Analysis of the Distributed Immune Inspection Equipment Sensor Scheduling Model Based on Adaptive Dynamic Probabilistic Particle Swarm Optimization

Based on the analysis of bacterial parasitic behavior and biological immune mechanism, this paper puts forward the basic idea and implementation method of an embedding adaptive dynamic probabilistic parasitic immune mechanism into a particle swarm optimization algorithm and constructs particle swarm optimization based on an adaptive dynamic probabilistic parasitic immune mechanism algorithm. The specific idea is to use the elite learning mechanism for the parasitic group with a strong parasitic ability to improve the ability of the algorithm to jump out of the local extreme value, and the host will generate acquired immunity against the parasitic behavior of the parasitic group to enhance the diversity of the host population’s particles. Parasitic behavior occurs when the number of times reaches a predetermined algebra. In this paper, an example simulation is carried out for the prescheduling and dynamic scheduling of immune inspection. The effectiveness of prescheduling for immune inspection is verified, and the rules constructed by the adaptive dynamic probability particle swarm algorithm and seven commonly used scheduling rules are tested on two common dynamic events of emergency task insertion and subdistributed immune inspection equipment failure. In contrast, the experimental data was analyzed. From the analysis of experimental results, under the indicator of minimum completion time, the overall performance of the adaptive dynamic probability particle swarm optimization algorithm in 20 emergency task insertion instances and 20 subdistributed immune inspection equipment failure instances is better than that of seven scheduling rules. Therefore, in the two dynamic events of emergency task insertion and subdistributed immune inspection equipment failure, the adaptive dynamic probabilistic particle swarm algorithm proposed in this paper can construct effective scheduling rules for the rescheduling of the system when dynamic events occur and the constructed scheduling. The performance of the rules is better than that of the commonly used scheduling rules. Among the commonly used scheduling rules, the performance of the FIFO scheduling rules is also better. In general, the immune inspection scheduling multiagent system in this paper can complete the prescheduling of immune inspection and process dynamic events of the inspection process and realize the prereactive scheduling of the immune inspection process.

Self-adaptive architectures in IoT systems: a systematic literature review

Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in bandwidth consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with simultaneous events of different types.In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.

Image-based high-throughput mapping of TGF-β-induced phosphocomplexes at a single-cell level

Protein interactions and posttranslational modifications orchestrate cellular responses to e.g. cytokines and drugs, but it has been difficult to monitor these dynamic events in high-throughput. Here, we describe a semi-automated system for large-scale in situ proximity ligation assays (isPLA), combining isPLA in microtiter wells with automated microscopy and computer-based image analysis. Phosphorylations and interactions are digitally recorded along with subcellular morphological features. We investigated TGF-β-responsive Smad2 linker phosphorylations and complex formations over time and across millions of individual cells, and we relate these events to cell cycle progression and local cell crowding via measurements of DNA content and nuclear size of individual cells, and of their relative positions. We illustrate the suitability of this protocol to screen for drug effects using phosphatase inhibitors. Our approach expands the scope for image-based single cell analyses by combining observations of protein interactions and modifications with morphological details of individual cells at high throughput.

Dynamic PRA Prospects for the Nuclear Industry

This review paper highlights approaches and tools available to the nuclear industry for dynamic probabilistic risk assessment (DPRA) using dynamic event trees. DPRA is an emerging methodology that has advantages as compared to traditional, static PRA predominantly owing to the addition of time dependent modeling. Traditional PRAs predefine events and outcomes into Event Trees (ET) and Fault Trees (FT), that are coupled with various combinations of Initiating Events (IE), Top Events (TE), branches, end states and sequences. A more complete depiction of the system and accident progression behavior can be quantified using DPRA to account for dynamic events such as those involving human actions. This paper discusses the strengths and needs of existing DPRA tools to align with the risk informed methodology currently used in the nuclear industry. DPRA is evolving during an exciting time in the nuclear industry with emerging advanced reactor designs also coming on the scene. Advanced nuclear (Gen IV) designs often incorporate passively safe systems that have less readily available data for traditional PRA due to their limited operating history. DPRA is a promising methodology that can address this challenge and demonstrate to the regulatory bodies and public that advanced designs operate within safety margins. In this light, the paper considers the historical role of PRA in the nuclear industry and motivation for considering dynamic PRA models. An introduction to the differences inherent in DPRA and how it complements and enhances existing PRA approaches is discussed. Additionally, a review of research from U.S national laboratories and universities features recent DPRA tool advancements that could be applied in the nuclear industry. These DPRA approaches and tools are summarized and examined to thoughtfully provide a path forward to best leverage existing research and integrate DPRA into advanced reactor design and analysis.

Holistic Approach in Integrating Passive Acoustic Logs with Static and Dynamic Data Improved Wells Integrity Understanding at Field Scale

Proactive well diagnostic surveillance helps with safe delivery of production by effective well management and risk mitigation. The objective of the paper is to demonstrate the data analytics approach utilizing passive acoustic technology in combination with conventional methods of detecting low magnitude dynamic events behind single or multiple casing strings. The results of integrated interpretation of passive acoustic wireline technology with the data from different sources helped to make optimal decision. Traditional well integrity diagnostic includes temperature and passive acoustic data analysis that are associated with high uncertainty. A newer generation of array passive acoustic technology with enhanced sensitivity capabilities was deployed offshore Azerbaijan. A combination of array passive acoustics data, single point temperature and distributed fiber optic data have been acquired during a multi-well campaign. Extensive review of well integrity history, downhole and surface gauge data incorporated with passive acoustic data from arrays of spectral sensors in time and depth domain helped to refine the process and evolve into a unique interpretation methodology. The comprehensive interpretation accounted for integration of all available static and dynamic data such as: fluids and formation pressure distribution along the borehole, cement bond logs evaluation, annuli pressure and temperature, production and downhole gauge measurements, fibre optic data, temperature and passive acoustic logs. This helped to understand the low scale dynamic events behind the casing and make an informed decision on safe and reliable well operations. The sensitivity of array passive acoustic technology proved successful in detecting subtle acoustic events where conventional methods failed or had limited success. Successful results have been achieved by customizing the logging program using a multiple well evolutionary approach that improved data quality and saved rig time. Interpretation and decisions derived from each well involved multi-disciplinary well review panel sessions with specialists from subsurface & geohazards, drilling & completions, production & operations departments. Case studies presented in this paper describe the interpretation approach of highly sensitive array passive acoustic sensors in combination with available static and dynamic point and distributed data. The logging program and interpretation approach used in this article could be considered as a basis for future applications in wells with similar design.

Advances in live imaging early mouse development: exploring the researcher's interdisciplinary toolkit

ABSTRACT Live imaging is an important part of the developmental biologist's armoury of methods. In the case of the mouse embryo, recent advances in several disciplines including embryo culture, microscopy hardware and computational analysis have all contributed to our ability to probe dynamic events during early development. Together, these advances have provided us with a versatile and powerful ‘toolkit’, enabling us not only to image events during mouse embryogenesis, but also to intervene with them. In this short Spotlight article, we summarise advances and challenges in using live imaging specifically for understanding early mouse embryogenesis.

CardIAP: Calcium images analyzer web application

MotivationIonic calcium (Ca2+) plays the role of the second messenger in eukaryotic cells associated with cellular functions of regulation of the cell cycle, such as transport, motility, gene expression, and metabolism (Permyakov and Kretsinger, 2009). The use of fluorometric techniques in isolated cells, loaded with Ca2+ sensitive fluorescent probes allows the quantitative measurement of dynamic events that occur in living, functioning cells. The Cardiomyocytes Images Analyzer Application (CardIAP) covers the need for tools to analyze and retrieve information from confocal microscopy images, in a systematic, accurate, and fast way.ResultsHere we present the CardIAP web app, an automated method for the identification of spatio-temporal patterns in a calcium fluorescence imaging sequence. Through this tool, users can analyze single or multiple Ca2+ transients from confocal line-scan images and obtain quantitative information on the dynamic response of the stimulated myocyte.Our web application also allows the user the extraction of data on calcium dynamics in downloadable tables and plots, simplifying the calculation of the alternation and discordance indices and their classification. CardIAP could assist in studying the underlying mechanisms of anomalous calcium release phenomena.Availability and implementationCardIAP is an open-source app, entirely developed in Python, which can be freely accessed and used at http://cardiap.herokuapp.com/.

The effects of aircraft use and available repair spares on aircraft sortie generation: a long-duration logistical wargaming simulation tool

Long-duration logistical wargames within the Air domain are complex and highly dynamic events that are driven by aircraft availability. In order to gain insight into the impact of aircraft use, this research developed a simulation tool that uses a stepwise approach for adjudication and provides the user many capabilities including, but not limited to, the ability to have multiple bases and types of aircraft. Daily aircraft availability and missions accomplished are two critical metrics of interest. Within the simulation, the user has the ability to control types of part failures, control parts availability, control maintenance capabilities, and control number of mission scheduled. Finally, the user can account for the possibility of attrition along with the effects of numerous major events present in real-life scenarios. This tool is validated through application of a space covering design along with regression modeling and shows that the tool is well-behaved, functions as expected, and can quickly provide meaningful insights into operational scenarios.

Anticipatory approach for dynamic and stochastic shipment matching in hinterland synchromodal transportation

This paper investigates a dynamic and stochastic shipment matching problem faced by network operators in hinterland synchromodal transportation. We consider a platform that receives contractual and spot shipment requests from shippers, and receives multimodal services from carriers. The platform aims to provide optimal matches between shipment requests and multimodal services within a finite horizon under spot request uncertainty. Due to the capacity limitation of multimodal services, the matching decisions made for current requests will affect the ability to make good matches for future requests. To solve the problem, this paper proposes an anticipatory approach which consists of a rolling horizon framework that handles dynamic events, a sample average approximation method that addresses uncertainties, and a progressive hedging algorithm that generates solutions at each decision epoch. Compared with the greedy approach which is commonly used in practice, the anticipatory approach has total cost savings up to 8.18% under realistic instances. The experimental results highlight the benefits of incorporating stochastic information in dynamic decision making processes of the synchromodal matching system.

Spatially and temporally resolved measurements of turbulent Rayleigh-Bénard convection by Lagrangian particle tracking of long-lived helium-filled soap bubbles

We present spatially and temporally resolved velocity and acceleration measurements of turbulent RayleighBénard convection spanning the whole volume (~ 1 m³) of a cylindrical sample with aspect ratio one. With the "Shake-The-Box" (STB) Lagrangian particle tracking (LPT) algorithm, we were able to instantaneously track up to 560,000 particles, corresponding to mean inter-particle distances down to 6 - 8 Kolmogorov lengths. We used the data assimilation scheme ‘FlowFit’, which involves continuity and Navier-Stokesconstraints, to map the scattered velocity and acceleration data on cubic grids, herewith recovering the smallest flow scales. Lagrangian and Eulerian visualizations reveal the dynamics of the large-scale circulation and its interplay with small scale structures, such as thermal plumes and turbulent background fluctuations. As a result, the complex time-dependent behavior of the LSC comprising azimuthal rotations, torsional oscillation and sloshing can be extracted from the data. Further, we found more seldom dynamic events, such as spontaneous reorientations of the LSC in the data from long-term measurements.