Conversion Tool Research Articles

Development of the automatic void generation module in GEOUNED conversion tool

The radiation transport simulation of complex nuclear facilities, such as ITER, involves the process to adapt the highly complex engineering CAD models to radiation transport computational codes. An essential feature of this process is the generation of void space. In this article, the automatic void generation module of the new GEOUNED conversion tool from CAD to MCNP geometry is presented. The module combines common approaches, such as the division of void space into void cells, with two new features: fusion of void cells and independent void regions definition with individual fill material assignation. In order to evaluate the performance of the module and the capabilities of the new features, the CAD model of the IFMIF-DONES Accelerator Systems (AS) is converted with different void mode configurations. The best configurations are determined comparing the computational performance and the void quality of the converted models.

TaxonTableTools - A comprehensive, platform-independent graphical user interface software to explore and visualise DNA metabarcoding data

DNA-based identification methods, such as DNA metabarcoding, are increasingly used as biodiversity assessment tools in research and environmental management. Although powerful analysis software exists to process raw data, the translation of sequence read data into biological information and downstream analyses may be difficult for end users with limited expertise in bioinformatics. Thus, the need for easy-to-use, graphical user interface (GUI) software to analyze and visualize DNA metabarcoding data is growing. Here we present TaxonTableTools (TTT), a new platform-independent GUI that aims to fill this gap by providing simple, reproducible analysis and visualization workflows. The input format of TTT is a so-called "TaXon table". This data format can easily be generated within TTT from two common file formats that can be obtained using various published DNA metabarcoding pipelines: a read table and a taxonomy table. TTT offers a wide range of processing, filtering and analysis modules. The user can analyze and visualize basic statistics, such as read proportion per taxon, as well as more sophisticated visualizations such as interactive Krona charts for taxonomic data exploration, or complex parallel category diagrams to assess species distribution patterns. Venn diagrams can be calculated to compare taxon overlap among replicates, samples, or analysis methods. Various ecological analyses can be produced directly, including alpha or beta diversity estimates, rarefaction analyses, and principal coordinate or non-metric multidimensional scaling plots. The taxonomy of a data set can be validated via the Global Biodiversity Information Facility (GBIF) API to check for synonyms and spelling mistakes. Furthermore, geographical distribution data can be automatically downloaded from GBIF. Additionally, TTT offers a conversion tool for DNA metabarcoding data into formats required for traditional, taxonomy-based analyses performed by regulatory bioassessment programs. Beyond that, TTT is able to produce fully interactive html-based graphics that can be analyzed in any web browser. The software comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) or the Python package index (https://pypi.org/project/taxontabletools/).

Mapping of the descriptive logic into RDF using binary relational data model

This paper is dedicated to the data integration problem. To establish relationships between data models is one of the key tasks in this solution. The descriptive logic and the relational data model are at the heart of a study. They have been used to create a mapping method on the theoretical level. The binary relational data model has been developed as a part of a mapping method. The previous studies are continued in this paper to prove on practice a mapping creation method between the descriptive logic and the binary relational data model. The method uses the binary relational data model as an integrating model. This paper continues the previous research of practical implementation of the mapping creation between the descriptive logic and the binary relational data model. The task to prove the theoretical mapping method on practice was formulated. A question how to map the binary relational data model into RDF-triples was considered. A brief overview of the R2R ML conversion tool was given. Triple maps were created to convert a conceptual information model of descriptive logic into RDF triplets with the help of R2R ML. Also, triples maps are described to convert basic mapping mechanisms into RDF with the help of R2R ML.

Analysis of TBLA (Transcript Based Lesson Analysis) SainsMastery of Mathematical Concepts

The Think Pair Share Learning Model assisted with conversion tool games is a model designed for student-centered learning. So that students as individual subjects and social fields become inseparable. The purpose of this study is to reveal the pattern of knowledge construction through conceptual mastery using Transcript Bassed Learning (TBLA). This study involved 28 students of grade 5 SDN Bendo. In this study, it was divided into 2 cycles, namely cycle 1 focused on the unit length conversion material while the cycle concentrated on the material addition and subtraction of length unit conversion. The activities in each cycle will be recorded via video or audio. Then the recording will be used as a transcript of the learning dialogue and then analyzed using Arvaja’s (2007) student response type. In addition to the transcript analysis, the students were given a written exam through the LKS (Student Worksheet). For the first cycle the class average value reached 75.36, while in the second cycle the average value reached 78.93. The percentage of completeness of all cycles has exceeded 80%.

Support NNEF execution model for NNAPI

With growing applications such as image recognition, speech recognition, ADAS, and AIoT, artificial intelligence (AI) frameworks are becoming popular in various industries. Currently, many choices for neural network frameworks exist for executing AI models in applications, especially for training/inference purposes, including TensorFlow, Caffe, MXNet, PyTorch, Core ML, TensorFlow Lite, and NNAPI. With so many different emerging frameworks, exchange formats are needed for different AI frameworks. Given this requirement, the Khronos group created a standard draft known as the Neural Network Exchange Format (NNEF). However, because NNEF is new, conversion tools for various AI frameworks that would allow the exchange of various AI frameworks remain missing. In this work, we fill this gap by devising NNAPI conversion tools for NNEF. Our work allows NNEF to execute inference tasks on host and Android platforms and flexibly invokes Android neural networks through the API (NNAPI) on the Android platform to speed up inference operations. We invoke NNAPI by dividing the input NNEF model into multiple submodels and let NNAPI execute these submodels. We develop an algorithm named BFSelector that is based on a classic breadth-first search and includes cost constraints to determine how to divide the input model. Our preliminary experimental results show that our support of NNEF on NNAPI can obtain a speedup of 1.32 to 22.52 times over the baseline for API 27 and of 4.56 to 211 times over the baseline for API 28, where the baseline is the NNEF-to-Android platform conversion without invoking NNAPI. The experiment includes AI models such as LeNet, AlexNet, MobileNet_V1, MobileNet_V2, VGG-16, and VGG-19.

Cartesian space robot manipulator clamping movement in ROS simulation and experiment

Abstract This paper studies the robot arm sorting position control based on robot operation system (ROS), which works depending on the characteristics of the robot arm sorting operation using the top method, to automate the sorting operation and improve the work efficiency of workpiece sorting. Through the ROS MoveIt! module, the sorting pose and movement path of the robotic arm are planned, the inverse kinematics of the sorting robotic arm is solved, and the movement pose characteristics of the sorting robotic arm are analysed. The robot arm model was created using Solidworks software, and the URDF model file of the robot arm was exported through the sw2urdf plugin conversion tool, and the parameters were configured. Based on ROS for 6-degree-of-freedom (DOF) robot motion simulation, random extended tree (RRT) algorithm from open motion planning library (OMPL) is selected. The robot motion planning analysis and sorting manipulator drive UR5 manipulator. The results show that the sorting pose and motion trajectory of the robot arm are determined by controlling the sorting pose of the sorting robot arm, and the maximum radius value of the tool centre point (TCP) rotation of the robot arm and the position of the workpiece are obtained. This method can improve the success rate of industrial sorting robots in grabbing objects. This analysis is of great significance to the research of robots’ autonomous object grabbing.

Twelve years of SAMtools and BCFtools.

BackgroundSAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods.FindingsThe first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines.ConclusionBoth SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.

Strategic disposal of flood debris via CO2-assisted catalytic pyrolysis

Recent abnormal climate changes resulted in the dramatic alternation of rainfall and flood patterns in many countries. The massive generation of flood debris, a mixture of soil (sediment), biomass, plastic, metal, and various hazardous materials, poses various environmental and public health problems. This study suggests a sustainable technical platform to convert the hazardous materials into value-added products. CO2-assisted pyrolysis was used to thermally convert flood debris into syngas (H2 and CO). CO2 enhanced the syngas production due to gas phase homogeneous reactions (HRs) between CO2 and volatile hydrocarbons evolved from pyrolysis of flood debris. For improvement of HRs in line with enhancement of syngas production, additional thermal energy and earth abundant catalyst were used. In particular, Ni/SiO2 catalyst increased more than one order of magnitude higher syngas production, comparing to non-catalytic pyrolysis. Synergistic effect of CO2 and Ni catalyst showed nearly 50% more production of syngas in reference to catalytic pyrolysis under N2. During flood debris pyrolysis, compositional matrix of flood debris was also determined by detecting index chemicals of waste materials that cannot be identified by naked eyes. Thus, this study confirmed that CO2-assisted pyrolysis is a useful tool for conversion of flood debris into value-added chemicals.

How do healthcare professionals experience communication with people with aphasia and what content should communication partner training entail?

Purpose Aphasia after stroke has been shown to lead to communication difficulties between healthcare professionals (HCP) and people with aphasia. Clinical guidelines emphasize the importance of teaching HCP to use supportive conversative techniques through communication partner training (CPT). The aim of this study is to explore and describe the experiences of HCP in communicating with people with aphasia and their needs and wishes for the content in CPT. Materials and methods The data were collected through qualitative semi-structured interviews with 17 HCP. HCP were recruited from two geriatric rehabilitation centres in the Netherlands and one academic hospital in Belgium. The interviews drew upon the qualitative research methodologies ethnography and phenomenology and were thematically analysed using the six steps of Braun & Clarke. Results Three themes were derived from the interviews. HCP experienced that communication difficulties impede healthcare activities (theme 1) and reported the need to improve communication through organizational changes (theme 2), changing the roles of SLTs (theme 3) and increasing knowledge and skills of HCP (theme 4). Conclusions According to HCP, communication difficulties challenge the provision of healthcare activities and lead to negative feelings in HCP. HCP suggest that communication can be improved by providing more time in the healthcare pathway of people with aphasia, adapting healthcare information to the needs of people with aphasia, commitment of physicians and managers, changing the roles of SLTs and improving knowledge and skills of HCP. Implications for rehabilitation Communication between healthcare professionals (HCP) and people with aphasia can be improved by training HCP to use supportive conversation techniques and tools. An important condition for successful implementation of communication partner trainings in healthcare centres is to identify the experiences of HCP with communication with people with aphasia and their needs and wishes for training content. This study shows that communication problems between HCP and people with aphasia impede diagnosis and therapy with considerable implications for healthcare quality. The suggestions that HCP have concerning the content of communication partner trainings can be placed under “education” and “implementation and post-training support.” HCP describe specific roles for speech-and language therapists to fulfil after the training and suggest two main changes that should be made at an organizational level.

In Situ Surface-Enhanced Raman Spectroscopy Characterization of Electrocatalysis with Different Nanostructures.

As energy demands increase, electrocatalysis serves as a vital tool in energy conversion. Elucidating electrocatalytic mechanisms using in situ spectroscopic characterization techniques can provide experimental guidance for preparing high-efficiency electrocatalysts. Surface-enhanced Raman spectroscopy (SERS) can provide rich spectral information for ultratrace surface species and is extremely well suited to studying their activity. To improve the material and morphological universalities, researchers have employed different kinds of nanostructures that have played important roles in the development of SERS technologies. Different strategies, such as so-called borrowing enhancement from shell-isolated modes and shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS)-satellite structures, have been proposed to obtain highly effective Raman enhancement, and these methods make it possible to apply SERS to various electrocatalytic systems. Here, we discuss the development of SERS technology, focusing on its applications in different electrocatalytic reactions (such as oxygen reduction reactions) and at different nanostructure surfaces, and give a brief outlook on its development.

Development of an Exploring System: An Integrated Approach to Children’s Math Education in China

In terms of the leap-forward development for education experiment, both the Chinese and English educational experiment have been very successful, while the progress of the children’s mathematics education experiment in China has been relatively slow. It is believed that the main reason for this is the lack of an efficient cognitive tool or system that supports students’ study and exploration and correspondingly covers the whole curriculum of children’s math education. In recent years, basic mathematics education cognitive tools have undergone rapid development, whereas the lack of a common normalization method for continued in-depth inquiry of the relationships among Objects, Algebra, and Geometry (OAG) has led to the fragmentation of students’ mathematical knowledge. Thus, it is difficult for students to develop a systematic understanding of mathematics. Therefore this project attempts to construct a Children’s Mathematics Exploring System (CMES), which integrates with cognition theory, method, and contents of basic mathematics education to query the essence of the relationships among OAG. Specifically, the functions of CMES include the following: (1) providing cognitive tools with typical characteristics for the relationships among OAG; (2) offering a general normalized open system on which sustainable in-depth research can be conducted and a conversion tool through which the complete textbook unit of the basic mathematics education can be converted to reveal the relationships among OAG; and (3) presenting a set of cognitive theories, methods, and a system of basic mathematics education to further explore the relationships among OAG. This project will accelerate the development of cognitive technology in basic mathematics education, widen its practical applications, reduce the difficulty of mathematics cognition, and finally improve the quality and efficiency of basic mathematics education.

Sono- and mechanochemical technologies in the catalytic conversion of biomass.

This tutorial review focuses on the valorisation of biomass by sonochemical and mechanochemical activation. Although several of the examples reported herein rely on the use of model compounds rather than native feedstocks, the conversion of lignocellulosic fractions into valuable compounds is a great opportunity with which to more sustainably exploit natural resources, from environmental, economic and social points of view. The use of non-conventional technologies that generate high-energy microenvironments can improve biomass deconstruction and the accessibility of catalysts, granting higher conversion and selectivity. The critical parameters in sonochemical and mechanochemical conversions have been analysed together with the most common devices and reactors, and the potential of sonocatalysis and mechanocatalysis as emerging tools for both catalytic and biocatalytic biomass conversion will be discussed. A SWOT (strengths, weaknesses, opportunities and threats) analysis will provide an overview of the effective feasibility of these approaches in a biorefinery context. Although these technologies offer indisputable advantages (mild reaction conditions, enhanced reaction rates and mass transfer), their mechanisms and the systematic adjustment of parameters to give optimal outcomes still require further investigation, which will pave the way for reproducible and scalable experiments. Indeed, process scale-up can be accomplished both in batch and flow mode. However, results are not particularly predictable, despite the accurate control of instrumental variables, because of the variability found in biomass sources and the complexity inherent in structures.

Construction of the Reverse Resource Recovery System of e-Waste Based on DLRNN.

The research on the reverse resource network of e-waste at home and abroad is still in its infancy, and most of it is only based on traditional forward logistics. Reverse resources are the process of moving goods from their typical final destination for recycling value or proper disposal. With the intensification of market competition and the strengthening of environmental protection legislation by the government, reverse resources are no longer a neglected corner in the supply chain. The DLRNN model of the e-waste reverse resource recovery system constructed in this paper can provide an important theoretical and empirical basis for the rational utilization of waste electronic products and fully tap the potential value of waste electronic products, which is of great significance to the recycling of natural resources. In this paper, a hybrid network framework DLRNN based on deep learning (DL) and cyclic neural network (RNN) is designed for problem classification. Experimental results show that the classification accuracy of this framework is improved by 2.4% on TREC and 2.5% on MSQC without additional word vector conversion tools.

The development of electrical energy conversion tools as learning media for the concept of energy sources

The purpose of this research is to produce electrical energy conversion tool as a learning medium. This research process is a research and development research which is commonly referred to as R&D. Sugiyono modified this research model. The instrument used was a questionnaire with a Likert scale which were assessed by media experts, material experts, physics teachers and students of Grade XI IPA Senior High School Lab school, Untad, Palu. The product quality data obtained were analyzed using descriptive analysis. The product quality based on the results of the analysis of the media expert’s assessment obtained a percentage value of 84% with the interpretation of “Very Good”. The product quality, based on the results of the assessment analysis by material experts, obtained a percentage value of 85% with the interpretation “Very Good”. For the results of the physics teacher responses on the perspective of the learning media developed, it obtained a percentage value of 92.2% with the interpretation “Strongly Agree”. For the results of the students’ responses in Grade XI Science Senior High School Lab school, Untad, Palu, on the limited test, the percentage value obtained was 85.95% which had the interpretation of, “Strongly Agree” regarding the use of the learning media. The results of the tests and questionnaire analysis indicated that the electric energy conversion tool as a learning medium developed is suitable for use in the learning process.

A Practical, Integrated Multi-Criteria Decision-Making Scheme for Choosing Cloud Services in Cloud Systems

Currently, with the rapid development and broad application of cloud computing technology, companies tend to use cloud services to build their applications or business systems. Selecting a trustworthy cloud service is a challenging multi-criteria decision-making (MCDM) problem. Moreover, decision makers are more inclined to use linguistic descriptions to assess the quality of service (QoS) for cloud services due to the limitation of the decision makers' knowledge and the vagueness of criteria information. Therefore, we propose a practical, integrated MCDM scheme for cloud service evaluation and selection of cloud systems, allowing decision makers to compare cloud services based on QoS criteria. First, to more accurately and effectively express the uncertainty of qualitative concepts, the cloud model is used as a conversion tool for qualitative and quantitative information to quantify linguistic terms. Second, given the shortcomings of traditional differentiating measures between cloud models, a more comprehensive distance measurement algorithm using cloud droplet distribution is proposed for the cloud model. The new distance measurement algorithm is applied to the calculation of cloud model similarity and the gray correlation coefficient. The dynamic expertise weights are determined by calculating the similarity between the expert evaluation cloud model and the arithmetic mean cloud model. Then, we propose a technique for order preference by similarity to an ideal solution (TOPSIS) improved by the grey relational analysis (GRA) to calculate the relative closeness of alternatives to the positive and negative ideal solutions and establish a multi-objective optimization model that maximizes the relative closeness of all alternatives to determine the weights of the criteria. Finally, we reconstructed the QoS evaluation criteria for cloud services from both application and service perspectives, and the classical TOPSIS is applied to generate alternative rankings. The practicability and robustness of the scheme were tested through the cloud service selection problem experienced by a real mining company's scheduling platform, which can provide practical references with the theoretical basis for the selection and evaluation of cloud services.

Conversion between the Modified Mini-Mental State Examination (3MSE) and the Mini-Mental State Examination (MMSE).

BackgroundThe Modified Mini‐Mental State Examination (3MSE) and the Mini‐Mental State Examination (MMSE) are two commonly used instruments for assessing cognitive function. Although conversion between 3MSE and MMSE is useful in applications such as integrative data analysis, there are limited published reports on the topic. Our objective is to provide a dual tool: (1) an item‐level conversion tool to score responses for deriving both 3MSE and MMSE measures, and (2) cross‐walk tables to facilitate quick conversion between 3MSE and MMSE.MethodsAn SAS program tool allows scoring of 3MSE item‐level responses into MMSE score. Using integrated data sets (n = 8346), actual 3MSE and MMSE scores obtained from the same individuals were linked to form cross‐walk tables.ResultsAn SAS conversion program was made available. Cross‐walk tables were derived. Validation sample shows bias is –0.11 (standard deviation = 1.02) in 3MSE→MMSE; the converse had substantially large bias.DiscussionThe 3MSE→MMSE conversion table can be used in clinical practice and legacy system data.

Feasibility and acceptability of the "Day 100 Talk": An interdisciplinary communication intervention during the first six months of childhood cancer treatment.

Communication gaps arise early in the childhood cancer trajectory and may persist. The authors conducted a pilot study of the feasibility and acceptability of a communication intervention, the Day 100 Talk (D100). D100 involves an interprofessional family conference during initial months of treatment between oncologists, psychosocial clinicians, and parents, facilitated by a 3-part conversation tool. The authors enrolled English-speaking parents of children with nonrelapsed, nonprogressive cancer who were receiving continuity care from enrolled pediatric oncologists and psychosocial clinicians at a single site. The a priori feasibility threshold was 60% parent completion of the D100 intervention. Surveys from parents and professionals and debrief interviews with professionals assessed D100 acceptability. Thirty-seven parents (77%) and 38 oncology professionals (67%) enrolled. Twenty of 33 evaluable parents (61%) participated in a D100 family conference. Most commonly, parents did not complete the D100 intervention because of scheduling difficulties related to clinical team constraints. All 17 parents who completed a post-D100 survey agreed or strongly agreed that D100 participation was helpful. In debrief interviews, professionals identified D100 benefits, namely, stepping back to the big picture and getting on the same page, and barriers related to logistical challenges and professionals' anticipatory dread. The D100 intervention pilot demonstrates high acceptability among parents of children with cancer. Despite meeting the prespecified feasibility threshold, findings highlight important barriers to D100 dissemination, namely, perceived burdens on professionals. Potential strategies to reduce burden may include using virtual visit platforms, incorporating D100 elements across multiple visits, or prioritizing intervention delivery to parents with the greatest need for enhanced communication.

A CHAT about health literacy - a qualitative feasibility study of the Conversational Health Literacy Assessment Tool (CHAT) in a Danish municipal healthcare centre.

BackgroundUnderstanding individual health literacy needs is crucial when designing supportive and effective health care. However, tools assessing health literacy in practice are lacking. The Conversational Health Literacy Assessment Tool (CHAT) was recently developed, but its ability to assess health literacy remains unexplored. We aimed to investigate the implementation and adoption of CHAT, its ability to increase awareness of health literacy among healthcare providers, and if CHAT could assess health literacy needs in patients.MethodsWe performed a qualitative feasibility study of CHAT among healthcare providers (nurses, physiotherapists and occupational therapists) who provide rehabilitation services for patients with noncommunicable diseases in a municipal healthcare centre in Denmark. The study used the RE‐AIM framework (Reach, Effectiveness, Adoption, Implementation and Maintenance) to structure interview guide and analysis. We collected qualitative data from four small focus groups with healthcare providers (n = 11). The data were analysed using a deductive thematic three‐step method for organising and interpreting data. All informants provided written informed consent prior to data collection.ResultsCHAT seems to be a feasible and efficient tool for assessing health literacy needs among individuals with different socio‐demographic characteristics and with different diagnoses. CHAT was easiest implemented and adopted by healthcare providers, who were already familiarly with the concept of health literacy. The informants emphasised that an introduction to CHAT and health literacy as concept was valuable for the adoption. Some of them felt frustrated that they did not have opportunity and options to meet the health literacy needs identified by CHAT.ConclusionsCHAT is a promising tool for assessing individual health literacy needs and increasing awareness of health literacy among healthcare providers. For successfully implementation of CHAT, we recommend developing a structured implementation programme, including an introduction to health literacy and an outline of the options for acting upon CHAT results.

Firefighter Occupational Cancer Risk: Starting the Conversation.

This study was conducted to determine if an educational session would increase the firefighter's knowledge of their occupational cancer risk and increase their motivation and confidence to have a conversation with their primary care provider (PCP) in regard to their occupational cancer risk. This study was an exploratory descriptive interventional pilot study. Firefighters self-reported using a pre-/post-educational intervention survey. Results were analyzed using a paired t test in IBM SPSS Version 23 to compare the pre-/post-test mean scores. There were statistically significant differences noted in the mean scores of the pre-/post-interventional survey tool. It seems an educational intervention can motivate and increase the firefighter's confidence to have a conversation with their PCP in regard to their occupational cancer risk. Firefighter perceptions of their "lived" care experience at the PCP level shared during study sessions also contributed to some key conclusions. These conclusions lead to the development of an informational wallet card for the firefighter to use as a conversational tool during a visit with their PCP. Motivating firefighters to have a conversation with their PCP in regard to their occupational cancer risk could potentially increase firefighter occupational cancer awareness at the PCP level and result in earlier detection of cancers in firefighters leading to decreased firefighter cancer mortality rates. Increasing informed firefighter conversations at the PCP level in regard to their occupational cancer risk could potentially increase appropriate cancer prevention guidance and earlier cancer screenings at the primary care level.

Enhanced production of polyhydroxybutyrate from syngas by using nanoscaled cellulose particles with a syngas-converting enzyme complex immobilized on Ralstonia eutropha

Synthetic gas (syngas) produced from pyrolysis or a blast furnace is a mixture of different ratio of H2, CO, N2, CH4, CO2 and other carbon-containing gas. CO2 and H2 are the key sources for Ralstonia eutropha, which uses CO2 to produce polyhydroxybutyrate (PHB) with a H2 uptake system. For the efficient conversion of syngas to accessible carbon source, nanoscaled cellulose particles with an enzyme complex consisting of carbon monoxide dehydrogenase (CooS), carbon monoxide binding unit (CooA) and carbonic anhydrase (CA) were assembled by minimized scaffoldin (mCbpA). In addition to this assembly, mCbpA-BspA (MB) established to bridge the nanoparticle assembly and R. eutropha was used to provide higher enzyme stability and activity. The highest PHB accumulation after 72 h of fermentation was 14.2 g/L in a closed serum bottle that contained the enzyme assembly immobilized on R. eutropha. This catalytic assembly may be a new potential conversion tool to produce biodegradable PHB from syngas.