Correlation of Royal Jelly composition with swarming tendency in Honey Bees (Apis mellifera): A Review

Honey bees are eusocial insects which respond to warm weather, abundant food source by increasing their population through swarming to ensure the survival of the colony. To maintain a superior colony a queen must have a nutrient-rich diet and high egg production. Royal jelly is a high-quality food which has numerous beneficial properties required for proper growth, development, survival of the queen. Factors like congestion, lack of adequate queen pheromone, abnormal queen pheromone, pathogenic infections, exposure to pesticides influence the queen quality which further promotes non-reproductive swarming behaviour. Worker bees analyse the queen condition to prepare for supersedure or emergency queen rearing. This review paper highlights the influence of royal jelly composition on the queen quality, the impact of queen quality on swarming tendency, correlation between royal jelly composition and swarming tendency.

Improved Dragonfly Algorithm with Neighbourhood Structures

Dragonfly algorithm is a recently proposed optimization algorithm inspired on the static and dynamic swarming behaviour of dragonflies. Because of its simplicity and effectiveness, DA has received interest of specialists from various fields. Premature convergence and local optima is an issue in Dragonfly Algorithm. Improved Dragonfly Algorithm with Neighbourhood Structures (IDANS) is proposed to overcome this drawback. Dragonfly Algorithm with Neighborhood structures utilizes candidate solutions in an iterative and intuitive process to discover promising areas in a search space. IDANS is then initialized with best value of dragonfly algorithm to further explore the search space. In order to improve the efficiency of IDANS, Neighbourhood structures such as Euclidean, Manhattan and Chebyshev are chosen to implement these structures on IDANS to obtain best results. The proposed method avoids local optima to achieve global optimal solutions. The Efficiency of the IDANS is validated by testing on benchmark functions and classical engineering problem called Gear train design problem. A comparative performance analysis between IDANS and other powerful optimization algorithms have been carried out and the results shows that IDANS gives better performance than Dragonfly algorithm. Moreover it gives competitive results in terms of convergence and accuracy when compared with other algorithms in the literature.

Economic Load Dispatch Incorporating Wind Power Using Hybrid Biogeography-Based Optimization

This article represents salp swarm algorithm (SSA) for the most favourable operating solution of economic load dispatch (ELD). For making the convergence first along with SSA, another optimization algorithm (i.e., BBO [biogeography;based optimization]) is also used. For lowering the operational cost, wind power is employed with thermal units. SSA is inspired by swarming behaviour of salp, which belongs to salpiside family. Salp possess a special kind of swarm while hunting for food and navigating. The recommended algorithm is executed on two systems of SIX units and 40 units. In both of the cases, load dispatch problem is carried out with renewable sources and also without renewable sources. Individually, BBO, SSA, and hybrid BBO-SSA are applied to all the test systems to justify effectiveness of hybrid BBO-SSA. Obtained results assure the prospective and advantages of recommended algorithm in contrast to algorithms mentioned in the article. Results come out to be very satisfying and reveal that hybrid BBO-SSA is a powerful algorithm to solve ELD problems.

Single-cell motile behaviour of $$Trypanosoma brucei $$ in thin-layered fluid collectives

We describe a system for the analysis of an important unicellular eukaryotic flagellate in a confining and crowded environment. The parasite Trypanosoma brucei is arguably one of the most versatile microswimmers known. It has unique properties as a single microswimmer and shows remarkable adaptations (not only in motility, but prominently so), to its environment during a complex developmental cycle involving two different hosts. Specific life cycle stages show fascinating collective behaviour, as millions of cells can be forced to move together in extreme confinement. Our goal is to examine such motile behaviour directly in the context of the relevant environments. Therefore, for the first time, we analyse the motility behaviour of trypanosomes directly in a widely used assay, which aims to evaluate the parasites behaviour in collectives, in response to as yet unknown parameters. In a step towards understanding whether, or what type of, swarming behaviour of trypanosomes exists, we customised the assay for quantitative tracking analysis of motile behaviour on the single-cell level. We show that the migration speed of cell groups does not directly depend on single-cell velocity and that the system remains to be simplified further, before hypotheses about collective motility can be advanced. Graphic abstract

Swarm robotics:design and implementation

This project presents a swarming and herding behaviour using simple robots. The main goal is to demonstrate the applicability of artificial intelligence (AI) in simple robotics that can then be scaled to industrial and consumer markets to further the ability of automation. AI can be achieved in many different ways; this paper explores the possible platforms on which to build a simple AI robots from consumer grade microcontrollers. Emphasis on simplicity is the main focus of this paper. Cheap and 8 bit microcontrollers were used as the brain of each robot in a decentralized swarm environment were each robot is autonomous but still a part of the whole. These simple robots don’t communicate directly with each other. They will utilize simple IR sensors to sense each other and simple limit switches to sense other obstacles in their environment. Their main objective is to assemble at certain location after initial start from random locations, and after converging they would move as a single unit without collisions. Using readily available microcontrollers and simple circuit design, semiconsistent swarming behaviour was achieved. These robots don’t follow a set path but will react dynamically to different scenarios, guided by their simple AI algorithm.

A Comprehensive Key for Identification of the “Swarming Conehead” Ruspolia Differens Serville, 1838 (Orthoptera: Tettigoniidae) Occurring in the Afro - Tropical Region

This work reviewed t h e behavioural, morphological and molecular characteristics of the lo n g - h o rned g r a ss h opp e r R u s po l i a d i f f e r e n s S e r v i l le or “ s en en e ” i n S wa h i li name ( O r t h op t e ra T e t t i g o n ii da e ), as apparent in literature. On that basis, the work has generated a comprehensive key for identification of this species. As widely known, this insect native to Afro - tropical region where it is widely edible and it ha s a characteristic s w a rm i n g behaviour that strikingly occurs during rainy season. Also, it has c o l ou r polymorphism with a total of six key sympatric colour forms, sex d i m o r p hi sm with m al e s possessing longer antennae and a u n i q u e pair o f active tongue - like m e t a t h o r a c i c f l a p s whereas the females have a corresponding pair of vestigial metathoracic nodules. Furthermore, t he species has a pair of distinct subequal black markings on the mid and hind tibia near the knee joint and a white inter - ocular oval mark that appears like a s imple eye. Its sister species which is Ruspolia nitidula S c o p o l i as verified by molecular phylogenetics i s e xc l u s i v e l y s ol i t a r y , mostly g r e en i sh an d P a l ea rc t i c r an g i n g i n A s i a , E u r op e a n d N o r t h e r n A f r i c a. Since swarming behaviour is a foremost diagnostic feature differentiating R. differens from other coneheads, it is worthwhile to demarcate the species with a common name “Swarming Conehead” adding to the existed names.

Response of insect swarms to dynamic illumination perturbations

Many animal species across taxa spontaneously form aggregations that exhibit collective behaviour. In the wild, these collective systems are unavoidably influenced by ubiquitous environmental perturbations such as wind gusts, acoustic and visual stimuli, or the presence of predators or other animals. The way these environmental perturbations influence the animals' collective behaviour, however, is poorly understood, in part because conducting controlled quantitative perturbation experiments in natural settings is challenging. To circumvent the need for controlling environmental conditions in the field, we study swarming midges in a laboratory experiment where we have full control over external perturbations. Here, we consider the effect of controlled variable light exposure on the swarming behaviour. We find that not only do individuals in the swarm respond to light changes by speeding up during brighter conditions but also the swarm as a whole responds to these perturbations by compressing and simultaneously increasing the attraction of individual midges to its centre of mass. The swarm-level response can be described by making an analogy to classical thermodynamics, with the state of the swarm moving along an isotherm in a thermodynamic phase plane.