DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

© Copyright 2021 The Author(s). At present, Solid-State Fermentation (SSF) is mainly controlled by artificial experience, and the product quality and yield are not stable. Therefore, predicting the quality and yield of SSF is of great significance for improving the utility of SSF. In this works, we propose a Deep Learning Utility Prediction (DLUP) scheme for the SSF in the Industrial Internet of Things (IIoT), including parameter collection and utility prediction of the SSF process. Furthermore, we propose a novel Edge-rewritable Petri net to model the parameter collection and utility prediction of the SSF process and further verify their soundness. More impor- tantly, DLUP combines the generating ability of Least Squares Generative Adversarial Networks (LSGAN) with the predicting ability of Fully Connected Neural Network (FCNN) to realize the utility prediction (usually use the alcohol concentration) of SSF. Experiments show that the proposed method predicts the alcohol concentration more accurately than the other joint prediction methods. In addition, the method in our paper provides evidences for setting the ratio of raw materials and proper temperature through numerical analysis.Tai Shan Industry Leading Talent Project (Grant Number: tscy20180416); Major Science and Technology Innovation Project of Shandong Province (Grant Number: 2019TSLH0214)

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 22nd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 29 papers presented in this volume were carefully reviewed and selected from 85 submissions. They deal with foundational research with a clear significance for software science

28th International Symposium on Temporal Representation and Reasoning (TIME 2021)

The 28th International Symposium on Temporal Representation and Reasoning (TIME 2021) was planned to take place in Klagenfurt, Austria, but had to move to an online conference due to the insecurities and restrictions caused by the pandemic. Since its frst edition in 1994, TIME Symposium is quite unique in the panorama of the scientifc conferences as its main goal is to bring together researchers from distinct research areas involving the management and representation of temporal data as well as the reasoning about temporal aspects of information. Moreover, TIME Symposium aims to bridge theoretical and applied research, as well as to serve as an interdisciplinary forum for exchange among researchers from the areas of artifcial intelligence, database management, logic and verifcation, and beyond

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tool

Principles of Markov automata

A substantial amount of today's engineering problems revolve around systems that are concurrent and stochastic by their nature. Solution approaches attacking these problems often rely on the availability of formal mathematical models that reflect such systems as comprehensively as possible. In this thesis, we develop a compositional model, Markov automata, that integrates concurrency, and probabilistic and timed stochastic behaviour. This is achieved by blending two well-studied constituent models, probabilistic automata and interactive Markov chains. A range of strong and weak bisimilarity notions are introduced and evaluated as candidate relations for a natural behavioural equivalence between systems. Among them, weak distribution bisimilarity stands out as a natural notion being more oblivious to the probabilistic branching structure than prior notions. We discuss compositionality, axiomatizations, decision and minimization algorithms, state-based characterizations and normal forms for weak distribution bisimilarity. In addition, we detail how Markov automata and weak distribution bisimilarity can be employed as a semantic basis for generalized stochastic Petri nets, in such a way that known shortcomings of their classical semantics are ironed out in their entirety.Ein beträchtlicher Teil gegenwärtiger ingenieurwissenschafter Probleme erstreckt sich auf Sys- teme, die ihrer Natur nach sowohl stochastisch als auch nebenläufig sind. Lösungsansätze fußen hierbei häufig auf der Verfügbarkeit formaler mathematischer Modelle, die es erlauben, die Spez- ifika jener Systeme möglichst erschöpfend zu erfassen. In dieser Dissertation entwickeln wir ein kompositionelles Modell namens Markov-Automaten, das Nebenläufigkeit mit probabilistis- chen und stochastischen Prozessen integriert. Dies wird durch die Verschmelzung der zweier bekannter Modellklassen erreicht, und zwar die der probabilistischen Automaten und die der interaktiven Markovketten. Wir entwickeln dabei ein Spektrum verschiedener, starker und schwacher Bisimulationsrelationen und beurteilen sie im Hinblick auf ihre Eignung als natür- liche Verhaltensäquivalenz zwischen Systemen. Die schwache Wahrscheinlichkeitsverteilungs- bisimulation sticht dabei als natürliche Wahl hervor, da sie die probabilistische Verzwei- gungsstruktur treffender abstrahiert als bisher bekannte Bisimulationsrelationen. Wir betra- chten des Weiteren Kompositionalitätseigenschaften, Axiomatisierungen, Entscheidungs- und Minimierungsalgorithmen, sowie zustandsbasierte Charakterisierungen und Normalformen für die schwache Wahrscheinlichkeitsverteilungsbisimulation. Abschließend legen wir dar, dass Markov-Automaten und die schwacheWahrscheinlichkeitsverteilungsbisimulation als Grundlage für eine verbesserte Semantik von verallgemeinerten stochastischen Petrinetzen dienen kann, welche bekannte Mängel der klassischen Semantik vollständig behebt

Twenty years of rewriting logic

AbstractRewriting logic is a simple computational logic that can naturally express both concurrent computation and logical deduction with great generality. This paper provides a gentle, intuitive introduction to its main ideas, as well as a survey of the work that many researchers have carried out over the last twenty years in advancing: (i) its foundations; (ii) its semantic framework and logical framework uses; (iii) its language implementations and its formal tools; and (iv) its many applications to automated deduction, software and hardware specification and verification, security, real-time and cyber-physical systems, probabilistic systems, bioinformatics and chemical systems