scholarly journals How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams

2020 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Lisa O’Bryan ◽  
Margaret Beier ◽  
Eduardo Salas
Keyword(s):  
Swarm Intelligence ◽  
Collective Intelligence ◽  
Group Level ◽  
Team Behavior ◽  
Targeted Interventions ◽  
Team Outcomes ◽  
Essential Components ◽  
Or Team ◽  
Behavioral Mechanisms ◽  
Insight Into

Researchers of team behavior have long been interested in the essential components of effective teamwork. Much existing research focuses on examining correlations between team member traits, team processes, and team outcomes, such as collective intelligence or team performance. However, these approaches are insufficient for providing insight into the dynamic, causal mechanisms through which the components of teamwork interact with one another and impact the emergence of team outcomes. Advances in the field of animal behavior have enabled a precise understanding of the behavioral mechanisms that enable groups to perform feats that surpass the capabilities of the individuals that comprise them. In this manuscript, we highlight how studies of animal swarm intelligence can inform research on collective intelligence in human teams. By improving the ability to obtain precise, time-varying measurements of team behaviors and outcomes and building upon approaches used in studies of swarm intelligence to analyze and model individual and group-level behaviors, researchers can gain insight into the mechanisms underlying the emergence of collective intelligence. Such understanding could inspire targeted interventions to improve team effectiveness and support the development of a comparative framework of group-level intelligence in animal and human groups.

QUALITY OF INDIVIDUAL AND GROUP LEVEL INTERVENTIONS FOR FIRST EPISODE PSYCHOSIS AT THE TERTIARY PSYCHIATRIC HOSPITAL IN UGANDA.

2020 ◽  
Author(s):  
Emmanuel Kiiza Mwesiga ◽  
Noeline Nakasujja ◽  
Lawrence Nankaba ◽  
Juliet Nakku ◽  
Seggane Musisi
Keyword(s):  
Psychiatric Hospital ◽  
Psychotic Disorders ◽  
First Episode Psychosis ◽  
First Episode ◽  
Group Level ◽  
Group Interventions ◽  
Episode Psychosis ◽  
Essential Components ◽  
The Individual

Introduction: Individual and group level interventions have the largest effect on outcomes in patients with the first episode of psychosis. The quality of these individual and group level interventions provided to first-episode psychosis patients in Uganda is unclear.Methods: The study was performed at Butabika National Psychiatric Teaching and referral hospital in Uganda. A retrospective chart review of recently discharged adult in-patients with the first episode of psychosis was first performed to determine the proportion of participants who received the different essential components for individual and group level interventions. From the different proportions, the quality of the services across the individual and group interventions was determined using the first-Episode Psychosis Services Fidelity Scale (FEPS-FS). The FEPS-FS assigns a grade of 1-5 on a Likert scale depending on the proportion of patients received the different components of the intervention. Results: The final sample included 156 first-episode psychosis patients. The median age was 27 years [IOR (24-36)] with 55% of participants of the female gender. 13 essential components across the individual and group interventions were assessed and their quality quantified. All 13 essential components had poor quality with the range of scores on the FEPS-FS of 1-3. Only one essential component assessed (use of single antipsychotics) had moderate quality.Discussion: Among current services at the National psychiatric hospital of Uganda, the essential for individual and group level interventions for psychotic disorders are of low quality. Further studies are required on how the quality of these interventions can be improved.

scholarly journals The role of race, religion, and partisanship in misperceptions about COVID-19

2021 ◽  
Vol 24 (4) ◽  
pp. 638-657
Author(s):  
James N. Druckman ◽  
Katherine Ognyanova ◽  
Matthew A. Baum ◽  
David Lazer ◽  
Roy H. Perlis ◽  
...  
Keyword(s):  
Lived Experiences ◽  
Media Exposure ◽  
Group Level ◽  
Political Issues ◽  
The Public ◽  
Media Usage ◽  
Targeted Interventions ◽  
Large Survey ◽  
Racial Ethnic

Concerns about misperceptions among the public are rampant. Yet, little work explores the correlates of misperceptions in varying contexts – that is, how do factors such as group affiliations, media exposure, and lived experiences correlate with the number of misperceptions people hold? We address these questions by investigating misperceptions about COVID-19, focusing on the role of racial/ethnic, religious, and partisan groups. Using a large survey, we find the number of correct beliefs held by individuals far dwarfs the number of misperceptions. When it comes to misperceptions, we find that minorities, those with high levels of religiosity, and those with strong partisan identities – across parties – hold a substantially greater number of misperceptions than those with contrasting group affiliations. Moreover, we show other variables (e.g., social media usage, number of COVID-19 cases in one’s county) do not have such strong relationships with misperceptions, and the group-level results do not reflect acquiescence to believing any information regardless of its truth value. Our results accentuate the importance of studying group-level misperceptions on other scientific and political issues and developing targeted interventions for these groups.

scholarly journals The correlation between socioeconomic factors and COVID-19 among immigrants in Norway: a register-based study

2021 ◽  
pp. 140349482110158
Author(s):  
Marte Kjøllesdal ◽  
Katrine Skyrud ◽  
Abdi Gele ◽  
Trude Arnesen ◽  
Hilde Kløvstad ◽  
...  
Keyword(s):  
Socioeconomic Factors ◽  
Low Income ◽  
Emergency Preparedness ◽  
Epidemiological Data ◽  
Group Level ◽  
Socioeconomic Indicators ◽  
Targeted Interventions ◽  
Low Income Families ◽  
Crowded Housing

Aim: Immigrants in Norway have higher COVID-19 notification and hospitalisation rates than Norwegian-born individuals. The knowledge about the role of socioeconomic factors to explain these differences is limited. We investigate the relationship between socioeconomic indicators at group level and epidemiological data for all notified cases of COVID-19 and related hospitalisations among the 23 largest immigrant groups in Norway. Methods: We used data on all notified COVID-19 cases in Norway up to 15 November 2020, and associated hospitalisations, from the Norwegian Surveillance System for Communicable Diseases and the emergency preparedness register at the Norwegian Institute of Public Health. We report notified COVID-19 cases and associated hospitalisation rates per 100,000 and their correlation to income, education, unemployment, crowded housing and years of residency at the group level. Results: Crowded housing and low income at a group level were correlated with rates of both notified cases of COVID-19 (Pearson`s correlation coefficient 0.77 and 0.52) and related hospitalisations (0.72, 0.50). In addition, low educational level and unemployment were correlated with a high number of notified cases. Conclusions: Immigrant groups living in disadvantaged socioeconomic positions are important to target with preventive measures for COVID-19. This must include targeted interventions for low-income families living in overcrowded households.

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

From Optimization to Clustering

2016 ◽  
pp. 1519-1544 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

scholarly journals Modeling Dynamic Behaviors within Population

2020 ◽  
Vol 34 (10) ◽  
pp. 13736-13737
Author(s):  
Nazgol Tavabi
Keyword(s):  
Social Media ◽  
Activity Patterns ◽  
Sensor Data ◽  
Physiological Data ◽  
Temporal Data ◽  
Group Level ◽  
Human Behaviors ◽  
Doctoral Research ◽  
Similarities And Differences ◽  
Insight Into

The abundance of temporal data generated by mankind in recent years gives us the opportunity to better understand human behaviors along with the similarities and differences in groups of people. Better understanding of human behaviors could be very beneficial in choosing strategies, from group-level to society-level depending on the domain. This type of data could range from physiological data collected from sensors to activity patterns in social media. Identifying frequent behavioral patterns in sensor data could give more insight into the health of a community and provoke strategies towards improving it; By analyzing patterns of behaviors in social media, platform's attributes could be adjusted to the user's needs.This type of modeling introduces numerous challenges that varies depending on the data. The goal of my doctoral research is to introduce ways to better understand and capture human behavior by modeling individual's behaviors as time series and extracting interesting patterns within them.

scholarly journals A Review of Various Swarm Intelligence Based Routing Protocols for Iot

2017 ◽  
Vol 5 (1) ◽  
pp. 50-63
Author(s):  
Shailja Agnihotri ◽  
K.R. Ramkumar
Keyword(s):  
Internet Of Things ◽  
Swarm Intelligence ◽  
Routing Protocols ◽  
Ad Hoc ◽  
Routing Algorithms ◽  
Link Failures ◽  
Routing Mechanism ◽  
Mobile Ad Hoc ◽  
Hoc Networks ◽  
Insight Into

The paper provides insight into various swarm intelligence based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of Internet of Things. These include the reliability, link failures, routing, heterogeneity etc. The MANETs and WSNs routing issues impose almost same requirements for IoT routing mechanism. The recent work of the worldwide researchers is focused on this area. protocols are based on the principles of swarm intelligence. The swarm intelligence is applied to achieve the optimality and the efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ACO technique tries to provide answers to many routing issues. Using the swarm intelligence and ant colony optimization principles, it has been seen that, the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT. As the various standard protocols available for MANETs and WSNs are not reliable enough, the paper finds the need of some efficient routing algorithms for IoT.

scholarly journals Exercise intolerance in pulmonary arterial hypertension: insight into central and peripheral pathophysiological mechanisms

2021 ◽  
Vol 30 (160) ◽  
pp. 200284
Author(s):  
Simon Malenfant ◽  
Marius Lebret ◽  
Émilie Breton-Gagnon ◽  
François Potus ◽  
Roxane Paulin ◽  
...  
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Recent Decade ◽  
Exercise Intolerance ◽  
Peak Oxygen Consumption ◽  
Targeted Interventions ◽  
Pulmonary Arterial ◽  
Cardinal Symptom ◽  
Insight Into

Exercise intolerance is a cardinal symptom of pulmonary arterial hypertension (PAH) and strongly impacts patients' quality of life (QoL). Although central cardiopulmonary impairments limit peak oxygen consumption (V′O2peak) in patients with PAH, several peripheral abnormalities have been described over the recent decade as key determinants in exercise intolerance, including impaired skeletal muscle (SKM) morphology, convective O2 transport, capillarity and metabolism indicating that peripheral abnormalities play a greater role in limiting exercise capacity than previously thought. More recently, cerebrovascular alterations potentially contributing to exercise intolerance in patients with PAH were also documented. Currently, only cardiopulmonary rehabilitation has been shown to efficiently improve the peripheral components of exercise intolerance in patients with PAH. However, more extensive studies are needed to identify targeted interventions that would ultimately improve patients' exercise tolerance and QoL. The present review offers a broad and comprehensive analysis of the present literature about the complex mechanisms and their interactions limiting exercise in patients and suggests several gaps in knowledge that need to be addressed in the future for a better understanding of exercise intolerance in patients with PAH.

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