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Smart communication protocols are becoming a key mechanism for improving communication performance in networks such as wireless sensor networks. However, the literature lacks mechanisms for simulating smart communication protocols in precision agriculture for decreasing production costs. In this context, the current work presents an agent-based simulator of smart communication protocols for efficiently managing pesticides. The simulator considers the needs of electric power, crop health, percentage of alive bugs and pesticide consumption. The current approach is illustrated with three different communication protocols respectively called (a) broadcast, (b) neighbor and (c) low-cost neighbor. The low-cost neighbor protocol obtained a statistically-significant reduction in the need of electric power over the neighbor protocol, with a very large difference according to the common interpretations about the Cohen’s d effect size. The presented simulator is called ABS-SmartComAgri and is freely distributed as open-source from a public research data repository. It ensures the reproducibility of experiments and allows other researchers to extend the current approach.

Understanding factors controlling primary production is fundamental for the protection, management, and restoration of ecosystems. Tropical seagrass ecosystems are among the most productive ecosystems worldwide, yielding tremendous services for society. Yet they are also among the most impaired from anthropogenic stressors, prompting calls for ecosystem-based restoration approaches. Artificial reefs (ARs) are commonly applied in coastal marine ecosystems to rebuild failing fisheries and have recently gained attention for their potential to promote carbon sequestration. Nutrient hotspots formed via excretion from aggregating fishes have been empirically shown to enhance local primary production around ARs in seagrass systems. Yet, if and how increased local production affects primary production at ecosystem scale remains unclear, and empirical tests are challenging. We used a spatially explicit individual-based simulation model that combined a data-rich single-nutrient primary production model for seagrass and bioenergetics models for fish to test how aggregating fish on ARs affect seagrass primary production at patch and ecosystem scales. Specifically, we tested how the aggregation of fish alters (i) ecosystem seagrass primary production at varying fish densities and levels of ambient nutrient availability and (ii) the spatial distribution of seagrass primary production. Comparing model ecosystems with equivalent nutrient levels, we found that when fish aggregate around ARs, ecosystem-scale primary production is enhanced synergistically. This synergistic increase in production was caused by nonlinear dynamics associated with nutrient uptake and biomass allocation that enhances aboveground primary production more than belowground production. Seagrass production increased near the AR and decreased in areas away from the AR, despite marginal reductions in seagrass biomass at the ecosystem level. Our simulation's findings that ARs can increase ecosystem production provide novel support for ARs in seagrass ecosystems as an effective means to promote (i) fishery restoration (increased primary production can increase energy input to the food web) and (ii) carbon sequestration, via higher rates of primary production. Although our model represents a simplified, closed seagrass system without complex trophic interactions, it nonetheless provides an important first step in quantifying ecosystem-level implications of ARs as a tool for ecological restoration.

The European Parliament requested EFSA to develop a holistic risk assessment of multiple stressors in honey bees. To this end, a systems‐based approach that is composed of two core components: a monitoring system and a modelling system are put forward with honey bees taken as a showcase. Key developments in the current scientific opinion (including systematic data collection from sentinel beehives and an agent‐based simulation) have the potential to substantially contribute to future development of environmental risk assessments of multiple stressors at larger spatial and temporal scales. For the monitoring, sentinel hives would be placed across representative climatic zones and landscapes in the EU and connected to a platform for data storage and analysis. Data on bee health status, chemical residues and the immediate or broader landscape around the hives would be collected in a harmonised and standardised manner, and would be used to inform stakeholders, and the modelling system, ApisRAM, which simulates as accurately as possible a honey bee colony. ApisRAM would be calibrated and continuously updated with incoming monitoring data and emerging scientific knowledge from research. It will be a supportive tool for beekeeping, farming, research, risk assessment and risk management, and it will benefit the wider society. A societal outlook on the proposed approach is included and this was conducted with targeted social science research with 64 beekeepers from eight EU Member States and with members of the EU Bee Partnership. Gaps and opportunities are identified to further implement the approach. Conclusions and recommendations are made on a way forward, both for the application of the approach and its use in a broader context. This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2021.EN-6608/full

Sociograms can represent the social relations between students. Some kinds of sociograms are more suitable than others for achieving a high academic performance of students. However, for now, at the beginning of an educative period, it is not possible to know for sure how the sociogram of a group of students will be or evolve during a semester or an academic year. In this context, the current approach presents an Agent-Based Simulator (ABS) that predicts the sociogram of a group of students taking into consideration their psychological profiles, by evolving an initial sociogram through time. This simulator is referred to as ABS-SOCI (ABS for SOCIograms). For instance, this can be useful for organizing class groups for some subjects of engineering grades, anticipating additional learning assistance or testing some teaching strategies. As experimentation, ABS-SOCI has been executed 100 times for each one of four real scenarios. The results show that ABS-SOCI produces sociograms similar to the real ones considering certain sociometrics. This similarity has been corroborated by statistical binomial tests that check whether there are significant differences between the simulations and the real cases. This experimentation also includes cross-validation and an analysis of sensitivity. ABS-SOCI is free and open-source to (1) ensure the reproducibility of the experiments; (2) to allow practitioners to run simulations; and (3) to allow developers to adapt the simulator for different environments.

Smallholder farms support the livelihoods of 2.5 billion people and their decisions on how to manage their land has profound consequences for the environment and the food security of billions of people. However, farmers' values, norms and resulting management practices are usually not formed in isolation. Triangulating multiple analytical, modelling and simulation methods, we investigated if and how social influence exerted through peer‐to‐peer information exchange affect soil nutrition management among 2734 Indonesian smallholder cocoa farmers across 30 different villages. The results show that the relational structures of these village‐based social networks strongly relate to farmers' use of fertiliser. In villages with highly centralised networks (i.e. hub‐and‐spoke networks where one or very few farmers holds disproportionately central position in the village network), a large majority of farmers report the same fertiliser use, and that practice is typically to avoid using fertilisers. By contrast, in less centralised networks, fertiliser use varies widely. The observed community‐level distributions of fertiliser use can be most closely reproduced through simulations by complex contagion mechanisms in which social influence is only exerted by opinion leaders that are much more socially connected than others. However, even such leaders' abilities to influence others to change fertiliser use may be limited in practice. The combination of our quantitative and qualitative findings provides significant policy implications for development programs targeting smallholder farming communities. An important practical lesson is that common interventions which primarily engage socially central farmers may not be effective in stimulating desired transitions in social‐ecological systems. Read the free Plain Language Summary for this article on the Journal blog. Petani kecil berperan dalam menopang kehidupan 2.5 milyar orang, dan keputusan mereka dalam mengelola lahan berdampak besar terhadap lingkungan serta ketahanan pangan milyaran orang. Namun, nilai‐nilai, norma‐norma, dan praktek pengelolaan yang dijalankan oleh para petani tersebut biasanya tidak terbentuk dalam isolasi. Menggunakan triangulasi berbagai metode analisis, pemodelan, dan simulasi, kami meneliti apakah dan bagaimana pengaruh sosial melalui pertukaran informasi sesama petani mempengaruhi manajemen nutrisi tanah pada 2734 petani kakao skala kecil di 30 desa di Indonesia. Hasil studi menunjukkan bahwa struktur relasional dari jaringan sosial berbasis desa ini sangat mempengaruhi penggunaan pupuk oleh petani. Di desa dengan jaringan yang sangat sentral (misalnya, jaringan hub‐and‐spoke dimana satu atau beberapa petani yang memiliki posisi sangat terpusat dalam jaringan desa), sebagian besar petani melaporkan penggunaan pupuk yang serupa, yang umumnya adalah menghindari penggunaan pupuk. Sebaliknya, dalam jaringan yang kurang terpusat, variasi penggunaan pupuk cenderung lebih luas. Distribusi penggunaan pupuk di tingkat komunitas yang kami amati dapat direproduksi dengan lebih akurat melalui simulasi menggunakan mekanisme penularan kompleks. Dalam mekanisme ini, pengaruh sosial hanya diberikan oleh pemimpin opini yang memiliki koneksi sosial jauh lebih luas dibandingkan dengan yang lain. Namun, kemampuan pemimpin seperti ini untuk mempengaruhi perubahan dalam penggunaan pupuk pada praktiknya mungkin terbatas. Gabungan dari temuan kuantitatif dan kualitatif kami memberikan implikasi kebijakan yang penting bagi program‐program pembangunan yang ditujukan untuk komunitas petani skala kecil. Salah satu pelajaran praktis yang penting adalah bahwa intervensi konvensional yang terutama menargetkan petani dengan posisi sentral mungkin tidak efektif dalam mendorong perubahan yang diharapkan dalam sistem sosial‐ekologis. Read the free Plain Language Summary for this article on the Journal blog.

The paper presents an agent‐based modeling and simulation approach to model the forest fuel supply chain for heating purposes (i.e., heating plants). The paper focuses on the simulation of the processes of timber harvesting by forest enterprises and the competition of heating plants for the limited resource of wood chips. In particular, the work identifies different stakeholders having an adaptive behavior—with respect to the overall market conditions and timber prices. The agent‐based model developed here—called SimFoMa—uses three types of agents—forest enterprises, heating plants, and traders. The agents are interacting in an environment that has rich information on the forests and road network. The SimFoMa model is applied to a test area, the province of Carinthia, Austria. We defined six different simulation scenarios that cover different market situations—from increasing timber prices, volatile market conditions, or decreasing market conditions—and evaluated the harvest patterns, transport distances and the forest itself. The paper utilizes the agent‐based modeling methodology to model the agent's adaptive behavior of the forest fuel supply chain and to model the competition of heating plants for forest fuels. To evaluate this phenomena we mainly analyze transport distances of the simulation runs. For the test area of Carinthia, the experiments show that the behavior of small forest owners influences the supply of forest fuels. Timber prices not meeting the expectations of small forest owners might not motivate them to produce timber and forest fuels. On the long run the overall forest fuel supply does not meet the demand in the test area Carinthia—hence it relies on biomass imports. Furthermore, we witnessed increasing transport distances from harvest site to heating plant. Recommendations for Resource Managers The results of the spatial Agent‐based simulation of the forest fuel market with agents competing for the limited resource forest biomass show that transport distances for forest fuels can vary and may increase over time. Hence, the planning of the forest fuels supply and the respective transport distances is crucial to reduce the carbon footprint of the timber for heating purposes. As small forest owners produce timber on a more irregular basis (based on the price in the market), the motivation of small forest owners is crucial for the steady supply of biomass for heating purposes—for the case of Carinthia. In the long run it is not possible to fulfill the demand of biomass for heating purposes for Carinthia, without imports of timber. Again, crucial is the motivation of small forest owners to produce timber.

While CRISPR‐Cas defence mechanisms have been studied on a population level, their temporal dynamics and variability in individual cells have remained unknown. Using a microfluidic device, time‐lapse microscopy and mathematical modelling, we studied invader clearance in Escherichia coli across multiple generations. We observed that CRISPR interference is fast with a narrow distribution of clearance times. In contrast, for invaders with escaping PAM mutations we found large cell‐to‐cell variability, which originates from primed CRISPR adaptation. Faster growth and cell division and higher levels of Cascade increase the chance of clearance by interference, while slower growth is associated with increased chances of clearance by priming. Our findings suggest that Cascade binding to the mutated invader DNA, rather than spacer integration, is the main source of priming heterogeneity. The highly stochastic nature of primed CRISPR adaptation implies that only subpopulations of bacteria are able to respond quickly to invading threats. We conjecture that CRISPR‐Cas dynamics and heterogeneity at the cellular level are crucial to understanding the strategy of bacteria in their competition with other species and phages. Synopsis Time‐lapse microscopy combined with computational modeling reveals new insights into the single‐cell biology of CRISPR‐Cas defense during invader DNA clearance in E. coli . CRISPR adaptation and interference over multiple generations can be tracked using time‐lapse microscopy. Direct interference is fast and efficient, while priming shows large variations between cells. Slower cell growth leads to earlier spacer acquisition within a population. Mathematical modeling shows reduced Cascade binding affinity for the target drives variation between cells during priming. Graphical Abstract Time‐lapse microscopy combined with computational modeling reveals new insights into the single‐cell biology of CRISPR‐Cas defense during invader DNA clearance in E .  coli .

Purpose: In this paper, the impact of Cooperative Adaptive Cruise Control (CACC) systems on traffic performance is examined using microscopic agent-based simulation. Using a developed traffic simulation model of a freeway with an on-ramp - created to induce perturbations and to trigger stop-and-go traffic, the CACC system's effect on the traffic performance is studied. The previously proposed traffic simulation model is extended and validated. By embedding CACC vehicles in different penetration levels, the results show significance and indicate the potential of CACC systems to improve traffic characteristics and therefore can be used to reduce traffic congestion. The study shows that the impact of CACC is positive but is highly dependent on the CACC market penetration. The flow rate of the traffic using CACC is proportional to the market penetration rate of CACC equipped vehicles and the density of the traffic. Design/methodology/approach: This paper uses microscopic simulation experiments followed by a quantitative statistical analysis. Simulation enables researchers manipulating the system variables to straightforwardly predict the outcome on the overall system, giving researchers the unique opportunity to interfere and make improvements to performance. Thus with simulation, changes to variables that might require excessive time, or be unfeasible to carry on real systems, are often completed within seconds. Findings: The findings of this paper are summarized as follow: * Provide and validate a platform (agent-based microscopic traffic simulator) in which any CACC algorithm (current or future) may be evaluated. * Provide detailed analysis associated with implementation of CACC vehicles on freeways. * Investigate whether embedding CACC vehicles on freeways has a significant positive impact or not.Research limitations/implications: The main limitation of this research is that it has been conducted solely in a computer laboratory. Laboratory experiments and/or simulations provide a controlled setting, well suited for preliminary testing and calibrating of the input variables. However, laboratory testing is by no means sufficient for the entire methodology validation. It must be complemented by fundamental field testing. As far as the simulation model limitations, accidents, weather conditions, and obstacles in the roads were not taken into consideration. Failures in the operation of the sensors and communication of CACC design equipment were also not considered. Additionally, the special HOV lanes were limited to manual vehicles and CACC vehicles. Emergency vehicles, buses, motorcycles, and other type of vehicles were not considered in this dissertation. Finally, it is worthy to note that the human factor is far more sophisticated, hard to predict, and flexible to be exactly modeled in a traffic simulation model perfectly. Some human behavior could occur in real life that the simulation model proposed would fail to model. Practical implications: A high percentage of CACC market penetration is not occurring in the near future. Thus, reaching a high penetration will always be a challenge for this type of research. The public accessibility for such a technology will always be a major practical challenge. With such a small headway safety gap, even if the technology was practically proven to be efficient and safe, having the public to accept it and feel comfortable in using it will always be a challenge facing the success of the CACC technology. Originality/value: The literature on the impact of CACC on traffic dynamics is limited. In addition, no previous work has proposed an open-source microscopic traffic simulator where different CACC algorithms could be easily used and tested. We believe that the proposed model is more realistic than other traffic models, and is one of the very first models to model the behavior CACC vehicles on freeways.

This article addresses the stock market as a complex system. The complexity of the stock market arises from the structure of the environment, agent heterogeneity, interactions among agents, and interactions with market regulators. We develop the idea of a meta‐model, which is a model of models represented in an agent‐based model that allows us to investigate this type of market complexity. The novelty of this article is the incorporation of various complexities captured by network theoretical models or induced by investment behavior. The model considers agents heterogeneous in terms of their strategies and investment behavior. Four investment strategies are included in the model: zero‐intelligence, fundamental strategy, momentum (trend followers), and adaptive trading strategy using the artificial neural network algorithm. In terms of behavior, the agents can be risk averse or loss occupied with overconfidence or conservative biases. The agents may interact with each other by sharing market sentiments through a structured scale‐free network. The market regulator controls the market through various control tools such as the risk‐free rate and taxation. Parameters are calibrated to the S&P500. The calibration is implemented using a scatter search heuristic approach. The model is validated using various stylized facts of stock return patterns such as excess kurtosis, auto‐correlation, and ARCH effect phenomena. Analysis at the macro and micro level of the market was performed by measuring the sensitivity of volatility and market capital and investigating the wealth distributions of the agents. We found that volatility is more sensitive to the model parameters than to market capital, and thus, the level of volatility does not affect market capital. In addition, the findings suggest that the efficient market hypothesis holds at the macro level but not at the micro level. © 2016 Wiley Periodicals, Inc. Complexity 21: 530–554, 2016

Underwater sensors provide one of the possibilities to explore oceans, seas, rivers, fish farms and dams, which all together cover most of our planet’s area. Simulators can be helpful to test and discover some possible strategies before implementing these in real underwater sensors. This speeds up the development of research theories so that these can be implemented later. In this context, the current work presents an agent-based simulator for defining and testing strategies for measuring the amount of fish by means of underwater sensors. The current approach is illustrated with the definition and assessment of two strategies for measuring fish. One of these two corresponds to a simple control mechanism, while the other is an experimental strategy and includes an implicit coordination mechanism. The experimental strategy showed a statistically significant improvement over the control one in the reduction of errors with a large Cohen’s d effect size of 2.55.