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With the technology development, enterprise applications are rapidly changing from the traditional centralized application architecture to the distributed, microservices architecture (MSA). However, with the further development of MSA, enterprises are facing new challenges while enjoying the convenience brought by microservices, e.g. how to manage a large number of services, how to quickly locate and troubleshoot these services, how to avoid the performance issues, etc. As the next generation of MSA, service mesh is attracting more and more attentions and can be used to solve above problems. On top of service mesh, this paper proposes one mechanism that largely customizes service mesh implementation technologies. Furthermore, we implement this mechanism in a real project of one local large financial company. The final result illustrates that the mechanism is not only feasible but also effective to solve aforementioned service governance problems.

Vaccination against infectious diseases has changed the future of the human species, saving millions of lives every year, both children and adults, and providing major benefits to society as a whole. Here we show, however, that national and sub-national coverage of vaccination varies greatly and major unmet needs persist. Although scientific progress opens exciting perspectives in terms of new vaccines, the pathway from discovery to sustainable implementation can be long and difficult, from the financing, development and licensing to programme implementation and public acceptance. Immunization is one of the best investments in health and should remain a priority for research, industry, public health and society. An overview of the effects of vaccines on global morbidity and mortality, vaccine safety issues, and the hurdles involved in proceeding from vaccine discovery to successful implementation.

Purpose Global competition has intensified pressure on small- and medium-sized enterprises (SMEs) to implement lean. Recently, the debate has converged to the role of lean implementation barriers (LIBs). The purpose of this paper is to contribute to this debate by exploring the LIBs in SMEs through three case studies. Design/methodology/approach A case study approach was employed followed by interpretive structural modelling (ISM) to model the interrelationship among the LIBs. Findings This study reveals that lack of management commitment, leadership and resources are the key barriers to lean implementation in SMEs in India. Furthermore, poor communication between different levels of the organisation and inadequate dissemination of the knowledge of lean benefits also creates hindrance in lean implementation. Managerial implications of the identified barriers for lean implementation in SMEs have been discussed. Originality/value The research regarding lean implementation in SMEs is scarce. This study is the first attempt of its kind to identify the lean barriers in a small industry setup through mathematical analysis.

Collaborative robotics have a large potential for use in industrial applications. Nevertheless, this potential is currently unrealized and one of the reasons is the challenges in planning and designing while considering the safety requirements of these new types of applications. In this article, we will use an exemplary application to describe the many design decisions that are made during the planning of an industrial application featuring human-robot collaboration. Our approach uses model-based systems engineering concepts for considering safety-related aspects of the application during the design phase. Using a software implementation based on our method, we will then compare design results including required floor space and cycle time for the same exemplary application and discuss the implications of our approach for planning other robotics tasks. With our method, the required separation area around the robot was reduced by up to over 66% for a situation featuring a specific robot to be operated at 100% of its maximum possible speed.

Marine resources are vital to the development of a country. Marine investigation can obtain more marine resources and acquire more marine environmental information. A common method used in the marine investigation consumes a large amount of both time and money. Thus, the scientific path planning is important for improving the efficiency and reducing the costs of the marine investigation. Currently, the most commonly used algorithms for path planning are the genetic algorithm (GA) and the ant colony optimization algorithm (ACOA). Through continuous iterations, the initial solutions obtained by GA and ACOA gradually approach the optimal solutions. However, the final solutions of both algorithms are often suboptimal solutions or local optimal solutions. In particular, in terms of the marine investigation path planning that involves enormous stations, both GA and ACOA are prone to premature and local optimal solutions, leading to the stagnation of the searching. Therefore, in order to solve these problems and save the costs of marine investigation, the ACOA and GA are combined to propose a hybrid algorithm for the further improvement in the quality of the solutions. Through the experiments and software implementation, the proposed hybrid algorithm is proved of high effectiveness and robustness, which could obtain the optimal path for single or multiple research vessels, thereby saving the time and costs of marine investigation path planning.

Purpose The purpose of this paper is to increase the understanding of how companies can implement rapid manufacturing (RM) (i.e. the use of additive manufacturing (AM) technologies for final part production) for mass customisation (MC), drawing upon the experiences of firms in the dental sector (one of the major users of AM technologies). Design/methodology/approach A framework for implementation of RM for MC was developed from the literature to guide the data gathering. Data from six case companies in the dental sector implementing RM for MC, supplemented with insights from their respective AM machine providers and software companies, were used to analyse how companies implement RM for MC and what considerations and challenges they face in the process. Findings The study shows how implementation of RM for MC entails different considerations depending on the stage of implementation and maturity of involved technologies. In total, 26 challenges have been identified that seem to play a crucial role in implementation. The paper suggests that RM can enable MC in manufacturing by achieving both a high number of units produced and as well as a high level of customisation of each product. Originality/value Based on the review of the literature, no case studies exist that investigate companies implementing RM for MC despite literature having suggested RM as an enabler for MC in manufacturing for many years.

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners' physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners' smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner. (DIPF/Orig.)

The paper presents the results of the study, which formed the methodological foundations for constructing a system of the urban programs' rational planning in the field of housing and communal services and the urban environment, which provide intellectual support for decision-making. The use of the considered problems and proposed methods for their solutions is advisable to put into practice in informatization and computerization of the urban processes.

This article presents the results of using ensemble algorithms for short-term hourly forecasting of electricity prices. Combining forecasts has proved itself to be the approach that is most useful in the following situations. There is uncertainty in choosing the most accurate forecasting method. There is uncertainty associated with the choice of input data and factors that should be taken into account when forecasting, it is necessary to avoid large forecasting errors, both in the direction of overstatement and in the direction of understatement of the studied indicator. The article describes the author's software implementation of the ensemble model of forecasting the time series (TS) based on the adaptive method in the R environment, as well as the results of a comparative analysis of the accuracy of forecasting TS electricity prices using the single (Holt-Winters, ARIMA) and ensemble models (OPERA, adaptive model). The results obtained allow concluding that the use of ensemble models in solving applied problems of forecasting time series is promising.

In the last decade, a technological shift in the bioinformatics field has occurred: larger genomes can now be sequenced quickly and cost effectively, resulting in the computational need to efficiently compare large and abundant sequences. Furthermore, detecting conserved similarities across large collections of genomes remains a problem. The size of chromosomes, along with the substantial amount of noise and number of repeats found in DNA sequences (particularly in mammals and plants), leads to a scenario where executing and waiting for complete outputs is both time and resource consuming. Filtering steps, manual examination and annotation, very long execution times and a high demand for computational resources represent a few of the many difficulties faced in large genome comparisons. In this work, we provide a method designed for comparisons of considerable amounts of very long sequences that employs a heuristic algorithm capable of separating noise and repeats from conserved fragments in pairwise genomic comparisons. We provide software implementation that computes in linear time using one core as a minimum and a small, constant memory footprint. The method produces both a previsualization of the comparison and a collection of indices to drastically reduce computational complexity when performing exhaustive comparisons. Last, the method scores the comparison to automate classification of sequences and produces a list of detected synteny blocks to enable new evolutionary studies.