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Feeder automation is important to improve the reliability of power supply for distribution network. Existing voltage‐time feeder automation scheme exists the problems of long fault processing time and the upstream switches of the fault point need to tolerate fault current twice and the upstream sections need to experience two short time power outage. In this paper, a quick fault location and isolation method for distribution network based on adaptive reclosing is proposed. Three key technologies of the strategy are introduced, including a phase segregated switch of the distribution network based on the permanent magnet operating structure, the phase selector based on transient current characteristics, and the temporary fault and permanent fault identification method based on the power frequency voltage characteristics. The proposed strategy can avoid the second impact on upstream switches and power‐off of the upstream section in case of the permanent fault and can effectively shorten the fault processing time.

Virtualization is instigating a paradigm shift in the networking industry, to keep up with emerging application's quality of service requirements, massive growth in traffic volume, and to reduce capital and operational expenditures. Network virtualization coupled with function virtualization enables network providers to offer on‐demand virtualized networks and services. Network slicing goes a step further by facilitating a new business model, namely Network‐as‐a‐Service, to offer dedicated and customized network slices (i.e. partitions of physical infrastructure) to multiple tenants, while ensuring proper isolation. However, this shift introduces new challenges for network providers and calls for intelligent and automated management. Artificial intelligence and machine learning are considered as enablers for the automated deployment and management of virtualized networks and services. This chapter exposes the state‐of‐the‐art research that leverages artificial intelligence and machine learning to address complex problems in deploying and managing virtualized networks and services. It also delineates open, prominent research challenges and opportunities to realize automated management of virtualized networks and services.

Active distribution network (ADN) technology, as an important trend of the future smart distribution grid, is able to effectively absorb distributed energy resource (DER), to reasonably optimise grid‐load operation characteristics, and to safely support the reliability of power supply. Through enhancing energy utilisation efficiency and friendly interaction with user access, ADN technology is also able to comprehensively improve the power supply reliability of the distribution network. However, distributed feeder automation (FA), as an important part of ADN technology, will also meet new problems and challenges with the access of DER in the distribution network. The formal method can analyse the correctness and effectiveness of a distributed fault processing algorithm from mathematical logic, which provides an important theoretical basis for distributed fault processing. The focus herein is on the formal description and verification of topology modelling in fault location, isolation, and service restoration (FLISR) based on distributed processing. By abstracting and simplifying the complex power system features, the adaptability of the formal method is solved. The logical correctness of the topology model in FLISR based on distributed processing is verified. Finally, the distributed local topology model and algorithm is verified through a formal method using an actual ADN example.

Internet of Things (IoT) networks involve interactions between multiple devices and systems, which may be part of different layers of the ecosystem. These systems cannot operate in isolation. In fact, they have significant interdependencies between them. Therefore, connectivity is crucial in the design of such systems and networks. This chapter provides an overview of the connectivity challenges in an IoT ecosystem from a wireless communication standpoint.

Objectives The advent of micro‐computed tomography (μCT) made cancellous bone more accessible than ever before. Nevertheless, the characterization of cancellous bone is made difficult by its inherent complexity and the difficulties in defining homology across datasets. Here we propose novel virtual methodological approaches to overcome those issues and complement existing methods. Materials and methods We present a protocol for the isolation of the whole cancellous region within a μCT scanned bone. This method overcomes the subsampling issues and allows studying cancellous bone as a single unit. We test the protocol on a set of primate bones. In addition, we describe a set of morphological indices calculated on the topological skeleton of the cancellous bone: node density, node connectivity, trabecular angle, trabecular tortuosity, and fractal dimension. The usage of the indices is shown on a small comparative sample of primate femoral heads. Results The isolation protocol proves reliable in isolating cancellous structures from several different bones, regardless of their shape. The indices seem to detect some functional differences, although further testing on comparative samples is needed to clarify their potential for the study of cancellous architecture. Conclusions The approaches presented overcome some of the difficulties of trabecular bone studies. The methods presented here represent an alternative or supporting method to the existing tools available to address the biomechanics of cancellous bone., The study of cancellous bone is hindered by its inherent complexity and difficulties in finding homology across samples. We introduce novel methods to overcome these issues. First, we propose a protocol to isolate cancellous bone from μCT scanned bones. Second, we developed an approach based on skeletonization to quantify cancellous complexity.

A critical aspect for obtaining accurate, reliable, and high-resolution estimates of nuclear DNA content is the release of nuclei from the cytoplasm in sufficient amounts, while maintaining their integrity throughout the analysis, protecting their DNA from degradation by endonucleases, and enabling stoichiometric DNA staining. In embryophytes, the most common method consists of chopping the plant material with a sharp razor blade to release nuclei into an isolation buffer, filtering the homogenate, and staining the nuclei in buffered suspension with a fluorochrome of choice. Despite the recent description of alternative approaches to isolate nuclei, the chopping procedure remains the most widely adopted method, due to its simplicity, rapidity, and effectiveness. In this review article, we discuss the specifics of nuclei isolation buffers and the distorting effects that secondary metabolites may have in nuclear suspensions and how to test them. We also present alternatives to the chopping procedure, options for filtering and fluorochromes, and discuss the applications of these varied approaches. A summary of the best practices regarding the isolation of plant nuclei for the estimation of nuclear DNA content is also provided.

Aptamers are short oligonucleotides isolated in vitro from randomized libraries that can bind to specific molecules with high affinity, and offer a number of advantages relative to antibodies as biorecognition elements in biosensors. However, it remains difficult and labor-intensive to develop aptamer-based sensors for small-molecule detection. Here, we provide a detailed review of the challenges and advances in the isolation and characterization of small-molecule-binding DNA aptamers and their utilization in sensors. First, we discuss in vitro methodologies for the isolation of aptamers, and provide strategies and guidance on selecting the appropriate strategy for generating aptamers with optimal binding properties for a given application. We next examine techniques for robust characterizing aptamer-target binding and structure. Afterwards, we discuss various small-molecule sensing platforms based on both original or engineered aptamers, and their strengths and limitations for detection applications. Finally, we conclude with a general workflow to develop aptamer-based small-molecule sensors for real-world applications.

Rural microfinance must contend with the triple challenges of isolation, small‐scale transactions, and risk. These challenges result in information asymmetries and transaction costs that render markets for financial services costly or missing. This paper examines how emerging digital technologies (e.g., mobile money, digital credit scoring, and earth observation) can reshape rural markets for savings, credit, and insurance services, especially in developing countries. Although our synthesis of the literature suggests reason for hope in all three domains, the imperfections of these digital technologies require evaluation and oversight if the resulting rural financial system is to be more efficient and equitable than its predecessor. JEL CLASSIFICATION D14; F63; R51

Two techniques that have received widespread attention for future networks are network sharing and network slicing . In this chapter, the relationship between the two is explored and the commonality between them is explained. In the simplest case, the resource can be set up as fixed blocks of spectrum allocated to a specific network operator (network sharing) or a particular set of services (network slicing). However, this can be inefficient in terms of use of resource and to overcome that inefficiency the concept of dynamic allocation is introduced, although that does introduce the possibility of inter‐cell interference between shares/slices. An extensive review of the literature in the area shows that such an approach will lead to significant benefits.Techniques to mitigate the interference are explained and it is shown that suitable control in the allocation of resources to users can lead to complete isolation between the slices, albeit at the expense of a small amount of the gain achieved through sharing. The effects of sharing spectrum or setting up slices in this way are demonstrated through example simulation results, although it is made clear that the example approach is used is just one way to achieve the mitigation required. In those example results the aim was to maximize the number of users who are allocated sufficient spectrum to achieve a capacity (in the Shannon sense) to meet their quality of service (QoS) requirements.

A single-step method for isolation of specific cells based on multiple surface markers will have unique advantages because of its scalability, efficacy, and mildness. Herein, we developed multi-aptamer-mediated proximity ligation method on live cell membranes that leverages a multi-receptor co-recognition design for enhanced specificity, as well as a robust in situ signal amplification design for improved sensitivity of cell isolation. We demonstrated the promising efficacy of our method on differentiating tumor cell subtypes in both cell mixtures and clinical samples. Owing to its simple and fast operation with excellent cell isolation sensitivity and accuracy, this approach will have broad applications in biological science, biomedical engineering, and personalized medicine.

Material flow control mechanisms determine: (i) whether an order should be released onto the shop floor; and (ii) whether a station should be authorized to produce. Well‐known approaches include Kanban, Drum‐Buffer‐Rope (DBR), Constant Work‐in‐Process (ConWIP), Paired‐cell Overlapping Loops of Cards with Authorization (POLCA), Workload Control (WLC), and Control of Balance by Card Based Navigation (COBACABANA). The literature typically treats these approaches as competing, meaning studies argue for the superiority of one over another. However, a closer look reveals that existing mechanisms either focus on order release (ConWIP, DBR, WLC, and COBACABANA) or on production authorization (Kanban and POLCA). This study therefore calls for a paradigm shift and argues that the different mechanisms may play complementary rather than competing roles. Using simulation, we assess the performance of COBACABANA and POLCA in a high‐variety make‐to‐order shop, a type of shop arguably in most need of material flow control given the importance of throughput times and delivery time adherence. Results demonstrate that COBACABANA outperforms POLCA, but the simultaneous adoption of both control mechanisms outperforms the use of either one in isolation. More specifically, adding POLCA production authorization to COBACABANA order release enables the superfluous direct load to be further reduced, resulting in shop floor throughput time reductions of between 15% and 26% while further reducing the percentage tardy and mean tardiness by up to 14%. Compared to no material flow control, the new combined mechanism realizes a reduction of almost 50% in the percentage tardy and more than 30% in mean tardiness.

This article describes two spatially‐explicit models created to allow experimentation with different societal responses to the COVID19 pandemic. We outline the work to date on modelling spatially‐explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalised models for mitigation provide significant advantages over a ‘one size fits all' approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.

Protein-protein interactions are one of the most basic and critical processes underlying biological functions. Thus, identification of the interacting proteins of a protein of interest and further elucidation of the roles of the interactions is critical for understanding the related biological processes. The yeast two-hybrid (Y2H) method is a popular approach for identifying protein-protein interactions. Once interacting proteins are identified, a comparison of the phenotypes of mutants lacking the specific protein-protein interaction with those of the wild-type strain is a powerful tool for uncovering the former interactions' biological significance. However, isolation of such interaction-defective mutants is often laborious. Here, I describe a novel and efficient approach for isolating such mutants that uses the Y2H technique with a modified Y2H vector, and provide an example of how this approach can be used to screen interaction-null/impaired mutants. Because the strategy is simple and the modification of a pre-existing Y2H vector is sufficient for the screening purpose, the same strategy can be applied to any existing two-hybrid system.

A methodology for the development of design tools for direct estimation of peak inelastic response in reduced-degree-of-freedom (RDOF) isolation and energy dissipation systems is presented. The suggested procedure is an extension of an earlier method addressing purely hysteretic isolation systems. Herein, the dynamic equation of motion is first normalised to reduce the number of design parameters that significantly affect the response. The sensitivity of normalised response quantities to the amplitude of the ground motion is then investigated through extensive parametric nonlinear dynamic analyses of isolated single-degree-of-freedom (SDOF) systems with linear viscous damping using code-based target spectra. Regression analysis is subsequently employed to develop generalised design equations (GDEs) suitable for design. Further investigations are made to address nonlinear viscous damping and the effect of the transverse component of seismic action in two-degree-of freedom (2DOF) systems under bidirectional excitation, making the procedure applicable to common bridge isolation schemes. GDEs constitute an alternative to equivalent linearisation approaches commonly adopted by codes, informing the selection among alternative isolation and energy dissipations schemes without requiring iterative analysis. The approach is incorporated in the Deformation-Based Design methodology for seismically isolated bridges in a forthcoming paper.

Regular maintenance is very important to ensure all the required types of machinery and equipment be kept 100% efficient for marine and offshore systems. Maintenance operations for marine and offshore systems are carried out by the seafarers/operators and it is they who are usually liable for any error. Previous studies have identified that about 80% of marine and offshore accidents occurred due to human error. Therefore, to address this concern human reliability assessment (HRA) is very important. However, an appropriate technique is required to estimate human error probability (HEP) for marine and offshore systems. Human error assessment and reduction technique (HEART) is applied to many industries to determine HEP. Recently, HEART was specifically developed for the maintenance operations of marine and offshore systems considering marine and offshore environmental and operational conditions. However, there is a deficiency in this technique as it does not provide a concrete method to determine the seafarers assessed proportion of effect (SAPOE) and it therefore, relies heavily on the judgment of a single expert. This study proposes a hybrid HEART to overcome the problem. The hybrid HEART utilizes the evidence theory to fuse an expert's opinion to determine APOA for each corresponding error producing condition (EPC). The proposed technique is applied to estimate HEP for the maintenance procedures of a condensate pump for an offshore oil and gas facility as a case study. The HEP values are calculated for each selected activity and comparison is provided. Based on the results, a performing pressure test and isolation leak test has the highest HEP 1.54E‐01 and depressurizing drain lines has the lowest 1.54E‐04. It is proposed that the application of this hybrid HEART will enable estimating HEP more accurately. Therefore, it will contribute to improving the overall safety level in the maintenance of marine and offshore systems.

The vast biodiversity of the microbial world and how little is known about it, has already been revealed by extensive metagenomics analyses. Our rudimentary knowledge of microbes stems from difficulties concerning their isolation and culture in laboratory conditions, which is necessary for describing their phenotype, among other things, for biotechnological purposes. An important component of the understudied ecosystems is methanogens, archaea producing a potent greenhouse-effect gas methane. Therefore, we created PhyMet2 , the first database that combines descriptions of methanogens and their culturing conditions with genetic information. The database contains a set of utilities that facilitate interactive data browsing, data comparison, phylogeny exploration and searching for sequence homologues. The most unique feature of the database is the web server MethanoGram, which can be used to significantly reduce the time and cost of searching for the optimal culturing conditions of methanogens by predicting them based on 16S RNA sequences. The database will aid many researchers in exploring the world of methanogens and their applications in biotechnological processes. PhyMet2 with the MethanoGram predictor is available at http://metanogen.biotech.uni.wroc.pl.

Firms collaborate in green product innovation (GPI) to develop products with less environmental impact. These products typically use less energy, have lower emissions and incorporate more environmentally friendly materials. In an industrial setting, firms often collaborate along the supply chain with customers and suppliers. This paper focuses on external and internal capabilities that firms need when collaborating in GPI. The paper builds on data from five large industrial firms in ten case studies, in which these firms collaborate with customers and suppliers to innovate green products to an industrial market. External and internal capabilities are investigated. The study points to the importance of finding a suitable partner. Partners need to have environmental expertise and contribute knowledge or technology that is new to the firm. Firms need to combine relational capabilities, such as trust, with contractual agreements in collaborative innovations. The findings point to the importance of knowledge management with the partner as well as internally in the firm. The study shows that no partner collaboration operates in isolation but is situated in a network context.

Summary A new seismic design manner, namely building mass damper (BMD), which is inspired from a combination of mid-story isolation and tuned mass damper design concepts, recently attracts immense attention. It is mainly because that the use of partial structural mass of the building as an energy absorber in the BMD design can overcome the drawback of limited response reduction due to insufficient added tuned mass in the conventional tuned mass damper design. In this study, an optimum BMD (OBMD) design approach, namely optimum dynamic characteristic control approach, based on a simplified 3-lumped-mass structure model is proposed to seismically protect both the superstructure (or tuned mass) and the substructure (or primary structure), respectively, above and below the control layer. A series of sensitivity analyses and experimental studies on different parameters, including mass, frequency, and damping ratios, of a building model designed with a BMD system were conducted. The test results verify the practical feasibility of the BMD concept as well as the effectiveness of the proposed OBMD design. Furthermore, by comparing with the numerical results of a mid-story isolated counterpart, it is demonstrated that the proposed OBMD design can have a comparable and even better control performance.

Many proteins contain intrinsically disordered regions (IDRs), functional polypeptide segments that in isolation adopt a highly flexible conformational ensemble instead of a single, well-defined structure. Disorder prediction methods, which can discriminate ordered and disordered regions from the amino acid sequence, have contributed significantly to our current understanding of the distinct properties of intrinsically disordered proteins by enabling the characterization of individual examples as well as large-scale analyses of these protein regions. One popular method, IUPred provides a robust prediction of protein disorder based on an energy estimation approach that captures the fundamental difference between the biophysical properties of ordered and disordered regions. This paper reviews the energy estimation method underlying IUPred and the basic properties of the web server. Through an example, it also illustrates how the prediction output can be interpreted in a more complex case by taking into account the heterogeneous nature of IDRs. Various applications that benefited from IUPred to provide improved disorder predictions, complementing domain annotations and aiding the identification of functional short linear motifs are also described here. IUPred is freely available for noncommercial users through the web server (http://iupred.enzim.hu and http://iupred.elte.hu) . The program can also be downloaded and installed locally for large-scale analyses.

Finding construction components in cluttered point clouds is a critical pre-processing task that requires intensive and manual operations. Accurate isolation of an object from point clouds is a key for further processing steps such as positive identification, scan-to-building information modeling (BIM), and robotic manipulation. Manual isolaton is tedious, time consuming, and disconnected from the automated tasks involved in the process. This article adapts and examines a method for finding objects within 3D point clouds robustly, quickly, and automatically. A local feature on a pair of points is employed for representing 3D shapes. The method has three steps: (1) offline model library generation, (2) online searching and matching, and (3) match refinement and isolation. Experimental tests are carried out for finding industrial (curvilinear) and structural (rectilinear) elements. The method is verified under various circumstances in order to measure its performance toward addressing the major challenges involved in 3D object finding. Results show that the method is sufficiently quick and robust to be integrated with automated process control frameworks.

In this chapter, the problem of integrated fault detection, isolation, and control (IFDIC) design of continuous-time Markovian jump linear systems with uncertain transition probabilities and subject to both energy bounded and peak bounded disturbances is introduced and addressed. A single Markovian jump module designated as the IFDIC under a mixed robust H∞/H_/L1 framework is considered to simultaneously achieve the desired detection, isolation, and control objectives. Conventional mixed robust H∞/H_/L1 approaches to the fault detection and isolation (FDI) problem lead to conservative results due to the selection of identical Lyapunov matrices.