Development Process Research Articles

Research and Application of Treatment Measures for Low-Yield and Low-Efficiency Coalbed Methane Wells in Qinshui Basin

China is rich in high-grade coalbed methane resources, accounting for one-third of the total amount of coalbed methane resources. Qinshui Basin is the main high ranking coalbed methane mining basin in China. In the early stage of CBM development, low-production and low-efficiency wells were formed in the process of block development because of an insufficient understanding of reservoir geological conditions. The existence of low-yield and low-efficiency wells with low output and a poor development benefit seriously restricts the efficient development of coalbed methane. In order to improve the overall development efficiency of coalbed methane fields, how to revitalize low-yield and low-efficiency wells is the main problem facing the development process of coalbed methane. With the deepening understanding of the study area geology, the formation of low-yield and low-efficiency wells has been basically identified. With the advancement of development technology, developers have the ability to retrofit some low-producing and inefficient wells. Low-production and low-efficiency wells are widely distributed. It is difficult to find the criteria for classifying low-producing and low-efficiency wells because of the great differences in geological conditions and reservoir physical properties in different blocks. In addition, the causes of a low-production and low-efficiency well are complex, as the same well is often caused by many reasons, and how to identify the causes of low-production and low-efficiency wells is difficult. In recent decades, developers have studied many methods to retrofit low-production wells, but the retrofit results are not satisfactory. How to choose an economical and efficient reservoir reconstruction method to revitalize low-production and low-efficiency wells is particularly important. This paper starts with the definition of low-production and low-efficiency wells in different blocks, combining an economic evaluation and productivity characteristics to judge whether they are low-production and low-efficiency wells, and defines the distribution of low-production and low-efficiency wells in blocks. The reasons for the formation of low-production and low-efficiency wells are analyzed with the geological characteristics, production dynamic performance, and engineering reconstruction effects. This paper makes a comparative analysis of the current relatively mature low-production and low-efficiency well treatment measures, clearly identifies the advantages and disadvantages of different treatment measures, and takes corresponding stimulation measures for different causes of low-production and low-efficiency wells. The research shows that there are 687 low-production and low-efficiency wells in block A, accounting for 69.4% of the total number of wells, and the low-production and low-efficiency wells account for a relatively large proportion; so, it is necessary to treat them. The main causes of low-production and low-efficiency wells are geology, engineering and drainage systems. The geological reason mainly refers to the low gas production of coalbed methane wells influenced by three factors: resource abundance, faults, and collapse columns. According to the different causes, three treatment measures of large-scale secondary fracturing, temporary plugging, and diversion fracturing and foam fracturing are put forward. The research method in this paper is targeted at different geological conditions so it can be used to guide the treatment of low-yield and low-efficiency wells in other CBM blocks, and it has very important significance for revitalizing the existing low-efficiency CBM assets and improving the development efficiency of CBM.

Development of interactive digital teaching materials based on 21st century skills in special purpose arabic language courses at STAI Attanwir Bojonegoro

Special Purpose Arabic Language refers to the teaching or learning of Arabic language tailored to specific needs or objectives. This means that Arabic language lessons are designed with a focus on its usage context in specific fields or activities, as opposed to a general approach to teaching Arabic. Special Purpose Arabic Language courses at the university level are tailored to specific needs and objectives in a professional context, producing graduates ready to face global challenges. The objectives of this research are (1) to analyze the process of developing interactive digital teaching materials based on 21st-century skills in Special Purpose Arabic Language courses at STAI Attanwir Bojonegoro, (2) to analyze the feasibility of interactive digital teaching materials based on 21st-century skills in Special Purpose Arabic Language courses at STAI Attanwir Bojonegoro, and (3) to analyze the effectiveness of interactive digital teaching materials based on 21st-century skills in Special Purpose Arabic Language courses at STAI Attanwir Bojonegoro. This research uses the Research and Development (R&D) method using the ADDIE model. Data collection techniques in this study use expert validation questionnaires, expert validation materials, student responses, and tests. The results of this research and development are as follows: (1) The development process consists of five steps: analysis, development, design, implementation, and evaluation, resulting in the production of interactive digital teaching materials based on 21st-century skills. (2) Based on validation results by experts, an average score of 90% was obtained. According to the Likert scale, the value of 90% falls into the category of very feasible because it is within the range of >61%. Meanwhile, the average percentage of student responses obtained an overall score of 88.5%, falling into the category of very feasible. (3) The results of the effectiveness test of teaching materials using a paired sample T-test showed a significant difference between pre-test and post-test scores with a significant value (2-tailed) of p = 0.000, <0.05. The null hypothesis (H0) in this study was rejected, and the alternative hypothesis (Ha) was accepted, indicating a significant difference between the two tests. Based on the data above, the developed teaching materials are effective in Special Purpose Arabic Language courses.

Manufacturing mesenchymal stromal cells in a microcarrier-microbioreactor platform can enhance cell yield and quality attributes: case study for acute respiratory distress syndrome

Mesenchymal stem and stromal cells (MSCs) hold potential to treat a broad range of clinical indications, but clinical translation has been limited to date due in part to challenges with batch-to-batch reproducibility of potential critical quality attributes (pCQAs) that can predict potency/efficacy. Here, we designed and implemented a microcarrier-microbioreactor approach to cell therapy manufacturing, specific to anchorage-dependent cells such as MSCs. We sought to assess whether increased control of the biochemical and biophysical environment had the potential to create product with consistent presentation and elevated expression of pCQAs relative to established manufacturing approaches in tissue culture polystyrene (TCPS) flasks. First, we evaluated total cell yield harvested from dissolvable, gelatin microcarriers within a microbioreactor cassette (Mobius Breez) or a flask control with matched initial cell seeding density and culture duration. Next, we identified 24 genes implicated in a therapeutic role for a specific motivating indication, acute respiratory distress syndrome (ARDS); expression of these genes served as our pCQAs for initial in vitro evaluation of product potency. We evaluated mRNA expression for three distinct donors to assess inter-donor repeatability, as well as for one donor in three distinct batches to assess within-donor, inter-batch variability. Finally, we assessed gene expression at the protein level for a subset of the panel to confirm successful translation. Our results indicated that MSCs expanded with this microcarrier-microbioreactor approach exhibited reasonable donor-to-donor repeatability and reliable batch-to-batch reproducibility of pCQAs. Interestingly, the baseline conditions of this microcarrier-microbioreactor approach also significantly improved expression of several key pCQAs at the gene and protein expression levels and reduced total media consumption relative to TCPS culture. This proof-of-concept study illustrates key benefits of this approach to therapeutic cell process development for MSCs and other anchorage-dependent cells that are candidates for cell therapies.

Versatile fitting approach for operando spectroscopic imaging ellipsometry of HfS2 oxidation

Facile mapping of 2D heterostructures and resolving anisotropic formation kinetics down to the monolayer level are critical to developing scalable interfacing solutions and unlocking their application potential in emerging nano-optoelectronics. We adapt a Kramers–Kronig constrained variational fitting algorithm for spectroscopic imaging ellipsometry (SIE) to facilitate multi-scale heterostructure analysis comprising films with unknown complex dielectric functions and demonstrate how this enables non-destructive, scalable mapping and operando capability for the model system of HfS2 oxidation. This methodology proves highly accurate for assessing the thickness of buried HfS2 layers, oxide quality, and lateral and vertical uniformity. We capture dynamic stack evolution during thermal oxidation up to 400 ∘C, providing insights into the temperature and time-dependent nature of self-limiting oxide growth and reaction kinetics that involve the localised trapping and release of sulphur reaction products. Our methodology is versatile in material and device horizons, and advantageously agnostic to the underlying substrate. Combined with the various modes of SIE operation, it unlocks fast, high-throughput, large-area capability to accelerate process development at the atomic scale.

Premixed calcium silicate-based ceramic sealers promote osteogenic/cementogenic differentiation of human periodontal ligament stem cells: A microscopy study.

To evaluate the effects of premixed calcium silicate based ceramic sealers on the viability and osteogenic/cementogenic differentiation of human periodontal ligament stem cells (hPDLSCs). The materials evaluated were TotalFill BC Sealer (TFbc), AH Plus Bioceramic Sealer (AHPbc), and Neosealer Flo (Neo). Standardized discs and 1:1, 1:2, and 1:4 eluates of the tested materials were prepared. The following in vitro experiments were carried out: ion release, cell metabolic activity 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, cell migration, immunofluorescence experiment, cell attachment, gene expression, and mineralization assay. Statistical analyses were performed using one-way ANOVA followed by Tukey's post hoc test (p < .05). Increased Ca2+ release was detected in TFbc compared to AHPbc and Neo (*p < .05). Biological assays showed a discrete cell metabolic activity and cell migration in Neo-treated cell, whereas scanning electronic microscopy assay exhibited that TFbc group had a better cell adhesion process of substrate attachment, spreading, and cytoskeleton development on the niche-like structures of the cement than AHPbc and Neo. The sealers tested were able to induce overexpression of the CEMP-1, ALP, and COL1A1 genes in the first days of exposure, particularly in the case of TFbc (***p < .001). All materials tested significantly increased the mineralization of hPDLSCs when compared to the negative control, although more pronounced calcium deposition was observed in the TFbc-treated cells (***p < .001). Our results suggested that TFbc promotes cell differentiation, both by increasing the expression of key osteo/odontogenic genes and by promoting mineralization of the extracellular matrix, whereas this phenomenon was less evident in Neo and AHPbc. RESEARCH HIGHLIGHTS: TFbc group had a better cell adhesion process of substrate attachment, spreading, and cytoskeleton development on the niche-like structures of the cement than AHPbc and Neo. The sealers tested were able to induce overexpression of the CEMP-1, ALP, and COL1A1 genes in the first days of exposure, particularly in the case of TFbc. All materials tested significantly increased the mineralization of hPDLSCs when compared to the negative control, although more pronounced calcium deposition was observed in the TFbc-treated cells.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.

The study of transporter proteins is key to understanding the mechanism behind multi-drug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

A Change from Negative to Positive of Later Adoption Using the Innovation Decision Process to Imply Sustainability for HR Chatbots of Private Companies in Thailand

The current adoption of human resource (HR) chatbots has created problems within organizations, such as stilted conversational flow and a limited range of queries and responses. This paper presents an analysis of the factors affecting these issues by employing a new conceptual model. The instances of rejection and acceptance of an HR chatbot were analyzed in this study using the innovation decision process. A survey of 251 employees from private companies in Thailand was conducted, scrutinizing their experiences of using HR chatbots. Then, the innovation decision process was utilized to identify the critical factors that influenced the shift in attitude from rejection to acceptance. The survey identified three key findings affecting employees’ negative attitudes towards the HR chatbot, namely, hesitation concerning the perceived ease-of-use (HPEOU), word of mouth (WoM), and personal innovation (PI). Additionally, our research also revealed that the way people perceive the level of risk associated with using the HR chatbot directly affects their intention to reject. This highlighted the significance of organizational development for facilitating the re-engagement of employees with the HR chatbot, and specifically, a focus on the elements of people (PP), processes (PC), technology (TE), and policy (PL). This study demonstrated the advances in process development within an organization and its corresponding policies. The validation of HR chatbots was influenced by a distinct corporate vision. This study provides guidelines for the implementation of HR chatbots for employees in private corporations in Thailand. The findings can assist in enhancing operational performance and the subsequent adoption of HR chatbots, resulting in the sustainable development of an efficient acceptance evaluation model of change from negative to positive. This model uses the innovation decision process to foster the sustainability of HR chatbots in private companies in Thailand.

Protocol for the Growth and Maturation of hiPSC-Derived Kidney Organoids using Mechanically Defined Hydrogels.

With recent advances in the reprogramming of somatic cells into induced Pluripotent Stem Cells (iPSCs), gene editing technologies, and protocols for the directed differentiation of stem cells into heterogeneous tissues, iPSC-derived kidney organoids have emerged as a useful means to study processes of renal development and disease. Considerable advances guided by knowledge of fundamental renal developmental signaling pathways have been made with the use of exogenous morphogens to generate more robust kidney-like tissues in vitro. However, both biochemical and biophysical microenvironmental cues are major influences on tissue development and self-organization. In the context of engineering the biophysical aspects of the microenvironment, the use of hydrogel extracellular scaffolds for organoid studies has been gaining interest. Two families of hydrogels have recently been the subject of significant attention: self-assembling peptide hydrogels (SAPHs), which are fully synthetic and chemically defined, and gelatin methacryloyl (GelMA) hydrogels, which are semi-synthetic. Both can be used as support matrices for growing kidney organoids. Based on our recently published work, we highlight methods describing the generation of human iPSC (hiPSC)-derived kidney organoids and their maturation within SAPHs and GelMA hydrogels. We also detail protocols required for the characterization of such organoids using immunofluorescence imaging. Together, these protocols should enable the user to grow hiPSC-derived kidney organoids within hydrogels of this kind and evaluate the effects that the biophysical microenvironment provided by the hydrogels has on kidney organoid maturation. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Directed differentiation of human induced pluripotent stem cells (hiPSCs) into kidney organoids and maturation within mechanically tunable self-assembling peptide hydrogels (SAPHs) Alternate Protocol: Encapsulation of day 9 nephron progenitor aggregates in gelatin methacryloyl (GelMA) hydrogels. Support Protocol 1: Human induced pluripotent stem cell (hiPSC) culture. Support Protocol 2: Organoid fixation with paraformaldehyde (PFA) Basic Protocol 2: Whole-mount immunofluorescence imaging of kidney organoids. Basic Protocol 3: Immunofluorescence of organoid cryosections.

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study

BackgroundHospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of natural language processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes. ObjectiveThis study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model. DesignA retrospective observational cohort study. Setting(s)This study was conducted at Ajou University School of Medicine in South Korea. ParticipantsData from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized. MethodsTwo distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC. ResultsAmong 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672–0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768–0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model. ConclusionsThis study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

Effects of endogenous testosterone on oscillatory activity during verbal working memory in youth.

Testosterone levels sharply rise during the transition from childhood to adolescence and these changes are known to be associated with changes in human brain structure. During this same developmental window, there are also robust changes in the neural oscillatory dynamics serving verbal working memory processing. Surprisingly, whereas many studies have investigated the effects of chronological age on the neural oscillations supporting verbal working memory, none have probed the impact of endogenous testosterone levels during this developmental period. Using a sample of 89 youth aged 6-14 years-old, we collected salivary testosterone samples and recorded magnetoencephalography during a modified Sternberg verbal working memory task. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting maps were subjected to whole-brain ANCOVAs examining the effects of testosterone and sex, controlling for age, during verbal working memory encoding and maintenance. Our primary results indicated robust testosterone-related effects in theta (4-7 Hz) and alpha (8-14 Hz) oscillatory activity, controlling for age. During encoding, females exhibited weaker theta oscillations than males in right cerebellar cortices and stronger alpha oscillations in left temporal cortices. During maintenance, youth with greater testosterone exhibited weaker alpha oscillations in right parahippocampal and cerebellar cortices, as well as regions across the left-lateralized language network. These results extend the existing literature on the development of verbal working memory processing by showing region and sex-specific effects of testosterone, and are the first results to link endogenous testosterone levels to the neural oscillatory activity serving verbal working memory, above and beyond the effects of chronological age.

LSAC-Net: A Lightweight Scale-aware CNN with Densely Connected Focal Modulation for Retinal Blood Vessel Segmentation

The diagnosis of sight-threatening eye conditions typically involves segmenting retinal structures in fundus images. The segmentation process enables clinicians to identify alterations in retinal morphology, facilitating the detection and diagnosis of various eye diseases. Despite recent developments in image processing, current approaches often fail to partition delicate arteries precisely. Although deep learning has great potential for segmenting medical images, the accuracy of the segmentation process may be limited due to its dependence on repetitive convolution and pooling processes, which may impair the representation of edge information. In this study, we present LSAC-Net, a pixel-level convolutional neural network (CNN) designed specifically for retinal vessel segmentation. Moreover, it is lightweight, with only 37.5 K learnable parameters. LSAC-Net integrates a unique scale-aware feature extraction block (SAFEB) and a densely connected focal modulation block (DCFMB) to improve segmentation accuracy. In addition, careful selection of filter numbers to avoid overlap effectively shortens training times and boosts computational performance, both of which improve overall model efficiency. We performed extensive tests on different aspects of retinal images to evaluate the robustness and generalizability of LSAC-Net. In particular, a thorough evaluation of the model's precision in retinal artery segmentation, a crucial task for ophthalmological diagnosis and treatment, was carried out. We evaluated the performance and efficacy of LSAC-Net with a particular focus on RBV. The model obtains 98.65% specificity, 97.54% accuracy, and 98.88% area under the curve surpassing existing approaches. Experimental results confirm that LSAC-Net is robust, generalizable, and maintains a high level of segmentation accuracy. These features highlight LSAC-Net's potential as a useful instrument for accurate and timely retinal image segmentation in a variety of therapeutic contexts.

Logic-driven, simulation-based risk engineering to ensure the sustainability of productive processes even with data scarcity

Designing and operating production systems and keeping them up to date at the speed of innovation to meet competition, consumer trends and sustainability requirements is a challenging task. The problem becomes even more challenging when the production system is highly uncertain and data-poor. This complexity is increasingly recognised, as is the value that risk engineering can bring to the design, operation, maintenance and upgrading of production systems to avoid compromising their viability and ensure their continued efficiency.The manuscript is guided by this challenge and has a twofold objective. First, it analyses and explains the importance of adopting a logic-driven and simulation-based risk engineering approach to support decision making. This objective is achieved by going to the root of the methodological constructs that characterise the methodologies available in the literature and by highlighting the main limitations. Secondly, it applies the HoRAM method to the use case of food banks to verify its suitability. The use case of food banks was chosen because it is characterised by more and unusual uncertainties compared to conventional production systems (such as uneven labour and raw material availability).The main contributions of this research are as follows. First, it provides a critical analysis of currently available risk assessment methodologies. It highlights the weaknesses and explains the implications of these weaknesses for decision support. Secondly, it concerns the development of a complete risk engineering process, offering an approach that goes beyond conventional risk engineering approaches that stop at the identification of the critical elements associated with the problem at hand. It explains the importance of closing the risk analysis loop by verifying the efficiency of the identified mitigation solutions.The results suggest that the proposed approach is suitable for supporting complex decision-making processes characterised by high uncertainty and data scarcity. Furthermore, due to the logic-driven nature of the proposed approach, non-experts can be involved and contribute to the analysis and engineering of risks. This allows to increase the situational awareness of decision makers and consequently their efficiency in making complex decisions.

Process design, performance comparison and operating test of the multi-stage vertical absorption heat exchanger realizing independent heating in three zones

Absorption heat exchangers applied in district heating systems can significantly reduce the outlet water temperature of the primary network, thereby expanding the heating scale and promoting low-temperature waste heat recovery. However, nearly 50% of the existing heating systems in high-rise buildings are divided into three independent zones. At present, there is a lack of research for absorption heat exchangers in this scenario. The main task of this paper is to address this issue to expand the application range of absorption heat exchangers. Firstly, two different processes are designed in this paper. The first process is to add a plate heat exchanger in the existing two-stage process, while the second process is a novel three-stage vertical process, consisting of three absorption heat pumps with different pressures and three sets of plate heat exchangers. Then, the operating performance, self-adjustment ability and the system cost of different processes are compared based on the simulation method. Although the price of Process 2 is 26.4% higher than that of Process 1, the efficiency of Process 2 is 20.3%-27.7% higher, and the self-regulation ability is significantly better. Thus, Process 2 has better overall performance. Moreover, the system development and operating test of Process 2 are conducted. Throughout the whole heating season, the overall efficiency of the system is 1.17–1.23. Without manual intervention, the relative standard deviations of the inlet water temperature of the secondary network and the actual heat supply rate ratio are less than 5% and 16% respectively, which proves that the system has great self-adjustment ability. The conclusions in this paper can guide the design and application of the absorption heat exchanger in three-zone independent heating scenarios.

Exploring the Biochemical Mechanisms of Fluoroquinolone Compounds against Tuberculosis by Utilizing Molecular Docking and Quantitative Structure-amino Acid Relationship

Background: Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb). It is one of the leading causes of death of 1.5 million people each year. TB can be treated by directly observed treatment short course (DOTS), but due to multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis, consequences can be devastating if the single DOTS dose is missed by the patient. MDR and XDR-TB require much more attention and time to control the infection. The longer period of tuberculosis treatment has side effects and it is expensive. Objectives: This alarming condition demands the development of novel processes to diagnose the disease in its early stage as well as to produce more promising antimicrobial chemotherapeutics. The current study aimed to explore molecular mechanisms involving docking simulation-based quantitative structureamino acid relationship (QSAAR) in order to have a better understanding of the interactions between the fluoroquinolones and Mtb DNA gyrase. Methods: In this study, 24 fluoroquinolone (FQ) compounds present in the literature were selected and docked against the Mtb DNA gyrase. Further, the relationship between the minimum inhibitory concentration of the compounds and interacting amino acids was assessed using QSAAR. Results: The study has established a novel method of formulating a quantitative structure-amino acid relationship. A significant correlation (R-value=0.829) between biological activity and the docked amino acid residues responsible for producing anti-tubercular activities has been obtained. Conclusion: The predicted residues captured in the developed model have been explored to report the Mtb virulence.

Developing an international consensus reporting guideline for intervention Fidelity in non-drug, non-surgical trials: The ReFiND protocol

BackgroundInadequate reporting of fidelity to interventions in trials limits the transparency and interpretation of trial findings. Despite this, most trials of non-drug, non-surgical interventions lack comprehensive reporting of fidelity. If fidelity is poorly reported, it is unclear which intervention components were tested or implemented within the trial, which also hinders research reproducibility. This protocol describes the development process of a reporting guideline for fidelity of non-drug, non-surgical interventions (ReFiND) in the context of trials. MethodsThe ReFiND guideline will be developed in six stages. Stage one: a guideline development group has been formed to oversee the guideline methodology. Stage two: a scoping review will be conducted to identify and summarize existing guidance documents on the fidelity of non-drug, non-surgical interventions. Stage three: a Delphi study will be conducted to reach consensus on reporting items. Stage four: a consensus meeting will be held to consolidate the reporting items and discuss the wording and structure of the guideline. Stage five: a guidance statement, an elaboration and explanation document, and a reporting checklist will be developed. Stage six: different strategies will be used to disseminate and implement the ReFiND guideline. DiscussionThe ReFiND guideline will provide a set of items developed through international consensus to improve the reporting of intervention fidelity in trials of non-drug, non-surgical interventions. This reporting guideline will enhance transparency and reproducibility in future non-drug, non-surgical intervention research.

SNNtrainer3D: Training Spiking Neural Networks Using a User-Friendly Application with 3D Architecture Visualization Capabilities

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model’s behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners.

Potential of Growing Energy Crops and Then Producing Liquid Fuels in Marginal Land: A China Scenario

To explore the potential of growing energy crops and then producing liquid fuels in marginal land in China, in this paper, the status quo of existing biomass resources and marginal land utilization in China have been first analyzed and the development process of biomass liquid fuels has then been reviewed. Secondly, using ethyl levulinate (EL) as our research subject, the production capacity of growing energy crops in marginal land and their potential to prepare liquid fuels have been explored. Finally, the problems in developing marginal land have been summarized, and proposed policy recommendations for marginal land development, energy crop cultivation, and liquid fuel development suitable for Chinese conditions. The results showed that the potential of sweet sorghum, cassava, jatropha curcas, and switchgrass cultivation in China in producing is 75.76 million tonnes, 1.52 million tonnes, 4.57 million tonnes, and 5.16 million tonnes, respectively. Among these crops, sweet sorghum and switch grass have a higher production capacity and are more suitable to be planted on marginal land and used to produce liquid fuels. The planting of these two crops has absorbed about a 111.11 million tonnes and 7.57 million tonnes of carbon dioxide (CO2), respectively, presenting significant carbon sequestration and oxygen-producing effects, which provides a theoretical basis for the analysis of land use change.

On the role of hydrogen inhibition in gas-phase methane pyrolysis for carbon capture and hydrogen production in a tubular flow reactor

The thermal pyrolysis of methane allows an economically competitive hydrogen production without direct CO2 emissions. Although several mechanisms for the process have already been proposed, especially the inhibitory effect of hydrogen as well as the process operation at increased pressure have not yet been fully clarified. In this context, the present work investigates the influence of hydrogen and argon as inert gas on product composition, methane conversion, and hydrogen selectivity as a function of temperature (1000 °C to 1600 °C), residence time (1s to 7s), molar dilution ratio (1:1 to 4:1), and pressure (1bar to 4bar) in a high-temperature reactor. Within the scope of this work, total differences in CH4 conversion of up to 50% could be observed at equal process parameters between an argon and hydrogen dilution, underlining the potential impact of diluents on the overall process. Moreover, increasing the pressure from 1bar to 4bar reduces the formation of byproducts significantly for both H2 and Ar as diluent, however with different mechanistic characteristics. The most remarkable difference is the formation of propylene that exclusively takes place in argon-diluted reaction gas mixtures. This occurrence persists unabated, even under elevated pressures and temperatures reaching as high as 1600 °C. We ascribe this phenomenon to the interaction of methyl and ethyl radicals, establishing it as an impasse to further reactions leading to the formation of solid products. Herewith, the study provides novel insights from a reaction engineering perspective as well as from a process development perspective and clarifies the role of the dilution gases hydrogen and argon on the methane pyrolysis reaction.

Generating suspension-adapted human mesenchymal stromal cells (S-hMSCs) for the scalable manufacture of extracellular vesicles

Human mesenchymal stromal cells (hMSCs) are a naturally adherent cell type and one of the most studied cellular agents used in cell therapy over the last 20 years. Their mechanism of action has been primarily associated with paracrine signalling, which has contributed to an increase in the number of studies focused on hMSC-related extracellular vesicles (EVs). In this study, we demonstrate for the first time that human telomerase reverse transcriptase (hTERT) immortalised hMSCs can be adapted to suspension culture, eliminating the need for microcarriers or other matrixes to support cell growth. This novel cell line, named suspension hMSCs (S-hMSCs), has a doubling time of approximately 55 hours, with a growth rate of 0.423 d−1. Regarding its immunophenotype characteristics, S-hMSCs retained close to 90% of CD73 and CD105 expression levels, with the CD90 receptor being downregulated during the adherent to suspension adaptation process. An RNA sequencing analysis showed an upregulation of the transcripts coding for CD44, CD46 and CD47 compared to the expression levels in AT-hMSCs and hTERT-hMSCs. The cell line herein established was able to generate EVs using a chemically defined medium formulation with these nanoparticles averaging 150 nm in size and displaying the markers CD63, CD81, and TSG101, while not expressing the negative marker calnexin. This body of evidence, combined with the visual confirmation of EV presence using transmission electron microscopy, demonstrates the EV-producing capabilities of the novel S-hMSCs. This cell line provides a platform for process development, drug discovery and translational studies in the EV field.

Biorefinery of red seaweed Palmaria palmata for production of bio-based chemicals and biofuels

The seaweed Palmaria palmata was used as feedstock for production of chemical/fuel precursors (acetone, butanol, ethanol) and biogas. Palmaria palmata consists of xylan as major cell wall polysaccharide, as well as galactan and glucan. First, the biomass was subjected to acid hydrolysis at laboratory scale to solubilise fermentable sugars using acetic or hydrochloric acid as catalysts. Differences were observed in composition and amenability to hydrolysis of seaweeds from different harvests. Highest saccharification yields were obtained using HCl (pH 1.7, 120 °C, up to 80 % of xylose). Hydrolysates were fermented to acetone-butanol-ethanol by Clostridium beijerinckii reaching yields of 0.28 g products/g consumed sugars. The process by-products (solids after acetic acid hydrolysis and spent fermentation broth) were used as feedstocks for anaerobic digestion showing biogas yields between 310 and 650 L/kg dry organic material when mixed in different ratios with sugar beet pulp. Subsequently, the hydrolysis and fermentation processes were upscaled up to a 100-L pilot volume where nanofiltration was implemented to increase sugar concentration and remove salts. While high monomeric sugar yields were replicated during upscaling, fermentation inhibition was observed in the upscaled process. This paper shows Palmaria palmata to be a suitable feedstock for the co-production of bio-butanol and biogas and highlights process development needs to desalt and detoxify seaweed hydrolysates prior to fermentation.