Multimodal Data Evaluation for Classification Problems

Social media data is currently the main input to a wide variety of research works in many knowledge fields. This kind of data is generally multimodal, i.e., it contains different modalities of information such as text, images, video or audio, mainly. To deal with multimodal data to tackle a specific task could be very difficult. One of the main challenges is to find useful representations of the data, capable of capturing the subtle information that the users who generate that information provided, or even the way they use it. In this paper, we analysed the usage of two modalities of data, images, and text, both in a separate way and by combining them to address two classification problems: meme's classification and user profiling. For images, we use a textual semantic representation by using a pre-trained model of image captioning. Later, a text classifier based on optimal lexical representations was used to build a classification model. Interesting findings were found in the usage of these two modalities of data, and the pros and cons of using them to solve the two classification problems are also discussed.

An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis

A simple method for estimating soybean yield under ideal environments in Japan is proposed. Several models that simulate soybean yield have been proposed in other countries; however, direct adaptation to Japanese species is difficult in terms of climatic and regional characteristics. In addition, they often require variety-specific information or various environmental information, which is sometimes hard to simulate. Therefore, we attempted to create a simple simulation model with meteorological data as the main input to the model. The proposed model ignores the features that need setting for each cultivated field and is composed of a statistical model instead of a physiological analysis for the sake of brevity. Although the prediction accuracy of the model needs to be improved, we can use it as a decision support system for soybean cultivation because it requires only location information and can be easily introduced by many farmers.

Big Data Analytics for Spatio-Temporal Service Orders Demand Forecasting in Electric Distribution Utilities

This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil.

Human visual gamma for color stimuli: When LGN drive is equalized, red is not special

Strong gamma-band oscillations in primate early visual cortex can be induced by spatially homogeneous, high-contrast stimuli, such as color surfaces. Compared to other hues, particularly strong gamma oscillations have been reported for red stimuli. However, precortical color processing and the resultant strength of input to V1 has often not been fully controlled for. This leaves the possibility that stronger responses to some hues were due to differences in V1 input strength. We presented stimuli that had equal luminance and color contrast levels in a color coordinate system based on color responses of the lateral geniculate nucleus, the main input source for area V1. With these stimuli, we recorded magnetoencephalography in 30 human subjects. We found narrowband color-induced gamma oscillations in early visual cortex, which, contrary to previous reports, did not differ between red and green stimuli of equal L-M cone contrast. Notably, blue stimuli with contrast exclusively on the S cone axis induced very weak gamma responses, as well as smaller event-related fields and poorer change detection performance. The strength of human color gamma responses could be well explained by the strength of thalamic input induced by each hue and does not show a clear red bias when this input strength is properly equalized.

Investigation of Energy Recovery from Municipal Solid Waste: A Case Study of Al-Karak City / Jordan

Municipal solid waste (MSW) from Al-Karak city is monitored and characterized. The MSW is sorted into 14 different waste categories. In addition, the MSW is separated into several size fractions, followed by sorting and characterization. The average daily production rate of waste is 61.5 ton/day. The MSW generated per capita is estimated to be 0.55 kg/capita/day. The main input MSW comprised a modest proportion of organic content 38.8 %. Other combustible fractions are present in significant proportions, allowing the waste to be utilized for energy recovery. Refuse-derived fuels (RDFs) may be made from waste fractions > 100 mm and 50 – 100 mm.

Financing conditions of renewable energy projects – results from an EU wide survey

This data note aims to present a dataset with values for financing conditions for renewable energy projects in Europe. This includes weighted average cost of capital, cost of debt, cost of equity, debt share, debt service coverage ratio and loan tenors. The dataset was elaborated in the framework of the "Auctions for Renewable Energy Support II" project (AURES II). The main goal of the AURES II project is to provide policy support to the Member States of the European Union in order to improve the effectiveness and cost-efficiency of auctions for renewable energy support. As part of the AURES II project, an extensive survey (structured interviews) was conducted between September 2019 and April 2020 with different stakeholders involved in the renewable energy industry, such as banks, project developing companies, and investment funds, among others. The technologies covered were solar photovoltaics (PV), wind onshore, and wind offshore. Interviewees were asked to provide values for financing conditions for specific projects (for certain cases, country estimates or ranges of values were provided). Spain, Portugal, Greece, Germany, and Denmark were selected as focus countries, for which the interviews also included qualitative questions to discuss the observed quantitative data in these countries. The presented data has been used as the main input to elaborate an AURES II report on renewable energy financing conditions in Europe.

Design Methodology for Interfacing DERs to Power Systems through VSC

This paper presents a methodology to connect distributed energy resources via an 84-pulse voltage source converter to three-phase system grid or load of standard or nonstandard voltage values. Transfer function blocks are included to illustrate interfacing among converters. The main input-output values to be considered in the application are detailed and the system can be modified to be included in other systems without loss of generality. The definition of the reactive component for supporting grid or load variations without degrading the overall performance is carried by for the DC-DC converter. A control variable for reducing the DC gain is used to improve the settling time. Our proposal defines the capacitive and inductive component values for an operating point and gives the option to reduce them when adding smoothed variations and adaptive controllers.

Colombia’s GHG Emissions Reduction Scenario: Complete Representation of the Energy and Non-Energy Sectors in LEAP

The signatory countries of the Paris Agreement must submit their updated Intended National Determined Contributions (INDCs) to the UNFCCC secretariat every five years. In Colombia, this activity was historically carried out with a wide set of diverse non-interconnected sector-specific models. Given the complexity of GHG emissions reporting and the evaluation of mitigation actions on a national scale, the need for a centralized platform was evident. Such approach would allow the integration and analysis of potential interactions among sectors, as well as to guarantee the homogeneity of assumptions and input parameters. In this paper, we describe the construction of an integrated bottom-up LEAP model tailored to the Colombian case, which covers all IPCC sectors. An integrated model facilitates capturing synergies and intersectoral interactions within the national GHG emissions system. Hence, policies addressing one sector and influencing others are identified and correctly assessed. Thus, 44 mitigation policies and mitigation actions were included in the model, in this way, identifying the sectors directly and being indirectly affected by them. The mitigation scenario developed in this paper reaches a reduction of 28% of GHG emissions compared with the reference scenario. The importance of including non-energy sectors is evident in the Colombian case, as GHG emission reductions are mainly driven by AFOLU. The first section describes the GHG emissions context in Colombia. Next, we describe the model structure, main input parameters, assumptions, considerations, and used LEAP functionalities. Results are presented from a GHG emissions accounting and energy demand perspective. The model allows for the correct estimate of the scope and potential of mitigation actions by considering indirect, unintended emissions reductions in all IPCC categories, as well as synergies with all mitigation actions included in the mitigation scenario. Moreover, the structure of the model is suitable for testing potential emission trajectories, facilitating its adoption by official entities and its application in climate policymaking.

Pharmaceutical Compounds in Aquatic Environments—Occurrence, Fate and Bioremediation Prospective

Various contaminants of emerging concern (CECs) have been detected in different ecosystems, posing a threat to living organisms and the environment. Pharmaceuticals are among the many CECs that enter the environment through different pathways, with wastewater treatment plants being the main input of these pollutants. Several technologies for the removal of these pollutants have been developed through the years, but there is still a lack of sustainable technologies suitable for being applied in natural environments. In this regard, solutions based on natural biological processes are attractive for the recovery of contaminated environments. Bioremediation is one of these natural-based solutions and takes advantage of the capacity of microorganisms to degrade different organic pollutants. Degradation of pollutants by native microorganisms is already known to be an important detoxification mechanism that is involved in natural attenuation processes that occur in the environment. Thus, bioremediation technologies based on the selection of natural degrading bacteria seem to be a promising clean-up technology suitable for application in natural environments. In this review, an overview of the occurrence and fate of pharmaceuticals is carried out, in which bioremediation tools are explored for the removal of these pollutants from impacted environments.

Water reuse in the new regulatory framework for basic sanitation

In practically all processes, water is used as the main input. The high levels of consumption by users have been causing adverse changes in the environment. For cities, the challenge lies in the collection and treatment of sewage, due to the concentration of people. In 2019 it was found that only 28.01% of sewage is collected in northeastern Brazil, and only 36.24% is treated, thus demonstrating the complexity of basic sanitation in Brazil. Since basic sanitation is a set of measures to preserve the environment, to prevent diseases and improve the quality of life, in 2020 the New Legal Framework for Basic Sanitation was published (Law 14.026/2020). The agenda on the reuse of water is growing, considering the water shortage in the country, and also because users depend on water in specific quantity and quality. There are reports of water reuse since ancient Greece and countries have been investing in reuse ever since. Legislation must not restrictively position itself to prevent reuse, but must act in an adequate manner to ensure the health and well-being of the population. Thus, the new legal framework for sanitation is aimed at reducing losses in water distribution, improving treatments, reusing sanitary effluents and using rainwater. The National Water and Sanitation Agency was responsible for drawing up regulations on reuse. With that, the objective of the research was to analyze the peculiarities presented by the new basic sanitation framework, considering the possible propositions for the reuse of water. The methodology used was based on bibliographical analysis on sanitation, supply and reuse of water, in addition to the analysis of current legislation. It was concluded that water reuse is an alternative to improve sanitation indicators in the country, since most water bodies in Brazil are supplied by rainwater, with the exception of the Amazon River, which receives water from the melting of the Andes mountain range. In addition, the new sanitation framework legitimized reuse, with a view to better use and management of water, since it is the main input for all production processes, in addition to standing out in the energy matrix, with hydroelectric plants.