A DISTRIBUTED APPROACH TO ANT COLONY OPTIMIZATION

Swarm Intelligence(SI) is the emergent collective intelligence of groups of simple agents. Economy is an example of SI. Simulating an economy using Ant Colony algorithms would allow prediction and control of fluctuations in the complex emergent behavior of the simulated system. Such a simulation is far beyond SI's capabilities, which is still in its infancy. This paper presents a distributed approach implementing Ant Colony Optimization(ACO). We present our agent based architecture of ACO and initial experimental results on the Travelling Salesman Problem. The innovation of our work consists of: i)representing network nodes as software agents, ii) representing software agents as software objects that are passed as messages between the nodes according to ACO rules.Swarm Intelligence, Ant Colony Optimization, Multi-Agent, Distributed, Heuristis

A numerical study of the correspondence between paths in a causal set and geodesics in the continuum

This paper presents the results of a computational study related to the path-geodesic correspondence in causal sets. For intervals in flat spacetimes, and in selected curved spacetimes, we present evidence that the longest maximal chains (the longest paths) in the corresponding causal set intervals statistically approach the geodesic for that interval in the appropriate continuum limit.Comment: To the celebration of the 60th birthday of Rafael D. Sorki

Introduction to the thematic issue on Intelligent systems, applications and environments for the industry of the future

Recent advances in the area of ubiquitous computing, ambient intelligence and intelligent environments are making inroads in business-oriented application domains. This issue of JAISE addresses core topics on the design, use and evaluation of smart applications and systems for the factory of the future, an emerging trend perhaps better known as Industry 4.0. The digital transformation in the enterprise envisioned by Industry 4.0 will entwine the cyber-physical world and real world of manufacturing to deliver networked production with enhanced process transparency. Production systems, data analytics and cloud-enabled business processes will interact directly with customers to realize the ambitious goal of single lot individualized manufacturing. This thematic issue features a survey and 5 research articles which address the modeling, designing, implementation, assessment and management of intelligent systems, applications and environments that will shape and advance the smart industry of the future.status: publishe

Unlocking novel therapies:cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties

Efficiency and Security of Process Transparency in Production Networks - A View of Expectations, Obstacles and Potentials

Much of the resilience and flexibility of production networks lies in the transparency of processes that allows timely perception of actual process states and adequate decisions or intervention at the proper point of the production system. Such degree of observability and permeability do, however, bear risks of malevolent tapping or interference with the information stream which, in the case of production systems, can put both business and physical processes at risk, requiring careful exploration of security threats in horizontal and vertical integration, and individual end-to-end connections likewise. Also, different levels of networked production present specific needs—high throughput and low time lag on the shop-floor level, or tolerances for confidence, gambling and bounded-rational views in cross-company relations—that may conflict with security policies. The paper presents a systematic summary of such apparently contradicting preferences, and possible approaches of reconciliation currently perceived to be relevant on various abstraction levels of production networks.status: publishe

Removal of turbidity and organic load from surface water by coagulation-flotation

The aim of this study is to monitor comparatively and operationally two drinking water treatment flows, a new based on flotation unit using prehydrolyzed aluminium coagulation agent in comparison with settling based on the conventional technological flow using aluminium sulphate coagulation agent. The new proposed and introduced flow exhibited greater drinking water treatment performance under the conditions of raw surface water characterized by low and medium loading in terms of turbidity and organic load. The new proposed technological flow allowed that all studied parameters characteristics to the drinking water for treated water to meet the requirements imposed by the legislative norms

Force and energy dissipation variations in non-contact atomic force spectroscopy on composite carbon nanotube systems

UHV dynamic force and energy dissipation spectroscopy in non-contact atomic force microscopy were used to probe specific interactions with composite systems formed by encapsulating inorganic compounds inside single-walled carbon nanotubes. It is found that forces due to nano-scale van der Waals interaction can be made to decrease by combining an Ag core and a carbon nanotube shell in the Ag@SWNT system. This specific behaviour was attributed to a significantly different effective dielectric function compared to the individual constituents, evaluated using a simple core-shell optical model. Energy dissipation measurements showed that by filling dissipation increases, explained here by softening of C-C bonds resulting in a more deformable nanotube cage. Thus, filled and unfilled nanotubes can be discriminated based on force and dissipation measurements. These findings have two different implications for potential applications: tuning the effective optical properties and tuning the interaction force for molecular absorption by appropriately choosing the filling with respect to the nanotube.Comment: 22 pages, 6 figure