Insensitivity and product-form decomposability of reallocatable GSMP

1993 ◽  
Vol 25 (2) ◽  
pp. 415-437 ◽  
Author(s):  
Masakiyo Miyazawa
Keyword(s):  
Stochastic Process ◽  
Stationary Distribution ◽  
Jump Process ◽  
Product Form ◽  
Balance Equations ◽  
Independent Components ◽  
Extended Sense ◽  
Wide Range ◽  
Standard Models

A stochastic process, called reallocatable GSMP (RGSMP for short), is introduced in order to study insensitivity of its stationary distribution. RGSMP extends GSMP with interruptions, and is applicable to a wide range of queues, from the standard models such as BCMP and Kelly's network queues to new ones such as their modifications with interruptions and Serfozo's (1989) non-product form network queues, and can be used to study their insensitivity in a unified way. We prove that RGSMP supplemented by the remaining lifetimes is product-form decomposable, i.e. its stationary distribution splits into independent components if and only if a version of the local balance equations hold, which implies insensitivity of the RGSMP scheme in a certain extended sense. Various examples of insensitive queues are given, which include new results. Our proofs are based on the characterization of a stationary distribution for SCJP (self-clocking jump process) of Miyazawa (1991).

Insensitivity and product-form decomposability of reallocatable GSMP

1993 ◽  
Vol 25 (02) ◽  
pp. 415-437 ◽  
Author(s):  
Masakiyo Miyazawa
Keyword(s):  
Stochastic Process ◽  
Stationary Distribution ◽  
Jump Process ◽  
Product Form ◽  
Balance Equations ◽  
Independent Components ◽  
Extended Sense ◽  
Wide Range ◽  
Standard Models

A stochastic process, called reallocatable GSMP (RGSMP for short), is introduced in order to study insensitivity of its stationary distribution. RGSMP extends GSMP with interruptions, and is applicable to a wide range of queues, from the standard models such as BCMP and Kelly's network queues to new ones such as their modifications with interruptions and Serfozo's (1989) non-product form network queues, and can be used to study their insensitivity in a unified way. We prove that RGSMP supplemented by the remaining lifetimes is product-form decomposable, i.e. its stationary distribution splits into independent components if and only if a version of the local balance equations hold, which implies insensitivity of the RGSMP scheme in a certain extended sense. Various examples of insensitive queues are given, which include new results. Our proofs are based on the characterization of a stationary distribution for SCJP (self-clocking jump process) of Miyazawa (1991).

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Polymer Chemistry

2004 ◽  
Keyword(s):  
Conducting Polymers ◽  
Liquid Crystalline ◽  
Polymer Chemistry ◽  
Chain Growth ◽  
Crystalline Polymers ◽  
Wide Range ◽  
Cyclic Oligomers ◽  
Step Growth

Polymer Chemistry: A Practical Approach in Chemistry has been designed for both chemists working in and new to the area of polymer synthesis. It contains detailed instructions for preparation of a wide-range of polymers by a wide variety of different techniques, and describes how this synthetic methodology can be applied to the development of new materials. It includes details of well-established techniques, e.g. chain-growth or step-growth processes together with more up-to-date examples using methods such as atom-transfer radical polymerization. Less well-known procedures are also included, e.g. electrochemical synthesis of conducting polymers and the preparation of liquid crystalline elastomers with highly ordered structures. Other topics covered include general polymerization methodology, controlled/"living" polymerization methods, the formation of cyclic oligomers during step-growth polymerization, the synthesis of conducting polymers based on heterocyclic compounds, dendrimers, the preparation of imprinted polymers and liquid crystalline polymers. The main bulk of the text is preceded by an introductory chapter detailing some of the techniques available to the scientist for the characterization of polymers, both in terms of their chemical composition and in terms of their properties as materials. The book is intended not only for the specialist in polymer chemistry, but also for the organic chemist with little experience who requires a practical introduction to the field.

scholarly journals Organelle Genetics in Plants

2021 ◽  
Vol 22 (4) ◽  
pp. 2104
Author(s):  
Pedro Robles ◽  
Víctor Quesada
Keyword(s):  
Rna Editing ◽  
Posttranscriptional Regulation ◽  
Genome Engineering ◽  
Plant Mitochondria ◽  
Plastid Dna ◽  
Chloroplast Genomes ◽  
Wide Range ◽  
Organelle Genetics ◽  
Selection Of

Eleven published articles (4 reviews, 7 research papers) are collected in the Special Issue entitled “Organelle Genetics in Plants.” This selection of papers covers a wide range of topics related to chloroplasts and plant mitochondria research: (i) organellar gene expression (OGE) and, more specifically, chloroplast RNA editing in soybean, mitochondria RNA editing, and intron splicing in soybean during nodulation, as well as the study of the roles of transcriptional and posttranscriptional regulation of OGE in plant adaptation to environmental stress; (ii) analysis of the nuclear integrants of mitochondrial DNA (NUMTs) or plastid DNA (NUPTs); (iii) sequencing and characterization of mitochondrial and chloroplast genomes; (iv) recent advances in plastid genome engineering. Here we summarize the main findings of these works, which represent the latest research on the genetics, genomics, and biotechnology of chloroplasts and mitochondria.

Characterization of the conditional stationary distribution in Markov chains via systems of linear inequalities

2020 ◽  
Vol 52 (4) ◽  
pp. 1249-1283
Author(s):  
Masatoshi Kimura ◽  
Tetsuya Takine
Keyword(s):  
Markov Chains ◽  
Stationary Distribution ◽  
Continuous Time ◽  
State Transition ◽  
Convex Polytope ◽  
Linear Inequalities ◽  
Systems Of Linear Inequalities ◽  
Free Sets ◽  
Practical Implications

AbstractThis paper considers ergodic, continuous-time Markov chains $\{X(t)\}_{t \in (\!-\infty,\infty)}$ on $\mathbb{Z}^+=\{0,1,\ldots\}$ . For an arbitrarily fixed $N \in \mathbb{Z}^+$ , we study the conditional stationary distribution $\boldsymbol{\pi}(N)$ given the Markov chain being in $\{0,1,\ldots,N\}$ . We first characterize $\boldsymbol{\pi}(N)$ via systems of linear inequalities and identify simplices that contain $\boldsymbol{\pi}(N)$ , by examining the $(N+1) \times (N+1)$ northwest corner block of the infinitesimal generator $\textbf{\textit{Q}}$ and the subset of the first $N+1$ states whose members are directly reachable from at least one state in $\{N+1,N+2,\ldots\}$ . These results are closely related to the augmented truncation approximation (ATA), and we provide some practical implications for the ATA. Next we consider an extension of the above results, using the $(K+1) \times (K+1)$ ( $K > N$ ) northwest corner block of $\textbf{\textit{Q}}$ and the subset of the first $K+1$ states whose members are directly reachable from at least one state in $\{K+1,K+2,\ldots\}$ . Furthermore, we introduce new state transition structures called (K, N)-skip-free sets, using which we obtain the minimum convex polytope that contains $\boldsymbol{\pi}(N)$ .

scholarly journals Characterization of a Novel CaCO3-Forming Alkali-Tolerant Rhodococcus erythreus S26 as a Filling Agent for Repairing Concrete Cracks

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2967
Author(s):  
Seunghoon Choi ◽  
Sungjin Park ◽  
Minjoo Park ◽  
Yerin Kim ◽  
Kwang Min Lee ◽  
...  
Keyword(s):  
Building Materials ◽  
Culture Fluid ◽  
Natural Phenomenon ◽  
Environment Friendly ◽  
Wide Range ◽  
Filling Agent ◽  
Calcium Source ◽  
Alkali Tolerance ◽  
High Alkali

Biomineralization, a well-known natural phenomenon associated with various microbial species, is being studied to protect and strengthen building materials such as concrete. We characterized Rhodococcus erythreus S26, a novel urease-producing bacterium exhibiting CaCO3-forming activity, and investigated its ability in repairing concrete cracks for the development of environment-friendly sealants. Strain S26 grown in solid medium formed spherical and polygonal CaCO3 crystals. The S26 cells grown in a urea-containing liquid medium caused culture fluid alkalinization and increased CaCO3 levels, indicating that ureolysis was responsible for CaCO3 formation. Urease activity and CaCO3 formation increased with incubation time, reaching a maximum of 2054 U/min/mL and 3.83 g/L, respectively, at day four. The maximum CaCO3 formation was achieved when calcium lactate was used as the calcium source, followed by calcium gluconate. Although cell growth was observed after the induction period at pH 10.5, strain S26 could grow at a wide range of pH 4–10.5, showing its high alkali tolerance. FESEM showed rhombohedral crystals of 20–60 µm in size. EDX analysis indicated the presence of calcium, carbon, and oxygen in the crystals. XRD confirmed these crystals as CaCO3 containing calcite and vaterite. Furthermore, R. erythreus S26 successfully repaired the artificially induced large cracks of 0.4–0.6 mm width.

scholarly journals A product form for the general stochastic matching model

2021 ◽  
Vol 58 (2) ◽  
pp. 449-468
Author(s):  
Pascal Moyal ◽  
Ana Bušić ◽  
Jean Mairesse
Keyword(s):  
Stationary Distribution ◽  
Product Form ◽  
Necessary Condition ◽  
Matching Model ◽  
Bipartite Matching ◽  
Compatibility Graph ◽  
Stochastic Matching ◽  
Dynamic Reversibility ◽  
Original Dynamic ◽  
Do So

AbstractWe consider a stochastic matching model with a general compatibility graph, as introduced by Mairesse and Moyal (2016). We show that the natural necessary condition of stability of the system is also sufficient for the natural ‘first-come, first-matched’ matching policy. To do so, we derive the stationary distribution under a remarkable product form, by using an original dynamic reversibility property related to that of Adan, Bušić, Mairesse, and Weiss (2018) for the bipartite matching model.

scholarly journals Isolation and Characterization of Strain Exiguobacterium sp. KRL4, a Producer of Bioactive Secondary Metabolites from a Tibetan Glacier

2021 ◽  
Vol 9 (5) ◽  
pp. 890
Author(s):  
Pietro Tedesco ◽  
Fortunato Palma Esposito ◽  
Antonio Masino ◽  
Giovanni Andrea Vitale ◽  
Emiliana Tortorella ◽  
...  
Keyword(s):  
Phenotypic Analysis ◽  
Biological Assays ◽  
C Elegans ◽  
Isolation And Characterization ◽  
Wide Range ◽  
Protein Encoding ◽  
Nematocidal Activity ◽  
Extremophilic Microorganisms ◽  
Unique Source

Extremophilic microorganisms represent a unique source of novel natural products. Among them, cold adapted bacteria and particularly alpine microorganisms are still underexplored. Here, we describe the isolation and characterization of a novel Gram-positive, aerobic rod-shaped alpine bacterium (KRL4), isolated from sediments from the Karuola glacier in Tibet, China. Complete phenotypic analysis was performed revealing the great adaptability of the strain to a wide range of temperatures (5–40 °C), pHs (5.5–8.5), and salinities (0–15% w/v NaCl). Genome sequencing identified KRL4 as a member of the placeholder genus Exiguobacterium_A and annotation revealed that only half of the protein-encoding genes (1522 of 3079) could be assigned a putative function. An analysis of the secondary metabolite clusters revealed the presence of two uncharacterized phytoene synthase containing pathways and a novel siderophore pathway. Biological assays confirmed that the strain produces molecules with antioxidant and siderophore activities. Furthermore, intracellular extracts showed nematocidal activity towards C. elegans, suggesting that strain KRL4 is a source of anthelmintic compounds.

scholarly journals Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 255
Author(s):  
Marie Tahon ◽  
Silvio Montresor ◽  
Pascal Picart
Keyword(s):  
Phase Error ◽  
Speckle Noise ◽  
Experimental Conditions ◽  
Benchmark Data ◽  
Wide Range ◽  
Phase Images ◽  
Different Depths ◽  
Phase Data ◽  
Do So

Digital holography is a very efficient technique for 3D imaging and the characterization of changes at the surfaces of objects. However, during the process of holographic interferometry, the reconstructed phase images suffer from speckle noise. In this paper, de-noising is addressed with phase images corrupted with speckle noise. To do so, DnCNN residual networks with different depths were built and trained with various holographic noisy phase data. The possibility of using a network pre-trained on natural images with Gaussian noise is also investigated. All models are evaluated in terms of phase error with HOLODEEP benchmark data and with three unseen images corresponding to different experimental conditions. The best results are obtained using a network with only four convolutional blocks and trained with a wide range of noisy phase patterns.

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