bluuebunny/crossref_metadata_2025 huggingface dataset:
Dantzig-Wolfe Decomposition for Cost Partitioning
Authors: Pommerening Florian , Keller Thomas , Halasi Valentina , Seipp Jendrik , Sievers Silvan , Helmert Malte
Published: September 2022

Optimal cost partitioning can produce high quality heuristic estimates even from small abstractions. It can be computed with a linear program (LP) but the size of this LP often makes this impractical. Recent work used Lagrangian decomposition to speed up the computation. Here we use a different decomposition technique called Dantzig-Wolfe decomposition to tackle the problem. This gives new insights into optimal cost partitioning and has several advantages over Lagrangian decomposition: our method detects when a cost partition is optimal; it can deal with general cost functions; and it does not consider abstractions in the linear program that do not contribute to the heuristic value. We also show the advantage of the method empirically and investigate several improvements that are useful for all cost partitioning methods.
Online Saturated Cost Partitioning for Classical Planning
Authors: Seipp Jendrik
Published: September 2022

Cost partitioning is a general method for admissibly summing up heuristic estimates for optimal state-space search. Most cost partitioning algorithms can optimize the resulting cost-partitioned heuristic for a specific state. Since computing a new cost-partitioned heuristic for each evaluated state is usually too expensive in practice, the strongest planners based on cost partitioning over abstraction heuristics precompute a set of cost-partitioned heuristics before the search and maximize over their estimates during the search. This makes state evaluations very fast, but since there is no better termination criterion than a time limit, it requires a long precomputation phase, even for the simplest planning tasks. A prototypical example for this is the Scorpion planner which computes saturated cost partitionings over abstraction heuristics offline before the search. Using Scorpion as a case study, we show that by incrementally extending the set of cost-partitioned heuristics online during the search, we drastically speed up the planning process and even often solve more tasks.
Iterative-deepening Bidirectional Heuristic Search with Restricted Memory
Authors: Shperberg Shahaf S. , Danishevski Steven , Felner Ariel , Sturtevant Nathan R.
Published: September 2022

The field of bidirectional heuristic search has recently seen great advances. However, the subject of memory-restricted bidirectional search has not received recent attention.
In this paper we introduce a general iterative deepening bidirectional heuristic search algorithm (IDBiHS) that searches simultaneously in both directions while controlling the meeting point of the search frontiers.
First, we present the basic variant of IDBiHS, whose memory is linear in the search depth. We then add improvements that exploit consistency and front-to-front heuristics. Next, we move to the case where a fixed amount of memory is available to store nodes during the search and develop two variants of IDBiHS: (1) A*+IDBiHS, that starts with A* and moves to IDBiHS as soon as memory is exhausted.
(2) A variant that stores partial forward frontiers until memory is exhausted and then tries to match each of them from the backward side.
Finally, we experimentally compare the new algorithms to existing unidirectional and bidirectional ones. In many cases our new algorithms outperform previous ones in both node expansions and time.
On the Compilability and Expressive Power of State-Dependent Action Costs
Authors: Speck David , Borukhson David , Mattmüller Robert , Nebel Bernhard
Published: September 2022

While state-dependent action costs are practically relevant and have been studied before, it is still unclear if they are an essential feature of planning tasks. In this paper, we study to what extent state-dependent action costs are an essential feature by analyzing under which circumstances they can be compiled away. We give a complete classification for all combinations of action cost functions and possible cost measures for the compilations.
Our theoretical results show that if both task sizes and plan lengths are to be preserved polynomially, then the boundary between compilability and non-compilability lies between FP and FPSPACE computable action cost functions (under a mild assumption on the polynomial hierarchy). Preserving task sizes polynomially and plan lengths linearly at the same time is impossible.
Data-Driven Decision-Theoretic Planning using Recurrent Sum-Product-Max Networks
Authors: Tatavarti Hari , Doshi Prashant , Hayes Layton
Published: September 2022

Sum-product networks (SPN) are knowledge compilation models and are related to other graphical models for efficient probabilistic inference such as arithmetic circuits and AND/OR graphs. Recent investigations into generalizing SPNs have yielded sum-product-max networks (SPMN) which offer a data-driven alternative for decision making that has predominantly relied on handcrafted models. However, SPMNs are not suited for decision-theoretic planning which involves sequential decision making over multiple time steps. In this paper, we present recurrent SPMNs (RSPMN) that learn from and model decision-making data over time. RSPMNs utilize a template network that is unfolded as needed depending on the length of the data sequence. This is significant as RSPMNs not only inherit the benefits of SPNs in being data driven and mostly tractable, they are also well suited for planning problems. We establish soundness conditions on the template network, which guarantee that the resulting SPMN is valid, and present a structure learning algorithm to learn a sound template. RSPMNs learned on a testbed of data sets, some generated using RDDLSim, yield MEUs and policies that are close to the optimal on perfectly-observed domains and easily improve on a recent batch-constrained RL method, which is important because RSPMNs offer a new model-based approach to offline RL.
Detecting Unsolvability Based on Separating Functions
Authors: Christen Remo , Eriksson Salomé , Pommerening Florian , Helmert Malte
Published: September 2022

While the unsolvability IPC sparked a multitude of planners proficient in detecting unsolvable planning tasks, there are gaps where concise unsolvability arguments are known but no existing planner can capture them without prohibitive computational effort. One such example is the sliding tiles puzzle, where solvability can be decided in polynomial time with a parity argument. We introduce separating functions, which can prove that one state is unreachable from another, and show under what conditions a potential function over any nonzero ring is a separating function. We prove that we can compactly encode these conditions for potential functions over features that are pairs, and show in which cases we can efficiently synthesize functions satisfying these conditions. We experimentally evaluate a domain-independent algorithm that successfully synthesizes such separating functions from PDDL representations of the sliding tiles puzzle, the Lights Out puzzle, and Peg Solitaire.
Who Needs These Operators Anyway: Top Quality Planning with Operator Subset Criteria
Authors: Katz Michael , Sohrabi Shirin
Published: September 2022

Top-quality planning in general and quotient top-quality planning in particular deal with producing multiple high-quality plans while allowing for their efficient generation, skipping equivalent ones. Prior work has explored one equivalence relation, considering two plans to be equivalent if their operator multi-sets are equal. This allowed omitting plans that are reorderings of previously found ones. However, the resulting sets of plans were still large, in some domains even infinite.
In this paper, we consider a different relation: two plans are related if one's operator multiset is a subset of the other's. We propose novel reformulations that forbid plans that are related to the given ones. While the new relation is not transitive and thus not an equivalence relation, we can define a new subset top-quality planning problem, with finite size solution sets. We formally prove that these solutions can be obtained by exploiting the proposed reformulations. Our empirical evaluation shows that solutions to the new problem can be found for more tasks than unordered top-quality planning solutions. Further, the results shows that the solution sizes significantly decrease, making the new approach more practical, particularly in domains with redundant operators.
Pattern Selection Strategies for Pattern Databases in Probabilistic Planning
Authors: Klößner Thorsten , Steinmetz Marcel , Torralba Álvaro , Hoffmann Jörg
Published: September 2022

Recently, pattern databases have been extended to probabilistic
planning, to derive heuristics for the objectives of goal probability
maximization and expected cost minimization. While this approach
yields both theoretical and practical advantages over techniques
relying on determinization, the problem of selecting the patterns in
the first place has only been scantily addressed as yet, through a
method that systematically enumerates patterns up to a fixed
size. Here we close this gap, extending pattern generation techniques
known from classical planning to the probabilistic case. We consider
hill-climbing as well as counter-example guided abstraction refinement
(CEGAR) approaches, and show how they need to be adapted to obtain
desired properties such as convergence to the perfect value function
in the limit. Our experiments show substantial improvements over
systematic pattern generation and the previous state of the art.
Biased Exploration for Satisficing Heuristic Search
Authors: Kuroiwa Ryo , Beck J. Christopher
Published: September 2022

Satisficing heuristic search such as greedy best-first search (GBFS) suffers from local minima, regions where heuristic values are inaccurate and a good node has a worse heuristic value than other nodes. Search algorithms that incorporate exploration mechanisms in GBFS empirically reduce the search effort to solve difficult problems. Although some of these methods entirely ignore the guidance of a heuristic during their exploration phase, intuitively, a good heuristic should have some bound on its inaccuracy, and exploration mechanisms should exploit this bound. In this paper, we theoretically analyze what a good node is for satisficing heuristic search algorithms and show that the heuristic value of a good node has an upper bound if a heuristic satisfies a certain property. Then, we propose biased exploration mechanisms which select lower heuristic values with higher probabilities. In the experiments using synthetic graph search problems and classical planning benchmarks, we show that the biased exploration mechanisms can be useful. In particular, one of our methods, Softmin-Type(h), significantly outperforms other GBFS variants in classical planning and improves the performance of Type-LAMA, a state-of-the-art classical planner.
Conflict-Directed Diverse Planning for Logic-Geometric Programming
Authors: Ortiz-Haro Joaquim , Karpas Erez , Toussaint Marc , Katz Michael
Published: September 2022

Robots operating in the real world must combine task planning for reasoning about what to do with motion planning for reasoning about how to do it -- this is known as task and motion planning. One promising approach for task and motion planning is Logic Geometric Programming (LGP) which integrates a logical layer and a geometric layer in an optimization formulation. The logical layer describes feasible high-level actions at an abstract symbolic level, while the geometric layer uses continuous optimization methods to reason about motion trajectories with geometric constraints.
In this paper we propose a new approach for solving task and motion planning problems in the LGP formulation, that leverages state-of-the-art diverse planning at the logical layer to explore the space of feasible logical plans, and minimizes the number of optimization problems to be solved on the continuous geometric layer. To this end, geometric infeasibility is fed back into planning by identifying prefix conflicts and incorporating this back into the planner through a novel multi-prefix forbidding compilation. We further leverage diverse planning with a new novelty criteria for selecting candidate plans based on the prefix novelty, and a metareasoning approach which attempts to extract only useful conflicts by leveraging the information that is gathered in the course of solving the given problem.
Optimising the Stability in Plan Repair via Compilation
Authors: Saetti Alessandro , Scala Enrico
Published: September 2022

Plan repair is the problem of solving a given planning problem by using a solution plan of a similar problem. Plan repair problems can arise in execution contexts, that is, when an agent performing the plan has to deal with some unexpected contingency that makes the given plan invalid. Repairing a plan works often much better than replanning from scratch, and is crucial when plans have to be kept stable. There is no planning system until now that guarantees to find plans at the minimum distance from an input plan. This paper presents the first approach to such a problem; we indeed introduce a simple compilation scheme that converts a classical planning problem into another where optimal plans correspond to plans with the minimum distance from an input plan. Our experiments using a number of planners show that such a simple approach can solve the plan repair problem optimally and more effectively than replanning from scratch for a large number of cases. Last but not least, the approach proves competitive with LPG-ADAPT.
Learning to Estimate Search Progress Using Sequence of States
Authors: Sudry Matan , Karpas Erez
Published: September 2022

Many problems of interest can be solved using heuristic search algorithms. When solving a heuristic search problem, we are often interested in estimating search progress, that is, how much longer until we have a solution. Previous work on search progress estimation derived formulas based on some relevant features that can be observed from the behavior of the search algorithm. In this paper, rather than manually deriving such formulas we leverage machine learning to learn more accurate search progress predictors automatically. We train a Long Short-Term Memory (LSTM) network, which takes as input sequences of nodes expanded by the search algorithm, and predicts how far along with the search we are. Importantly, our approach still treats the search algorithm as a black box and does not look into the contents of search nodes. An empirical evaluation shows our technique outperforms previous search progress estimation techniques.
Loopless Top-K Planning
Authors: Von Tschammer Julian , Mattmüller Robert , Speck David
Published: September 2022

In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the application, plans with such loops are of little importance because they are dominated by a loopless representative and can prevent more meaningful plans from being found.

In this paper, we motivate and introduce loopless top-k planning. We show how to enhance the state-of-the-art symbolic top-k planner, symK, to obtain an efficient, sound and complete algorithm for loopless top-k planning. An empirical evaluation shows that our proposed approach has a higher k-coverage than a generate-and-test approach that uses an ordinary top-k planner, which we show to be incomplete in the presence of zero-cost loops.
Uniform Machine Scheduling with Predictions
Authors: Zhao Tianming , Li Wei , Zomaya Albert Y.
Published: September 2022

The revival in learning theory has provided us with improved capabilities for accurate predictions. This work contributes to an emerging research agenda of online scheduling with predictions by studying the makespan minimization in uniformly related machine non-clairvoyant scheduling with job size predictions. Our task is to design online algorithms that effectively use predictions and have performance guarantees with varying prediction quality. We first propose a simple algorithm-independent prediction error measurement to quantify prediction quality. To effectively use the predicted job sizes, we design an offline improved 2-relaxed decision procedure approximating the optimal schedule. With this decision procedure, we propose an online O(min{log eta, log m})-competitive algorithm that assumes a known prediction error. Finally, we extend this algorithm to construct a robust O(min{log eta, log m})-competitive algorithm that does not assume a known error. Both algorithms require only moderate predictions to improve the well-known Omega(log m) lower bound, showing the potential of using predictions in managing uncertainty.
Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics
Authors: Pingen Geert L. J. , Van Ommeren Christian R. , Van Leeuwen Cornelis J. , Fransen Ruben W. , Elfrink Tijmen , De Vries Yorick C. , Karunakaran Janarthanan , Demirović Emir , Yorke-Smith Neil
Published: September 2022

Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.
Building Resource-Dependent Conditional Plans for an Earth Monitoring Satellite
Authors: Pralet Cédric , Doose David , Anxionnat Julien , Pouly Jérémie
Published: September 2022

This paper introduces a conditional planning and execution system for an Earth monitoring satellite. This system builds plans containing optional acquisitions that are activated or not at execution time depending on the amount of energy actually available. One originality is that the energy activation thresholds computed on the ground take into account the capacity of the satellite to use an heliocentric pointing or switch off the payload when acquisitions are canceled. Another originality is that the conditional planner proposed uses several energy propagation models, from conservative models containing margins on power production to optimistic models that allow opportunistic acquisitions to be planned.
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari
Authors: Ayton Benjamin , Asai Masataro
Published: September 2022

Width-based planning has shown promising results on Atari 2600 games using pixel input, while using substantially fewer environment interactions than reinforcement learning. Recent width-based approaches have computed feature vectors for each screen using a hand designed feature set (Rollout-IW) or a variational autoencoder trained on game screens (VAE-IW), and prune screens that do not have novel features during the search. We propose Olive (Online-VAE-IW), which updates the VAE features online using active learning to maximize the utility of screens observed during planning. Experimental results across 55 Atari games demonstrate that it outperforms Rollout-IW by 42-to-11 and VAE-IW by 32-to-20. Moreover, Olive outperforms existing work based on policy-learning (π-IW, DQN) trained with 100 times the training budget by 30-to-22 and 31-to-17, and a state of the art data-efficient reinforcement learning (EfficientZero) trained with the same training budget and ran with 1.8 times the planning budget by 18-to-7 in the Atari 100k benchmark, without any policy learning. The source code and the appendix are available at github.com/ibm/atari-active-learning and arxiv.org/abs/2109.15310 .
Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods
Authors: Ferber Patrick , Geißer Florian , Trevizan Felipe , Helmert Malte , Hoffmann Jörg
Published: September 2022

How can we train neural network (NN) heuristic functions for classical
planning, using only states as the NN input? Prior work addressed this
question by (a) per-instance imitation learning and/or (b) per-domain
learning. The former limits the approach to instances small enough for
training data generation, the latter to domains where the necessary
knowledge generalizes across instances. Here we explore three methods
for (a) that make training data generation scalable through
bootstrapping and approximate value iteration. In particular, we
introduce a new bootstrapping variant that estimates search effort
instead of goal distance, which as we show converges to the perfect
heuristic under idealized circumstances. We empirically compare these
methods to (a) and (b), aligning three different NN heuristic function
learning architectures for cross-comparison in an experiment of
unprecedented breadth in this context. Key lessons are that our
methods and imitation learning are highly complementary; that
per-instance learning often yields stronger heuristics than per-domain
learning; and the LAMA planner is still dominant but our methods
outperform it in one benchmark domain.
Learning Multi-Agent Action Coordination via Electing First-Move Agent
Authors: Ruan Jingqing , Meng Linghui , Xiong Xuantang , Xing Dengpeng , Xu Bo
Published: September 2022

Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously, and asynchronous action coordination between agents is rarely considered. This paper introduces a bi-level multi-agent decision hierarchy for coordinated behavior planning. We propose a novel election mechanism in which we adopt a graph convolutional network to model the interaction among agents and elect a first-move agent for asynchronous guidance. We also propose a dynamically weighted mixing network to effectively reduce the misestimation of the value function during training. This work is the first to explicitly model the asynchronous multi-agent action coordination, and this explicitness enables to choose the optimal first-move agent. The results on Cooperative Navigation and Google Football demonstrate that the proposed algorithm can achieve superior performance in cooperative environments. Our code is available at https://github.com/Amanda-1997/EFA-DWM.
Active Grammatical Inference for Non-Markovian Planning
Authors: Topper Noah , Atia George , Trivedi Ashutosh , Velasquez Alvaro
Published: September 2022

Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L* has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian reward signal, which must be inferred from experiments. We call this setting non-Markovian planning, as opposed to non-Markovian RL. The proposed approach leverages L* to explicate an automaton structure for the underlying planning objective. We exploit the environment model to learn an automaton faster and integrate it with value iteration to accelerate the planning. We compare against recent non-Markovian RL solutions which leverage grammatical inference, and establish complexity results that illustrate the difference in runtime between grammatical inference in planning and RL settings.
Actor-Focused Interactive Visualization for AI Planning
Authors: Cantareira Gabriel Dias , Canal Gerard , Borgo Rita
Published: September 2022

As we grow more reliant on AI systems for an increasing variety of applications in our lives, the need to understand and interpret such systems also becomes more pronounced, be it for improvement, trust, or legal liability. AI Planning is one type of task that provides explanation challenges, particularly due to the increasing complexity in generated plans and convoluted causal chains that connect actions and determine overall plan structure. While there are many recent techniques to support plan explanation, visual aids for navigating this data are quite limited. Furthermore, there is often a barrier between techniques focused on abstract planning concepts and domain-related explanations. In this paper, we present a visual analytics tool to support plan summarization and interaction, focusing in robotics domains using an actor-based structure. We show how users can quickly grasp vital information about actions involved in a plan and how they relate to each other. Finally, we present a framework used to design our tool, highlighting how general PDDL elements can be converted into visual representations and further connecting concept to domain.
Imágenes en circulación. Una aproximación a la cultura visual digital desde la crisis de la especificidad del medio fotográfico
Authors: Martínez Luna Sergio
Published: September 2022

El artículo estudia la cuestión de la especificidad del medio en el contexto de las transformaciones de la digitalización, en especial la condición actual de la fotografía. La digitalización intensifica las contradicciones del discurso de la autonomía del medio fotográfico, pues diluye las diferencias entre imagen fijay en movimiento e incluso entre imagen analógica y digital. Las imágenes interconectadas en red son simultáneamente singulares y múltiples, presentan una movilidad interna y externa, y sus formas de aparición y conectividad son dependientes de operaciones que desbordan la percepción humana. Cuestionando términos como el de flujo y circulación el artículo propone la articulación entre escalas y ritmos, entre una mirada sobre las imágenes a la vez distante y concentrada, capaz de abordar críticamente las condiciones actuales de producción y recepción de las expresiones culturales.
FamiliarizArte. Del cuento del lobo a la distopía del confinamiento
Authors: Martínez Vérez Victoria , Montero Seoane Antonio , Sardá Miragaya Alba
Published: September 2022

Introducción: el presente trabajo analiza los resultados del proyecto FamiliarizArte, galardonado con el Premio Espiral de educación de ámbito nacional. Dicho proyecto utiliza la mediación artística, a través de la contemplación de cuatro obras: Xentes que ollan (Personas que miran) de Díaz Pardo, Casas de Antonio Tenreiro, Vista da Coruña (Vista de La Coruña) de Urbano Lugrís y Zoolóxico (Zoológico) de Lago Rivera, pertenecientes al fondo pictórico del Museo de Belas Artes da Coruña (Noroeste de España), con el fin de provocar un contexto reflexivo que permita analizar el impacto de la Covid-19. Método: para ello, se realiza una propuesta de actividad entre tres instituciones: el CIFP Anxel Casal, que actúa como proponente, la Escuela Infantil de Montealto, quien acoge e impulsa el proyecto y el Museo de Bellas Artes, quien cede su fondo pictórico, para la composición de una narrativa visual que sirve como marco contemplativo y reflexivo. Resultados y conclusiones: los resultados del proyecto, analizados a través de la técnica cualitativa de panel (De Keulenaer, 2008), muestran cómo las pinturas elegidas han permitido que las familias de la escuela construyan un relato emocional de lo acontecido durante la pandemia, situando las vivencias en un contexto manejable, que reflexiona acerca del miedo y da forma a la esperanza.
Production of Al(HI)-K(I)-Ti(IV)-sulphate-containing leach liquor from metakaolinite-containing ash derived from South African coal fines
Authors: Collins A.C. , Strydom C.A. , Matjie R.H. , Bunt J.R. , van Dyk J.C.
Published: September 2022

South African discard coal fines and K2CO3 blends were heated in a laboratory-scale rotary kiln to produce ashes for H2SO4 leaching tests. The optimized H2SO4 leaching conditions of 6.12 mol.dm3 (M) H2SO4, solid to liquid ratios 1:5 and 1:10, and 8o°C for 8 hours were used. K2CO3 was added to increase the dissolution efficiency of K. The objective was to determine if the Al present in metakaolinite (Al2O3.2SiO2), the Al, K, and Ti in the alumino-silicate glasses, and the Ti in rutile (TiO2) in the ashes could be selectively dissolved in H2SO4. XRF results show that the ashes formed at 700°C dissolved more efficiently (87% Al, 89% K and 23% Ti) compared to the ashes formed at 1050°C. This can be attributed to the presence of Al2O3.2SiO2, K2CO3 melt, K2CO3 remnants, KAl(SO4)2, and K-aluminosilicate glass in these ashes. XRD results indicate that the ashes prepared at 1050°C contained anorthite (CaAl^Og), microcline (KAlSi3O8), pseudomullite (Al2.SiO2), and silicon spinel (2Al2O3.3SiO2), which are either insoluble or only sparingly soluble in H2SO4. These minerals resulted in the lower dissolution efficiencies of Al and K. Based on the high dissolution efficiencies of Al and K for the ashes produced at 700°C, coal fines blended with K2CO3 could possibly be utilized as feedstocks for the production of aluminium(III), potassium(I), and titanium(IV) and a sulphate-containing leach liquor. Furthermore, the environmental issues and costs associated with the handling and disposal of large volumes of coal fines will also be resolved.
Changing Debates and Shifting Landscapes in Science Studies: Exploring How Graduate Students with Varied Backgrounds Think About the Role of Value-Judgments in Science
Authors: Ramachandran Aishwarya , Achar Jerry , Green Georgia , Hanson-Wright Brynley , Leiter Sophie , Öberg Gunilla
Published: September 2022

Few studies consider how changes in science studies education might reduce barriers to fruitful engagement with scientific practices. This paper is co-authored by the participants and instructor of a small interdisciplinary graduate seminar at the University of British Columbia (UBC) in Vancouver, Canada. The seminar reflected on the role of value-judgments in science, considering the learning experiences of a science studies student (AR, first author) and four students (of a total of six students registered in the seminar) who have backgrounds in the sciences (JA, GG, BHW, SL), their responses to course materials, and outlines lessons learned with respect to interdisciplinary communication. AR was surprised to find that the science students enjoyed reading and engaging with science studies texts as she thought they would be apprehensive about the epistemic content, but they thought the texts effectively illustrated that science is influenced by social factors. Instead of expressing concerns about epistemic issues, the science students’ critiques pertained to the length of texts and writing style. They also felt that some texts “unfairly” attacked scientists, and could be “dry,” “abstract,” and overly “problem-focused” without offering concrete solutions. This study suggests that interventions which explicitly encourage conversation and collaboration between students in science studies and the sciences more broadly can play a crucial role in dismantling unknowingly held simplistic views of other disciplines. It also speaks to the critical necessity of broad interdisciplinary scholarship which explicitly includes both the natural sciences and humanities. AR noted she initially believed that science students would react negatively to outsiders’ critiques of the sciences and concluded that science studies education ought to include meaningful engagement with practicing scientists, which is rarely the case. This study illustrates the importance of using texts which have a style and vocabulary not felt as disparaging towards scientists when introducing science students or researchers to concepts in science studies. It also points to the need for studies investigating how students from different research backgrounds may learn to “see” their use of jargon and the implicit assumptions they make about their listeners’ familiarity or understanding of a specific idea.
ONTOLOGI HURUF NUN MENUJU TITIK SEMIOTIKA
Authors: Chaer Hasanuddin , Rasyad Abdul , Sirulhaq Ahmad
Published: September 2022

Semiotics is the study of signs, symbols, and communication in people’s lives. This study which analyzes the letter nun in the Arabic Alphabet using Ferdinand de Saussure’s theory of semiotics, “dyadic semiotics” aims to reveal the breadth and depth of the meaning of the letter nun at the beginning of Surah al-Qalam. This study uses a descriptive method with a content analysis study approach, comprising data collection, data processing, and interpretation. The source of data are the Arabic script. The results of the study show that the ontological symbol for the letter nun is a means of teaching the sign through an established paradigm of semiotics to investigate the meaning element of the letter nun, which is the symbol of the Golden Embryo as a form of symbolic interaction throughout the cosmic order to reproduce and fertilize the world’s egg cell which is symbolized by the cup-like element of the letter nun as the “ovary” and the dot of the letter as a symbol of the fusion between the ‘egg cell and sperm cell’ to become the seed of the eternal universe towards the point of perfection of cosmic semiotic regeneration. All cosmic semiotic processes always occur in all life processes such as the semiotic processes that occur through human reproductive organs such as the testes in males and the ovaries in females in every fertilization process of the zygote. Every order of the cosmos, including human beings, originates from the process of two forms of signs, namely, that which can be seen and observed (signifier) and that which is only to be thought about through ideas (signified).
SAWER: MELAMPAUI RUANG DAN MELUASKAN Jangkauan Interaksi pada Pertunjukan Dangdut
Authors: Raditya Michael H. B.
Published: September 2022

This article deals with the issues of sawer that have appeared over time, both in live and online performances. This topic is interesting because sawer is intertwined in the interaction and participation of two active subjects, namely the singer and her audience. In live performances, the exchange between singers and their audiences happens in the same space and time. On the other hand, accommodating sawer in online dangdut performances is a challenge. However, in 2020 there was a lot of interaction and participation—and even sawer—in online shows. I believe this is not the first glimpse of this phenomenon because online sawer places dangdut beyond its physical space and expands the range of interaction between the performer and the audience. In the wider context, this phenomenon is proof of cosmopolitanism and globalization in the online world. According to that hypothesis, this article attempts to articulate the many varieties and complexities of the sawer phenomenon. Although there is an issue about globalization and its impact (read: reduction of diversity), through dangdut—and sawer— the issue can be negotiated freely and easily. In this research, literature studies and digital ethnography will be used to investigate the phenomenon. There are two findings in this research, first, the patterns and process of digital sawer; and second, how society responds to the global world using local ways and logic. It can be concluded that through Dangdut, the global platform is used locally and will remain local, especially looking at the quantity and activeness of Indonesian netizens.
Síndrome de Kounis en envenenamiento por accidente apídico, una revisión narrativa
Authors: Cabrera-Rojas Diana Lizeth , Muñoz-Ortiz Edison
Published: September 2022

El síndrome de Kounis se define, en general, por la aparición de eventos coronarios agudos asociados a síntomas anafilácticos. A pesar de que su mecanismo fisiopatológico es aún incierto, se ha postulado durante años la actividad vasoespástica coronaria secundaria a hipersensibilidad tipo I como un factor desencadenante. En el contexto de accidente apídico por un ataque masivo de abejas debe considerarse el síndrome de envenenamiento y el efecto de daño miocárdico directo por los componentes del veneno como mecanismo causal.
Recientemente se han reportado casos de síndrome de Kounis en el contexto de infección por SARS-CoV-2. Estos hallazgos abren la posibilidad de que haya otros procesos de lesión implicados, dentro de los que se destacan la tormenta de citoquinas. Esta revisión narrativa pretende examinar los mecanismos de daño en el síndrome de Kounis a partir del envenenamiento por accidente apídico y su relación con el desarrollo de complicaciones a corto plazo.
Meningitis bacteriana en neonatos: un estudio descriptivo multicéntrico en la ciudad de Medellín, Colombia
Authors: Zúñiga Yulieth Alexandra , Vélez-Martínez Luis Felipe , López-García Leidy Carolina , Beltrán-Arroyave Claudia Patricia , Cornejo-Ochoa José William
Published: September 2022

Introducción: la meningitis bacteriana es una infección grave que requiere un abordaje terapéutico empírico de inicio temprano. En Colombia existe poca información sobre esta enfermedad en la etapa neonatal y es indispensable contar con datos actualizados que orienten la antibioticoterapia de manera oportuna.
Objetivo: describir las características clínicas, microbiológicas y epidemiológicas de la meningitis neonatal bacteriana en tres centros hospitalarios de la ciudad de Medellín durante los años 2010 a 2019.
Métodos: se realizó un estudio descriptivo a partir de todos los registros médicos electrónicos de recién nacidos atendidos en tres hospitales de la ciudad de Medellín desde 2010 hasta 2019. Se revisaron las historias clínicas con diagnóstico de egreso de meningitis bacteriana y que contaran con aislamiento microbiológico. Los datos fueron extraídos de SAP® y Servinte Clinical Suite Enterprise® y registrados en una hoja de cálculo de Google®.
Resultados: se revisaron 125 historias con diagnóstico de meningitis neonatal. Sólo se incluyeron 42 casos con aislamiento microbiológico. La edad gestacional promedio fue de 35 semanas, y los patógenos aislados con mayor frecuencia fueron Escherichia coli (40,5 %) y Streptococcus agalactiae (40,5 %). En el 71,4 % la infección meníngea fue de clasificación tardía según la edad. Los principales síntomas fueron irritabilidad 56,1 % y fiebre 48,8 %.
Conclusiones: los agentes causales más frecuentes encontrados en esta población fueron Streptococcus agalactiae y Escherichia coli. Los síntomas principales fueron irritabilidad y fiebre. En un gran porcentaje de neonatos no se evidenciaron síntomas sugestivos de infección del sistema nervioso central. Por otra parte, la prematuridad fue predominante en la población estudiada.
Analyzing the Concept and Field of Inquiry of English as a Lingua Franca from a Decolonial Perspective
Authors: Rosa Gabriela da Costa , Duboc Ana Paula
Published: September 2022

Recent epistemological and ontological revisions demonstrate a change within the elf geopolitics of knowledge as voices from the global South begin to claim themselves as knowledge producers within a field strongly marked by the European hegemony. This article aims at analyzing the concept and field of inquiry of English as a Lingua Franca (ELF) from a decolonial perspective. Founded on the work of one the authors and departing from the decolonial exercise of identification-interrogation-interruption, the article identifies the main ELF tenets, brings up different voices in the field, and interrogates who these voices belong to, and where they come from. Findings show a complex weave of meanings still marked by coloniality traces in which hegemonic European views place themselves as hybris del punto cero despite the multiplicity of elf views and practices all over the globe. The authors advocate for attentive and critical reading of elf, particularly, with regards to where knowledge is generated and who generates such knowledge if one wishes to delink from self-assured global north elf epistemologies. They also propose a decolonial praxis in the reading of elf as a pre-condition towards the interruption of coloniality.
Canciones y refranes de México y Centroamérica. Incorporación en cursos de español como segunda lengua y como lengua extranjera en Canadá
Authors: Potvin Cynthia , Bonilla Ramos María Fernanda
Published: September 2022

En este artículo destacamos la relevancia lingüística y cultural de los refranes y canciones populares de México y Centroamérica para incluirlos en cursos de ele institucionalizados, concretamente en Canadá, así como para conciliar el desarrollo de las competencias intercultural y lingüística. Analizamos 30 refranes y 10 canciones y los clasificamos según los niveles y léxico temático del Plan Curricular (PC). Los resultados indican la presencia de rasgos lingüísticos —por ejemplo, nahuatlismos— y culturales propios de México y Centroamérica que normalmente están ausentes de los cursos y manuales de ele, cuya inclusión permitirá formar hablantes interculturales y ciudadanos globales.
Implementación de unidades didácticas sobre lectura crítica y argumentación pragmadialéctica en geología
Authors: Londoño Vásquez David Alberto , Diez María Angélica , Casadio Silvio , Rodríguez Maximiliano Nicolás , Ramírez Mejía Laura Sofía
Published: September 2022

En este artículo se abordan prácticas educativas de lectoescritura y argumentación pragmadialéctica en cursos universitarios de geología en dos universidades argentinas. Se emplearon encuestas, entrevistas, un grupo focal, unidades didácticas y relatos de prácticas docentes en la recolección de la unidad de análisis. Se concluye que fue enriquecedor que los docentes disciplinares se formen en lectura y escritura, pues redunda en la comprensión y producción textual disciplinar, acentuándose los procesos participativos, críticos y reflexivos.
Validación de una prueba informatizada para la evaluación de procesos perceptivos auditivos y visuales
Authors: Muñetón Ayala Mercedes Amparo , Estévez Monzó Adelina , Vásquez Arango Claudia Fernanda , Ortiz González María del Rosario , Ramírez Santana Gustavo
Published: September 2022

En esta investigación se valida la prueba PRAVI como instrumento para medir las habilidades perceptivas auditivas y visuales en estudiantes de Educación Primaria. Para ello, se analizaron las variables: fiabilidad, validez de constructo, validez externa y pendientes de crecimiento en función del grado. Los resultados presentaron alfas de Cronbach adecuados para la tarea JOT y la tarea ID en el factor visual lingüístico (.76/.77, respectivamente), auditivo lingüístico (.77/.75, respectivamente), auditivo no lingüístico (.75/.76, respectivamente) y visual no lingüístico (.75.71, respectivamente). Se concluye que PRAVI muestra pendientes de crecimiento adecuadas revelando sensibilidad a los grados escolares.
Algunas creencias y actitudes lingüísticas de hablantes del español de España hacia las variedades hispánicas
Authors: Ariño Bizarro Andrea , Bernad Castro Julia
Published: September 2022

Este trabajo amplía el estudio de las creencias y actitudes lingüísticas de los hablantes nativos del español de España con respecto a las demás variedades hispánicas. Para ello, treinta y seis hablantes nativos participaron en una encuesta sociolingüística de carácter mixto. Según los resultados, los participantes privilegian el español de España frente al americano. así como manifiestan diferencias actitudinales con respecto a las variedades americanas. Estas creencias lingüísticas, además, motivan actitudes lingüísticas en los hablantes, como su preferencia por el doblaje audiovisual en español de España y la supremacía del castellano como variedad lingüística de prestigio.
«Brava gente brasiliana» (II): o discurso brasilianista e seus equívocos lexicológicos
Authors: Santa Rosa Matos Marcos Paulo , Souza Cruz Itana Virginia , Faye Pedrosa Cleida Emilia
Published: September 2022

Este trabalho analisa os argumentos lexicológicos adotados por movimentos ufanistas e separatistas do Brasil para distinguir os adjetivos brasiliano e brasileiro e defender a adoção daquele em detrimento deste último: i) o sufixo –eiro denota profissão ou ofício, não formando gentílicos e possuindo conotação pejorativa; ii) o sufixo –ano é, por excelência, o formador de gentílicos em língua portuguesa, homologamente ao que ocorre em outras línguas. Para tanto, discorremos sobre a formação do adjetivo pátrio brasileiro e seu emprego como signo de nacionalidade, bem como investigamos os valores morfossemânticos dos sufixos –eiro e –ano.
La traducción de la poesía femenina china al catalán: antologías
Authors: Bai Zhimeng
Published: September 2022

En este artículo se realiza un estudio sobre la traducción de la poesía femenina china al catalán, a partir de las antologías publicadas, las composiciones seleccionadas y sus traducciones. Este trabajo pretende alcanzar los siguientes objetivos: sintetizar la evolución de dichas antologías desde el siglo xx, revelar sus características en diferentes períodos, analizar las diferentes estrategias adoptadas por los traductores, y formular posibles líneas futuras de investigación.
Navegando com Erêndira nas areias do real e do mágico
Authors: Da Silva Tavares Alves Carla Rosane
Published: September 2022

O trabalho analisa o conto «A incrível e triste história da Cândida Erêndira e sua avó desalmada», de Gabriel García Márquez, revelando como se dão as relações dialógicas. A pesquisa bibliográfica e hermenêutica apoiou-se no conceito do fantástico, para explicitação do realismo mágico e daí percorreu um caminho até a análise da ideologia. O realismo mágico caracteriza-se pela inclusão de elementos do fantástico ou mítico na ficção aparentemente realista. A palavra do emissor endereçada ao receptor mostra uma relação social carregada de ideologia. A narrativa em apreço constitui-se na sobreposição de dois parâmetros: o real e o irreal.
Corporate social responsibility as a mechanism to promote health institutions in Colombia
Authors: Hernández-Palma Hugo Gaspar , Pitre-Redondo Remedios Catalina , Mendoza-Casseres Daniel
Published: September 2022

Corporate Social Responsibility (CSR) has been positioned in recent years as an alternative to support the management of various institutions to meet the needs of stakeholders with whom it interacts in the production dynamics. In this sense, the purpose of the study is to analyze the contributions that can be derived from the incorporation of CSR guidelines in the management of Health Provider Institutions (or IPS) as a mechanism for their promotion. Based on a documentary design, this paper explores the academic literature related to CSR and its potential for integration with health institutions in Colombia. Therefore, the problem is addressed using qualitative approach tools by researching the main journals and mainstream articles. CSR integration into management brings multiple benefits for the organization; specifically, in the health sector it significantly supports activities of a social nature, thus achieving better stakeholder perception.