bluuebunny/crossref_metadata_2025 huggingface dataset:
The siege of Dalmatovsky monastery during the peasant unrest of 1842
Authors: Menshchikov Igor , Pavlutckikh Timofei
Published: June 2022

The article deals the peasant unrest of 1842–1843, known as the potato riots. The epicenter of the riots of 1842 was in the Trans-Ural uezdes (districts) of the Perm Governorate (Kamyshlovsky and Shadrinsky). The peasants seized the volost boards and churches, demanded from the rural authorities and the clergy to issue them a "sales charter", according to which they were transferred to serfs. Fearing the anger of the crowd, several country authorities fled to the Dalmatov Dormition Monastery to find refuge outside its walls. However, peasants from the surrounding villages began to gather in the town of Dalmatovo, they captured the Dalmatian volost head and the clerk and demanded that they be given a "sales charter" under the threat of torture. At this time, the treasurer of the monastery, Hieromonk Nicholas, ordered the gates of the monastery to be locked and threatened the rebels that he would fire a cannon. Then, as part of a religious procession, he proceeded to the volost administration house and freed the captive country authorities. After that, he, not meeting resistance, returned with them to the monastery. These events were portrayed by some Soviet historians, such as Nicolay Druzhinin, Sergey Tokarev, as a heroic siege of the Dalmatov monastery. However, an analysis of archived and published sources demonstrates that there was no siege in fact, the peasants were not going to seize the monastery and did not interfere with the movement from it, and the firing from the monastery was carried out with blank charges. When the military detachments arrived the next day, most of the peasants fled, several dozen were arrested without resistance and put on trial. Thus, we are dealing with a historiographical myth based on soviet ideology and an uncritical attitude towards the sources and works of other historians.
Factors associated with COVID-19 contagion in healthcare workers of a level III hospital in Bogota?, Colombia: recommendations for contagion mitigation
Authors: Espitia-Hernandez Diana Marcela
Published: June 2022

Background: The COVID-19 pandemic has had a significant impact on healthcare workers who are at a greater risk of contagion given their higher level of exposure. However, the various factors contributing to this heightened risk have not been identified so far. Objective: To identify the factors associated with COVID-19 contagion in healthcare workers. Methods Cross-sectional study using data from healthcare workers of a high complexity hospital between March 2020 and March 2021. The sociodemographic characteristics of the contagion cases were analyzed, and a Chi2 test was carried out to identify the variables to be measured as part of the multinomial regression. Results: Overall, 382 positive cases were reported in a population of 1544 hospital workers, for an institutional prevalence of 24.74%. Of these, 71.2% were women, 65.8% worked in the clinical area, and the highest percentage of contagion cases occurred among licensed practical nurses (38.22%). A significant association was found between inhospital contagion and all the variables included in the model, with p<0.000 in the adjusted model. Conclusions: The highest percentage of contagions occurred among healthcare professionals working in the clinical area. However, the majority of the cases are the result of inside and outside social contacts and not of patient care. Steps must be taken, considering that clinical staff is essential for managing the current health crisis and learning lessons for future similar circumstances.
Demographic and Clinical Differences Between Applied Definitions of Medical Complexity
Authors: Heneghan Julia A. , Goodman Denise M. , Ramgopal Sriram
Published: June 2022

OBJECTIVES

To identify the degree of concordance and characterize demographic and clinical differences between commonly used definitions of multisystem medical complexity in children hospitalized in children’s hospitals.

METHODS

We conducted a retrospective, cross-sectional cohort study of children <21 years of age hospitalized at 47 US Pediatric Health Information System-participating children’s hospitals between January 2017 to December 2019. We classified patients as having multisystem complexity when using 3 definitions of medical complexity (pediatric complex chronic conditions, pediatric medical complexity algorithm, and pediatric chronic critical illness) and assessed their overlap. We compared demographic, clinical, outcome, cost characteristics, and longitudinal healthcare utilization for each grouping.

RESULTS

Nearly one-fourth (23.5%) of children hospitalized at Pediatric Health Information System-participating institutions were identified as meeting at least 1 definition of multisystem complexity. Children with multisystem complexity ranged from 1.0% to 22.1% of hospitalized children, depending on the definition, with 31.2% to 95.9% requiring an ICU stay during their index admission. Differences were seen in demographic, clinical, and resource utilization patterns across the definitions. Definitions of multisystem complexity demonstrated poor agreement (Fleiss’ κ 0.21), with 3.5% of identified children meeting all 3.

CONCLUSIONS

Three definitions of multisystem complexity identified varied populations of children with complex medical needs, with poor overall agreement. Careful consideration is required when applying definitions of medical complexity in health services research, and their lack of concordance should result in caution in the interpretation of research using differing definitions of medical complexity.
Introduzindo o tema da corrupção nas análises sobre mudanças dos modelos de gestão da saúde
Authors: Andreazzi Maria de Fátima Siliansky de
Published: June 2022

O trabalho, recorte de uma pesquisa sobre privatização da saúde no município do Rio de Janeiro, aborda a principal questão levantada na literatura nacional e internacional sobre os resultados das reformas gerencialistas da Administração pública no contexto do neoliberalismo, que é a falta de controle sobre as parcerias público-privadas e os contratos de gestão. No caso, as Organizações Sociais de Saúde (OSS) têm frequentemente sido envolvidas em processos de corrupção. Faz-se uma breve revisão sobre a corrupção, abordando suas dimensões quantitativas e as concepções correntes. O marco teórico adotado é o marxismo, em especial, a partir de análises de formações econômico-sociais pós ou semicoloniais, onde tem sido encontrado, na contemporaneidade, níveis significativos de corrupção. O caso do Rio de Janeiro abordou 14 OSS que estabeleceram contratos com a prefeitura entre 2009 e 2015 utilizando fontes públicas oficiais, jornais e processos do Ministério Público. Em somente quatro OSS não se identificaram irregularidades, a partir das fontes citadas. As mais comuns envolveram irregularidades na prestação de contas, sobrepreços e pagamento de propinas a agentes públicos. Aponta ao final, que, a contrarreforma do Estado na saúde, ao contrário do proposto nos anos 1990, parece estar facilitando a corrupção, indo contra os princípios da administração pública que orientaram a Constituição de 1988.
A harmonia possível das coisas
Authors: Santos Pedro Brum
Published: June 2022

A presente reflexão objetiva apontar componentes textuais para ilustrar modos de funcionamento do discurso na narrativa de José Saramago. Papel do autor, posição do narrador, ordenações temporais, amarrações tropológicas e atualização da modalidade romance histórico, dentre outros aspectos, como se procura demonstrar, colocam-se em ordem de uma diversidade de recursos que concorrem para a pluralidade de sentidos, objeto e efeito de vigor artístico e alcance abrangente.
First records of Tylopilus glutinosus Iqbal Hosen (Boletaceae) from Shorea robusta-dominated forests in tropical India: morphological description and phylogenetic estimation
Authors: Chakraborty Dyutiparna , Gelardi Matteo , Hembrom Manoj Emanuel , Ghosh Aniket
Published: June 2022

During the routine survey for exploring the hidden macrofungal wealth of tropical Sal (Shorea robusta Roth) forests in West Bengal and Jharkhand (India), we found a specimen similar to Tylopilus plumbeoviolaceous (Snell & E.A.Dick) Snell & E.A.Dick. After careful morphological observations and phylogenetic analysis, the species was found to be conspecific with Tylopilus glutinosus Iqbal Hosen, a recently established taxon from Bangladesh. We report T. glutinosus for the first time from India and provide a detailed description, figures, a multigene phylogenetic analysis, and comprehensive comparisons with similar species. A distributional map is also provided.
Diversity and Abundance of Insects Pollinator of Chayote (Sechium edule (Jacq.) Swartz
Authors: Alfawwaz Muhammad Dzaky , Permana Agus Dana , Putra Ramadhani Eka
Published: June 2022

Chayote plants (Sechium edule) with monoecious characters require a pollination process. The pollination process requires pollinating agents to increase its productivity, one of which is insects. This research aimed to determine the diversity and abundance of insects pollinator on chayote plants. Observation of diversity and abundance used a scan sampling method. Pollinator insects observations were carried out in 3 time periods, morning, afternoon, and evening on male and female flowers. We measured environmental parameters such as temperature, humidity, wind speed, and light intensity. Eight species of wild insects pollinated chayote flowers, Apis cerana, Apis dorsata, Lasioglossum leucozonium, Polistes sagittarius, Phimenes flavopictus, Campsomeriella annulata, Lucilia sericata, and Musca domestica. The insect pollinators community had moderate diversity (1.23), a relatively dynamic community (0.59), and moderate dominance (0.62), with A. cerana, which had been the dominant insect pollinator with a relative abundance of 61.63%. Musca domestica and L. sericata were (0,58%) the least dominant insect pollinator with a relative abundance of 0.58%. This research concludes that the insects pollinator of chayote has a moderate level of diversity, relatively dynamic community, and average dominance.
EN PRENSA INFLUENCIA DEL COMPORTAMIENTO PRO AMBIENTAL DE PROFESORES SOBRE ESTUDIANTES UNIVERSITARIOS EN CUARENTENA EN PRENSA
Authors: ARGÜERO FONSECA A. , MARTÍNEZ SOTO J. , MARCHIORO D. M. , REYNOSO GONZÁLEZ O. U.
Published: June 2022

Las crisis profundas que amenazan el bienestar y la existencia, podrían sensibilizar a las personas y conducirlas al desarrollo de comportamientos más solidarios, especialmente con la naturaleza. Durante la cuarentena por COVID-19, la educación remota favoreció el acercamiento de profesores y alumnos, al compartir espacios personales a través de las videollamadas. Derivado de lo anterior, la presente investigación tuvo como objetivo determinar cuál es la influencia que tienen los comportamientos proambientales del profesor como modelo, sobre sus alumnos después de una capacitación en psicología ambiental. Para evaluar este efecto, se realizó un estudio cuantitativo, transeccional de alcance correlacional a 33 estudiantes universitarios con edades entre 19 y 24 años (M=21.69; DE=1.70), 19 mujeres y 14 hombres, elegidos a conveniencia, a quienes se les aplicaron las escalas de conductas proambientales (alfa = 0.70) y la de expectativa de comportamiento ambiental de otros (alfa = 0.77), a través de un formulario de Google. Los resultados indican valores altos de conducta proambiental propia e intermedios en las expectativas de comportamiento proambiental del docente. Además, se encontró una correlación significativa de tipo positiva entre las variables (p <.001, r =.54). Como conclusión, se puede afirmar que, a mayor expectativa de comportamiento proambiental de los profesores desde la percepción de los estudiantes, mayor es su conducta proambiental adquirida, lo que podría significar un desafío para las instituciones universitarias y un compromiso para lograr que la educación ambiental indirecta, muestre los resultados esperados para el cumplimiento de las metas ambientales de la agenda pública.
EDUCAÇÃO REMOTA NO CONTEXTO DA COVID 19 EM MOÇAMBIQUE: UM OLHAR SOBRE AS CONDIÇÕES DE ACESSO: UM OLHAR SOBRE AS CONDIÇÕES DE ACESSO
Authors: Teixeira Ricardo Antonio Gonçalves , Gonçalves António Cipriano Parafino , Jorge Adérito Nandja
Published: June 2022

O presente artigo aborda o contexto da educação básica de Moçambique elege como compreender, a partir das medidas de mitigação da COVID 19 estudadas pelo setor de educação, como diferentes realidades geradas para analisar a viabilidade de atividades de ensino remoto, por meio de aulas de educação em Moçambique. Para tanto, valeu-se dos dados do Recenseamento Geral da População e Habitação de Moçambique, a partir dos censos de 2007 e 2017, bem como do estudo realizado pela Faculdade de Educação da Universidade Eduardo Mondlane, junto com o Movimento Educação para Todos (MEPT ). Como resultado, dentre outros, uma pesquisa mostra a falta de escolarização da população moçambicana em idade escolar, cuja realidade se assevera em adultos com idade igual ou superior a 20 anos e, em especial, às mulheres; associa a não escolarização e o abandono escolar ao casamento e maternidade prematura; e, indica, se mantém no processo escolar redução das mulheres percentuais de redução dos custos, efeitos importantes. Quanto ao professor medidas de mitigação da COVID 19, no contexto da educação pedagógica emergencial, o estudo revela que as condições precárias de atendimento estrutural e infraestruturais disponíveis, seja por parte das escolas, dos a partir e das famílias , impactam no acesso e qualidade da aprendizagem dos educandos.
AVALIAÇÃO DA INCIDÊNCIA DE DORES NA COLUNA E OUTRAS ARTICULAÇÕES NO PERÍODO DE HOME OFFICE DURANTE A PANDEMIA DA COVID-19
Authors: Batista Flamarion dos Santos , Melcher João Leonardo Dendevitz Gil , Carvalho Karina
Published: June 2022

Qual o impacto que a pandemia da COVID-19 trouxe para a saúde osteomuscular dos trabalhadores formais em home office? Para responder esta pergunta foi realizado estudo prospectivo observacional por meio da coleta de dados via questionário online, no período de setembro de 2020 a outubro de 2021. Foram criadas questões sobre o perfil dos trabalhadores formais, frequência de atividades físicas, dores na coluna e outras articulações e demais fatores associados ao trabalho remoto e ao isolamento social. Foi obtida amostra de 98 participantes. Como conclusão, verificou-se alteração do padrão de atividades físicas e, a quantidade de horas trabalhadas no período, acarretou piora e/ou surgimento de dores.
Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks
Authors: Cheng Chen , Yang Haiqin , King Irwin , Lyu Michael
Published: June 2022

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.
Ontological Smoothing for Relation Extraction with Minimal Supervision
Authors: Zhang Congle , Hoffmann Raphael , Weld Daniel
Published: June 2022

Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations' arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervisedtechnique that learns extractors for a set of minimally-labeledrelations. Ontological smoothing has three phases. First, itgenerates a mapping between the target relations and a backgroundknowledge-base. Second, it uses distant supervision toheuristically generate new training examples for the targetrelations. Finally, it learns an extractor from a combination of theoriginal and newly-generated examples. Experiments on 65 relationsacross three target domains show that ontological smoothing candramatically improve precision and recall, even rivaling fully supervisedperformance in many cases.
Querying Linked Ontological Data through Distributed Summarization
Authors: Fokoue Achille , Meneguzzi Felipe , Sensoy Murat , Pan Jeff
Published: June 2022

As the semantic web expands, ontological data becomes distributed over a large network of data sources on the Web. Consequently, evaluating queries that aim to tap into this distributed semantic database necessitates the ability to consult multiple data sources efficiently. In this paper, we propose methods and heuristics to efficiently query distributed ontological data based on a series of properties of summarized data. In our approach, each source summarizes its data as another RDF graph, and relevant section of these summaries are merged and analyzed at query evaluation time. We show how the analysis of these summaries enables more efficient source selection, query pruning and transformation of expensive distributed joins into local joins.
Solving Temporal Problems Using SMT: Weak Controllability
Authors: Cimatti Alessandro , Micheli Andrea , Roveri Marco
Published: June 2022

Temporal problems with uncertainty are a well established formalism to model time constraints of a system interacting with an uncertain environment. Several works have addressed the definition and the solving of controllability problems, and three degrees of controllability have been proposed: weak, strong, and dynamic. In this work we focus on weak controllability: we address both the decision and the strategy extraction problems. Extracting a strategy means finding a function from assignments to uncontrollable time points to assignments to controllable time points that fulfills all the temporal constraints. We address the two problems in the satisfiability modulo theory framework. We provide a clean and complete formalization of the problems, and we propose novel techniques to extract strategies. We also provide experimental evidence of the scalability and efficiency of the proposed techniques.
Using Expectations to Drive Cognitive Behavior
Authors: Kurup Unmesh , Lebiere Christian , Stentz Anthony , Hebert Martial
Published: June 2022

Generating future states of the world is an essential component of high-level cognitive tasks such as planning. We explore the notion that such future-state generation is more widespread and forms an integral part of cognition. We call these generated states expectations, and propose that cognitive systems constantly generate expectations, match them to observed behavior and react when a difference exists between the two. We describe an ACT-R model that performs expectation-driven cognition on two tasks – pedestrian tracking and behavior classification. The model generates expectations of pedestrian movements to track them. The model also uses differences in expectations to identify distinctive features that differentiate these tracks. During learning, the model learns the association between these features and the various behaviors. During testing, it classifies pedestrian tracks by recalling the behavior associated with the features of each track. We tested the model on both single and multiple behavior datasets and compared the results against a k-NN classifier. The k-NN classifier outperformed the model in correct classifications, but the model had fewer incorrect classifications in the multiple behavior case, and both systems had about equal incorrect classifications in the single behavior case.
Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble
Authors: Chakraborty Prithwish , Marwah Manish , Arlitt Martin , Ramakrishnan Naren
Published: June 2022

Local and distributed power generation is increasingly relianton renewable power sources, e.g., solar (photovoltaic or PV) andwind energy. The integration of such sources into the power grid ischallenging, however, due to their variable and intermittent energyoutput. To effectively use them on alarge scale, it is essential to be able to predict power generation at afine-grained level. We describe a novel Bayesian ensemble methodologyinvolving three diverse predictors. Each predictor estimates mixingcoefficients for integrating PV generation output profiles but capturesfundamentally different characteristics. Two of them employ classicalparameterized (naive Bayes) and non-parametric (nearest neighbor) methods tomodel the relationship between weather forecasts and PV output. The thirdpredictor captures the sequentiality implicit in PV generation and uses motifsmined from historical data to estimate the most likely mixture weights usinga stream prediction methodology. We demonstrate the success and superiority of ourmethods on real PV data from two locations that exhibit diverse weatherconditions. Predictions from our model can be harnessed to optimize schedulingof delay tolerant workloads, e.g., in a data center.
Improving Twitter Retrieval by Exploiting Structural Information
Authors: Luo Zhunchen , Osborne Miles , Petrovic ́ Saša , Wang Ting
Published: June 2022

Most Twitter search systems generally treat a tweet as a plain text when modeling relevance. However, a series of conventions allows users to tweet in structural ways using combination of different blocks of texts.These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured document (e.g., web pages) retrieval. In this paper we utilize the structure of tweets, induced by these blocks, for Twitter retrieval. A set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring tweets can achieve state-of-the-art performance. Our approach does not rely upon social media features, but when we do add this additional information, performance improves significantly.
Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization
Authors: Nie Feiping , Huang Heng , Ding Chris
Published: June 2022

As an emerging machine learning and information retrieval technique, the matrix completion has been successfully applied to solve many scientific applications, such as collaborative prediction in information retrieval, video completion in computer vision, \emph{etc}. The matrix completion is to recover a low-rank matrix with a fraction of its entries arbitrarily corrupted. Instead of solving the popularly used trace norm or nuclear norm based objective, we directly minimize the original formulations of trace norm and rank norm. We propose a novel Schatten $p$-Norm optimization framework that unifies different norm formulations. An efficient algorithm is derived to solve the new objective and followed by the rigorous theoretical proof on the convergence. The previous main solution strategy for this problem requires computing singular value decompositions - a task that requires increasingly cost as matrix sizes and rank increase. Our algorithm has closed form solution in each iteration, hence it converges fast. As a consequence, our algorithm has the capacity of solving large-scale matrix completion problems. Empirical studies on the recommendation system data sets demonstrate the promising performance of our new optimization framework and efficient algorithm.
Symbolic Synthesis of Observability Requirements for Diagnosability
Authors: Bittner Benjamin , Bozzano Marco , Cimatti Alessandro , Olive Xavier
Published: June 2022

Given a partially observable dynamic system and a diagnoser observing its evolution over time, diagnosability analysis formally verifies (at design time) if the diagnosis system will be able to infer (at runtime) the required information on the hidden part of the dynamic state. Diagnosability directly depends on the availability of observations, and can be guaranteed by different sets of sensors, possibly associated with different costs. In this paper, we tackle the problem of synthesizing observability requirements, i.e. automatically discovering a set of observations that is sufficient to guarantee diagnosability. We propose a novel approach with the following characterizing features. First, it fully covers a comprehensive formal framework for diagnosability analysis, and enables ranking configurations of observables in terms of cost, minimality, and diagnosability delay. Second, we propose two complementary algorithms for the synthesis of observables. Third, we describe an efficient implementation that takes full advantage of mature symbolic model checking techniques. The proposed approach is thoroughly evaluated over a comprehensive suite of benchmarks taken from the aerospace domain.
Transfer Learning with Graph Co-Regularization
Authors: Long Mingsheng , Wang Jianmin , Ding Guiguang , Shen Dou , Yang Qiang
Published: June 2022

Transfer learning proves to be effective for leveraging labeled data in the source domain to build an accurate classifier in the target domain. The basic assumption behind transfer learning is that the involved domains share some common latent factors. Previous methods usually explore these latent factors by optimizing two separate objective functions, i.e., either maximizing the empirical likelihood, or preserving the geometric structure. Actually, these two objective functions are complementary to each other and optimizing them simultaneously can make the solution smoother and further improve the accuracy of the final model. In this paper, we propose a novel approach called Graph co-regularized Transfer Learning (GTL) for this purpose, which integrates the two objective functions seamlessly into one unified optimization problem. Thereafter, we present an iterative algorithm for the optimization problem with rigorous analysis on convergence and complexity. Our empirical study on two open data sets validates that GTL can consistently improve the classification accuracy compared to the state-of-the-art transfer learning methods.
Heart Rate Topic Models
Authors: Van Esbroeck Alexander , Chia Chih-Chun , Syed Zeeshan
Published: June 2022

A key challenge in reducing the burden of cardiovascular disease is matching patients to treatments that are most appropriate for them. Different cardiac assessment tools have been developed to address this goal. Recent research has focused on heart rate motifs, i.e., short-term heart rate sequences that are over- or under-represented in long-term electrocardiogram (ECG) recordings of patients experiencing cardiovascular outcomes, which provide novel and valuable information for risk stratification. However, this approach can leverage only a small number of motifs for prediction and results in difficult to interpret models. We address these limitations by identifying latent structure in the large numbers of motifs found in long-term ECG recordings. In particular, we explore the application of topic models to heart rate time series to identify functional sets of heart rate sequences and to concisely describe patients using task-independent features for various cardiovascular outcomes. We evaluate the approach on a large collection of real-world ECG data, and investigate the performance of topic mixture features for the prediction of cardiovascular mortality. The topics provided an interpretable representation of the recordings and maintained valuable information for clinical assessment when compared with motif frequencies, even after accounting for commonly used clinical risk scores.
Similarity Is Not Entailment — Jointly Learning Similarity Transformation for Textual Entailment
Authors: Yokote Ken-ichi , Bollegala Danushka , Ishizuka Mitsuru
Published: June 2022

Predicting entailment between two given texts is an important task upon which the performance of numerous NLP tasks depend on such as question answering, text summarization, and information extraction. The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similarity measures and, (b) the optimal non-linear combination of those transformation functions to predict textual entailment. Our method consistently outperforms numerous baselines, reporting a micro-averaged F-score of 46.48 on the RTE- 7 benchmark dataset. The proposed method is ranked 2-nd among 33 entailment systems participated in RTE-7, demonstrating its competitiveness over numerous other entailment approaches. Although our method is statistically comparable to the current state-of-the-art, we require less external knowledge resources.
Evaluating Temporal Plans in Incomplete Domains
Authors: Morwood Daniel , Bryce Daniel
Published: June 2022

Recent work on planning in incomplete domains focuses on constructing plans that succeed despite incomplete knowledge of action preconditions and effects. As planning models become more expressive, such as in temporal planning, the types of incompleteness may not only change, but plans become more challenging to evaluate. The primary difficulty to temporal plan evaluation is accounting for temporal constraints that may not be satisfied under all interpretations of the incomplete domain. In this work, we formulate incomplete temporal plan evaluation as a generalization of the temporal consistency problem, called partial temporal consistency. We present a knowledge compilation approach that is combined with symbolic constraint propagation and model counting algorithms for counting the number of incomplete domain model interpretations under which a plan is consistent. We present an evaluation that identifies the aspects of incomplete temporal plans most impact performance.
Covering Number as a Complexity Measure for POMDP Planning and Learning
Authors: Zhang Zongzhang , Littman Michael , Chen Xiaoping
Published: June 2022

Finding a meaningful way of characterizing the difficulty of partially observable Markov decision processes (POMDPs) is a core theoretical problem in POMDP research. State-space size is often used as a proxy for POMDP difficulty, but it is a weak metric at best. Existing work has shown that the covering number for the reachable belief space, which is a set of belief points that are reachable from the initial belief point, has interesting links with the complexity of POMDP planning, theoretically. In this paper, we present empirical evidence that the covering number for the reachable belief space (or just ``covering number", for brevity) is a far better complexity measure than the state-space size for both planning and learning POMDPs on several small-scale benchmark problems. We connect the covering number to the complexity of learning POMDPs by proposing a provably convergent learning algorithm for POMDPs without reset given knowledge of the covering number.
Exploring Mixed-Initiative Interaction for Learning with Situated Instruction in Cognitive Agents
Authors: Mohan Shiwali , Laird John
Published: June 2022

Human-agent interaction for learning with instruction canbe viewed on a continuum of instructor/agent control. At one extreme are systems that learn by instructor-driveninteractions, such as learning by demonstration, examples,or imitation. The other extreme of the continuum isoccupied by systems where instructor interaction is limited to responding to the questions posed by the agent or toproviding feedback on agent’s performance. There are advantages to an approach that explores a mixed-initiative instructional dialog. We describe an approach to mixed-initiative interaction based on collaborative discourse theory that allows Soar agents to learn from situated instruction.
Heuristic Search Comes of Age
Authors: Sturtevant Nathan , Felner Ariel , Likhachev Maxim , Ruml Wheeler
Published: June 2022

In looking back on the last five to ten years of work in heuristic search a few trends emerge. First, there has been a broadening of research topics studied. Second, there has been a deepened understanding of the theoretical foundations of search. Third, and finally, there have been increased connections with work in other fields. This paper, corresponding to a AAAI 2012 invited talk on recent work in heuristic search, highlights these trends in a number of areas of heuristic search. It is our opinion that the sum of these trends reflects the growth in the field and the fact that heuristic search has come of age.
Leveraging Crowdsourcing to Detect Improper Tasks in Crowdsourcing Marketplaces
Authors: Baba Yukino , Kashima Hisashi , Kinoshita Kei , Yamaguchi Goushi , Akiyoshi Yosuke
Published: June 2022

Controlling the quality of tasks is a major challenge in crowdsourcing marketplaces. Most of the existing crowdsourcing services prohibit requesters from posting illegal or objectionable tasks. Operators in the marketplaces have to monitor the tasks continuously to find such improper tasks; however, it is too expensive to manually investigate each task. In this paper, we present the reports of our trial study on automatic detection of improper tasks to support the monitoring of activities by marketplace operators. We perform experiments using real task data from a commercial crowdsourcing marketplace and show that the classifier trained by the operator judgments achieves high accuracy in detecting improper tasks. In addition, to reduce the annotation costs of the operator and improve the classification accuracy, we consider the use of crowdsourcing for task annotation. We hire a group of crowdsourcing (non-expert) workers to monitor posted tasks, and incorporate their judgments into the training data of the classifier. By applying quality control techniques to handle the variability in worker reliability, our results show that the use of non-expert judgments by crowdsourcing workers in combination with expert judgments improves the accuracy of detecting improper crowdsourcing tasks.
Sources of Variation in the Carbon Footprint of Hemodialysis Treatment
Authors: Sehgal Ashwini R. , Slutzman Jonathan E. , Huml Anne M.
Published: June 2022

Significance Statement

Studies have demonstrated that hemodialysis facilities have a high environmental effect because the treatment requires large amounts of energy, water, and supplies. However, data regarding how much greenhouse gas emissions from hemodialysis treatment vary across facilities, treatments, and emission contributors have been lacking. In this study, the authors estimated magnitude and sources of variation in the carbon footprint of hemodialysis treatment. They found that the annual emissions per hemodialysis facility are equivalent to emissions from the annual energy use of 93 homes, and emissions per treatment are equivalent to driving an average automobile for 238 km (149 miles). Carbon footprints across facilities, treatments, and emission contributors also varied substantially. Understanding the magnitude and variation in greenhouse gas emissions may help identify measures to reduce the environmental effect of hemodialysis treatment.

Background

Greenhouse gas emissions from hemodialysis treatment in the United States have not been quantified. In addition, no previous studies have examined how much emissions vary across facilities, treatments, and emission contributors.

Methods

To estimate the magnitude and sources of variation in the carbon footprint of hemodialysis treatment, we estimated life-cycle greenhouse gas emissions in carbon dioxide equivalents (CO2-eq) associated with 209,481 hemodialysis treatments in 2020 at 15 Ohio hemodialysis facilities belonging to the same organization. We considered emissions from electricity, natural gas, water, and supply use; patient and staff travel distance; and biohazard and landfill waste.

Results

Annual emissions per facility averaged 769,374 kg CO2-eq (95% CI, 709,388 to 848,180 kg CO2-eq). The three largest contributors to total emissions were patient and staff transportation (28.3%), electricity (27.4%), and natural gas (15.2%). Emissions per treatment were 58.9 kg CO2-eq, with a three-fold variation across facilities. The contributors with the largest variation in emissions per treatment were transportation, natural gas, and water (coefficients of variation, 62.5%, 42.4%, and 37.7%, respectively). The annual emissions per hemodialysis facility are equivalent to emissions from the annual energy use in 93 homes; emissions per treatment are equivalent to driving an average automobile for 238 km (149 miles).

Conclusions

Similar medical treatments provided in a single geographic region by facilities that are part of the same organization may be expected to have small variations in the determinants of greenhouse gas emissions. However, we found substantial variation in carbon footprints across facilities, treatments, and emission contributors. Understanding the magnitude and variation in greenhouse gas emissions may help identify measures to reduce the environmental effect of hemodialysis treatment.
Exploración de tórax, sistema cardiovascular y pulmones
Authors: Mendivil de la Ossa José Alberto , Gómez Duque Lina María
Published: June 2022

Esta guía de práctica de los cursos en Semiología del Paciente Sano y Semiología Clínica se desarrolló con base en los textos guía para el syllabus de estos cursos del programa de Medicina, que corresponden a los textos "Manual Seidel de exploración física" [1] y "Semiología médica Cediel" [2]. La guía comprende los aspectos básicos de la semiotecnia y la interpretación de los hallazgos semiológicos del examen del tórax, sistema cardiovascular y pulmones, siguiendo este orden: 1) evaluación de la morfología por inspección y palpación del tórax; 2) evaluación del sistema cardiovascular, a través de los signos clínicos que informan sobre la precarga, la poscarga y el compartimento cardiaco, y 3) evaluación de la fisiología de pulmones y sistema respiratorio a través de la inspección, palpación, percusión, auscultación y signos indirectos de sus alteraciones.
Growing indium antimonide single crystals with a diameter of 100 mm by the modified Chochralsky method
Authors: Kozlov R. Yu. , Kormilitsina S. S. , Molodtsova E. V. , Zhuravlev E. O.
Published: June 2022

At present, all over the world there is a tendency to increase the diameters of single crystals of both elementary semiconductors and semiconductor compounds. There are reports indicating the use of single crystals of III-V semiconductors with a diameter of four to six inches. So far, indium antimonide single crystals up to 75 mm in diameter have been obtained in Russia.Indium antimonide is the element base of the broadest field of solid-state electronics — optoelectronics. On its basis, linear and matrix photodetectors are manufactured, operating in the spectral wavelength range of 3-5 microns, which are used as a viewing element in thermal imaging systems.In this work, we selected the thermal growth conditions and obtained indium antimonide single crystals 100 mm in diameter in the crystallographic direction [100]. The solution of this problem has made it possible to significantly increase the yield of suitable photodetectors.Single crystals 100 mm in diameter were grown by the Czochralski method in a two-stage process. The design of the graphite heating unit was enlarged and matched to a working crucible with a diameter of 150 millimeters and a load of 4.5-5 kg.The Van der Pauw method was used to measure the electrical properties of the obtained single crystals, which corresponded to the standard parameters of undoped indium antimonide. Using an optical microscope, the etching pits were counted using the 9-field method. The dislocation density in crystals with a diameter of 100 mm was ≤ 100 cm-2 and corresponded to the values ​for crystals of 50 mm.
The Effect of Digital Stories on 3rd Graders’ Achievement, Attitudes and Motivation in Science Lesson
Authors: BAŞAR Tarık
Published: June 2022

This research aimed to determine the effect of digital stories on 3rd grade students’ achievement, attitudes, and motivation in science lesson. For this purpose, explanatory sequential design, one of the mixed methods, was used in the research. The study group, composed of primary school 3rd grade students, was identified via convenience sampling method. The students in the experimental group watched the digital stories prepared by the researcher in the experimental process of the research. 10 cognitive learning outcomes related to the units "Our five senses", "Let's recognize the force" and "Let's know the matter" included the Primary School Third Grade Science Curriculum of Turkey were taken as the basis during the preparation of the digital stories used within the scope of the research. As a result of the quantitative data obtained from the research, it was concluded that experimental group students’ achievement, attitudes, and motivation were higher in the science lesson compared to control group students’ achievement, attitudes, and motivation. Based on the results of the qualitative data obtained from the research, it was determined that the students found digital stories entertaining, interesting and intriguing. In addition, it was concluded that digital stories facilitate students’ learning process and provide them with the opportunity to associate what is learned in the lesson with daily life. It is recommended to integrate digital stories into the current Science Curriculum in line with the results obtained from the research.
Physicochemical and antioxidant properties of Coffea arabica honey from Western Oromia, Ethiopia
Authors: Admassu Addi , Tura Bareke , Deresa Kebebe Tolera Kumsa , Kasim Roba
Published: June 2022

Coffea arabica is one of the most widely consumed and marketed commodities in the world. The study was designed to characterize C.arabica honey for botanical composition, physicochemical parameters, and antioxidant properties of honey. Twelve honey samples of C. arabica honey were collected during the flowering period of coffee flowers from the Zander hive. The physicochemical properties of honey and the Botanical origin of honey were determined based on Harmonized methods of the International Honey Commission. The antioxidant power of the coffee monofloral honey samples was determined by dissolving 1.5 gm of honey with 25 ml distilled water and mixing it with 25ml methanol and placed at 25◦C for sixty minutes of maceration using a temperature shaker. The pollen count percentage from honey indicated that all honey samples collected from Gera, Gomma, Yayu, and Manna districts were identified as coffee monofloral honey representing 84%, 93%, 75%, and 73 % of pollen count respectively. The mean moisture, ash, HMF, EC, FA, pH, fructose, glucose and sucrose content of Coffea arabica honey were 22.48%, 0.21%, 11.88, 0.49 mS/cm, 13.44 meq/Kg, 3.32, 32.77%, 32.9%, and 3.57% respectively. The total phenol and flavonoid content range from 42.1-82.1 and 21.7-59.7 mg/100 g of GAE/g respectively while the radical scavenging activity ranges from 60.2- 66.3%. The pollen analysis of honey from the area is coffee monofloral honey since its pollen count exceeds more than 45% and the honey quality also meets the Ethiopian and International standards. The antioxidant power of Coffee honey has a considerable amount of polyphenolics which have relevant antiradical activity.
Does Prior Night’s Sleep Impact Next Day’s Executive Functioning? It Depends on an Individual’s Average Sleep Quality
Authors: Yu Dian , Goncalves Carolina , Yang Pei-Jung , Geldhof G. John , Michaelson Laura , Ni Yue , Lerner Richard M.
Published: June 2022

Executive functioning (EF) is a series of fundamental goal-directed cognitive abilities that enable effective learning. Differences in daily sleep quality may covary with fluctuations in EF among youth. Most studies linking sleep to EF rely on between-person differences and average effects for the sample. This study employed an intensive longitudinal design and examined the within-person relations between self-reported prior night’s sleep quality and next day’s EF. Students from Grades 4 to 12 (M age= 14.60, SD = 2.53) completed three behavioral EF tasks repeatedly across approximately one semester. The final analytic sample included 2898 observations embedded in 73 participants. Although, on average, sleep did not significantly covary with EF, there was heterogeneity in within-person sleep-EF relations. Moreover, individuals’ average sleep quality moderated within-person effects. For individuals with low mean sleep quality, a better-than-usual sleep quality was linked to better EF performance. However, for individuals with high mean sleep quality, better-than-usual sleep quality was linked to worse EF performance. Understanding person-specific relations between sleep and EF can help educators optimize EF and learning on a daily basis and produce positive academic outcomes across longer time periods.