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The taxonomic classification of viral sequences is frequently used for the rapid identification of pathogens, which is a key point for when a viral outbreak occurs. Both Oxford Nanopore Technologies (ONT) MinION and the Illumina (NGS) technology provide efficient methods to detect viral pathogens. Despite the availability of many strategies and software, matching them can be a very tedious and time-consuming task. As a result, we developed PIMGAVir and Vir-MinION, two metagenomics pipelines that automatically provide the user with a complete baseline analysis. The PIMGAVir and Vir-MinION pipelines work on 2nd and 3rd generation data, respectively, and provide the user with a taxonomic classification of the reads through three strategies: assembly-based, read-based, and clustering-based. The pipelines supply the scientist with comprehensive results in graphical and textual format for future analyses. Finally, the pipelines equip the user with a stand-alone platform with dedicated and various viral databases, which is a requirement for working in field conditions without internet connection.

To create a concept of the marketing strategy, it is necessary to analyse the factors affecting the purchasing decisions of consumers. For the variables: production, import, export, and manufacturer's price we examine their impact on the marketing of poultry meat in the EU-28 in 2009 and 2011. Countries are grouped into clusters, their properties are analysed in relation to the mentioned variables. With multiple regression analysis, we find that there is a statistical correlation between high production and de-pending on the variable, and between the imports and exports as the independent vari-ables. Based on the analysed data in the researched countries, we conclude that the qualitative development of the production of poultry meat required implementing sophis-ticated agricultural policy with low inputs prices and exploit all available spare re-sources.

This study aimed to validate a newly-developed instrument, The Writing Strategies for Self-Regulated Learning (SRL) Questionnaire, with respect to its multifaceted structure of SRL strategies in English as a foreign language (EFL) writing. A total of 790 undergraduate students from 6 universities in Northeast China volunteered to be participants. Confirmatory factor analyses (CFA) through structural equation modeling (SEM) were applied to evaluate 3 hypothesized models. The results of the CFA validated a 9-factor correlated model of second language (L2) writing strategies for SRL with satisfactory psychometric characteristics. Model comparisons confirmed a hierarchical, multidimensional structure of SRL as the best model, in which self-regulation, as a higher order construct, accounted for the correlations of the 9 lower-order writing strategies, pertaining to cognitive, metacognitive, social-behavioral, and motivational regulation aspects. Multiple regression analysis revealed that 6 out of 9 SRL strategies had significant predictive effects on EFL writing proficiency. The empirical evidence lends preliminary support to a transfer of SRL theory from educational psychology to the field of L2/EFL education, particularly L2/EFL writing. Implications of these findings are discussed. (Verlag).

Purpose This study aims to examine the influence of intellectual capital and knowledge management on competitive advantage with the mediation role of innovativeness in the Pakistan manufacturing industry. Moreover, differentiation strategy is used as a moderator between innovativeness and competitive advantage. Design/methodology/approach The data was collected from 387 manufacturing firms in Pakistan through questionnaires. Purposive random sampling was used to collect data. The partial least square structural equation modeling (PLS-SEM) method is used to test the proposed hypotheses. This study followed multiple regression analyses to see the influence of intellectual capital, knowledge management, innovativeness and differentiation strategy on competitive advantage. Findings The results elucidate that intellectual capital and knowledge management significantly determines innovativeness and competitive advantage. Moreover, innovativeness significantly mediates between intellectual capital, knowledge management and competitive advantage. Besides, innovativeness significantly determines competitive advantage. Business strategies significantly lead to competitive advantage. Finally, business strategies significantly moderate between innovativeness and competitive advantage. Practical implications The research highlight an important issue that how manufacturing sector management uses intellectual capital, knowledge management, innovativeness and business strategies in determining competitive advantage. Besides, it covers the gap and assists the management of the manufacturing sector to focus on exogenous constructs to examine competitive advantage. Originality/value This study adds value to the body of knowledge by focusing on predictors that impact competitive advantage. This initial study determines intellectual capital and knowledge management influence on competitive advantage and innovativeness as a mediator by using resource orchestration theory. Moreover, differentiation strategy is used as moderating variable between innovativeness and competitive advantage. The managers, students and researchers can obtain benefits from this study.

This chapter contains sections titled: Graphic Representations in Time‐Attribute Space Graphic Representation in Geographic Space Hybrid Representations with Spatial and Time‐Attribute Axes Time as a Visual Variable Concluding Remarks References

We investigated the roles of classroom supports for multiple motivations and engagement in students' informational text comprehension, motivation, and engagement. A composite of classroom contextual variables consisting of instructional support for choice, importance, collaboration, and competence, accompanied by cognitive scaffolding for informational text comprehension, was provided in four-week instructional units for 615 grade 7 students. These classroom motivational-engagement supports were implemented within integrated literacy/history instruction in the Concept-Oriented Reading Instruction (CORI) framework. CORI increased informational text comprehension compared with traditional instruction (TI) in a switching replications experimental design. Students' perceptions of the motivational-engagement supports were associated with increases in students' intrinsic motivation, value, perceived competence, and increased positive engagement (dedication) more markedly in CORI than in TI, according to multiple regression analyses. Results extended the evidence for the effectiveness of CORI to literacy/history subject matter and informational text comprehension among middle school students. The experimental effects in classroom contexts confirmed effects from task-specific, situated experimental studies in the literature.

By simultaneously considering the supported truss configuration optimization and optimal attitude–vibration control in rigid–flexible coupling (RFC) structure, this study proposes a novel integrated uncertain optimal structure-control design strategy with interval uncertainties. Based on the principle of energy equivalence, the flexible support truss of the RFC structure is simplified using an equivalent beam model, which can significantly reduce the degree of freedom of the model and improve design efficiency on the premise of satisfying the analysis accuracy of the static and dynamic characteristics of the complete truss structure. The Lagrangian method is applied to establish an RFC structure model including a central rigid body, equivalent flexible truss and free end mass. Given the difficulty of quantifying the multi-source uncertainty encountered by actual RFC structures, the structural optimization and control system design in this study considers them as interval uncertainty. As long as the uncertainty bounds are known, the uncertainty propagation in the integrated design strategy can be quantified using interval dimension-wise analysis. The time-independent interval reliability-based frequency constraint and time-dependent interval reliability-based dynamic response constraint are both constituted for the proposed integrated uncertain optimal design strategy, which is solved using an advanced multi-objective optimization algorithm. One numerical example is applied to verify the proposed method. An optimum integrated design layout with a lightweight truss configuration and a low energy consumption control system is obtained.

The spread of antibiotic-resistant bacteria poses a substantial threat to morbidity and mortality worldwide. Due to its large public health and societal implications, multidrug-resistant tuberculosis has been long regarded by WHO as a global priority for investment in new drugs. In 2016, WHO was requested by member states to create a priority list of other antibiotic-resistant bacteria to support research and development of effective drugs. We used a multicriteria decision analysis method to prioritise antibiotic-resistant bacteria; this method involved the identification of relevant criteria to assess priority against which each antibiotic-resistant bacterium was rated. The final priority ranking of the antibiotic-resistant bacteria was established after a preference-based survey was used to obtain expert weighting of criteria. We selected 20 bacterial species with 25 patterns of acquired resistance and ten criteria to assess priority: mortality, health-care burden, community burden, prevalence of resistance, 10-year trend of resistance, transmissibility, preventability in the community setting, preventability in the health-care setting, treatability, and pipeline. We stratified the priority list into three tiers (critical, high, and medium priority), using the 33rd percentile of the bacterium's total scores as the cutoff. Critical-priority bacteria included carbapenem-resistant Acinetobacter baumannii and Pseudomonas aeruginosa, and carbapenem-resistant and third-generation cephalosporin-resistant Enterobacteriaceae. The highest ranked Gram-positive bacteria (high priority) were vancomycin-resistant Enterococcus faecium and meticillin-resistant Staphylococcus aureus. Of the bacteria typically responsible for community-acquired infections, clarithromycin-resistant Helicobacter pylori, and fluoroquinolone-resistant Campylobacter spp, Neisseria gonorrhoeae, and Salmonella typhi were included in the high-priority tier. Future development strategies should focus on antibiotics that are active against multidrug-resistant tuberculosis and Gram-negative bacteria. The global strategy should include antibiotic-resistant bacteria responsible for community-acquired infections such as Salmonella spp, Campylobacter spp, N gonorrhoeae, and H pylori. World Health Organization.

A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero–one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently.

Mobile robots play a pivotal role in advancing smart manufacturing technologies. However, existing Obstacle avoidance path Planning (OP) algorithms for mobile robots suffer from low stability and applicability. Therefore, this paper proposes an enhanced Secret Bird Optimization Algorithm (SBOA)-based OP algorithm for mobile robots to address these challenges, termed AGMSBOA. Firstly, an adaptive learning strategy is introduced, where individuals enhance the diversity of the algorithm’s population by summarizing relationships among candidates of varying quality, thereby strengthening the algorithm’s ability to locate globally optimal obstacle avoidance path regions. Secondly, a group learning strategy is incorporated by dividing the population into learning and teaching groups, enhancing the algorithm’s exploitation capabilities, improving the accuracy of obstacle avoidance path planning, and reducing actual runtime. Lastly, a multiple population evolution strategy is proposed, which balances the exploration/exploitation phases of the algorithm by analyzing the nature of different individuals, improving the algorithm’s ability to escape suboptimal obstacle avoidance path traps. Subsequently, AGMSBOA was used to solve the OP problem on five maps and two OP problems in real-world environments. The experiments illustrate that AGMSBOA achieves more than 5% performance improvement in path length and a 100–win rate in runtime metrics, as well as faster convergence and stability of the solution. Therefore, AGMSBOA proposed in this paper is an efficient, robust, and robust OP method for mobile robots.