Rich Model Research Articles

Modelling Blow Fly (Diptera: Calliphoridae) Spatiotemporal Species Richness and Total Abundance Across Land-Use Types.

Geographic Information Systems provide the means to explore the spatial distribution of insect species across various land-use types to understand their relationship with shared or overlapping spatiotemporal resources. Blow fly species richness and total fly abundance were correlated among six land-use types (residential, commercial, waste, woods, roads, and agricultural crop types) and distance to streams. To generate multivariate models of species richness and total fly abundance, blow fly trapping sites were chosen across the land-use gradient of Windsor-Essex County (Ontario, Canada) using a stratified random sampling approach. Sampling occurred in mid-June (spring), late August (summer), and late October (fall). Spring species richness correlated highest to residential (-), woods (-), distance to streams (+), and tomato fields (+) in models across all three land-use buffer scale distances (0.5, 1, 2 km), with waste (+/-), roads (-), wheat/corn (-), and commercial (-) correlating at only two of the three scales. Spring total fly abundance correlated with all but one land-use variable across all buffer scale distances, but the distance to streams (+), followed by orchards/vineyards (+) exhibited the greatest importance to these models. Summer blow fly species richness correlated with roads (-) and commercial (+) across all buffer distances, whereas at two of three buffer distances wheat/corn (-), residential (+), distance to streams (+), waste (-), and orchards/vineyards (+) were also important. Summer total fly abundance correlated to models with distance to streams (+), orchards/vineyards (+), and sugar beets/other vegetables (+) at the 2 km scale. Species richness and total abundance models at the 0.5 km buffer distance exhibited the highest correlation, lowest root mean square error, and similar prediction error to those derived at larger buffer distances. This study provides baseline methods and models for future validation and expansion of species-specific knowledge regarding adult blow fly relationships with spatiotemporal resources across land-use types and landscape features.

Adaptively remeshed multiphysical modeling of resistance forge welding with experimental validation of residual stress fields and measurement processes

Welding processes used in the production of pressure vessels impart residual stresses in the manufactured component. Computational modeling is critical to predicting these residual stress fields and understanding how they interact with notches and flaws to impact pressure vessel durability. In this work, we present a finite element model for a resistance forge weld and validate it using laboratory measurements. Extensive microstructural changes, near-melt temperatures, and large localized deformations along the weld interface pose significant challenges to Lagrangian finite element modeling. The proposed modeling approach overcomes these roadblocks in order to provide a high-fidelity simulation that can predict the residual stress state in the manufactured pressure vessel; a rich microstructural constitutive model accounts for material recrystallization dynamics, a frictional-to-tied contact model is coordinated with the constitutive model to represent interfacial bonding, and adaptive remeshing is employed to alleviate severe mesh distortion. An interrupted-weld approach is applied to the simulation to facilitate comparison to displacement measures. Several approaches are employed for residual stress measurement in order to validate the finite element model: neutron diffraction, the contour method, and the slitting method. Model-measurement comparisons are supplemented with detailed simulations that reflect the configurations of the residual-stress measurement processes themselves. The model results show general agreement with experimental measurements, and we observe some similarities in the features around the weld region. Factors that contribute to model-measurement differences are identified. Finally, we conclude with some discussion of the model development and residual stress measurement strategies, including how to best leverage the efforts put forth here for other types of related weld problems.

Bi-colored expansions of geometric theories

This paper concerns the study of expansions of models of a geometric theory T by a color predicate p, within the framework of the Fraïssé-Hrushovski construction method. For each α∈(0,1], we define a pre-dimension function δα on the class of Bi-colored models of T∀ and consider the subclass Kα+ consisting of models with hereditary positive δα. We impose certain natural conditions on T that enable us to introduce a complete Π2-theory Tα for the rich models in Kα+. We show how the transfer of certain model-theoretic properties, such as NIP and strong-dependence, from T to Tα, depends on whether α is rational or irrational.

Re-Envisioning the Plant Disease Triangle: Full Integration of the Host Microbiota and a Focal Pivot to Health Outcomes.

The disease triangle is a structurally simple but conceptually rich model that is used in plant pathology and other fields of study to explain infectious disease as an outcome of the three-way relationship between a host, a pathogen, and their environment. It also serves as a guide for finding solutions to treat, predict, and prevent such diseases. With the omics-driven, evidence-based realization that the abundance and activity of a pathogen are impacted by proximity to and interaction with a diverse multitude of other microorganisms colonizing the same host, the disease triangle evolved into a tetrahedron shape, which features an added fourth dimension representing the host-associated microbiota. Another variant of the disease triangle emerged from the recently formulated pathobiome paradigm, which deviates from the classical "one pathogen" etiology of infectious disease in favor of a scenario in which disease represents a conditional outcome of complex interactions between and among a host, its microbiota (including microbes with pathogenic potential), and the environment. The result is a version of the original disease triangle where "pathogen" is substituted with "microbiota." Here, as part of a careful and concise review of the origin, history, and usage of the disease triangle, I propose a next step in its evolution, which is to replace the word "disease" in the center of the host-microbiota-environment triad with the word "health." This triangle highlights health as a desirable outcome (rather than disease as an unwanted state) and as an emergent property of host-microbiota-environment interactions. Applied to the discipline of plant pathology, the health triangle offers an expanded range of targets and approaches for the diagnosis, prediction, restoration, and maintenance of plant health outcomes. Its applications are not restricted to infectious diseases only, and its underlying framework is more inclusive of all microbial contributions to plant well-being, including those by mycorrhizal fungi and nitrogen-fixing bacteria, for which there never was a proper place in the plant disease triangle. The plant health triangle also may have an edge as an education and communication tool to convey and stress the importance of healthy plants and their associated microbiota to a broader public and stakeholdership.

The IPM cycle: An instructional tool for promoting students' engagement in modeling practices and construction of models

AbstractThe importance of models and modeling in science education is well‐recognized, yet there exists significant polysemy among these terms within the literature. This ambiguity often leads to confusion, particularly regarding whether modeling represents an expected student performance, an instructional strategy to promote such performance, or both. Moreover, the construction of models has been depicted as both the objective of modeling and a distinct phase within modeling‐based instruction. Additionally, the expression of models has often been overlooked despite its significance as a crucial modeling practice. In an endeavor to shed light into these complexities associated with modeling in science education, this paper pursues a twofold aim. First, it theoretically presents and justifies the Instruction Performance Modeling (IPM) cycle, drawing on numerous previous contributions to the field, as a practical and specific instructional tool designed to clarify some problematic concepts both regarding modeling instruction and modeling practice. Second, it provides empirical evidence regarding the type of modeling performance exhibited by students involved in instruction guided by the IPM. This study applies discourse analysis to the multimodal productions of preservice teachers attending a lab‐based workshop on the topic of flotation. The main findings reveal that students' modeling performance, while exhibiting certain patterns such as the Introductory pattern or the Evaluation‐Revision one, predominantly manifests as a disorganized sequence of modeling practices. This result is consistent with certain precedents in the modeling literature but contrasts with the expected outcomes of well‐established approaches like Generation‐Evaluation‐Modification. Furthermore, the study aims to highlight the rich, meaningful, and productive modeling practices occurring in instructional scenarios guided by the IPM cycle.

Application of Virtual Reality Technology Based on Convolutional Neural Network in Digital Media Art Creation

On the basis of the convolutional neural network based on the absolute value layer, a convolutional neural network based on multi-level parallel is proposed. This model adds shortcut connections between different convolutional layers of the convolutional neural network. The shortcut connections allow the feature map output from the shallower convolutional layers in the network to be directly used as branch information, which is similar to the deep convolutional layer. The feature maps are combined to obtain a feature map with more comprehensive information. By using the features of the shallow convolutional layer and the deep convolutional layer at the same time, the embedded traces it learns are more accurate. The final experimental results prove that the multi-level parallel convolutional neural network has a significant improvement in detection performance compared with the rich model art creation analysis algorithm, even when confronting the HILL art creation algorithm with an embedding rate of 0.4bpp.

Enhancing the performance of convolutional neural network image-based steganalysis in spatial domain using Spatial Rich Model and 2D Gabor filters

Image-based steganalysis problem has attracted many researchers, and several solutions have been proposed. Deep learning-based methods are the most promising as they provide superior performance. Convolutional Neural network(CNN) based steganalysis methods are designed to improve the detection rate. Unlike traditional CNN models, CNN-based steganalysis requires careful design of preprocessing layers with filter initialization to obtain a good performance. In this paper, we established a CNN model that consists of two convolution layers for preprocessing and feature extraction, and four fully connected layers for classification. The preprocessing layer uses a set of efficient filter banks consisting of SRM and 2D Gabor filters. We conducted experiments using grayscale cover images from a popular and publicly available BOSSbase_1.01 database and Alask_v2 database with consideration for two different image sizes. The results showed that the proposed CNN model outperforms many state-of-the-art studies in two out of three well-known adaptive spatial domain steganography algorithms (S-UNIWARD, HUGO) and provides a close result for (WOW) algorithm when using the database with 512 × 512 images. On the other hand, the proposed model outperforms many state-of-the-art studies in the three algorithms when using the database with the original image size (256 × 256). Using image size 256, and the S-UNIWARD algorithm, the proposed model improved the detection accuracy rate by 13%, and 4.25% payloads of 0.2 and 0.4 bpp respectively compared to the previously best-known model (GBRAS-Net). The proposed model achieved 7.4% and 6.27% improvement in the detection accuracy for both payloads 0.2 and 0.4 bpp respectively using the HUGO algorithm compared with the previously best-known model (GBRAS-Net). For the WOW algorithm, the proposed model is slightly behind the best model (GBRAS-Net) but was able to obtain a close result for both payloads of 0.2 and 0.4 bpp, respectively. Using an image size of 512, the proposed model achieved 31.26%, 21.51%, 6.84%, 4.22%, and 1.96% improvement in the detection rate for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over S-UNIWARD algorithm compared to the previously best-known model (H-CNN). In addition, the proposed model achieved 27.60%, 23.69%, 12.66%, 5.27%, and 6.23% improved detection accuracy for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over HUGO algorithm compared with the previously best-known model (H-CNN). Finally, the proposed model provided 57.81%, 46.84%, 28.29%, 20.34%, and 13.79% improvement in the detection rate for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over WOW algorithm compared to the previously best-known model (H-CNN).

Creative Tourism: Two Decades of Conceptual Evolution and Characterization

The concept of “creative tourism”, emerging at the century’s start, responds to the “creative turn” in tourist destinations, aligning with the evolving creative class, clusters, and cities. This study employs a bibliometric analysis to examine the evolution and academic focus on creative tourism over the past two decades. Using databases like Scopus and Web of Science, we analyzed key trends, thematic developments, and the impact of creative industries on tourism. Our study reveals significant growth in creative tourism research, emphasizing creativity’s role in enhancing tourism experiences and formulating sustainable, culturally rich models. It underscores the importance of integrating creative industries into tourism and the positive impact on local economies and community development. This analysis not only maps the academic trajectory of creative tourism but also highlights its practical implications for future tourism management and sustainable community development.

Use of lichens as bioindicators of contamination by agrochemicals and metals.

The presence or absence of lichens serves as an indicator of the condition of an ecosystem and the degree to which it is contaminated by various agents, such as agrochemicals and metals. Evaluating the use of lichens as bioindicators of agrochemical contamination could provide a more comprehensive perspective of current contamination levels. Monitoring was conducted over a 4-month period in two study areas: one was a well-conserved area contaminated by metals, and the other was an area surrounded by agricultural crops contaminated by agrochemicals. Data on the presence and abundance of lichens in each study area were recorded at 10 monitoring points, a procedure that was repeated 16 times (every 15days), and concentrations of heavy metals and "organophosphate" agrochemicals in the lichens collected were measured by means of Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES) and Gas Chromatography (GC), respectively. Generalized linear mixed models were used to assess abundance and richness, while general linear mixed models were used to attain Shannon diversity and Simpson dominance indices. Moreover, a multivariate analysis was performed in order to compare the lichen communities in both areas. The results indicated differences between the area contaminated by metals and that contaminated by agrochemicals in terms of abundance and Simpson's dominance index, while no differences were found in the case of the richness and diversity models. The PERMANOVA analysis additionally showed differences between the lichen communities in the two areas. The results also demonstrated that Canoparmelia caroliniana bioaccumulated metals in both areas. The levels of barium, cadmium, and sodium were higher in the area contaminated by metals, while concentrations of chromium and copper were higher in the area contaminated by agrochemicals. Finally, the concentrations of agrochemicals were higher in the area contaminated by agrochemicals and included toxic substances such as Methylparathion and Parathion, which are prohibited in Ecuador. In conclusion, this research underscores the importance of lichens as precise indicators of environmental health and contamination by agrochemicals and metals.

Comparing comparison: Smith and Davidson

Comparison, as a method in the study of religion, has received considerable criticism in recent years. Much of the discussion addresses what we call the dilemma of religious comparison: either similarities between the cases compared are real and the differences accidental, or the differences are real and the similarities merely apparent. The first path is held to artificially impose similarity by wrongly privileging one of the things being compared, which imposes an essentialist view, taking its characteristic as the measure of all comparable cases. The other path exaggerates differences between comparands, undermining the very premise of comparison. We argue that this apparent dilemma presupposes a representational conception of meaning, namely that the task of comparison is to discover what kind of thing is represented by the cases being compared. However, there are other conceptions of meaning that might can potentially avoid or resolve the dilemma, by reorientating the basic conception of comparison, as opposed to entering into details of recent critical debates. This article argues that interpretationism, an approach to meaning associated with philosopher Donald Davidson does just this. JZ Smith did not explicitly adopt any semantic theory. But we demonstrate that he implicitly endorsed all of its main elements in his work on comparison. Smith’s writings represent a rich and detailed model of how comparison can be carried out under an interpretationist semantics. This article has two important implications for the study of religion. First, it dissolves the dilemma of religious comparison, suggesting a firmer theoretical and methodological basis for comparative studies. Second, it underlines that Smith’s work serves as a powerful and practical model of comparative method, as constructed on this basis. As a corollary, an interdisciplinary side-benefit, reading Smith in light of Davidson also makes important contributions to philosophy of language.

Stabilisation of extra virgin olive oil‐in‐water emulsions prepared by citrus pectin using green tea and grape seed phenolic extracts and catechin

SummaryThis study aimed at the solution of stability problems of extra virgin olive oil rich (oil:aqueous phase ratio = 50:50) model oil‐in‐water emulsions prepared with pectin using green tea (GTE) and grape seed extracts (GSE), and (+)‐catechin (CAT). Olive oil‐in‐water emulsion prepared with pectin at 0.5% (w/v) without phenolics lost 36% of its stability within 1 day. GTE at 1% (w/v) caused a limited increase in emulsion stability while emulsions with GSE at ≥0.25% or CAT at 1% maintained >90% of their stability after 14 days. GSE and CAT reduced the emulsion droplet size (2.6‐ and 1.9‐fold) while GTE increased the emulsion droplet size (1.5‐fold). Emulsions with GSE showed the smallest and most densely packed lipid droplets. The highest phenolic content and antioxidant activity in emulsions were obtained with CAT followed by GSE and GTE. This work is significant in that it showed the possibility of using GSE to obtain stable extra virgin olive oil emulsion‐based functional foods.

Analysis of The Readiness of The Regional Government of Malinau District North Kalimantan Province in Commemorate Digital Transformation

One of the efforts to realize the goals of bureaucratic reform is the modernization of the government bureaucracy which focuses on the orientation of public services to community satisfaction through optimizing the use of information technology. The purpose of this research is to analyse the readiness of the Regional Government of Malinau Regency in commemorate digital transformation and to identify supporting and inhibiting factors for the readiness of the Regional Government of Malinau Regency in commemorate digital transformation. This study uses a qualitative research design with descriptive methods, by adapting the An Integration Information Rich E-Readiness Assessment Tool readiness analysis model, proposed by Mutula & Brakel 2006. This research uses a triagulation analysis method on observation data, interviews and documentation in the field. The research results of the five dimensions of assessment show 1) Information Readiness with information access indicators categorized as ready and information mechanism indicators categorized as quite ready; 2) Institutional Readiness with Organizational Structure indicators categorized as ready, ICT Policy indicators categorized as quite ready and ICT Budget Readiness categorized as ready; 3) HR readiness with HR composition indicators categorized as quite ready, ICT HR qualification indicators categorized as unprepared, and ICT HR preparation indicators categorized as unprepared. 4) Infrastructure Availability with hardware and software infrastructure indicators categorized as ready and internet connection indicators categorized as quite ready; 5) External Environment Readiness with telecommunications service quality indicators categorized as quite ready.

Surface Defect-Extended BIM Generation Leveraging UAV Images and Deep Learning.

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks.

Poor transferability of richness models: Predicting plot-scale plant diversity in the Waterberg, South Africa

Modelling measures of biodiversity for understudied taxa or regions is one method to address taxonomic and geographic biases in biodiversity data. However, modelling biodiversity metrics, such as species richness, to unsampled areas is only useful if predictions are reliable. As a result, testing the transferability of richness models is necessary for assessing the potential for models to predict to unsampled areas. Here we test the transferability of plant richness models between two reserves to examine if the richness-environment relationship from one reserve can accurately estimate richness patterns in the other reserve, using the vascular plant species richness of the Waterberg region (savanna biome; northern South Africa) as a model system. Six richness response variables (total species, grass species, herb species, woody species, genus, and family richness) and a set of 16 predictor variables were analysed with three modelling approaches and two statistical techniques to build: (1) models comprising all available predictor variables, (2) models using a subset of predictor variables chosen based on model performance, and (3) models using a subset of predictor variables that reduce the difference in the environmental conditions between the two reserves. The models’ performance in the training area varied considerably, but soil variables were consistently the most important predictors of plant richness. However, the transferability of all the models was consistently poor across all modelling approaches and both techniques, possibly reflecting the degree to which each reserve contains novel environments absent from the other reserve (despite being separated by only c. 60 km and sharing vegetation types). Due to the poor performance of these richness models, they are currently not useful for predicting richness to other areas in the vicinity of the reserves or in the broader region. However, in areas, like the Waterberg region, that have high plant diversity and are poorly sampled, there may be value in continued development of richness models to address biodiversity gaps, thereby providing better data to inform conservation decisions.

A domain-independent agent architecture for adaptive operation in evolving open worlds

1Angry Birds Competition. Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed discrete-continuous worlds that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents' models to perform effectively. HYDRA is based upon PDDL+, a rich modeling language for planning in mixed, discrete-continuous environments. It augments the planning module with visual reasoning, task selection, and action execution modules for closed-loop interaction with complex environments. HYDRA implements a novel meta-reasoning process that enables the agent to monitor its own behavior from a variety of aspects. The process employs a diverse set of computational methods to maintain expectations about the agent's own behavior in an environment. Divergences from those expectations are useful in detecting when the environment has evolved and identifying opportunities to adapt the underlying models. HYDRA builds upon ideas from diagnosis and repair and uses a heuristics-guided search over model changes such that they become competent in novel conditions. The HYDRA framework has been used to implement novelty-aware agents for three diverse domains - CartPole++ (a higher dimension variant of a classic control problem), Science Birds (an IJCAI competition problem1), and PogoStick (a specific problem domain in Minecraft). We report empirical observations from these domains to demonstrate the efficacy of various components in the novelty meta-reasoning process.

Lookahead Pathology in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is a search paradigm that first found prominence with its success in the domain of computer Go. Early theoretical work established the soundness and convergence bounds for Upper Confidence bounds applied to Trees (UCT), the most popular instantiation of MCTS; however, there remain notable gaps in our understanding of how UCT behaves in practice. In this work, we address one such gap by considering the question of whether UCT can exhibit lookahead pathology in adversarial settings --- a paradoxical phenomenon first observed in Minimax search where greater search effort leads to worse decision-making. We introduce a novel family of synthetic games that offer rich modeling possibilities while remaining amenable to mathematical analysis. Our theoretical and experimental results suggest that UCT is indeed susceptible to pathological behavior in a range of games drawn from this family.

Unleashing potential: Model-based reform benchmarking for EU Member States

Supply-side policies take centre stage in the EU's post-pandemic recovery plans. This paper employs a benchmarking approach to quantify the potential impact of structural reforms in the EU Member States. Based on a comprehensive collection of structural indicators and a rich endogenous growth model, we evaluate reforms in five policy areas: (i) market competition and regulation; (ii) taxation; (iii) skills and education; (iv) labour markets; and (v) research and development. For each indicator and Member State, we simulate the closing of half of the gap with the EU's best performers, implying ambitious reforms for countries with significant distance to the frontier. For these stylised reforms, we find significant potential gains in employment and output, raising EU GDP by around 2% and 8% after five and twenty years, respectively. In the long run, the policies can increase EU GDP by over 20%. The policies also reduce economic disparities between countries, given different scope for reform. For countries with a sizable distance to the best performers, increases in potential GDP could exceed 40% when halving the gap across all indicators. Among the reforms considered here, human capital investment emerges as central for enhancing growth potential. In addition, we find synergies across reforms and countries and assess the sensitivity to alternative assumptions on technology dynamics in our model.

How can a behavioral economics lens contribute to implementation science?

BackgroundImplementation science in health is an interdisciplinary field with an emphasis on supporting behavior change required when clinicians and other actors implement evidence-based practices within organizational constraints. Behavioral economics has emerged in parallel and works towards developing realistic models of how humans behave and categorizes a wide range of features of choices that can influence behavior. We argue that implementation science can be enhanced by the incorporation of approaches from behavioral economics.Main bodyFirst, we provide a general overview of implementation science and ways in which implementation science has been limited to date. Second, we review principles of behavioral economics and describe how concepts from BE have been successfully applied to healthcare including nudges deployed in the electronic health record. For example, de-implementation of low-value prescribing has been supported by changing the default in the electronic health record. We then describe what a behavioral economics lens offers to existing implementation science theories, models and frameworks, including rich and realistic models of human behavior, additional research methods such as pre-mortems and behavioral design, and low-cost and scalable implementation strategies. We argue that insights from behavioral economics can guide the design of implementation strategies and the interpretation of implementation studies. Key objections to incorporating behavioral economics are addressed, including concerns about sustainment and at what level the strategies work.ConclusionScholars should consider augmenting implementation science theories, models, and frameworks with relevant insights from behavioral economics. By drawing on these additional insights, implementation scientists have the potential to boost efforts to expand the provision and availability of high quality care.

Insights on selective Pb adsorption via O 2p orbit in UiO-66 containing rich-zirconium vacancies

Herein, we constructed defective UiO-66 with rich Zr vacancy structure model, in which the defective structure was verified by various characterizations. Also, the Pb adsorption experiments affirmed that defective UiO-66 could display better adsorption and selective adsorption ability than that of perfect UiO-66. The results of partial density of states (PDOS) and Mulliken charge population indicated that the blue shift of O 2p and Zr 4d orbit induced the electron rearrangement of atoms closed to the bonding sites, while the positive charge number of Zr atoms decreased than before. Combining with the expansion of pore size, Pb atom was more inclined to transfer and bond with unsaturated coordination oxygens. More significantly, quantitative structure-activity relationships (QSARs) demonstrated that selective capture of Pb instead of Zn, Cu, Cd and Hg displayed by defective UiO-66 was determined jointly by bond strength, adsorption energy and electron transfer. This work provided some theoretical direction for the purpose of the fabrication of adsorbent and the investigation of mechanism.

Population parameters of Drosophila larval cooperative foraging.

Cooperative foraging behavior can be advantageous when there is a common exploitable resource. By cooperating, members of the group can take advantage of the potential of increased efficiency of working together as well as equitable distribution of the product. An experimental signature of cooperative foraging is an Allee effect where at a certain number of individuals, there is a peak of fitness. What happens when there are intruders especially ones that do not contribute to any work required for foraging? Drosophila larvae secrete digestive enzymes and exodigest food. Under crowded conditions in liquid food these larvae form synchronized feeding clusters which provides a fitness benefit. A key for this synchronized feeding behavior is the visually guided alignment between adjacent larvae in a feeding cluster. Larvae who do not align their movements are excluded from the groups and subsequently lose the benefit. This may be a way of editing the group to include only known members. To test the model, the fitness benefit from cooperative behavior was further investigated to establish an Allee effect for a number of strains including those who cannot exodigest or cluster. In a standard lab vial, about 40 larvae is the optimal number for fitness. Combinations of these larvae were also examined. The expectation was that larvae who do not contribute to exodigestion are obligate cheaters and would be expelled. Indeed, obligate cheaters gain greatly from the hosts but paradoxically, so do the hosts. Clusters that include cheaters are more stable. Therefore, clustering and the benefits from it are dependent on more than just the contribution to exodigestion. This experimental system should provide a rich future model to understand the metrics of cooperative behavior.