General Knowledge Research Articles

Participatory and Place-Based Socioeconomic Knowledge Generation: An Experience in Community-Based Research Pedagogy

This article uses fieldnotes along with student and practitioner feedback to recount the challenges, benefits, and broader learnings of engaging master’s students in a participatory research seminar. The students developed research proposals about a real-world socioeconomic challenge with and for local practitioners. Proposals were consistent with the principles and practices of participatory action research (PAR). The planning, implementation, and assessment of this course was informed by feminist scientific philosophies of collaboration, situatedness, partiality, accountability, and a sensitivity to power dynamics. In line with both PAR and SoTL principles, there was an explicit emphasis on partnership, reflexivity, and broad forms of learning in both the classroom and practitioner meetings. The students were challenged by the unfamiliarity of the research approach, the need to navigate a new way of working directly with stakeholders, as well as the responsibility to the community that participatory approaches espouse. Despite the challenges, the students were eager to soak up local knowledges, reflect on their role as researchers, and contribute constructively if they could.

Learning Generalizable and Composable Abstractions for Transfer in Reinforcement Learning

Reinforcement Learning (RL) in complex environments presents many challenges: agents require learning concise representations of both environments and behaviors for efficient reasoning and generalizing experiences to new, unseen situations. However, RL approaches can be sample-inefficient and difficult to scale, especially in long-horizon sparse reward settings. To address these issues, the goal of my doctoral research is to develop methods that automatically construct semantically meaningful state and temporal abstractions for efficient transfer and generalization. In my work, I develop hierarchical approaches for learning transferable, generalizable knowledge in the form of symbolically represented options, as well as for integrating search techniques with RL to solve new problems by efficiently composing the learned options. Empirical results show that the resulting approaches effectively learn and transfer knowledge, achieving superior sample efficiency compared to SOTA methods while also enhancing interpretability.

Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow

Time-series generation has crucial practical significance for decision-making under uncertainty. Existing methods have various limitations like accumulating errors over time, significantly impacting downstream tasks. We develop a novel generation method, DT-VAE, that incorporates generalizable domain knowledge, is mathematically justified, and significantly outperforms existing methods by mitigating error accumulation through a cumulative difference learning mechanism. We evaluate the performance of DT-VAE on several downstream tasks using both semi-synthetic and real time-series datasets, including benchmark datasets and our newly curated COVID-19 hospitalization datasets. The COVID-19 datasets enrich existing resources for time-series analysis. Additionally, we introduce Diverse Trend Preserving (DTP), a time-series clustering-based evaluation for direct and interpretable assessments of generated samples, serving as a valuable tool for evaluating time-series generative models.

COMMA: Co-articulated Multi-Modal Learning

Pretrained large-scale vision-language models such as CLIP have demonstrated excellent generalizability over a series of downstream tasks. However, they are sensitive to the variation of input text prompts and need a selection of prompt templates to achieve satisfactory performance. Recently, various methods have been proposed to dynamically learn the prompts as the textual inputs to avoid the requirements of laboring hand-crafted prompt engineering in the fine-tuning process. We notice that these methods are suboptimal in two aspects. First, the prompts of the vision and language branches in these methods are usually separated or uni-directionally correlated. Thus, the prompts of both branches are not fully correlated and may not provide enough guidance to align the representations of both branches. Second, it's observed that most previous methods usually achieve better performance on seen classes but cause performance degeneration on unseen classes compared to CLIP. This is because the essential generic knowledge learned in the pretraining stage is partly forgotten in the fine-tuning process. In this paper, we propose Co-Articulated Multi-Modal Learning (COMMA) to handle the above limitations. Especially, our method considers prompts from both branches to generate the prompts to enhance the representation alignment of both branches. Besides, to alleviate forgetting about the essential knowledge, we minimize the feature discrepancy between the learned prompts and the embeddings of hand-crafted prompts in the pre-trained CLIP in the late transformer layers. We evaluate our method across three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts. Experimental results demonstrate the superiority of our method by exhibiting a favorable performance boost upon all tasks with high efficiency. Code is available at https://github.com/hulianyuyy/COMMA.

Digital Knowledge Management in the Arab Region (Challenges and Solutions)

The Arab world faced many development challenges (at the educational, economic, and educational levels...), especially after the outbreak of the Corona pandemic, which imposed the need for digital transformation based on managing digital knowledge and achieving sustainable development. Nowadays, the concept of "knowledge economy" concept has become a necessity; given that global competitiveness is tautly related to the government’s ability to support knowledge generation and application to foster economic growth. Considering the disparity between the Arab countries in terms of technological progress, and the use of modern technologies, besides the absence of a joint Arab digital cooperative agenda, in addition to the growing digital divide that occurs for several reasons (political, financial, social, technical, scientific...); what are the chances of achieving the sustainable development goals 2030? Through his study, we attempted to shed light on the need to invest in digital transformation and digital knowledge management to achieve sustainable development. We study the current situation in the Arab region, and the challenges it faces, then we highlight the expected future scenarios if the Arab States continue to proceed in the same. Ultimately we present some possible solutions that could contribute create regional cooperation between the Arab countries, and bridging, or at least reducing the digital divide, by establishing an Arab agenda for digital development by the year 2040.

CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models

As an indispensable ingredient of intelligence, commonsense reasoning is crucial for large language models (LLMs) in real-world scenarios. In this paper, we propose CORECODE, a dataset that contains abundant commonsense knowledge manually annotated on dyadic dialogues, to evaluate the commonsense reasoning and commonsense conflict detection capabilities of Chinese LLMs. We categorize commonsense knowledge in everyday conversations into three dimensions: entity, event, and social interaction. For easy and consistent annotation, we standardize the form of commonsense knowledge annotation in open-domain dialogues as "domain: slot = value". A total of 9 domains and 37 slots are defined to capture diverse commonsense knowledge. With these pre-defined domains and slots, we collect 76,787 commonsense knowledge annotations from 19,700 dialogues through crowdsourcing. To evaluate and enhance the commonsense reasoning capability for LLMs on the curated dataset, we establish a series of dialogue-level reasoning and detection tasks, including commonsense knowledge filling, commonsense knowledge generation, commonsense conflict phrase detection, domain identification, slot identification, and event causal inference. A wide variety of existing open-source Chinese LLMs are evaluated with these tasks on our dataset. Experimental results demonstrate that these models are not competent to predict CORECODE's plentiful reasoning content, and even ChatGPT could only achieve 0.275 and 0.084 accuracy on the domain identification and slot identification tasks under the zero-shot setting. We release the data and codes of CORECODE at https://github.com/danshi777/CORECODE to promote commonsense reasoning evaluation and study of LLMs in the context of daily conversations.

Knowledge and Attitudes Regarding the Vaccination of Brazilian Immigrants in Portugal: Risks When Returning to Their Country of Origin?

Vaccination is one of the main advancements in public health in the prophylaxis of infectious diseases. We intend to describe the general knowledge about vaccines/vaccination among Brazilian immigrants in Portugal, characterize their attitudes toward vaccination, and describe their knowledge of the yellow fever (YF) vaccine. A cross-sectional study was conducted using a self-completion questionnaire (face-to-face or remote). A total of 542 people participated in the study; the mean age was 36.81 years; 40.1% were male; 44.8% had their 12th year of schooling; and 27.0% had resided for ≥10 years in Portugal. Regarding general knowledge about vaccination, 53.8% answered at least 6/8 questions correctly. A total of 37.1% tended to have a favorable attitude toward vaccination. Concerning traveling, 76.7% attributed the risk of disease at the destination as the main reason for accepting vaccines. A total of 89.3% knew that there was a risk of YF in Brazil. A total of 40% answered correctly only one question about the YF vaccine; 21.6% did not answer any questions correctly. Thus, most of the Brazilian immigrants in this study have high general knowledge about vaccines/vaccination, few have a favorable attitude, and their knowledge about the YF vaccine is scarce. This could limit vaccination adherence when visiting Brazil, making health education actions necessary to increase knowledge and prevent YF risks.

Application of Omics and Bioinformatics Technologies in Response to COVID-19 Pandemic

The medical biotechnology community has undertaken significant endeavors to gain a comprehensive understanding of SARS-CoV-2’s biology and pathogenesis mechanisms. Omics approaches and technologies have been widely employed in the fight against SARS-CoV-2. Since the onset of the virus outbreak, researchers have demonstrated how recent omics and bioinformatics technological advancements have contributed to the diagnosis, vaccine development, treatment, and control of disease transmission. Studies conducted since the outbreak have been collected and summarized, with a focus on bioinformatics approaches and their contribution to controlling this pandemic. Developments and advanced omics technology in connection to the COVID-19 pandemic have been analyzed. The multi-omics technology, which offers various strategies in identifying potential diagnostics, therapeutics, studies of variants of concern, and drug repurposing approaches, has been assessed. Pandemic response has seen the application of multi-omics and pan-genomics approaches, including genomics, metabolomics, transcriptomics, proteomics, epigenomics, clustered regularly interspaced short palindromic repeats (CRISPR) technology, host-pathogen interactions, artificial intelligence, and machine learning in various research areas. Additionally, bioinformatics and mathematical modeling have played a significant role in disease control. The use of smart technologies to control virus transmission and predict patients’ health conditions and treatment outcomes has also been crucial. Transcriptome analysis has emerged as a major application, contributing to the generation of new knowledge on viral sequences and intracellular signaling pathways that regulate viral infection and pathogenesis mechanisms. The sequencing of the virus has paved the way for the use of omics technologies and an integrative technique in combating the pandemic. In general, the advancement of omics technology during this pandemic has been fascinating and has contributed a significant role to the science of health biotechnology in general and omics and bioinformatics in particular.Keywords: bioinformatics, coronavirus, COVID-19, omics, SARS COV-2

La ciudadanía digital: metaanálisis sobre investigaciones en México

The present study was a meta-analysis compiling research on digital citizenship conducted in Mexico in 2011 - 2023. It was guided by the objective of arguing, analyzing and categorizing the lines of knowledge generation and application (LGAC) related to digital citizenship in Mexico. The methodology applied was meta-analysis where 88 research studies were documented: 47 papers, 33 journal articles and 8 theses. The studies found support the existence of six LGAC in Mexico. It was concluded that the studies are related to understanding the way in which society appropriates digital technologies, interactions through social networks and the manifestations of digital identity. Another point to highlight is that the study of digital citizenship has been used to analyze the acquisition of digital skills and attitudes, social issues addressed by digitalization, the roles played by educational actors in the virtual environment and the promotion of multiculturalism. The studies found provide relevance to the LGAC, and allow concluding that digital citizenship is a topic present in research on educational technology in Mexico.

Validating ‘GIS-UBEM’—A Residential Open Data-Driven Urban Building Energy Model

The study of energy consumption in buildings, particularly residential ones, brings with it significant socio-economic and environmental implications, as it accounts for approximately 40% of CO2 emissions, 18% in the case of residential buildings, in Europe. On a number of levels, energy consumption serves as a key parameter in urban sustainability indicators and energy plans. Access to data on energy consumption is crucial for energy planning, management, knowledge generation, and awareness. Urban Building Energy Models (UBEMs), which are emerging tools for simulating energy consumption at neighborhood scale, allow for more efficient intervention and energy rehabilitation planning. However, UBEM validation requires reliable reference data, which are often challenging to obtain at urban scale due to privacy concerns and data accessibility issues. Recent advances, such as automation and open data utilization, are proving promising in addressing these challenges. This study aims to provide a standardized UBEM validation process by presenting a case study that was carried out utilizing open data to develop bottom-up engineering models of residential energy demand at urban scale, with a resolution level of individual buildings, and a subsequent adjustment and validation using reference tools. This study confirms that the validated GIS-UBEM model heating and cooling demands and consumption fall within the confidence bands of ±15% and ±12.5%, i.e., the confidence bands required for the approval of official alternative simulation methods for energy certification. This paves the way for its application in urban-scale studies and practices with a well-established margin of confidence, covering a wide range of building typologies, construction models, and climates comparable to those considered in the validation process. The primary application of this model is to determine the starting point and subsequent evaluation of improvement scenarios at a district scale, examining issues such as massive energy rehabilitation interventions, energy planning, demand analysis, vulnerability studies, etc.

Analysis of Integrated Coastal Management (ICM) of Panama

The following document addresses the current situation of coastal management in Panama, using the “Decalogue” proposed by Barragán (2001 and 2004) as a framework to assess coastal management. This Decalogue encompasses key aspects such as policy, regulations, competencies, institutions, tools, training and education, resources, knowledge and information, sustainability education, and participation. Prior to the implementation of Panama’s National Ocean Policy in 2022, challenges were observed in these aspects, including overfishing, coastal degradation, threats to biodiversity, marine pollution, and other issues. Adherence to international agreements and the enactment of policies like the National Ocean Policy have strengthened regulations and promoted participation and knowledge generation in coastal management. As Panama strives to achieve its goals of marine conservation and sustainable development, increased training and education are anticipated, along with a focus on ocean culture and collaboration among entities and stakeholders. Keywords: Coastal management, National Ocean Policy, ICZM Decalogue, Panama, coastal sustainability, marine conservation.

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.

Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks

A Structural Hole Spanner (SHS) is a set of nodes in a network that act as a bridge among different otherwise disconnected communities. While several solutions exist for SHS discovery, they often suffer from high runtime, particularly in large-scale networks. Additionally, discovering SHSs across diverse networks poses another challenge, as the traditional one-model-fits-all approach fails to capture inter-graph differences, especially in diverse networks. Therefore, there is an urgent need of developing effective solutions for discovering SHSs in large-scale and diverse networks. Inspired by the recent advancement of graph neural network approaches on various graph problems, we propose graph neural network-based models to discover SHS nodes in large-scale and diverse networks. Our approach transform the problem into a learning problem and propose an efficient model GraphSHS, that exploits both the network structure and node features to discover SHS nodes in large scale networks, endeavouring to lessen the computational cost while maintaining high accuracy. To effectively discover SHSs across diverse networks, we propose another model Meta-GraphSHS based on meta-learning that learns generalizable knowledge from diverse training graphs (instead of directly learning the model) and utilizes the learned knowledge to create a customized model to identify SHSs in each new graph. Theoretically, we demonstrate that the depth of the proposed graph neural network model should be at least Ω(n/logn) to accurately calculate the SHSs discovery problem. We evaluate the performance of the proposed models through extensive experiments on synthetic and real-world datasets. Our experimental results show that GraphSHS discovers SHSs with high accuracy and is at least 167.1 times faster than the comparative methods on large-scale real-world datasets. Furthermore, Meta-GraphSHS effectively discovers SHSs across diverse synthetic networks with an accuracy of 96.2%.

The ethically significant difference between dual use and slippery slope arguments, in relation to CRISPR-Cas9: philosophical considerations and ethical challenges

Biomedical research, on the one hand, contributes to important goals from generation of knowledge about the human body to the development and testing of therapeutics of all kinds. On the other hand, it can produce serious and sometimes unforeseeable consequences. In the ethical analysis of these two aspects of biomedical research, two important argumentative strategies play a major role. First, slippery slope arguments are used to warn of potential risks and to highlight knowledge-based limitations. Second, a dual-use problem describes the challenge that already established techniques can be used for both morally wrong and morally right purposes. These two argumentative strategies appear to share several similarities, which will be investigated in this article. For this purpose, the article will first provide clear working definitions for both types of argument. This lays the foundation for the further ethical analysis. In a second step, and in order to investigate the similarities and differences of the argumentative strategies with an example from current ethical debates, CRISPR-Cas9, a currently very promising tool of genome editing, will be examined. The extent to which the possible applications of this genome editing tool can be addressed by slippery slope arguments or the dual use problem will be investigated. For this purpose, selected studies involving the use of CRISPR-Cas9 will be examined. Based on this two-step, analytic and example-based comparison of slippery-slope argumentation and dual-use considerations, the article will detail the ethically relevant difference between the two argumentative strategies and at the same time contribute to the ongoing ethical debate about CRISPR-Cas9.

Improving strategic planning for nature: Panacea or pandora’s box for the built and natural environment?

This paper assesses how strategic planning for nature can be improved for England’s built and natural environment using mainstreaming and landscape-scale concepts. Whilst both concepts feature in academic literature, there has been limited attention on their role as catalytic agents for strategic planning. Addressing this gap, evidence is used from two stakeholder workshops involving 62 senior policy experts managing a range of operational and hypothetical strategic spatial planning challenges. The results reveal a significantly weakened strategic planning arena characterised by policy disintegration, short termism and uncertainty. Key findings highlight the fallacy of pursuing strategic planning for nature in isolation from wider policy integration fusing environmental, economic and social components from the outset. Current barriers to progress include institutional inertia, technocratic vocabularies and neoliberalist priorities exacerbated by a weak underlying theory. Conversely opportunities for mainstreaming processes may help knowledge generation and exchange within transdisciplinary partnerships, whilst landscape scale thinking can improve understanding of issues using natures inherent geometry transforming processes and outcomes. The paper recommends the adoption of strategic planning pathways using mainstreaming and landscape-scale approaches working in tandem. Whilst focused on the English context, our findings are transferable to other planning systems in the Global North, especially those championing neoliberal market led policies.

Assessment of dental bur sterilization awareness among general dentists in Uttarakhand

This study assesses general dentists' knowledge and adherence to dental bur sterilization practices in Uttarakhand, aiming to enhance patient safety and clinical precision by identifying implementation gaps. Evaluate practitioners' understanding of optimal sterilization protocols for burs, pinpoint areas for improvement, and establish a baseline of current practices in general dentistry. A state-wide distribution of a structured questionnaire gathered responses from 237 general dentists. The survey focused on sterilization equipment usage, frequency, and compliance with industry guidelines. Quantitative data were analyzed for insights into current practices. Preliminary findings show varying awareness and adherence levels among practitioners. While a significant proportion demonstrated commendable knowledge, specific gaps and deviations were identified, suggesting areas for improvement. Continuous education and reinforcement of best practices in bur sterilization are crucial within the general dentistry community. Addressing identified gaps can elevate the standard of care, ensuring patient safety and precision in dental procedures. Ongoing education and awareness campaigns are vital for sustained improvement in bur sterilization practices.

Climate action from a gender perspective: A systematic review of the impact of climate policies on inequality

In 1992 the United Nations committed to promoting a gender perspective in all environmental and development programs and to establishing mechanisms for assessing the impact of environmental policies on women. However, 30 years later most countries acknowledge that they have not integrated that perspective into policy assessments. This paper provides the first systematic literature review (based on the PRISMA approach) of the impacts of climate policies on inequality from a gender perspective. The results show that although this is an emerging research area, the number of works is still very low: we find only 29 papers, most of them written in the last 4 years. There is also a clear lack of quantitative analysis; and only 5 studies provide an ex ante impact assessment. A more in depth-analysis shows that the existing analyses often use gender merely as an additional explanatory variable, but key aspects for a real gender-oriented analysis such as power relations, intersectionality and gender mainstreaming are missing. The gender perspective is typically more absent in studies led by men. If science wants to contribute to the generation of knowledge that is useful for tackling some of the greatest challenges of the 21st century, such as the transition to a socioeconomic model that is more respectful with the ecological limits of the planet and gender equality, it is important to expand knowledge in this area but also to reconnect with feminist theory.

Medical Students' Knowledge, Attitudes Toward, and Identification of Adverse Childhood Experiences and Trauma-Informed Care.

Adverse childhood experiences (ACEs) are traumatic experiences that occur prior to age 18 years and can have a long-term impact on adult physical and mental health. Knowledge of ACEs, including the link between ACEs and health, and trauma-informed care (TIC), is essential for medical professionals to ensure respectful and compassionate care for those with a history of childhood adversity. This study examined medical students' knowledge, attitudes toward, and identification of ACEs and TIC to inform curricular efforts. Using a cross-sectional design, students were recruited from 2 medical schools, one allopathic (medical degree) and one osteopathic (doctor of osteopathic medicine degree) to complete an electronic needs assessment survey. The survey included a patient vignette to assess students' ability to identify ACEs as contributing factors to patient health. The students were blind to the full purpose of the study to obtain an objective measurement of their ability to identify ACEs as contributors to patient health. Additional questions targeting knowledge and attitudes of ACEs and TIC were devised from previously published surveys with supplemental study-designed questions. Two hundred forty students completed the survey. Results demonstrated variable ability to identify ACEs; strong general knowledge of ACEs, with less developed practical knowledge; and overall positive attitudes toward ACEs and TIC. No differences were found between the medical degree and doctor of osteopathic medicine degree, or year in program. Medical students would benefit from curricular efforts that help to increase knowledge of the link between ACEs and health, increase practical application of ACEs and TIC, and improve their ability to identify ACEs in clinical scenarios.

Social Media - A New Chapter in Social Problems or A New Technique to Study Social Problems

The term "Mass Media" has been drastically replaced by “Social Media” and the change has made the greatest impact in our society. With the advancement in technology especially in the field of communication, knowledge dissemination process through social media has been widely affected. The degree of accessibility and validity of particular knowledge shared through social media remains questionable as it challenges the fundamental way of knowledge generation, knowledge sharing and knowledge validation process. Though through social media, knowledge is spread in faster and swift manner but unsupervised use of social media remains at centre as a propeller to new age social problems associated with it. Because of this, social media has been considered both as a “subject” and a “tool” when it comes to understand social problems. It felt important to take this study from the social work perspective in order to understand how social work academician operationalise “social media”. A qualitative study was taken up with students and teachers associated with social work institutions based in Gujarat with an objective to see their views on social media. The study explored if the social media was considered as an add-on to existing social problems in the syllabus and society or they considered the social media as a platform to study existing social problems; more specifically psychosocial issues. This study takes a critical take on social media as a tool to be used in their field work and as one of the associated learning methods to learn social work practice.

Wider institutional research cultures and their influence on patient and public involvement and engagement in health research – An institutional ethnography

Focus on patient and public involvement and engagement (PPIE) is increasing in health policy and research governance. PPIE is considered by some to be a democratic right, and by others to be a way to improve health care and research outcomes and implementation. Most recently, policy makers, funders and (clinical) research institutions are making PPIE a strategic requirement for health research urging researchers to invite patients and relatives into their research activities.Our study is based in a Danish university hospital where PPIE has been introduced as one of five strategic research goals. We investigated how researchers experienced this new practice and how their research practices connect to the wider context of the Danish health care system. Ten cases were studied during a year using observations, interviews, and document analysis. As our method of inquiry, we used institutional ethnography to look at researchers’ work from their perspective and to understand how PPIE practices are part of a larger institutional research culture reaching far beyond the individual. We found that current research culture has implications for the selection of patients and relatives and for what they are asked to do. Researchers who experienced that PPIE outcomes aided their existing research practices felt motivated. Researchers who engaged patients and relatives before it was a strategy, were ideologically driven and their approaches resulted in an increased diversity of inclusion and researcher assimilation.These findings add to the current knowledge on PPIE practices and help us understand that further development towards collaborative research practices require a change in key performance indicators and training and perhaps call for attention to our shared acceptance of knowledge generation in research.