Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Cephalopods Between Science, Art, and Engineering: A Contemporary Synthesis

ABSTRACT Cephalopods are outstanding animals. For centuries, they have provided a rich source of inspiration to many aspects of human cultures, from art, history, media and spiritual beliefs to the most exquisite scientific curiosity. Given their high esthetical value and 'mysteriously' rich behavioral repertoire they have functioned as boundary objects (or subjects) connecting seemingly distinct thematic fields. Interesting aspects of their being span from the rapid camouflaging ability inspiring contemporary art practices, to their soft and fully muscular body that curiously enough inspired both gastronomy and (soft) robotics. The areas influenced by cephalopods include ancient mythology, art, behavioural science, neuroscience, genomics, camouflage technology and bespoken robotics. Although these might seem far related fields, in this manuscript we want to show how the increasing scientific and popular interest in this heterogeneous class of animals have indeed prompted a high level of integration between scientific, artistic and sub-popular culture. We will present an overview of the birth and life of cephalopod investigations from the traditional study of ethology, neuroscience, and biodiversity to the more recent and emerging field of genomics, material industry and soft robotics. Within this framework, we will attempt to capture the current interest and progress in cephalopod scientific research that lately met both the public interest and the 'liberal arts' curiosity

Artificial Camouflage Systems With Centralized and Decentralized Pattern Formation

In nature, many animals display the ability to camouflage themselves in the surroundings by adapting their shapes, colors, and textures. How can we engineer artificial systems that have similar ability? Inspired by those natural camouflage, this thesis focuses on exploring and designing the artificial camouflage systems. This thesis first surveys the components needed to design possible systems for camouflage capability, from visual perception, camouflage patterns, pattern formation, to color-changing cells. It then reviews the mathematical models proposed for pattern formation in the literature, most of them based on the reaction-diffusion model. This thesis also surveys the artificial camouflage materials and systems inspired by camouflage animals. A distributed camouflage system for swarm robotics and smart materials is developed and demonstrated with the `Droplet' swarm robotics platform. The system consists of distributed algorithms of dominant color detection, local pattern decision, global pattern consensus, and pattern formation among the swarm. The distributed camouflage system is tested with both simulation and hardware experiments, and the results show that the system can perform camouflage and is robust and scalable. To continue the development of the artificial camouflage systems, two pattern formation algorithms empowered by deep learning are proposed. The first is a decentralized pattern formation with deep reinforcement learning. The second is a centralized pattern formation with generative adversarial networks. Both of them show that they are able to generate the desired patterns for the systems. To provide an efficient camouflage assessment, a 3D simulation is proposed to apply the camouflage patterns on the to-hide objects, and the simulation is followed by object detectors to report the camouflage scores. The images of camouflaged objects are captured with a set of differently configured cameras and passed through a deep-learning based detector to obtain the detection score. Each camera can be set with the distance and angle relative to the object to evaluate if the camouflage is robust to different perspectives. To sum up, this thesis not only provides centralized and decentralized algorithmic foundations for camouflage materials consisting of large numbers of smart particles, but also presents an efficient pipeline for testing artificial camouflage systems.</p

L\'evy walks

Random walk is a fundamental concept with applications ranging from quantum physics to econometrics. Remarkably, one specific model of random walks appears to be ubiquitous across many fields as a tool to analyze transport phenomena in which the dispersal process is faster than dictated by Brownian diffusion. The L\'{e}vy walk model combines two key features, the ability to generate anomalously fast diffusion and a finite velocity of a random walker. Recent results in optics, Hamiltonian chaos, cold atom dynamics, bio-physics, and behavioral science demonstrate that this particular type of random walks provides significant insight into complex transport phenomena. This review provides a self-consistent introduction to L\'{e}vy walks, surveys their existing applications, including latest advances, and outlines further perspectives.Comment: 50 page

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

Exploring the relationship between spatial cognitive ability and movement ecology

Spatial cognitive ability is hypothesised to be a key determinant of animal movement patterns. However, empirical demonstrations linking intra-individual variations in spatial cognitive ability with movement ecology are rare. I reared ~200 simultaneously hatched pheasant chicks per year over three years in standardised conditions without parents, controlling for the confounding effects of experience, maternal influences and age. I tested the chicks on spatial cognitive tasks from three weeks old to obtain measures of inherent, early-life spatial cognitive ability. Each year, I released birds when 10 weeks old into an open-topped enclosure in woodland. Birds dispersed from this enclosure after about one-month. Importantly, all birds were released into the same, novel area simultaneously, thus their experiences and opportunities were standardised. I remotely tracked pheasant movement through either RFID antenna placed under 43 supplementary feeders situated throughout our field site (2016) or by using a novel reverse-GPS tracking system (2017-2018). Spatial cognitive ability, determined through binary spatial discrimination (2016) or a Barnes maze (2017), was related to the diversity of foraging sites an individual used (Chapter 2: 2016). Those with better spatial cognitive ability used a more diverse range of artificial feeders than poor performing counterparts, perhaps to retain a buffer of alternative foraging sites where resource profitability was known. I found no relationship between the timing of daily foraging onset between birds of differing cognitive ability (Chapter 3; 2016), which I had hypothesised to be a consequence of birds developing efficient routes between refuges and feeders. After establishing a reverse GPS system on our field site (Chapter 4: 2017), I collected more detailed information about pheasant movement and found that birds with higher accuracy scores on the cognition tasks initially moved between foraging and resting sites more slowly than inaccurate birds in novel environments, perhaps to gather more detailed information. Accurate birds increased their speed over one month to match the same speed as inaccurate birds. All birds increased the straightness of their routes at a similar rate. Lastly, I found intraspecific differences in the orientation strategy that birds used to solve a dual strategy maze task (Chapter 5: 2018). These differences predicted habitat use after release: birds that utilised landmarks (allocentric strategies) showed less aversion to urban habitats (farm buildings/yards) than egocentric/mixed strategy birds, which is potentially due to the presence of large, stable landmarks within these habitats. In this thesis, I provide several empirical links between spatial cognitive ability and movement ecology across a range of ecological contexts. I suggest that very specific cognitive processes may govern particular movement behaviours and that there is not one overarching general spatial ability.European Commissio

Assessing the current landscape of AI and sustainability literature:Identifying key trends, addressing gaps and challenges

The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research.</p

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40