Hybrid Bare Bones Fireworks Algorithm for Load Flow Analysis of Islanded Microgrids

In this chapter, a hybrid bare bones fireworks algorithm (HBBFWA) is proposed and its application in solving the load flow problem of islanded microgrid is demonstrated. The hybridization is carried out by updating the positions of generated sparks with the help of grasshopper optimization algorithm (GOA) mimicking the swarming behavior of grasshoppers. The purpose of incorporating GOA with bare bones fireworks algorithm (BBFWA) is to enhance the global searching capability of conventional BBFWA for complex optimization problems. The proposed HBBFWA is applied to perform the load flow analysis of a modified IEEE 37-Bus system. The performance of the proposed HBBFWA is compared against the performance of BBFWA in terms of computational time, convergence speed, and number of iterations required for convergence of the load flow problem. Moreover, standard statistical analysis test such as the independent sample t-test is conducted to identify statistically significant differences between the two algorithms.

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.

Complete Genome Sequence of the Nonmotile Myxococcus xanthus Strain NM

Myxobacteria exhibit multicellular swarming behavior, which depends on the coordination of cell motility. Unlike other myxobacteria, Myxococcus xanthus NM is not capable of forming swarms due to a defective motility system. Here, we present the 9.35-Mbp genome sequence of this nonmotile myxobacterium.

Swarming Behavior in Anopheles gambiae (sensu lato): Current Knowledge and Future Outlook

Effective management of insect disease vectors requires a detailed understanding of their ecology and behavior. In Anopheles gambiae sensu lato (s.l.) (Diptera: Culicidae) mating occurs during swarming, but knowledge of their mating behavior under natural conditions is limited. Mosquitoes mate in flight over specific landmarks, known as swarm markers, at particular locations. Swarms consist of males; the females usually approach the swarm and depart following copulation. The number of mating pairs per swarm is closely associated with swarm size. The shape and height of swarm markers vary and may depend on the environmental conditions at the swarm’s location. Male–male interactions in mosquito swarms with similar levels of attractive flight activity can offer a mating advantage to some individuals. Flight tone is used by mosquitoes to recognize the other sex and choose a desirable mate. Clarifying these and other aspects of mosquito reproductive behavior can facilitate the development of population control measures that target swarming sites. This review describes what is currently known about swarming behavior in Anopheles gambiae s.l., including swarm characteristics; mating within and outside of swarms, insemination in females, and factors affecting and stimulating swarming.

First record of a mass swarming behavior of Chauliognathus vestitus Champion, 1914 (Coleoptera: Cantharidae) with comments on other Chauliognathus from Colima, México

In this work we document a case of mass swarming of the Mexican soldier beetle Chauliognathus vestitus Champion, 1914 from El Terrero locality, within the Sierra de Manantlan Biosphere Reserve, in Colima, Mexico. Two other species of soldier beetles Chauliognathus togatus Waterhouse, 1878 and Chauliognathus sp. were observed at El Terrero, coexisting with C. vestitus, but their numbers were considerably lower. An updated list of Chauliognathus species from the state of Colima is included. Our observations are the record, to our knowledge, of the second cantharid species exhibiting a mass flying behavior among more than 450 described Chauliognathus in the world, and the first record of this behavior among soldier beetles in the American continent.

Neutrophils self-limit swarming to contain bacterial growth in vivo

Neutrophils communicate with each other to form swarms in infected organs. Coordination of this population response is critical for the elimination of bacteria and fungi. Using transgenic mice, we found that neutrophils have evolved an intrinsic mechanism to self-limit swarming and avoid uncontrolled aggregation during inflammation. G protein–coupled receptor (GPCR) desensitization acts as a negative feedback control to stop migration of neutrophils when they sense high concentrations of self-secreted attractants that initially amplify swarming. Interference with this process allows neutrophils to scan larger tissue areas for microbes. Unexpectedly, this does not benefit bacterial clearance as containment of proliferating bacteria by neutrophil clusters becomes impeded. Our data reveal how autosignaling stops self-organized swarming behavior and how the finely tuned balance of neutrophil chemotaxis and arrest counteracts bacterial escape.

Strong attraction of male mosquitoes (Aedes aegypti) to humans

Female Aedes aegypti mosquitoes integrate multiple sensory cues to locate human hosts for blood meals. While male mosquitoes do not blood feed, male Ae. aegypti swarm around and land on humans in nature. Basrur et al. (2020) generated male Aedes aegypti lacking the fruitless gene and discovered that they gained strong attraction to humans, similar to female mosquitoes. The authors assume that host-seeking is a female-specific trait, which they confirmed through experiments. However, all experiments were performed under confined laboratory conditions which appear to inhibit swarming behavior. We used semi-field experiments to demonstrate robust attraction of male Ae. aegypti to humans. Human-baited traps captured up to 25% of released males within 15 min, whereas control traps without humans as bait failed to capture males. Rapid attraction to humans was further demonstrated through videography. Males swarmed around and landed on human subjects, with no activity recorded in paired unbaited controls. The absence of female Ae. aegypti in these experiments rules out a hypothesis by Basrur et al. (2020) that males are attracted not to the human, but to host-seeking females near humans. Finally, we confirm the lack of male attraction to humans in small laboratory cages, even when using recently field-collected males. Our direct observations of male mosquito attraction to humans refute a key assumption of Basrur et al. (2020) and raise questions around conditions under which fruitless prevents male host-seeking. Male mosquito attraction to humans is likely to be important for mating success in wild populations and its basis should be further explored.

Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder

Enzyme-powered nanomotors are an exciting technology for biomedical applications due to their ability to navigate within biological environments using endogenous fuels. However, limited studies into their collective behavior and demonstrations of tracking enzyme nanomotors in vivo have hindered progress toward their clinical translation. Here, we report the swarming behavior of urease-powered nanomotors and its tracking using positron emission tomography (PET), both in vitro and in vivo. For that, mesoporous silica nanoparticles containing urease enzymes and gold nanoparticles were used as nanomotors. To image them, nanomotors were radiolabeled with either 124I on gold nanoparticles or 18F-labeled prosthetic group to urease. In vitro experiments showed enhanced fluid mixing and collective migration of nanomotors, demonstrating higher capability to swim across complex paths inside microfabricated phantoms, compared with inactive nanomotors. In vivo intravenous administration in mice confirmed their biocompatibility at the administered dose and the suitability of PET to quantitatively track nanomotors in vivo. Furthermore, nanomotors were administered directly into the bladder of mice by intravesical injection. When injected with the fuel, urea, a homogeneous distribution was observed even after the entrance of fresh urine. By contrast, control experiments using nonmotile nanomotors (i.e., without fuel or without urease) resulted in sustained phase separation, indicating that the nanomotors’ self-propulsion promotes convection and mixing in living reservoirs. Active collective dynamics, together with the medical imaging tracking, constitute a key milestone and a step forward in the field of biomedical nanorobotics, paving the way toward their use in theranostic applications.