturated fatty acids, vitamins, and minerals. Several studies have investigated seaweed's gelling and thickening properties for use in the food industry. This chapter provides an overview of the potential applications of seaweed extracts and whole seaweeds as functional ingredients to enhance the nutritional, textural, and sensory attributes of food products (e.g., dairy, meat, bakery, and other products). Based on the studies, seaweed in the form of powder or extract can improve food products' nutritional, textural, and sensory properties. Additionally, seaweed also affects food products' health properties. Furthermore, seaweed's impact differs considerably depending on the species and concentration used, so seaweed-based commercial products need to be optimally formulated. According to the study, adding seaweed extracts or whole seaweeds to the diet had a positive impact on health, shelf-life, and overall food quality.
The 2018 Mexican presidential election represents a distinctive case study, extensively monitored and analyzed through social media streams and news media data. This election holds particular significance in political science due to the high level of citizen participation and the intense political activity observed on social networks. A substantial volume of data was generated and shared by individuals aged 18 to 35, who notably used political memes as a medium for political expression. This paper presents a comprehensive spatiotemporal data analytics framework designed to uncover and explain the complex political trends and insights related to Mexican citizens' voting intentions during the 2018 presidential election. The dataset analyzed spans the entire presidential campaign and includes diverse geographic regions within Mexico, from the northern, central, and southern areas. It consists of a corpus of 20,000 tweets, 300 Facebook memes, and detailed election results data. Our findings reveal significant trends and insights that link social media data with official election outcomes, even indicating a clear preference for the winning candidate well before election day.
Diabetes is a chronic disease, which is characterized by high levels of glucose in the blood and by too little insulin production or when it cannot be used effectively. The number of people who develop type 1 diabetes is increasing every year, it usually appears in the childhood or youth, and can develop during the development of the fetus in the womb, feeding during the first years of life, etc. Regarding type 2 diabetes, it occurs mostly from forty years and people suffering from obesity or other chronic diseases. This work presents the development of a web system application to support the pre-diagnosis of type II diabetes, with a precision acceptable using classification machine learning algorithms. The detection and diagnosis process will be facilitated with the use of machine learning algorithms for this, a framework will be developed using information based on health databases to anticipate whether the patient presents symptoms of diabetes or not, providing a basic diagnosis to anticipate the level threat with greater accuracy.
