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Background Sequence-based identification is one of the most effective methods for species-level identification of nontuberculous mycobacteria (NTM). However, it is time-consuming because of the bioinformatics processes involved, including sequence trimming, consensus sequence generation, and public database searches. We developed a simple and fully automated software that enabled species-level identification of NTM from trace files, SnackNTM (https://github.com/Young-gonKim/SnackNTM). Methods JAVA programing language was used for software development. The SnackNTM diagnostic algorithm utilized 16S rRNA gene sequences, according to the Clinical & Laboratory Standards Institute guidelines, and an rpoB gene region was adjunctively utilized to narrow down the species. The software performance was validated using trace files of 234 clinical cases, comprising 217 consecutive cases and 17 additionally selected cases of unique species. Results SnackNTM could analyze multiple cases at once, and all the bioinformatics processes required for sequence-based NTM identification were automatically performed with a single mouse click. SnackNTM successfully identified 95.9% (208/217) of consecutive clinical cases, and the results showed 99.0% (206/208) agreement with manual classification results. SnackNTM successfully identified all 17 cases of unique species. In a processing time comparison test, the analysis and reporting of 30 cases, which took 150 minutes manually, took only 40 minutes with SnackNTM. Conclusions SnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.

Background Associations between IgA nephropathy (IgAN) and HLA-DRB1 and -DQB1 alleles have been reported in several ethnic groups. We investigated the association of HLA-DRB1 and -DQB1 alleles with the predisposition for IgAN and disease progression to end-stage kidney disease (ESKD) in Korean patients. Methods We analyzed HLA-DRB1 and -DQB1 genotypes in 399 IgAN patients between January 2000 and January 2019 using a LIFECODES sequence-specific oligonucleotide (SSO) typing kit (Immucor, Stamford, CT, USA) or a LABType SSO Typing Test (One Lambda, Canoga Park, CA, USA). Alleles with a significant difference in two-digit resolution were further analyzed using in-house sequence-based typing and sequence-specific primer PCR. As controls, 613 healthy hematopoietic stem cell donors were included. Kidney survival was analyzed in 281 IgAN patients with available clinical and laboratory data using Cox regression analysis. Where needed, P-values were adjusted using Bonferroni correction. Results The allele frequencies of HLA-DRB1*04:05 (corrected P [Pc]

Background SARS‐CoV‐2 pandemic is currently ongoing, meanwhile vaccinations are rapidly underway in some countries. The quantitative immunoassays detecting antibodies against spike antigen of SARS‐CoV‐2 have been developed based on the findings that they have a better correlation with the neutralizing antibody. Methods The performances of the Abbott Architect SARS‐CoV‐2 IgG II Quant, DiaSorin LIAISON SARS‐CoV‐2 TrimericS IgG, and Roche Elecsys anti‐SARS‐CoV‐2 S were evaluated on 173 sera from 126 SARS‐CoV‐2 patients and 151 pre‐pandemic sera. Their correlations with GenScript cPass SARS‐CoV‐2 Neutralization Antibody Detection Kit were also analyzed on 173 sera from 126 SARS‐CoV‐2 patients. Results Architect SARS‐CoV‐2 IgG II Quant and Elecsys anti‐SARS‐CoV‐2 S showed the highest overall sensitivity (96.0%), followed by LIAISON SARS‐CoV‐2 TrimericS IgG (93.6%). The specificities of Elecsys anti‐SARS‐CoV‐2 S and LIAISON SARS‐CoV‐2 TrimericS IgG were 100.0%, followed by Architect SARS‐CoV‐2 IgG II Quant (99.3%). Regarding the correlation with cPass neutralization antibody assay, LIAISON SARS‐CoV‐2 TrimericS IgG showed the best correlation (Spearman rho = 0.88), followed by Architect SARS‐CoV‐2 IgG II Quant and Elecsys anti‐SARS‐CoV‐2 S (all rho = 0.87). Conclusions The three automated quantitative immunoassays showed good diagnostic performance and strong correlations with neutralization antibodies. These assays will be useful in diagnostic assistance, evaluating the response to vaccination, and the assessment of herd immunity in the future., The performances of three automated quantitative immunoassays detecting antibodies against SARS‐CoV‐2 spike protein, Abbott SARS‐CoV‐2 IgG II Quant; DiaSorin LIAISON SARS‐CoV‐2 TrimericS IgG; Roche Elecsys anti‐SARS‐CoV‐2 S were comparable, with superior sensitivity and specificity. The three immunoassays demonstrated strong correlations with GenScript cPass virus neutralization test.

Background For HLA genotyping, PCR sequence-specific oligonucleotide (SSO) methods using the Luminex platform are widely used. We evaluated the performance of LabType-SSO (One Lambda, USA) in Koreans. Methods LabType-SSO were performed on 50 residual DNA samples analyzed by sequence-based typing (SBT) for all HLA-A, -B, -C, -DRB1, and -DQB1 alleles with gene frequency > 0.1% in Koreans. Results The LabType-SSO results were in complete agreement with SBT at the 2-digit level. For 4-digit level, 9 HLA-A alleles, 1 HLA-B allele, 3 HLA-C alleles, neither HLA-DRB1 nor -DQB1 allele showed ambiguous results for assignment of most probable types considering HLA gene frequency in Koreans. In addition, two cases of DQB1*04:01 allele were incorrectly assigned to DQB1*04:02. Conclusions LabType-SSO tests showed accurate assignment of 2-digit level and LabType-SSO HLA-DRB1 test showed correct 4-digit most probable HLA type. The tests can be useful as intermediate resolution typing for solid organ transplantation.

The pandemic caused by COVID-19 brought on many challenges. Sustaining manufacturing production is one of them. Some market segments may experience reductions in demands, while other market segments may experience explosions in demand. Companies have to manage these changing needs and supply uncertainties while keeping employees safe and remaining profitable. This paper characterizes the problems encountered in setting up new production lines in two different industries and describes solutions adopted and envisioned.

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The rapid increase in the quantity of customer data has promoted the necessity to analyse these data. Recent progress in text mining has enabled analysis of unstructured text data such as customer suggestions, customer complaints and customer feedback. Much research has been attempted to use insights gained from text mining to identify customer needs to guide development of market-oriented products. However, the previous research has a drawback that identifies limited customer needs based on product features. To overcome the limitation, this paper presents application of text mining analysis of customer complaints to identify customers&rsquo, true needs by using the Outcome-Driven Innovation (ODI) method. This paper provides a method to analyse customer complaints by using the concept of job. The ODI-based analysis contributes to identification of customer latent needs during the pre-execution and post-execution steps of product use by customers that previous methods cannot discover. To explain how the proposed method can identify customer requirements, we present a case study of stand-type air conditioners. The analysis identified two needs that experts had not identified but regarded as important. This research helps to identify requirements of all the points at which customers want to obtain help from the product.

The complexity of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of Smart Manufacturing Systems (SMS). This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and integrated to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified.

Predictive maintenance is a maintenance strategy of diagnosing and prognosing a machine based on its condition. Compared with other maintenance strategies, the predictive maintenance strategy has the advantage of lowering the maintenance cost and time. Thus, many studies have been conducted to develop a predictive maintenance model based on a growth of prediction methodology. However, these studies tend to focus on building the predictive model and measuring its performance, rather than selecting the appropriate components for predictive maintenance. Nevertheless, selecting the predictive maintenance policy and target component are as important as model selection and performance measurement. In this paper, a selection method is proposed to improve component selection by referencing current literature and industry expert knowledge. The results of this research can serve as a foundation for further studies in this area.

As companies move forward to source globally, supply chain management has gained attention more than ever before. In particular, the discovery and selection of capable suppliers has become a prerequisite for a global supply chain operation. Manufacturing e-marketplaces have helped companies quickly and effectively discover new suppliers and/or buyers for their products and services. However, as the requirements and capabilities in isolation, their true meanings may not be uniformly interpreted by each other. The issue of semantics between suppliers and buyers, then, remains an obstacle. The main objective of this paper is to propose a semantic web-based supplier discovery system for building a long-term strategic supply chain. Specifically, (1) a key ontology is developed to represent the supplier’s capability information and the buyer’s requirements; (2) supplier’s potential capability is reasoned; and (3) and buyer’s requirements are semantically matched with supplier’s capability based on a similarity ...

Smart manufacturing is defined by high degrees of automation. Automation, in turn, is defined by clearly defined processes. The use of standards in this environment is not just commonplace, but essential to creating repeatable processes and reliable systems. As with the rest of society, manufacturing systems are becoming more tightly connected through advances in information and communication technologies (ICT). As a result, manufacturers are able to receive information from their business partners and operational units much more quickly and are expected to respond quickly as well. Quick responses to changes in a manufacturing system are much more challenging than the responses that we have come to expect in other aspects of our lives. Manufacturing revolves around heavy capital investments to rapidly produce large amounts of product in anticipation of steady streams of commerce. Changes under these conditions not only disrupt the operations, slowing the production of goods, but also create difficulties with managing the capital investments. These are challenges manufacturers face daily. A large part of these challenges is understanding how best to refit manufacturing facilities to respond to variability, and how to plan new production facilities. By analyzing the information that is available in a manufacturing system, manufacturers can make more informed decisions as to how to respond to change. Advances in the technological infrastructure underlying manufacturing systems are enabling more reliable and timely flow of information across all levels of the manufacturing operation. We propose that effective utilization of such operational information will enable more automated, agile responses to the changing conditions, i.e. Smart Manufacturing. In this paper, we analyze the sources and the standards supporting the flow of that information throughout the enterprise. The analysis is based an intersection of two reference models: the Factory Design and Improvement (FDI) process and the ISA88 hierarchical model of manufacturing operations. The FDI process consists of a set of high-level activities for designing and improving manufacturing operations. The ISA88 hierarchical model specifies seven levels of control within a manufacturing enterprise.

Smart manufacturing, today, is the ability to continuously maintain and improve performance, with intensive use of information, in response to the changing environments. Technologies for creating smart manufacturing systems or factories are becoming increasingly abundant. Consequently, manufacturers, large and small, need to correctly select and prioritize these technologies correctly. In addition, other improvements may be necessary to receive the greatest benefit from the selected technology. This paper proposes a method for assessing a factory for its readiness to implement those technologies. The proposed readiness levels provide users with an indication of their current factory state when compared against a reference model. Knowing this state, users can develop a plan to increase their readiness. Through validation analysis, we show that the assessment has a positive correlation with the operational performance.

Discovering the suppliers capable of manufacturing the parts that satisfy buyer requirements via current online market places remains difficult due to semantic differences between what the suppliers can produce and what the buyer wants to acquire. One of the promising approaches to overcome the semantic diffe-rence is to adopt an ontology to describe the suppliers’ manufacturing capabilities and the buyer requir-ements that range widely from manufacturing costs to eco-friendly design. Such an ontology dedicated to supplier discovery has yet to be developed. MSDL(Manufacturing Service Description Language) provides the basis for defining terms and their relationships in the ontology. Thus, the objective of this paper is to extend MSDL into a new ontology suitable for supplier discovery in mold manufacturing industry. In addition, a new ontology development method for supplier discovery will be proposed. Finally prototype demonstrations are provided to show a feasibility of the proposed ontology in mold manufacturing domains.

Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using detailed, precise, and appropriate data for intelligent decision-making. The intricacy of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of SMS. This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard modeling techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and harmonized to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified.

Smart manufacturing has the potential to address many of the challenges faced by industry. However, the manufacturing community often needs assistance to leverage available technologies to improve their systems. To assure the performance of these technologies, this paper proposes a shared knowledge base that collects problem areas, solutions, and best practices for manufacturing technology. An Implementation Risk Assessment Framework (IRAF) is also described to identify the primary weaknesses of technologies in specific manufacturing contexts. Such approaches have the potential to stimulate new ideas and drive standardization activities critical to scale up and deploy smart manufacturing technologies successfully and quickly.

Smart Manufacturing Systems have the ability to adapt to rapidly changing requirements. Software components of the manufacturing system—services under Service Oriented Architecture—must be configured dynamically to meet such requirements. Currently, software vendors provide packaged services, so they are not easily reconfigurable. Thus, engineers or production managers face difficulty in composing services with the appropriate functionality and quality. The objective of this paper is to discuss high-level requirements for such a unit service concept and provide an initial use case to illustrate how the unit service concept may apply new technologies to improve service. We propose a decomposition of target service according to standard model, and we claim the limitations of decomposed unit services, and new technologies and opportunities for each decomposed services.

As companies move forward to source globally, supply chain management has gained attention more than ever before. In particular, the discovery and selection of capable suppliers has become a prerequisite for a global supply chain operation. Manufacturing e-marketplaces have helped companies discover new suppliers and/or buyers fast and effective for their products and services. Due to the description of requirements and capabilities in isolation, their true meanings may not be uniformly interpreted from each other. The issue of semantics between suppliers and buyers, then, remains an obstacle.