Making a Difference: Planning for Engaged Participation in Environmental Research

Despite the rapid and accelerating rate of global environmental changes, too often research that has the potential to inform more sustainable futures remains disconnected from the context in which it could be used. Though transdisciplinary approaches (TDA) are known to overcome this disconnect, institutional barriers frequently prevent their deployment. Here we use insights from a qualitative comparative analysis of five case studies to develop a process for helping researchers and funders conceptualize and implement socially engaged research within existing institutional structures. The process we propose is meant to help researchers achieve societal as well as scientific outcomes relatively early in a project, as an end in itself or en route to greater engagement later. If projects that have a strong foundation of dialog and shared power wish to use TDA within current institutional and academic structures, we suggest that they focus on three process-based factors to increase their chances for success: (1) the maturity of relationships within a collaboration, (2) the level of context knowledge present within the collaborative team, and (3) the intensity of the engagement efforts within the project.

Development of Down Syndrome Research Over the Last Decades–What Healthcare and Education Professionals Need to Know

Down syndrome (DS) is the most prevalent neurodevelopmental disorder, with a known genetic cause. Besides facial dysmorphologies and congenital and/or acquired medical conditions, the syndrome is characterized by intellectual disability, accelerated aging, and an increased likelihood of an early onset Alzheimer's disease in adulthood. These common patterns of DS are derived from the long-held standard in the field of DS research, that describes individuals with DS as a homogeneous group and compares phenotypic outcomes with either neurotypical controls or other neurodevelopmental disorders. This traditional view has changed, as modern research pinpoints a broad variability in both the occurrence and severity of symptoms across DS, arguing for DS heterogeneity and against a single “DS profile.” Nevertheless, prenatal counseling does not often prioritize the awareness of potential within-group variations of DS, portraying only a vague picture of the developmental outcomes of children with DS to expectant parents. This mini-review provides a concise update on existent information about the heterogeneity of DS from a full-spectrum developmental perspective, within an interdisciplinary context. Knowledge on DS heterogeneity will not only enable professionals to enhance the quality of prenatal counseling, but also help parents to set targeted early interventions, to further optimize daily functions and the quality of life of their children.

Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things

Power Internet of Things (abbreviated as PIoT) is the information infrastructure to provide ubiquitous perception ability for smart grid (abbreviated as SG). To better deploy and utilize PIoT, its perception ability must be comprehensively assessed in terms of technical performance and economic benefits. However, at present, there is no assessment framework for PIoT due to the high diversity and heterogeneousness of SG scenarios. Additionally, there is information overlap between metrics in the assessment framework. The assessment model which could remove redundant information between metrics and simplify the assessment framework is an urgent demand to improve the effectiveness and timeliness of assessment. Consequently, first, aiming at the power system requirements of complex and diverse, a general assessment framework is put forward to assess the ability of PIoT in terms of technology and economy. Next, the requirement characteristics of power distribution scenario (abbreviated as PDS) are precisely analyzed with active context-knowledge orchestration technology. The general assessment framework is instantiated to build an instantiation assessment scheme in PDS. Moreover, an assessment model is established based on the instantiation assessment scheme to assess the efficiency of PIoT in Beijing. Finally, the assessment model is further refined with the machine learning technology to improve the efficiency of assessment. This refinement model achieves the extraction of 4-dimensional metrics from 23-dimensional metrics for assessment and finally improves assessment efficiency by 82.6%.

On the Functional Features of Acronyms and Abbreviations in On-Line Discourse

It has been established that such important factors as social, cultural, gender, etc. factors have a great impact on the creation of virtual reality. Recent studies have shown that the choice of linguistic and multilingual means depends, to a considerable extent, on the communicative strategies and mechanisms adopted by the language users. In the present article, the authors focus is on the functionality of different contractions and acronyms, which are widely used in online and offline communication, and are closely related to the all-important question of virtual time and space saving. The application of the cotextual descriptive method with a particular attention to the field of cotext and context knowledge in speech analysis allows the authors to emphasize the decisive role of the quantitative և qualitative uses of abbreviations in identifying the users' social and cultural characteristics.

Application of a management and storage system for knowledge generated from simulation projects as a teaching and assessment tool

During the development of a simulation project, people involved in the study acquire greater knowledge of the system being simulated. However, this knowledge is usually not externalized and ends up being lost at the end of the project. In this context, knowledge management allied with information technology may assist in information management and enable the collection, storage, and dissemination of knowledge. Thus, this paper aims to discuss and present a knowledge management system to manage and to store the knowledge generated by analysts in simulation projects. Our intention was to demonstrate a way that the knowledge can be stored and accessed at any moment by anyone to recover the development process and avoid mistakes during a simulation project. In order to achieve this goal, some graduate and undergraduate simulation courses at a Brazilian federal university were used as objects of study. At the end of the study, it was concluded that knowledge management integrated with information technology contributes to the work of simulation analysts by supporting information and knowledge management throughout the stages of the simulation projects.

Technological troubleshooting based on sentence embedding with deep transformers

In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine.

Detecting and explaining unfairness in consumer contracts through memory networks

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.

Contextual Cueing Accelerated and Enhanced by Monetary Reward: Evidence From Event-Related Brain Potentials

The vital role of reward in guiding visual attention has been supported by previous literatures. Here, we examined the motivational impact of monetary reward feedback stimuli on visual attention selection using an event-related potential (ERP) component called stimulus-preceding negativity (SPN) and a standard contextual cueing (CC) paradigm. It has been proposed that SPN reflects affective and motivational processing. We focused on whether incidentally learned context knowledge could be affected by reward. Both behavior and brain data demonstrated that contexts followed by reward feedback not only gave rise to faster implicit learning but also obtained a larger CC effect.