Groups Of Different Sizes Research Articles

Population Density Affects Drosophila Male Pheromones in Laboratory-Acclimated and Natural Lines.

In large groups of vertebrates and invertebrates, aggregation can affect biological characters such as gene expression, physiological, immunological and behavioral responses. The insect cuticle is covered with hydrocarbons (cuticular hydrocarbons; CHCs) which reduce dehydration and increase protection against xenobiotics. Drosophila melanogaster and D. simulans flies also use some of their CHCs as contact pheromones. In these two sibling species, males also produce the volatile pheromone 11-cis-Vaccenyl acetate (cVa). To investigate the effect of insect density on the production of CHCs and cVa we compared the level of these male pheromones in groups of different sizes. These compounds were measured in six lines acclimated for many generations in our laboratory - four wild-type and one CHC mutant D. melanogaster lines plus one D. simulans line. Increasing the group size substantially changed pheromone amounts only in the four D. melanogaster wild-type lines. To evaluate the role of laboratory acclimation in this effect, we measured density-dependent pheromonal production in 21 lines caught in nature after 1, 12 and 25 generations in the laboratory. These lines showed varied effects which rarely persisted across generations. Although increasing group size often affected pheromone production in laboratory-established and freshly-caught D. melanogaster lines, this effect was not linear, suggesting complex determinants.

Social interactions among ants are impacted by food availability and group size.

Social interactions are important for how societies function, conferring robustness and resilience to environmental changes. The structure of social interactions can shape the dynamics of information and goods transmission. In addition, the availability and types of resources that are transferred might impact the structure of interaction networks. For example, storable resources might reduce the required speed of distribution and altering interaction structure can facilitate such change. Here, we use Camponotus fragilis ants as a model system to examine how social interactions are impacted by group size, food availability, and food type. We compare global- and individual-level network measures across experiments in which groups of different sizes received limited or unlimited food that is either favorable and cannot be stored (carbohydrates), or unfavorable but with a potential of being stored (protein). We found that in larger groups, individuals interacted with more social partners and connected more individuals, and interaction networks became more compartmentalized. Furthermore, the number of individuals that ants interacted with and the distance they traveled both increased when food was limited compared to when it was unlimited. Our findings highlight how biological systems can adjust their interaction networks in ways that relate to their function. The study of such biological flexibility can inspire novel and important solutions to the design of robust and resilient supply chains.

Interaction structure constrains the emergence of conventions in group communication

Real-world communication frequently requires language producers to address more than one comprehender at once, yet most psycholinguistic research focuses on one-on-one communication. As the audience size grows, interlocutors face new challenges that do not arise in dyads. They must consider multiple perspectives and weigh multiple sources of feedback to build shared understanding. Here, we ask which properties of the group's interaction structure facilitate successful communication. We used a repeated reference game paradigm in which directors instructed between one and five matchers to choose specific targets out of a set of abstract figures. Across 313 games (N = 1,319 participants), we manipulated several key constraints on the group's interaction, including the amount of feedback that matchers could give to directors and the availability of peer interaction between matchers. Across groups of different sizes and interaction constraints, describers produced increasingly efficient utterances and matchers made increasingly accurate selections. Critically, however, we found that smaller groups and groups with less-constrained interaction structures ("thick channels") showed stronger convergence to group-specific conventions than large groups with constrained interaction structures ("thin channels"), which struggled with convention formation. Overall, these results shed light on the core structural factors that enable communication to thrive in larger groups.

C9H7NBrX (X = Cl, Br, NO3): Three Excellent Birefringent Crystals with Distinct Optical Anisotropy Regulated by Anions.

A large optical anisotropy is the most important parameter of birefringent crystals. Integrating π-conjugated groups with large polarizable anisotropy into target compounds is a common strategy for constructing brilliant birefringent crystals. However, the key problem is to enhance the density of the birefringence-active units and further arrange them parallelly. In this study, three novel birefringent crystals, C9H7NBrX (X = Cl, Br, NO3), are successfully synthesized by introducing a new birefringence-active [C9H7NBr]+ unit. Interestingly, these compounds feature similar layered structures but exhibit different optical anisotropies at 550nm (0.277 for C9H7NBrCl, 0.328 for C9H7NBrBr, and 0.401 for C9H7NBrNO3) owing to the different anions in them. Particularly, the small trigonal planar NO3 anions perfectly fill the interstices of the π-conjugated [C9H7NBr]+ groups with large optical anisotropy, with the resulting compound C9H7NBrNO3 showing superior optical properties compared to the others. The above findings provide strategies for designing new optical materials with large birefringence by matching birefringence-active groups of different sizes. Additionally, a new theory for predicting and comparing the polarizability anisotropy of compounds is proposed, which would guide in exploring large birefringent crystals.

Female-biased birth sex ratio in a female dispersal primate suggests local resource competition.

Group living may entail local resource competition (LRC) which can be reduced if the birth sex ratio (BSR) is biased towards members of the dispersing sex who leave the group and no longer compete locally with kin. In primates, the predicted relationship between dispersal and BSR is generally supported although data for female dispersal species are rare and primarily available from captivity. Here, we present BSR data for Phayre's leaf monkeys (Trachypithecus phayrei crepusculus) at the Phu Khieo Wildlife Sanctuary, Thailand (N = 104). In this population, nearly all natal females dispersed, while natal males stayed or formed new groups nearby. The slower reproductive rate in larger groups suggests that food can be a limiting resource. In accordance with LRC, significantly more females than males were born (BSR 0.404 males/all births) thus reducing future competition with kin. This bias was similar in 2-year-olds (no sex-differential mortality). It became stronger in adults, supporting our impression of particularly fierce competition among males. To better evaluate the importance of BSR, more studies should report sex ratios throughout the life span, and more data for female dispersal primates need to be collected, ideally for multiple groups of different sizes and for several years.

Research on the initial tensile stiffness of T‐stub based on correlation

AbstractSemi‐rigid steel and composite joints are widely used in steel structures for their effectiveness and low constructional costs and mechanical property research on the semi‐rigid connections could not be ignored. There are still limitations to the performance analysis of mechanical components in connections based on component method, such as T‐stub. This study mainly focuses on the modification of the stiffness formula of T‐stub. Ten groups of different sizes of T‐stub were designed for monotonic loading tests and accurate finite element models were established for comparable study with the experimental results. Parametric analysis was performed on the finite element models, and the influence of each parameter on the tensile stiffness of T‐stub was analyzed. Finally, based on the correlation between tensile stiffness and the parameters, using the probabilistic design system (PDS) module, a T‐stub initial tensile stiffness theoretical formula which has a wide range of application and easier to use is proposed.

Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem

This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid while minimizing path costs. Three distinct analyses were conducted: an overall analysis assessing colony performance across different-sized groups, a group analysis examining the performance of each partitioned group, and a pheromone distribution analysis discerning correlations between temporal pheromone distribution and ant navigation. From the results, a dynamic correlation emerged between the degree of colony partitioning and solution quality within the ACO algorithm framework.

“Experiencing perception”: A collection of class demonstrations and activities to engage students in sensation & perception courses

ABSTRACT Demonstrations have been used to illustrate perceptual phenomena for many years, since they are useful in increasing students’ interest. I have been teaching Perception and Attention for the past 15 years and I have often used demonstrations in class. Due to the pandemic, I had to find alternative demonstrations that did not require direct manipulation and could be easily performed from home. This led to changes in the selection of demonstrations and also in my teaching strategies. In this paper I present the result of this process, which is a collection of 15 class activities. Each activity includes complementary materials (reading assignments and reflection questions) to increase students’ engagement and learning. In presenting this collection of activities I will also reflect on some of the challenges and opportunities that I have found regarding the use of demonstrations in class with groups of different sizes and cultural backgrounds.

Controlled Surface Functionalization Using Aryldiazonium Salts for the Development of Aptasensing Platforms

It is well known that both the aptamer surface density and surface chemistry can have a strong influence on the analytical performance of aptasensors based on target-induced conformational changes1. In this context, we report here an improved protocol for obtaining aptasensing platforms which allows fine-tuning the DNA aptamer surface coverage. First, glassy carbon substrates were functionalized with ethynylphenyl groups through the electrochemical reduction of silyl-protected ethynylphenyl diazonium tetrafluoroborates followed by deprotection with tetrabutylammonium fluoride2. The successful removal of the protecting groups (triisopropylsilyl-, triphenylsilyl- and tris(biphen-4-yl)silyl-) was confirmed by X-ray photoelectron spectroscopy and cyclic voltammetry in the presence of Fe(CN)6 3-/4- as soluble redox probe. Following “click” post-modification with suitable derivatization reagents, the surface coverage with ethynylphenyl groups was assessed using complementary techniques such as XPS, cyclic voltammetry and chronocoulometry. For example, the F/C surface ratio of substrates derivatized with 1-(2,2,2-trifluoroethoxy)-6-azidohexane provided an indication of the functionalization degree, and the integration of Fc/Fc+ voltammetric peaks for substrates modified with N-(6-azidohexyl)ferrocenecarboxamide allowed a quantitative determination of the surface coverage. Both techniques showed a good correlation between the protective group size and the surface coverage with alkyne groups. Likewise, we observed a similar trend for substrates derivatized with azide-modified oligonucleotides, where the surface packing density was determined based on the chronocoulometric response of Ru(NH3)6 3+, a cationic redox probe which binds with the negatively charged phosphate groups from the oligonucleotide backbone. As proof of concept, we further developed an electrochemical molecular beacon aptasensor employing a ferrocene-labeled quinine aptamer. We demonstrate that the aptamer surface density, and ultimately the analytical performance of molecular beacon aptasensors, can be effectively fine-tuned by employing silyl protecting groups of different sizes.Acknowledgements This work was supported by a grant from the Romanian Ministry of Education and Research, CNCS-UEFISCDI, project number PN-III-P4-ID-PCE-2020-2474, within PNCDI III. References Onaş, A. M., Dascălu, C., Raicopol, M. D. & Pilan, L. Critical Design Factors for Electrochemical Aptasensors Based on Target-Induced Conformational Changes: The Case of Small-Molecule Targets. Biosensors 12, (2022).Leroux, Y. R., Fei, H., Noël, J.-M. M., Roux, C. & Hapiot, P. Efficient covalent modification of a carbon surface: Use of a silyl protecting group to form an active monolayer. J. Am. Chem. Soc. 132, 14039–14041 (2010).

Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.

Regulating the π–π interactions of copolymer gelators: Effective molecular design of highly stretchable and tough ionogels for wearable ionotronics

The emergence of stretchable ionic conductors has facilitated the development of wearable devices that can perceive changes in their surrounding environment and interpret external stimuli. An example of this is ionic skin, a form of artificial skin that is worn constantly for the real-time monitoring of temperature, pressure, and tension. However, ionic skins are vulnerable to physical damage and stress, which reduces their functionality, performance, and longevity. To obtain mechanically durable ionic skins, the toughness of their constituent ionogels must be improved by increasing their strength and/or stretchability. However, the molecular design and synthesis of these materials remain challenging due to the inherent trade-off between mechanical durability and ionic conductivity. In the present study, we introduced three styrene derivatives containing pendant groups of different sizes to copolymer gelators as ionic liquid (IL)-solvophobic components that form physically crosslinked domains and compared the mechanical and conductivity performance of the resulting ionogels. As the packing density of the styrene decreased, the IL-solvophobic domains became softer and more deformable due to the weaker physical crosslinking via π–π interactions. The optimal ionogel exhibited high stretchability (∼1200 %), excellent toughness (∼3.4 MJ·m−3), and high transparency (∼98 %). The successful use of the optimal ionogel in ionic skin sensors and deformable alternating-current electroluminescent displays demonstrated its broad range of practical applications.

Graph-Based Generalization of Galam Model: Convergence Time and Influential Nodes

We study a graph-based generalization of the Galam opinion formation model. Consider a simple connected graph which represents a social network. Each node in the graph is colored either blue or white, which indicates a positive or negative opinion on a new product or a topic. In each discrete-time round, all nodes are assigned randomly to groups of different sizes, where the node(s) in each group form a clique in the underlying graph. All the nodes simultaneously update their color to the majority color in their group. If there is a tie, each node in the group chooses one of the two colors uniformly at random. Investigating the convergence time of the model, our experiments show that the convergence time is a logarithm function of the number of nodes for a complete graph and a quadratic function for a cycle graph. We also study the various strategies for selecting a set of seed nodes to maximize the final cascade of one of the two colors, motivated by viral marketing. We consider the algorithms where the seed nodes are selected based on the graph structure (nodes’ centrality measures such as degree, betweenness, and closeness) and the individual’s characteristics (activeness and stubbornness). We provide a comparison of such strategies by conducting experiments on different real-world and synthetic networks.

Separation and characterization of Gastrodia elata polysaccharides based on asymmetrical flow field-flow fractionation: steric transition phenomenon

Asymmetrical flow field-flow fractionation (AF4), a gentle tool for the separation and characterization of particles and macromolecules, has attracted increased interest in recent years owing to its broad dynamic size range and utilization of "open channel" voids in the packing or stationary phase. A steric transition phenomenon in which the sample elution mode change from the normal mode to the steric/hyperlayer mode occurs. Accurate characterization by AF4 requires the absence of steric transition, particularly when the sample has a broad size distribution, because the effect of the combination of different modes is difficult to interpret. In this study, the relative molecular mass (M), radius of gyration (Rg), and conformation of Gastrodia elata polysaccharides (GEPs) were characterized using AF4 coupled with online multi-angle light scattering (MALS) and differential refractive index (dRI) detection (AF4-MALS-dRI). Steric transition was observed during GEP separation by AF4 owing to the broad size distribution of the molecules. This phenomenon would result in the inaccurate characterization of the GEPs in terms of M and Rg because two GEP groups of different sizes may elute together. In this study, the effects of constant and exponentially decaying cross-flow rates, sample mass concentration, and spacer thickness on steric transition were systematically investigated. The results indicated that a high GEP mass concentration (i. e., 0.75 mg/mL) can lead to steric transition. The spacer thickness affected the resolution and retention time of the GEPs and changed the steric transition point (di). An exponentially decaying cross-flow rate not only adjusted the di of the polydisperse GEP samples but also improved the GEP resolution and shortened the analysis time. The influence of steric transition was solved under the following operating conditions: injected GEP mass concentration=0.5 mg/mL; injection volume=50 μL; spacer thickness=350 μm; detector flow rate=1.0 mL/min; and cross-flow rate exponentially decayed from 0.2 to 0.05 mL/min with a half-life of 2 min. Moreover, the influence of GEP origins and ultrasound treatment time on the M and Rg distributions and conformation of GEPs were investigated under the optimized operating conditions. The results showed that the M and Rg distributions of Yunnan and Sichuan GEPs decreased with increasing ultrasound time. When the ultrasound treatment time was 15 min, the Yunnan GEPs had a loosely hyperbranched chain conformation, whereas the Sichuan GEPs had a spherical conformation. When the ultrasound treatment time was increased to 30 or 60 min, the GEPs from both Yunnan and Sichuan had a hyperbranched chain conformation, indicating that ultrasound treatment resulted in GEP degradation. Under the same extraction conditions, GEPs from Yunnan had larger M and Rg values than those from Sichuan. AF4-MALS-dRI showed good repeatability for the characterization of GEPs under the optimized operating conditions. The relative standard deviations of Rg and M were 0.5% and 1.7%, respectively. The data presented in this study can be used as a starting point for in-depth studies on the structural bioactivity of GEPs.

Information dissemination evolution under group feedback

In this paper, based on the feedback mechanism from the perspective of network groups, the evolutionary characteristics and laws of group networks under information dissemination are studied. First, the network is divided into groups of different sizes, and each group is given a dynamically changing group activity and a positive degree of response to different categories of information. Second, a feedback-based model of information dissemination in group networks is developed, which takes into account the differences between same-group and cross-group dissemination of information. Next, the model is applied to a scale-free network and a small-world network for simulation experiments. The experimental results show that, under the feedback mechanism, the main factor affecting the final evolutionary results of each group size in the small-world network is group positivity, which has little relationship with the initial size; similarly, the main factor affecting the average degree of each group in the scale-free network is also group positivity, which has nothing to do with the initial average degree. Finally, the method is applied to a real network to verify the rationality and effectiveness of the proposed model.

QoE-aware NOMA user grouping in 5G mobile communications using a multi-stage interval type-2 fuzzy set model

The user grouping has a significant impact on the performance of non-orthogonal multiple access (NOMA) systems. The present work is focused on downlink NOMA user grouping leveraging the type-2 fuzzy set (T2FS). The main drawback of conventional user grouping methods is low efficiency for middle users which degrades the overall system performance. To overcome this problem, a 2-step user grouping process is proposed that includes identifying group candidates and selecting the most qualified one. The first step is handled by introducing a group membership competency criterion based on T2FS modeling due to its capability to handle extra uncertainty in real-world phenomena. The second step involves adding the most qualified candidate to the desired group, given the additional power imposed on the group. The key contribution of the paper is twofold: (i) it affords a multi-stage structure to evaluate the competency of users to join a particular group relying on T2FS modeling. It allows groups of different sizes to be formed, depending on the network channel status and quality of experience (QoE) requirements, and (ii) the additional power imposed on the group is exploited as a measure to select the final group. In this regard, the analysis of interference that each user brings to others in the same group is taken into account. The performance of the proposed scheme is evaluated by simulations and compared with other methods. The obtained results indicate the efficiency of the proposed approach in terms of total power consumption and symbol error rate.

Effect of Multimedia E-Book Use on the Information Literacy of Nursing Students and Health Communication in Student-Led Large- and Small-Group Community Health Education Sessions

The integration of digital technology into healthcare is critical for health communication. This study analyzed a group of nursing students who applied multimedia health education e-books to different groups of varying sizes to explore the efficiency of implementing health communication and nursing information literacy in the nursing industry. If medical personnel can make appropriate use of technology, combine medical operations with information systems, and disseminate the purpose of health to groups of different sizes properly, the quality of patient care will improve. Thirty-two junior nursing students at the college level were divided into three groups, each subject to a 3-week internship. After the internship, a questionnaire survey was conducted. Additionally, nine nursing students were interviewed in a 45-min semistructured format. Regarding the effectiveness of nursing students using multimedia e-books to implement health communication and the development of nursing information literacy, the statistical analysis results demonstrated no significant differences between large groups and small groups. However, their 5-point Likert scale average values were all greater than 4, indicating that regardless of group size, their feedback on using e-books was positive. This means multimedia e-books can effectively help nursing students practice health communication application effectiveness and develop nursing information literacy.

Automatic Tumor Identification from Scans of Histopathological Tissues

Latest progress in development of artificial intelligence (AI), especially machine learning (ML), allows to develop automated technologies that can eliminate or at least reduce human errors in analyzing health data. Due to the ethics of usage of AI in pathology and laboratory medicine, to the present day, pathologists analyze slides of histopathologic tissues that are stained with hematoxylin and eosin under the microscope; by law it cannot be substituted and must go under visual observation, as pathologists are fully accountable for the result. However, a profuse number of automated systems could solve complex problems that require an extremely fast response, accuracy, or take place on tasks that require both a fast and accurate response at the same time. Such systems that are based on ML algorithms can be adapted to work with medical imaging data, for instance whole slide images (WSIs) that allow clinicians to review a much larger number of health cases in a shorter time and give the ability to identify the preliminary stages of cancer or other diseases improving health monitoring strategies. Moreover, the increased opportunity to forecast and take control of the spread of global diseases could help to create a preliminary analysis and viable solutions. Accurate identification of a tumor, especially at an early stage, requires extensive expert knowledge, so often the cancerous tissue is identified only after experiencing its side effects. The main goal of our study was to expand the ability to find more accurate ML methods and techniques that can lead to detecting tumor damaged tissues in histopathological WSIs. According to the experiments that we conducted, there was a 1% AUC difference between the training and test datasets. Over several training iterations, the U-Net model was able to reduce the model size by almost twice while also improving accuracy from 0.95491 to 0.95515 AUC. Convolutional models worked well on groups of different sizes when properly trained. With the TTA (test time augmentation) method the result improved to 0.96870, and with the addition of the multi-model ensemble, it improved to 0.96977. We found out that flaws in the models can be found and fixed by using specialized analysis techniques. A correction of the image processing parameters was sufficient to raise the AUC by almost 0.3%. The result of the individual model increased to 0.96664 AUC (a more than 1% better result than the previous best model) after additional training data preparation. This is an arduous task due to certain factors: using such systems’ applications globally needs to achieve maximum accuracy and improvement in the ethics of Al usage in medicine; furthermore if hospitals could give scientific inquiry validation, while retaining patient data anonymity with clinical information that could be systemically analyzed and improved by scientists, thereby proving Al benefits.

A multi-objective grey hierarchical group consensus model and its application

PurposeThe paper attempts to construct a novel multi-objective grey hierarchical group consensus approach to deal with the group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among decision makers (DMs).Design/methodology/approachTo deal with these group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among DMs non-cooperative behavior in uncertain information systems, considering the influence of coordination cost and the degree of group consensus, based on the idea of grey situation decision-making, the authors establish a multi-objective grey hierarchical group consensus model, and design different invalid decision elimination rules for decision-making groups of different sizes, and use a case verifies the effectiveness and feasibility of the model.FindingsWith the continuous improvement of the coordination cost budget, the degree of consensus of all departments and the overall consensus tend to be stable, and will no longer change with the increase of the coordination cost budget. The cost required by each department is basically consistent with the response trend of the cost required to coordinate the overall situation to the pre-set lower limit of group consensus.Originality/valueThe proposed approach can succeed in identifying DMs' information, and mine the DMs' information and help make a relatively more scientific decision.

Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections.

Testing remains a key tool for managing health care and making health policy during the coronavirus pandemic, and it will probably be important in future pandemics. Because of false negative and false positive tests, the observed fraction of positive tests-the surface positivity-is generally different from the fraction of infected individuals (the incidence rate of the disease). In this paper a previous method for translating surface positivity to a point estimate for incidence rate, then to an appropriate range of values for the incidence rate consistent with the model and data (the test range), and finally to the risk (the probability of including one infected individual) associated with groups of different sizes is illustrated. The method is then extended to include asymptomatic infections. To do so, the process of testing is modeled using both analysis and Monte Carlo simulation. Doing so shows that it is possible to determine point estimates for the fraction of infected and symptomatic individuals, the fraction of uninfected and symptomatic individuals, and the ratio of infected asymptomatic individuals to infected symptomatic individuals. Inclusion of symptom status generalizes the test range from an interval to a region in the plane determined by the incidence rate and the ratio of asymptomatic to symptomatic infections; likelihood methods can be used to determine the contour of the rest region. Points on this contour can be used to compute the risk (defined as the probability of including one asymptomatic infected individual) in groups of different sizes. These results have operational implications that include: positivity rate is not incidence rate; symptom status at testing can provide valuable information about asymptomatic infections; collecting information on time since putative virus exposure at testing is valuable for determining point estimates and test ranges; risk is a graded (rather than binary) function of group size; and because the information provided by testing becomes more accurate with more tests but at a decreasing rate, it is possible to over-test fixed spatial regions. The paper concludes with limitations of the method and directions for future work.

Cooperation in Networked Collective-Action Groups: Information Access and Norm Enforcement in Groups of Different Sizes

Norms, typically enforced via sanctions, are key to resolving collective-action problems. But it is often impossible to know what each individual member is contributing to group efforts and enforce cooperation accordingly. Especially as group size increases, people commonly have access to the behaviors of—and can sanction—only those to whom they are tied in a broader network. Here we integrate two streams of research: one conceptualizing ties in networked collective-action groups as access to information about what others are doing and a second where ties represent information plus opportunities to enforce cooperation via punishment. While both have pointed to the cooperation benefits of more ties in the network, we argue that these benefits will depend on group size and whether ties provide access to information about what others are doing or whether they also entail opportunities for norm enforcement. Our experiment demonstrates that densely tied information networks facilitate cooperation but only when the group size is small. When people can also enforce their ties’ cooperation, however, densely tied networks particularly benefit larger groups. The results demonstrate how network-level properties and individual-level tie patterns intersect to promote contributions in small and large collective-action groups.