Análisis de los efectos ambientales de la generación de energía undimotriz en el puerto Quequén, provincia de Buenos Aires, República Argentina

Desde hace dos siglos se produce una vertiginosa utilización de los recursos energéticos no renovables, es por ello que varios países se están volcando hacia tecnologías que hagan uso de las energías alternativas sustentables para la generación de energía eléctrica, siendo éstas objeto de estudio y experimentación. No obstante los beneficios que otorgan estas energías, es necesario estudiar y analizar los impactos ambientales potenciales, tanto positivos como negativos, producto de la instalación y operación de los dispositivos que las generan. El presente artículo aborda la identificación y ponderación de los impactos ambientales potenciales que podrían producirse por la instalación de un dispositivo electromecánico capaz de aprovechar la energía undimotriz (proveniente de las ondas de mar) en la escollera sur del puerto Quequén, provincia de Buenos Aires.For two centuries there was an intense use of non-renewable energy resources that is the reason which several countries are turning to technologies that make use of sustainable alternative energy for power generation, these being the subject of study and experimentation. However the benefits granted by these energies, it is necessary to study and analyze the potential environmental impacts, both positive and negative, resulting from the installation and operation of the devices that generate them. This article deals with the identification and weighting of potential environmental impacts that could result from the installation of an electromechanical device capable of harnessing wave energy (from sea waves) in the south jetty of puerto Quequén, province of Buenos Aires.Fil: Jauregui, Martín. Universidad Tecnológica Nacional; ArgentinaFil: Haim, Pablo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Liftschitz, Ana Julia. Universidad Tecnológica Nacional; ArgentinaFil: Pelissero, Mario Alberto. Universidad Tecnológica Nacional; Argentin

What are the type, direction, and strength of species, community, and ecosystem responses to warming in aquatic mesocosm studies and their dependency on experimental characteristics? A systematic review protocol

Background Mesocosm experiments have become increasingly popular in climate change research as they bridge the gap between small-scale, less realistic, microcosm experiments, and large-scale, more complex, natural systems. Characteristics of aquatic mesocosm designs (e.g., mesocosm volume, study duration, and replication) vary widely, potentially affecting the magnitude and direction of effect sizes measured in experiments. In this global systematic review we aim to identify the type, direction and strength of climate warming effects on aquatic species, communities and ecosystems in mesocosm experiments. Furthermore, we will investigate the context-dependency of the observed effects on several a priori determined effect moderators (ecological and methodological). Our conclusions will provide recommendations for aquatic scientists designing mesocosm experiments, as well as guidelines for interpretation of experimental results by scientists, policy-makers and the general public. Methods We will conduct a systematic search using multiple online databases to gather evidence from the scientific literature on the effects of warming experimentally tested in aquatic mesocosms. Data from relevant studies will be extracted and used in a random effects meta-analysis to estimate the overall effect sizes of warming experiments on species performance, biodiversity and ecosystem functions. Experimental characteristics (e.g., mesocosm size and shape, replication-level, experimental duration and design, biogeographic region, community type, crossed manipulation) will be further analysed using subgroup analyses

Efficacy and mechanisms underlying a gamified attention bias modification training in anxious youth: protocol for a randomized controlled trial

Background Attention bias modification training (ABMT) and cognitive behavioral therapy (CBT) likely target different aspects of aberrant threat responses in anxiety disorders and may be combined to maximize therapeutic benefit. However, studies investigating the effect of ABMT in the context of CBT have yielded mixed results. Here, we propose an enhanced ABMT to target the attentional bias towards threat, in addition to classic CBT for anxiety disorders in youth. This enhanced ABMT integrates the modified dot-probe task used in previous studies, where a target is always presented at the previous location of the neutral and not the simultaneously presented threatening stimulus, with a visual search, where the targets are always presented distally of threatening distractors. These two training elements (modified dot-probe and visual search) are embedded in an engaging game to foster motivation and adherence. Our goal is to determine the efficacy of the enhanced ABMT in the context of CBT. Further, we aim to replicate two previous findings: (a) aberrant amygdala connectivity being the neurobiological correlate of the attentional bias towards threat at baseline; and (b) amygdala connectivity being a mediator of the ABMT effect. We will also explore moderators of treatment response (age, sex, depressive symptoms and irritability) on a behavioral and neuronal level. Methods One hundred and twenty youth (8–17 years old) with a primary anxiety disorder diagnosis all receive CBT and are randomized to nine weeks of either active or control ABMT and symptom improvement will be compared between the two study arms. We will also recruit 60 healthy comparison youth, who along with eligible anxious youth, will be assessed with the dot-probe task during fMRI (anxious youth: before and after training; healthy volunteers: second measurement twelve weeks after initial assessment). Discussion The present study will contribute to the literature by (1) potentially replicating that aberrant amygdala connectivity mediates the attentional bias towards threat in anxious youth; (2) determining the efficacy of enhanced ABMT; and (3) advancing our understanding of the mechanisms underlying ABMT. Trial registration Clinicaltrials.gov: NCT03283930 Trial registration date: September 14th 2017. The trial registration took place retrospectively. Data acquisition started February 1st 2017

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives