bluuebunny/crossref_metadata_2025 huggingface dataset:
Cleaning Noisy and Heterogeneous Metadata for Record Linking across Scholarly Big Datasets
Authors: Sefid Athar , Wu Jian , Ge Allen C. , Zhao Jing , Liu Lu , Caragea Cornelia , Mitra Prasenjit , Giles C. Lee
Published: August 2019

Automatically extracted metadata from scholarly documents in PDF formats is usually noisy and heterogeneous, often containing incomplete fields and erroneous values. One common way of cleaning metadata is to use a bibliographic reference dataset. The challenge is to match records between corpora with high precision. The existing solution which is based on information retrieval and string similarity on titles works well only if the titles are cleaned. We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset. The blocking function uses the classic BM25 algorithm to find the matching candidates from the reference data that has been indexed by ElasticSearch. The core components use supervised methods which combine features extracted from all available metadata fields. The system also leverages available citation information to match entities. The combination of metadata and citation achieves high accuracy that significantly outperforms the baseline method on the same test dataset. We apply this system to match the database of CiteSeerX against Web of Science, PubMed, and DBLP. This method will be deployed in the CiteSeerX system to clean metadata and link records to other scholarly big datasets.
DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information
Authors: Wang Lijing , Chen Jiangzhuo , Marathe Madhav
Published: August 2019

Influenza-like illness (ILI) is among the most common diseases worldwide. Producing timely, well-informed, and reliable forecasts for ILI is crucial for preparedness and optimal interventions. In this work, we focus on short-term but highresolution forecasting and propose DEFSI (Deep Learning Based Epidemic Forecasting with Synthetic Information), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods. In DEFSI, we build a two-branch neural network structure to take both within-season observations and between-season observations as features. The model is trained on geographically highresolution synthetic data. It enables detailed forecasting when high-resolution surveillance data is not available. Furthermore, the model is provided with better generalizability and physical consistency. Our method achieves comparable/better performance than state-of-the-art methods for short-term ILI forecasting at the state level. For high-resolution forecasting at the county level, DEFSI significantly outperforms the other methods.
Early Detection of Vacant Parking Spaces Using Dashcam Videos
Authors: Wu Ming-Che , Yeh Mei-Chen
Published: August 2019

A major problem in metropolitan areas is finding parking spaces. Existing parking guidance systems often adopt fixed sensors or cameras that cannot provide information from the driver’s point of view. Motivated by the advent of dashboard cameras (dashcams), we develop neural-network-based methods for detecting vacant parking spaces in videos recorded by a dashcam. Detecting vacant parking spaces in dashcam videos enables early detection of spaces. Different from conventional object detection methods, we leverage the monotonicity of the detection confidence with respect to the distance away of the approaching target parking space and propose a new loss function, which can not only yield improved detection results but also enable early detection. To evaluate our detection method, we create a new large dataset containing 5,800 dashcam videos captured from 22 indoor and outdoor parking lots. To the best of our knowledge, this is the first and largest driver’s view video dataset that supports parking space detection and provides parking space occupancy annotations.
Determining Solvability in the Birds of a Feather Card Game
Authors: Araki Shuto , Uribe Juan Pablo Arenas , Wilkerson Zach , Bogaerts Steven , Byers Chad
Published: August 2019

Birds of a Feather is a single-player card game in which cards are arranged in a grid. The player attempts to combine stacks of cards under certain rules, with the goal being to combine all cards into a single stack. This paper highlights several approaches for efficiently classifying whether a randomlychosen state has a single-stack solution. These approaches use graph theory and machine learning concepts to prune a state’s search space, resulting in significant reductions in runtime relative to a baseline search.
Computational Intractability and Solvability for the Birds of a Feather Game
Authors: Hoshino Richard , Notarangelo Max
Published: August 2019

In this paper, we analyze Birds of a Feather (BoaF), a perfectinformation one-player card game that is the subject of the 2019 EAAI Undergraduate Research Challenge. We prove that the generalized N × N BoaF game is NP-complete, and then explore the one million deals in the 4×4 BoaF testbed. We present several graph-theoretic algorithms to prove that 1880 of these million deals are unsolvable, and conclude the paper with two search algorithms that efficiently show that all of the remaining 998,120 deals are in fact solvable.
Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game
Authors: Kucharski Bryon , Deihim Azad , Ergezer Mehmet
Published: August 2019

This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.
Get IT Scored Using AutoSAS — An Automated System for Scoring Short Answers
Authors: Kumar Yaman , Aggarwal Swati , Mahata Debanjan , Shah Rajiv Ratn , Kumaraguru Ponnurangam , Zimmermann Roger
Published: August 2019

In the era of MOOCs, online exams are taken by millions of candidates, where scoring short answers is an integral part. It becomes intractable to evaluate them by human graders. Thus, a generic automated system capable of grading these responses should be designed and deployed. In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS). We propose and explain the design and development of a system for SAS, namely AutoSAS. Given a question along with its graded samples, AutoSAS can learn to grade that prompt successfully. This paper further lays down the features such as lexical diversity, Word2Vec, prompt, and content overlap that plays a pivotal role in building our proposed model. We also present a methodology for indicating the factors responsible for scoring an answer. The trained model is evaluated on an extensively used public dataset, namely Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS shows state-of-the-art performance and achieves better results by over 8% in some of the question prompts as measured by Quadratic Weighted Kappa (QWK), showing performance comparable to humans.
An Integrative Framework for Artificial Intelligence Education
Authors: Langley Pat
Published: August 2019

Modern introductory courses on AI do not train students to create intelligent systems or provide broad coverage of this complex field. In this paper, we identify problems with common approaches to teaching artificial intelligence and suggest alternative principles that courses should adopt instead. We illustrate these principles in a proposed course that teaches students not only about component methods, such as pattern matching and decision making, but also about their combination into higher-level abilities for reasoning, sequential control, plan generation, and integrated intelligent agents. We also present a curriculum that instantiates this organization, including sample programming exercises and a project that requires system integration. Participants also gain experience building knowledge-based agents that use their software to produce intelligent behavior.
Concept Extraction and Prerequisite Relation Learning from Educational Data
Authors: Lu Weiming , Zhou Yangfan , Yu Jiale , Jia Chenhao
Published: August 2019

Prerequisite relations among concepts are crucial for educational applications. However, it is difficult to automatically extract domain-specific concepts and learn the prerequisite relations among them without labeled data.In this paper, we first extract high-quality phrases from a set of educational data, and identify the domain-specific concepts by a graph based ranking method. Then, we propose an iterative prerequisite relation learning framework, called iPRL, which combines a learning based model and recovery based model to leverage both concept pair features and dependencies among learning materials. In experiments, we evaluated our approach on two real-world datasets Textbook Dataset and MOOC Dataset, and validated that our approach can achieve better performance than existing methods. Finally, we also illustrate some examples of our approach.
A Neural Network Approach for Birds of a Feather Solvability Prediction
Authors: Sang Benjamin , Yoon Sejong
Published: August 2019

Birds of a Feather is a single player, perfect information card game. The game can have multiple board sizes with larger boards introducing larger search spaces that grow exponentially. In this paper, we investigate the solvability of the game, aiming at building a machine learning method to automatically classify whether a given board state has a solution path or not. We propose a method based on image-based features of the board state and deep neural network. Experimental results show that the proposed method can make reasonable predictions of the solvability of a game at an arbitrary stage of the game.
Automating Analysis and Feedback to Improve Mathematics Teachers’ Classroom Discourse
Authors: Suresh Abhijit , Sumner Tamara , Jacobs Jennifer , Foland Bill , Ward Wayne
Published: August 2019

Our work builds on advances in deep learning for natural language processing to automatically analyze transcribed classroom discourse and reliably generate information about teachers’ uses of specific discursive strategies called ”talk moves.” Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Currently, providing teachers with detailed feedback about the talk moves in their lessons requires highly trained observers to hand code transcripts of classroom recordings and analyze talk moves and/or one-on-one expert coaching, a time-consuming and expensive process that is unlikely to scale. We created a bidirectional long short-term memory (bi-LSTM) network that can automate the annotation process. We have demonstrated the feasibility of this deep learning approach to reliably identify a set of teacher talk moves at the sentence level with an F1 measure of 65%.
PopBots: Designing an Artificial Intelligence Curriculum for Early Childhood Education
Authors: Williams Randi , Park Hae Won , Oh Lauren , Breazeal Cynthia
Published: August 2019

PopBots is a hands-on toolkit and curriculum designed to help young children learn about artificial intelligence (AI) by building, programming, training, and interacting with a social robot. Today’s children encounter AI in the forms of smart toys and computationally curated educational and entertainment content. However, children have not yet been empowered to understand or create with this technology. Existing computational thinking platforms have made ideas like sequencing and conditionals accessible to young learners. Going beyond this, we seek to make AI concepts accessible. We designed PopBots to address the specific learning needs of children ages four to seven by adapting constructionist ideas into an AI curriculum. This paper describes how we designed the curriculum and evaluated its effectiveness with 80 Pre-K and Kindergarten children. We found that the use of a social robot as a learning companion and programmable artifact was effective in helping young children grasp AI concepts. We also identified teaching approaches that had the greatest impact on student’s learning. Based on these, we make recommendations for future modules and iterations for the PopBots platform.
A Preliminary Report of Integrating Science and Computing Teaching Using Logic Programming
Authors: Zhang Yuanlin , Wang Jianlan , Bolduc Fox , Murray William G. , Staffen Wendy
Published: August 2019

This paper presents a framework to integrate Science and Computing teaching using Logic Programming. We developed two modules: one for chemistry and the other for chemistry and physics. They are implemented in an elective course for 8th graders. Through clinical interviews, video taped class observations, exit interviews and our own experiences with the class, Logic Programming based approach is accessible to the students.
Artificial Intelligence Competencies for Data Science Undergraduate Curricula
Authors: Danyluk Andrea , Buck Scott
Published: August 2019

In August 2017, the ACM Education Council initiated a task force to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force is seeking to define what the computing contributions are to this new field, in order to provide guidance for computer science or similar departments offering data science programs of study at the undergraduate level. The ACM Data Science Task Force has completed the initial draft of a curricular report. The computing-knowledge areas identified in the report are drawn from across computing disciplines and include several sub-areas of AI. This short paper describes the overall project, highlights AI-relevant areas, and seeks to open a dialog about the AI competencies that are to be considered central to a data science undergraduate curriculum.
Predicting Unsolvable Deals in the Birds of a Feather Solitaire Game
Authors: Hoshino Richard , Kahn Maximilian
Published: August 2019

In this paper, we analyze Birds of a Feather (BoaF), a solitaire game played with 16 cards. While the large majority of deals are solvable, the set of unsolvable deals share certain characteristics that can be determined from the adjacency matrix of the corresponding “compatibility graph”. We create a binary decision tree based on just three variables to predict whether a given deal is solvable. Our predictive model, tested on 30,000 random deals, correctly classifies over 99.9% of our data.
Designing Preferences, Beliefs, and Identities for Artificial Intelligence
Authors: Conitzer Vincent
Published: August 2019

Research in artificial intelligence, as well as in economics and other related fields, generally proceeds from the premise that each agent has a well-defined identity, well-defined preferences over outcomes, and well-defined beliefs about the world. However, as we design AI systems, we in fact need to specify where the boundaries between one agent and another in the system lie, what objective functions these agents aim to maximize, and to some extent even what belief formation processes they use.

The premise of this paper is that as AI is being broadly deployed in the world, we need well-founded theories of, and methodologies and algorithms for, how to design preferences, identities, and beliefs. This paper lays out an approach to address these problems from a rigorous foundation in decision theory, game theory, social choice theory, and the algorithmic and computational aspects of these fields.
Towards Fluid Machine Intelligence: Can We Make a Gifted AI?
Authors: Davidson Ian , Walker Peter B.
Published: August 2019

Most applications of machine intelligence have focused on demonstrating crystallized intelligence. Crystallized intelligence relies on accessing problem-specific knowledge, skills and experience stored in long term memory. In this paper, we challenge the AI community to design AIs to completely take tests of fluid intelligence which assess the ability to solve novel problems using problem-independent solving skills. Tests of fluid intelligence such as the NNAT are used extensively by schools to determine entry into gifted education programs. We explain the differences between crystallized and fluid intelligence, the importance and capabilities of machines demonstrating fluid intelligence and pose several challenges to the AI community, including that a machine taking such a test would be considered gifted by school districts in the state of California. Importantly, we show existing work on seemingly related fields such as transfer, zero-shot, life-long and meta learning (in their current form) are not directly capable of demonstrating fluid intelligence but instead are task-transductive mechanisms.
Labor Division with Movable Walls: Composing Executable Specifications with Machine Learning and Search (Blue Sky Idea)
Authors: Harel David , Marron Assaf , Rosenfeld Ariel , Vardi Moshe , Weiss Gera
Published: August 2019

Artificial intelligence (AI) techniques, including, e.g., machine learning, multi-agent collaboration, planning, and heuristic search, are emerging as ever-stronger tools for solving hard problems in real-world applications. Executable specification techniques (ES), including, e.g., Statecharts and scenario-based programming, is a promising development approach, offering intuitiveness, ease of enhancement, compositionality, and amenability to formal analysis. We propose an approach for integrating AI and ES techniques in developing complex intelligent systems, which can greatly simplify agile/spiral development and maintenance processes. The approach calls for automated detection of whether certain goals and sub-goals are met; a clear division between sub-goals solved with AI and those solved with ES; compositional and incremental addition of AI-based or ES-based components, each focusing on a particular gap between a current capability and a well-stated goal; and, iterative refinement of sub-goals solved with AI into smaller sub-sub-goals where some are solved with ES, and some with AI. We describe the principles of the approach and its advantages, as well as key challenges and suggestions for how to tackle them.
Explainable, Normative, and Justified Agency
Authors: Langley Pat
Published: August 2019

In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area.
Meaningful Explanations of Black Box AI Decision Systems
Authors: Pedreschi Dino , Giannotti Fosca , Guidotti Riccardo , Monreale Anna , Ruggieri Salvatore , Turini Franco
Published: August 2019

Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.
Building Ethically Bounded AI
Authors: Rossi Francesca , Mattei Nicholas
Published: August 2019

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI’s freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven examplebased approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.
Recommender Systems: A Healthy Obsession
Authors: Smyth Barry
Published: August 2019

We propose endurance sports as a rich and novel domain for recommender systems and machine learning research. As sports like marathon running, triathlons, and mountain biking become more and more popular among recreational athletes, there exists a growing opportunity to develop solutions to a number of interesting prediction, classification, and recommendation challenges, to better support the complex training and competition needs of athletes. Such solutions have the potential to improve the health and well-being of large populations of users, by promoting and optimising exercise as part of a productive and healthy lifestyle.
Learning and the Unknown: Surveying Steps toward Open World Recognition
Authors: Boult T. E. , Cruz S. , Dhamija A.R. , Gunther M. , Henrydoss J. , Scheirer W.J.
Published: August 2019

As science attempts to close the gap between man and machine by building systems capable of learning, we must embrace the importance of the unknown. The ability to differentiate between known and unknown can be considered a critical element of any intelligent self-learning system. The ability to reject uncertain inputs has a very long history in machine learning, as does including a background or garbage class to account for inputs that are not of interest. This paper explains why neither of these is genuinely sufficient for handling unknown inputs – uncertain is not unknown, and unknowns need not appear to be uncertain to a learning system. The past decade has seen the formalization and development of many open set algorithms, which provably bound the risk from unknown classes. We summarize the state of the art, core ideas, and results and explain why, despite the efforts to date, the current techniques are genuinely insufficient for handling unknown inputs, especially for deep networks.
Machine Learning with Crowdsourcing: A Brief Summary of the Past Research and Future Directions
Authors: Sheng Victor S. , Zhang Jing
Published: August 2019

With crowdsourcing systems, labels can be obtained with low cost, which facilitates the creation of training sets for prediction model learning. However, the labels obtained from crowdsourcing are often imperfect, which brings great challenges in model learning. Since 2008, the machine learning community has noticed the great opportunities brought by crowdsourcing and has developed a large number of techniques to deal with inaccuracy, randomness, and uncertainty issues when learning with crowdsourcing. This paper summarizes the technical progress in this field during past eleven years. We focus on two fundamental issues: the data (label) quality and the prediction model quality. For data quality, we summarize ground truth inference methods and some machine learning based methods to further improve data quality. For the prediction model quality, we summarize several learning paradigms developed under the crowdsourcing scenario. Finally, we further discuss several promising future research directions to attract researchers to make contributions in crowdsourcing.
Um diálogo entre a Educação Ambiental e a Ciência por meio do filme ‘’Sonhos Tropicais’’: Uma contribuição para a formação inicial de professores.
Authors: Castro Andressa Aparecida , Borém Rosângela Alves Tristão , Gonçalves Laise Vieira , Nascimento Júnior Antonio Fernandes
Published: August 2019

A formação inicial de professores precisa ser repensa, é necessário que os licenciados em formação contemplem não só os conteúdos mas também as práticas metodológicas, buscando promover a educação como uma prática social, atuando na formação de cidadãos críticos, reflexivos e transformadores que compreendam o meio em que estão inserido, para assim, transforma-lo. Diante desta questão o presente trabalho tem como objetivo demonstrar o diálogo entre a educação ambiental e a ciência a partir do filme brasileiro: ‘’Sonho Tropical’’ dirigido por André Sturm e estreou comercialmente no Brasil em 2002, o filme retrata a situação do Rio de Janeiro, mostrando um ambiente influenciado tanto por questões socais, politicas, econômicas, sociais e outras. Para analise dos resultados, utilizou-se a pesquisa qualitativa, por meio da categorização temática. Como resultado foi obtido três categorias: ‘’a ciência procura meios para erradicar problemas’’, ‘’saneamento básico’’ e ‘’o ser humano altera o meio ambiente’’, estas categorias mostram como é necessário que a educação ambiental seja trabalhada através de um processo continuo, para quebrar o conceito conservacionista de meio ambiente, e como o cinema auxilia nesta construção do conceito, o que evidencia a necessidade de se apropriar destes conceitos para formação inicial de professores críticos transformadores.

PALAVRAS-CHAVE: Educação Ambiental, Arte, Formação de professores
Análise ambiental do processo turístico da segunda residência e suas implicações
Authors: Carvalho Lucivan Macena de , Tonetti Emerson Luis , Santos Everaldo dos
Published: August 2019

A atividade turística de modo geral é considerada um fenômeno de nível internacional e por essa razão tem sido motivo de vários estudos. Dentre os segmentos diversos, a segunda residência, é um tema ainda pouco estudado pelo setor acadêmico, mas que de modo geral, abrange os setores econômicos, culturais, sociais e ambientais. Essa segmentação turística requer uma atenção especial devido à ausência no Brasil de estudos mais aprofundados e suas possíveis implicações. O presente artigo resulta de uma pesquisa teórica e empírica, sendo seu objetivo principal evidenciar o aumento do consumo gerado pelo turismo de segunda residência no balneário Ipanema, no litoral do estado do Paraná. Com este objetivo foi levantado a distribuição do uso do solo em residencial permanente e ocasional, comercial permanente e ocasional, elaborar um croqui com essa distribuição e feito inferências a partir da literatura que discutem os problemas ambientais decorrentes da sazonalidade do turismo no balneário Ipanema, no município de Pontal do Paraná. Dentre os principais resultados encontrados na pesquisa está a distribuição dos domicílios ocasionais e permanentes posicionados praticamente de forma opostas, o aumento no consumo de energia elétrica, água, efluentes e resíduos sólidos na alta temporada. Tais resultados evidenciam a elevação do consumo sazonal e a potencial sobrecarga ambiental que sofre o balneário em estudo, acarretando em transtornos para a população local.
PALAVRAS-CHAVE: segunda residência; turismo e meio ambiente; balneário Ipanema.
Efeitos ecotoxicológicos de metais aos organismos aquáticos
Authors: Marques Márjori Brenda Leite , Américo-Pinheiro Juliana Heloisa Pinê
Published: August 2019

Em ecossistemas aquáticos, a presença de metais pesados são características oriundas de processos naturais, tais como, processos geológicos e físicos, mas também ou antrópicos como a aplicação de agrotóxicos, efluentes industriais, domésticos e mineração. Nos corpos hídricos os metais tendem a se acumular no sedimento sendo liberado em colunas d’agua e incorporados ao longo da cadeia alimentar, podendo afetar afetando a biota, sendo incorporados ao longo da cadeia alimentar e consequentemente podendo causar danos irreversíveis nos aos ecossistemas. Esse trabalho objetivou analisar a ecotoxicidade de metais pesados em organismos de diferentes níveis tróficos por meio de uma revisão bibliográfica sobre trabalhos que visaram avaliaram o efeito agudo e crônico de metais em organismos, focando nas consequências observadas correlacionando o tipo de metal e aos diferentes bioensaios.

PALAVRAS-CHAVE: Mercúrio. Cobre. Danio rerio.
A mobilidade urbana nas cidades brasileiras: realidade, desafios e perspectivas
Authors: Rabelo Noara Silva
Published: August 2019

Ao discutir a cidade, é praticamente intrínseco falar de mobilidade. A intensificação dos problemas urbanos, incluindo aqueles relacionados à mobilidade, têm estimulado a adoção de ações inovadoras na busca da melhoria da qualidade de vida das cidades, sustentabilidade e a humanização dos espaços urbanos. A adequação das cidades baseada nestes princípios está, cada vez mais, sendo adotada através de metodologias de integração das pessoas com o espaço urbano por meio de mudanças no espaço público e incentivo ao transporte sustentável. Pode-se conjecturar que uma das causas dos problemas de mobilidade urbana relaciona-se ao uso indiscriminado do transporte individual em detrimento da utilização de transportes sustentáveis. Neste trabalho, foi analisada a utilização de diferentes modais e seus respectivos impactos no meio urbano através da revisão de literatura, estudos de caso, dados estatísticos e reportagens. Também analisou a atual realidade da mobilidade urbana expondo os futuros impactos causados em relação à qualidade de vida das pessoas, apresentando algumas alternativas para reverter a atual situação. Diante das análises apresentadas, pode-se compreender que a mobilidade urbana é intrínseca a cada indivíduo em seus diversos e variáveis deslocamentos. É importante o entendimento de que o sistema de transporte coletivo e a utilização de meios não motorizados devem ser valorizada para que proporcione à cidade condições mais sustentáveis, e que provoque a assimilação do sistema de transportes como um todo pela população, de modo a alcançar a melhoria na qualidade de vida de todos.
Desempenho energético em unidades habitacionais de interesse social certificadas pelo Selo Casa Azul: Caso do edifício HAB 2
Authors: Perbeils Henriette da Silva
Published: August 2019

O Selo Casa Azul da Caixa econômica tem sido uma ferramenta importante na produção de habitação de interesse social sustentável, desde a sua criação em 2010, já certificou diversos empreendimentos voltados para habitação social em diferentes regiões do Brasil. Nesse contexto, este artigo tem como objetivo a análise do desempenho energético que o selo pode atribuir a unidades habitacionais de um edifício destinado a habitação popular. O empreendimento escolhido é o edifício HAB 2 e situa-se na comunidade do Morro da Babilônia, Rio de Janeiro, Brasil. Foi um dos resultados do programa Morar Carioca verde realizado na comunidade, e ganhou o Selo Casa Azul em nível ouro, tornando-se o primeiro empreendimento voltado para habitação popular a conquistar uma certificação ambiental, na cidade do Rio de Janeiro. Para analisar o desempenho energético, foram selecionadas duas unidades habitacionais do edifício, localizadas em condições mais desfavoráveis em termos de orientação solar das fachadas. Para avaliação foi utilizado o método prescritivo RTQ-R do Procel. Os dados referentes aos resultados são sistematizados e apresentados em tabelas. Por meio deles, comprovou-se que as duas unidades habitacionais avaliadas obtiveram bom desempenho energético, atingido o nível B de eficiência energética. Realizou-se também, de forma complementar, uma análise dos critérios alcançados pelo edifício HAB 2, visando detectar a distribuição da pontuação obtida nas categorias do Selo. Os resultados são expostos em gráficos e tabelas e revelam que o empreendimento contemplou todas as categorias estabelecidas, adotando inclusive a energia solar para aquecimento da água, na categoria de eficiência energética.
APREENSÃO DO ESPAÇO URBANO: Um exercício analítico do Parque Halfeld por meio de suas dimensões morfológicas
Authors: Costa Carlos Frederico Ribeiro , De Souza Thaísa Barboza , De Lana Juliana Nazareth , Barra Lima Rosilene de Oliveira , Barbosa Pereira Ana Aparecida
Published: August 2019

O presente trabalho aborda a metodologia de leitura do espaço urbano exposta por Maria Elaine Kohlsdorf em seu livro intitulado “A Apreensão da Forma da Cidade”, por meio de uma adaptação estruturada em um roteiro para pesquisa de campo. De acordo com essa metodologia, admite-se que os espaços possuem qualidades capazes de despertar naqueles que o frequentam o sentimento de identificação e orientação, com maior ou menor facilidade dependendo de suas características. Adotou-se como objeto de estudo o Parque Halfeld, localizado no município de Juiz de Fora, por sua representatividade histórica e por sua localização bem no centro da malha urbana da cidade. Este artigo apresenta o rebatimento dessa metodologia de análise no referido espaço, apresentando três níveis de apreensão da forma da cidade: o nível da percepção, o nível da imagem mental e o nível da representação geométrica. Os resultados permitiram concluir que a identidade de um espaço está diretamente ligada à conservação do mesmo e do seu entorno, o que expõe a necessidade de se pensar a qualificação da paisagem local a partir de uma abordagem sistêmica.
Avaliação de impacto de negócios sociais e teoria da mudança
Authors: Sugahara Cibele Roberta , Rodrigues Patricia Peres
Published: August 2019

As empresas de Negócios Sociais fazem uso de mecanismos de mercado para, por meio da sua atividade principal, buscar soluções para problemas socioambientais. Os Negócios Sociais integram a lógica dos diferentes setores econômicos e oferecem produtos e serviços de qualidade à população, ao propor soluções que possam combater a pobreza, diminuir a desigualdade e propor alternativas sustentáveis para o uso de recursos naturais. O objetivo deste artigo é apontar a importância da Teoria da Mudança como um meio de avaliação de impacto em Negócios Sociais, a fim de evidenciar quais são os elementos essenciais do processo de avaliação. Esta pesquisa adotou a metodologia da pesquisa bibliográfica para identificar a importância da Teoria da Mudança, uma forma de utilizar evidências para articular as relações entre as necessidades do público da sua iniciativa, os serviços prestados e os resultados atingidos. Dentre os resultados, ressalta-se que no processo de avaliação de impacto, a elaboração da Teoria da Mudança, quando alinhada aos objetivos da transformação que se almeja alcançar, pode determinar a mensuração do resultado e impacto gerado ao público envolvido.
PALAVRAS-CHAVE: Negócios Sociais. Avaliação de Impacto. Teoria da Mudança.
PBMC Electroporation buffer - 1SM v1
Authors: Chicaybam Leonardo , Bonamino Martin
Published: August 2019

Cellular genetic modification has become an excellent alternative for the treatment of some diseases. A promising example is T cells expressing the chimeric antigen receptor (CAR-T) for the treatment of some tumors. However, this therapy is expensive and laborious because it often involves the use of viral vectors. We have shown that our in-house developed buffer, called 1SM (Chicabuffers), can be efficiently used to electropore primary cells to express CAR using Lonza Nucleofector IIb.
Determination of effects of amniotic membrane homogenate on the growth of the uropathogenic Escherichia coli, S. aureus and S. marcescens strains v1
Authors: Sket Tina , Zeleznik Ramuta Taja , Starčič Erjavec Marjanca , Erdani Kreft Mateja
Published: August 2019

Purpose: Due to the emergence and spread of bacterial strains resistant to antibiotics, the development of new antimicrobials is imperative. The antimicrobial effect of the amniotic membrane (AM), the innermost part of placenta, has been explored to a limited extent so far. Materials and Methods:We collected 12 biological samples of AM homogenates, which we used for testing of AM’s antimicrobial effect on 4 pathogens, including the clinical strain of uropathogenic Escherichia coli (UPEC), the wild-type strain of Staphylococcus aureus, and the wild-type strain and a clinical strain of Serratia marcescens. To quantify and better understand the antibacterial effect of AM, we monitored the effect of AM homogenate on bacterial growth using plate count method and agar diffusion method. Additionally, minimal inhibitory concentrations (MICs) for AM homogenate dilutions were determined and S. marcescens growth in AM homogenate alone was evaluated. Results: Our results clearly demonstrated that AM homogenate had a bacteriostatic effect on studied UPEC and S. aureus. Interestingly, when used in lower concentrations, the AM homogenate had a bactericidal effect on both strains. In contrast, S. marcescenswas completely resistant to the growth-inhibitory substances of AM homogenate. In fact, its growth was slightly accelerated in liquid culture medium in the presence of AM homogenate and the strain was able to grow in undiluted, 2-fold and 4-fold diluted AM homogenate as well. Conclusion: Obtained results illustrated that AM homogenate could be a candidate for treatments and prevention of UPEC and S. aureus infections, but not that of S. marcescens, whose growth is enhanced by AM homogenate. Moreover, the established liquid culture medium assay can be used as a time- and cost-effective method for a personalized evaluation of drug effect on the growth of chosen bacterial strains with parallel testing of resistance or susceptibility to multiple drugs. Importantly, the susceptibility of bacteria to AM homogenate in solid and liquid culture media is encouraging for its use in biomedical applications.
Quantitative whole-slide image analysis of CD3 and CD8 immunohistochemical stains v2
Authors: Yoo Seungyeon
Published: August 2019

Given the virtual slide images of CD3 and CD8 immunohistochemical stains, this protocol provides the following quantitative measures of tumor immune microenvironment: Density of intraepithelial/stromal tumor-infiltrating lymphocytes (TIL) Proportion of stroma within an annotated tumor area Ratio of intraepithelial and stromal TIL Ratio of CD8 and CD3 TIL Once you annotate the tumor area manually, it segments the area into 1000 x 1000-pixel tiles. This enables you to assess not only the overall density of TIL and stroma, but also their mean, minimum, median, maximum and variance over the tiles.
International Law in Russia, Ukraine, and Belarus: Modern Integration Projects
Authors: Kalinichenko Paul , Petrov Roman , Karliuk Maksim
Published: August 2019

Modern challenges for international law application in the former USSR countries are inextricably linked to the regional integration issues. Despite seeking closer rapprochement with the EU, Russia never dropped its ambitions as a spearhead of political, economic and legal integration within the post-Soviet area. Belarus actively participates in the post-Soviet integration projects while seeking improvements of EU-Belarus relations. However, Ukraine embarked upon a long and challenging path of deeper political and economic integration with the EU and aligning its legal system with the EU acquis. Against this backdrop, this article studies the constitutional dimension of three post-Soviet republic’s engagement in regional integration projects identifying the problematic issues in the application of international law.
Anti-Rawls or the Russian Way of Eurasian Integration
Authors: Zakharova Maria , Przhilenskiy Vladimir
Published: August 2019

This article examines Eurasian integration in the context of the ideas which accompany it and make possible the implementation of its practices, especially law-making and law-enforcement practices. The central theme of the article is the competition among values and social technologies claiming to play an integrating role. The starting point of this analysis is the theory of justice by John Rawls presented in the form of many interpretations thereof by legal theorists, as well as experts in the field of political and moral philosophy. It is examined based on assessments made from the standpoint of the politico-legal and socio-historical development of the West, as well as on attempts to look at this theoretical concept from a different cultural and civilizational point of view. Detailed consideration is given to the ideas and images of justice formed within the philosophical symbiosis of Confucianism and Legalism and providing a value-based legal identity of the Chinese civilization. The article shows that the ideas and values of the Rawlsian theory of justice are rooted in the political and legal history of European civilization and the dependence thereof on the philosophical and theoretical language of European enlighteners and even on the Indo-European national language family. As the main alternative to the neoliberal theory of justice, the article studies the philosophical and theoretical and politico-legal heritage of the Eurasianists. The theory of Eurasian law advanced by representatives of this movement is analyzed in depth. This type of legal relations, based on obligations, is considered as a special type of law capable of uniting heterogeneous entities without requiring their full unification or depriving them of their civilizational and value-based peculiarities. The authors analyze the real experience of economic and politico-legal integration, both within the framework of international organizations  and  at  the  level  of  inter-governmental  [inter-country]  cooperation. An assessment is made of the justifiability of the claims of Eurasianist philosophy regarding its ability to successfully provide integration processes in this part of the world.
Court of the Eurasian Economic Union: The Beginning
Authors: Neshataeva Tatiana , Myslivskiy Pavel
Published: August 2019

This article is based on 7.5 years of work experience in the Courts of the Eurasian Economic Community (EurAsEC) and Eurasian Economic Union (EAEU) and, therefore, finalizes the major problems the mentioned judicial institutions faced with in the beginning of their functioning. The Eurasian Economic Union in post-Soviet space was created five years ago with minimal established doctrinal and practical perceptions of how such an organization may interact with State sovereignty and whether it constituted the first step for the subsequent emergence of a new State. The authors believe that the EAEU should not be confused with the State and should be considered a new type of international organization, supranational, to which member States transferred competence. In this organization the Court plays an important role – its main function is to ensure the uniform application of Union law by hearing disputes and providing advisory opinions in various spheres of integration and, therefore, establish practice having an erga omnes effect in the law of the Union and national legal systems of States. Just as in other international courts, the main role in the EAEU Court functioning is played by judges nominated by national governments, sometimes without taking into account the sui generis character of their future work. Taking into consideration the valuable experience from other international courts and organizations (such as the International Court of Justice, European Court of Human Rights, International Law Association), the authors suggest ways for improvement of future Court functioning by creating mechanisms that would monitor the qualities of candidates for the post of the EAEU Court judge.
Institutional and Legal Development of EAEU and EU in Comparative Perspective
Authors: Entin Mark , Voynikov Vadim
Published: August 2019

Despite the relatively short history of its development, the Eurasian Economic Union (EAEU) is becoming more confident about itself as a successful integration project. At the same time, there is a growing interest in the EAEU by the political elite and scientific community in Russia and abroad. The EAEU is investigated from different points of view, but almost no research is carried out without a comparative legal analysis of the EAEU and the European Union (EU). Both unions belong to the same type of integration organizations; the EAEU was largely created in the image of the EU. However, an analysis of the institutional and legal structure of the EAEU and the EU shows there are fundamental differences between the two unions concerning the principles of their functioning. This article substantiates the fact that supranational constitutionalization within the EU is not typical for the EAEU and is even harmful. At the same time, the technical tools developed by the EU can be useful to the EAEU for resolving current challenges of ensuring sustainability and self-affirmation in the international arena. This experience is of importance in view of the crisis experienced by the EU, since only they were able to manifest what institutional and legal decisions are working within the framework of an integration association, and which should be discarded. It is vital that the EAEU not repeat the mistakes and miscalculations of the EU.