Research Applications Research Articles

Bibliometric Analysis of Research in Pediatrics Related to Virtual and Augmented Reality: A Systematic Review.

The amount of research exploring the use of virtual reality [VR] and augmented reality [AR] technologies in health care has exploded. This has resulted in a massive body of work, making it difficult to obtain all of the research. The objective of this study was to map out and put together the scientific output of research and global trends in virtual and AR in pediatrics. Publications were collected from the Web of Science [WoS] database. The R tool was used to categorize and evaluate the research outputs, as well as the most productive and influential countries, journals, institutions, authors, articles, subject areas, and the latest research themes. The most utilized and co-occurring keywords were also examined. Texts, tables, and images were used to assess and describe the retrieval of findings. The research was based on information from 7423 publications. The strongest growth in publications occurred in 2020. The most productive and influential country was the USA. The journal was Pediatrics, the author was G Riva, and the institution was the University of Washington. The most frequently occurring keywords were simulation, rehabilitation, and stroke. The main research themes were therapy, surgical education, and rehabilitation. Pain, stroke, anxiety, depression, fear, dementia, and neurodegenerative illnesses were all common medical issues investigated. VR studies have mainly focused on surgical education or procedures, simulation technologies, and neurological conditions. Neurological conditions are linked to balance, gait, and rehabilitation, reflecting the prevalence of these disease groups. This article provides a thorough overview of VR and AR studies in the healthcare field. This work will allow academics, policymakers, and practitioners to gain a deeper understanding of the evolution of VR and AR studies in the healthcare field and its potential practical implications. Future VR and AR research should focus on bridging the gap between VR and AR healthcare research and clinical applications. Emerging trends in related fields, such as navigation, rehabilitation, stroke, dementia, and VR exposure therapy, should be given special attention.

Research progress on emotion recognition by combining virtual reality environment and electroencephalogram signals

Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people's quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research's application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.

Model Predictive Control of Magnetic Helical Swimmers in Two-Dimensional Plane

Magnetic micro/mini-swimmers have a great potential application in biomedical research and have gained broad attention. Recent research studies focus on the automatic control methods of magnetic micro/mini-swimmers, such as artificial intelligence methods. However, their autonomous manipulation remains a challenge since they are subject to various disturbances from the external environment and model uncertainties. The current methods employ a status observer to estimate these disturbances and uncertainties. In this paper, we apply a data-driven technique that utilizes the nonlinear approximation ability of neural networks (NNs). To be specific, a flexible structure of NNs, Broad Learning System (BLS), is employed to model the input-output mapping relationship between the direction of the rotating magnetic field and the swimming direction of the helical miniature swimmer. Then, according to the dual-variable decoupling control, a levitation controller is formulated and a path following controller is proposed based on a planar three-degree-of-freedom model of magnetic helical swimmers, which is inspired by the model of wheeled mobile robots. Simulations and experiments are conducted to quantitatively validate the proposed control method using different planar paths. The experiment results show that the mean absolute error of the path following control is about 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the body length which is less than 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm$</tex-math> </inline-formula> . Our proposed control method provides a preliminary study to alleviate the impact of disturbances and uncertainties on the control performance of magnetic micro/mini-swimmers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is mainly motivated by the potential application of artificial intelligence methods in magnetic micro/mini-robot community, especially for the applications that reject disturbances and uncertainties. In practice, a path planner is employed to compute a pre-defined reference path that connects the start and the targeted locations. Then our control method is applied to the magnetic helical swimmer and it is guided to follow the reference path. Tackling external disturbances and model uncertainties is still challenging during the control progress. Neural network-based technique is an intuitive approach since their nonlinear modeling ability and generalization are suitable for estimating these disturbances and uncertainties. As for training the neural-network model, the training data about the angle parameters should be recorded by manual control of the helical swimmer. These angle parameters define the rotating axis of the uniform rotating magnetic field and the self-rotating axis of the magnetic helical swimmer. According to the planar motion model, the optimal controller is formulated using the feedback distance error and angle error, and the sum of the control signal and the prediction of the compensating model is used as the final control input. Our electromagnetic coil system features easy operation and configuration of cameras or other sensors. Simulations and experiments validate the performance of the neural network-based compensating method and the proposed optimal control method using magnetic micro/mini-swimmers.

Global Publication Trends and Research Hotspots of the Immune System and Osteoporosis: A Bibliometric and Visualization Analysis from 2012 to 2022.

Osteoporosis (OP) is a systemic bone metabolism disorder in which the immune system and bone metabolism interact. The purpose of this study was to explore the research status, hot spots and trends regarding the influence of the immune system on OP and to provide a basis for research directions and applications in this field. We searched and collected literature about the immune system and OP published from 2012 to 2022 in the Web of Science Core Collection database. All the included studies were subjected to bibliometrics analysis using Hiplot Pro, VOSviewer and CiteSpace software to produce statistics and visual analyses of the literature output, countries, institutions, authors, keywords and journals. A total of 1201 papers were included, and the number of citations of these articles reached 31,776. The number of publications and citations on the immune system and OP has increased year by year. The top three countries with the greatest number of papers published were China, the United States of America (USA) and Italy. The two institutions with the largest number of papers published were Sichuan University and Soochow University, both located in China. De Martinis Massimo (Italy) and Ginaldi Lia (Italy) are prolific authors in this field. The representative academic journals are Osteoporosis International, Frontiers in Immunology, Journal of Bone and Mineral Research, PloS One and Bone. The results of the keyword cooccurrence analysis showed that the research topics in this field mainly focused on T cells, cytokines, signaling pathways, vitamin D, postmenopausal OP and immune diseases. The keyword burst results showed that zoledronic acid, chain fatty acids and gut microbiota are the frontiers and trends of future research on this topic. The influence of the immune system on OP has been widely studied, and the current research in this field focuses on the effect or mechanism of immune-related cytokines, signaling pathways and vitamin D on OP. Future research trends in this field should focus on the immune regulation mechanism and clinical transformation of zoledronic acid, chain fatty acids and the gut microbiota in OP.

Drought Responses in Three Apple Cultivars Using an Autonomous Sensor-based Irrigation System

Irrigation decision support systems evolving in the domestic temperate tree fruit production industry incorporate measures of soil moisture status, which diverges from classic physiological indicators of edaphic stress. This study used an autonomous sensor-based irrigation system to impose a water deficit (soil matric potential targets of –25, –40, –60, and –80 kPa) on ‘Autumn Gala’, ‘CrimsonCrisp’, and ‘Golden Delicious’ apple (Malus domestica) cultivars grafted to ‘Budagovsky 9’ rootstock in the greenhouse (n = 60). It was hypothesized that relationships between physiological plant function, assessed via infrared gas exchange and chlorophyll fluorescence, and the soil matric potential may be used to advance emerging irrigation decision support systems. Complications arising from defoliation by day 11 at –60 and –80 kPa indicate the generation of substrate-specific soil–water relationships in research applications of autonomous sensor-based irrigation systems. ‘Autumn Gala’ carbon assimilation rates at –80 kPa declined from day 0 to day 8 (9.93 and 5.86 μmol⋅m–2⋅s–1 carbon dioxide), whereas the transpiration rate was maintained, potentially reducing observed defoliation as other cultivars increased transpiration to maintain carbon assimilation. Correlation matrices revealed Pearson’s r ≤ |0.43| for all physiological metrics considered with soil matric potential. Nevertheless, exploratory regression analysis on predawn leaf water potential, carbon assimilation, transpiration, stomatal conductance, and nonphotochemical quenching exposed speculatively useful data and data shapes that warrant additional study. Nonlinear piecewise regression suggested soil matric potential may useful as a predictor for the rate of change in predawn leaf water potential upon exposure to a water deficit. The critical point bridging the linear spans, –30.6 kPa, could be useful for incorporating in emerging irrigation decision support systems.

Development of a novel machine learning-based approach for brain function assessment and integrated software solution

Industry 5.0 emphasizes human-centered solutions. However, bridging the gap between human conditions and engineering systems remains a challenge in this era. Promoting the development of cybernetics applications in the field of rehabilitation is imperative. In the healthcare sector, the evaluation of rehabilitation products’ ergonomics and the dynamic assessment of the human body are of utmost importance. Amid the burgeoning prominence of data-driven product design concepts, ergonomics methods have emerged as indispensable evaluative tools. Nevertheless, the integration of data from multiple sources remains a contemporary challenge; one must extract meaningful information from diverse data sets while enhancing data analysis and fusion techniques. In this study, we collect multimodal neurobiological signals using electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to devise a robust multimodal rehabilitation data fusion method based on graph convolutional network (GCN), particularly focusing on the motor imagery (MI) task. To achieve a more holistic evaluation, we incorporate granger causality (GC) and brain region adjacency matrix as supplementary features, integrating electrophysiological and hemodynamic perspectives. This integration aims to enhance information complementarity and foster a comprehensive understanding of the neural processes involved in MI. Our results demonstrate the superiority of the multimodal fusion method: it achieves higher average accuracy and improved stability, as indicated by the reduced standard deviation of accuracy. This enhanced performance suggests its potential regarding broader research applications. Building upon our successful findings, we establish an intelligent algorithmic rehabilitation platform based on multimodal neural data. This platform not only holds significant clinical value, facilitating precision and personalized medicine, but also propels the advancement of medical practices, paving the way for a more tailored and effective approach to patient care. Our research makes progresses in the areas of rehabilitation modeling, equipment design, intelligence, and interdisciplinary collaboration through the in-depth study and extension of cybernetic principles and techniques.

Survey on Context Based Identification of Customer Name Variations using ML Techniques

Abstract: The demand for automated validation of new customer company accounts, with a specific emphasis on resolving name variations within large databases, is steadily increasing. This surge in demand is a direct response to the significant scale at which data for new customers is being input into CRM systems, necessitating manual validation and correction processes. The primary objective of this survey paper is to identify machine learning methods capable of addressing the challenge of resolving name variations in extensive databases. To achieve this goal, we propose a multi-step approach. Firstly, we employ an approximate string matching algorithm with high computational speed to identify matches with a very high percentage of accuracy. In cases where such matches are not found in the system, we utilize named entity recognition techniques to annotate company names and their suffixes, such as INC, PVT, LTD, CORP, and CO, which may appear in various forms. To resolve abbreviation disambiguity, we explore the application of machine learning algorithms, including the naïve Bayes classifier, decision trees, and Support Vector Machines. In this survey paper, we conclude by presenting potential approximate string matching algorithms, a named entity recognition method, and a model for resolving abbreviation disambiguity. Our review not only provides a comprehensive overview of the current state of research in this area but also highlights gaps in the existing knowledge, offering valuable insights for future research and practical application.

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Mucus-Mimicking Mucin-Based Hydrogels by Tandem Chemical and Physical Crosslinking.

Mucosal tissues represent a major interface between the body and the external environment and are covered by a highly hydrated mucins gel called mucus. Mucus lubricates, protects and modulates the moisture levels of the tissue and is capitalized in transmucosal drug delivery. Pharmaceutical researchers often use freshly excised animal mucosal membranes to assess mucoadhesion and muco-penetration of pharmaceutical formulations which may struggle with limited accessibility, reproducibility, and ethical questions. Aiming to develop a platform for the rationale study of the interaction of drugs and delivery systems with mucosal tissues, in this work mucus-mimicking mucin-based hydrogels are synthesized by the tandem chemical and physical crosslinking of mucin aqueous solutions. Chemical crosslinking is achieved with glutaraldehyde (0.3% and 0.75% w/v), while physical crosslinking by one or two freeze-thawing cycles. Hydrogels after one freeze-thawing cycle show water content of 97.6-98.1%, density of 0.0529-0.0648gcm⁻3, and storage and loss moduli of ≈40-60 and ≈3-5Pa, respectively, that resemble the properties of native gastrointestinal mucus. The mechanical stability of the hydrogels increases over the number of freeze-thawing cycles. Overall results highlight the potential of this simple, reproducible, and scalable method to produce artificial mucus-mimicking hydrogels for different applications in pharmaceutical research.

More than just Alkene Construction – Re‐using Wittig Reactions/Reagents in Biomacromolecular Labeling, Imaging, Sequencing and Modification

AbstractClassical organic chemical reactions are essential for modern synthetic chemistry and offer valuable insights for chemical biology research. The pioneering bioorthogonal chemistry, based on the Staudinger reaction, is a prime example. However, the biocompatibility of classic “name” reactions like the Wittig reaction is still not fully explored. This versatile reaction efficiently converts carbonyl groups into olefins using phosphorus ylides, making it valuable in synthetic chemistry. Despite being in the early stages of development, the Wittig reaction and its reagents have various applications in peptide, protein, DNA, and RNA research. However, they have limitations such as low activity and efficiency, requiring organic solvents. Future directions may include developing Wittig reagents with improved biostability, simplifying the method, and creating multi‐labeling methods. Improving light‐activated Wittig reactions and interdisciplinary integration can further advance bioorthogonal chemistry. As technology advances, the Wittig reaction is poised to make greater contributions to molecular biology, cell biology, and biochemical modification research.

Targeted Degradation of Cell-Surface Proteins via Chaperone-Mediated Autophagy by Using Peptide-Conjugated Antibodies.

Cell-surface proteins are important drug targets but historically have posed big challenges for the complete elimination of their functions. Herein, we report antibody-peptide conjugates (Ab-CMAs) in which a peptide targeting chaperone-mediated autophagy (CMA) was conjugated with commercially available monoclonal antibodies for specific cell-surface protein degradation by taking advantage of lysosomal degradation pathways. Unique features of Ab-CMAs, including cell-surface receptor- and E3 ligase-independent degradation, feasibility towards different cell-surface proteins (e.g., epidermal growth factor receptor (EGFR), programmed cell death ligand 1 (PD-L1), human epidermal growth factor receptor 2 (HER2)) by a simple change of the antibody, and successful tumor inhibition in vivo, make them attractive protein degraders for biomedical research and therapeutic applications. As the first example employing CMA to degrade proteins from the outside in, our findings may also shed new light on CMA, a degradation pathway typically targeting cytosolic proteins.

Energy and exergy analysis of the cryogenic system for HTS magnets based on an open-cycle G-M/J-T cryocooler

High-field magnets have been developed for a broad range of research applications. High-temperature superconducting magnets have received considerable attention due to their potential use in high-field applications. The High Magnetic Field Laboratory of the Chinese Academy of Sciences (CHMFL) is currently designing and constructing high-temperature superconducting (HTS) magnets which need the cooling capacity about 5 W to 10 W at 4.5 K in order to ensure the stability of the system. To meet their operational requirements, a miniature cryogenic system based on the Gifford-McMahon/Joule-Thomson (G-M/J-T) cryocooler has been designed and analyzed. In this research, the performance of the cryogenic system for HTS magnets is investigated systematically by employing both energy analysis and entropy analysis techniques. The results highlight the importance of several key factors, including high pressures, precooling temperature at cooling stages, and J-T exchanger efficiency, for improving the cooling capacity of the cryogenic system. Lowering the precooling temperature can enhance the system cooling capacity and also cause an increase in the precooling capacity. As the pressure increases, the cooling capacity of the system reaches its maximum point at 11.31 bar The J-T heat exchanger accounts for the largest exergy loss rate, 35.2–43.7%. The findings provide technical guidelines for the subsequent experiments and performance optimization of cryogenic system.

Detecting abnormal cell behaviors from dry mass time series

The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

Abstract 6219: Type V collagen promotes lung adenocarcinoma metastasis by regulating tumor mechanical stress and the implication for prognosis and therapeutic strategies

Abstract Introduction: The mechanical environment of the human body influences physiological and pathological processes, and understanding these biomechanical aspects holds promise for medical research. Tumor microenvironment changes, including extracellular matrix (ECM) alterations, result the changing of mechanical characteristics of the tissue, play a crucial role in cancer progression and metastasis, including lung adenocarcinoma (LUAD), one of a major histology type of lung cancer. This study focuses on elucidating how mechanical characteristics, especially those related to the ECM, influence LUAD and the signaling pathways involved. Methods: Cell culture, transfection, Western blot, immunofluorescence, immunohistochemistry, Transwell assay, scratch wound-healing assay were used to investigate the behavior changes of LUAD cells under different treatment. Bioinformatics analyses, and machine learning-based integrative approaches were employed to build the SVM_Score, which was built with relevant base membrane (BM) genes. Patient-derived tumor organoids, myofibroblasts, and co-culture systems were established. Sorafenib, which can inhibited collagen and fibronectin synthesis, was used to explore the proliferation and migration changing of lung cancer cells co-cultured with myofibroblasts. Results: Mechanical stress, simulated by matrix application, enhanced LUAD cell migration and invasion, correlating with ECM alterations and EMT pathway activation. SVM_Score predicted LUAD patient prognosis and EMT propensity across multiple datasets, revealing its robust prognostic capabilities. Lower SVM_Scores were associated with worse survival outcomes, increased cancer-related pathways, higher Tumor Mutation Burden and altered immune microenvironment characteristics. SVM_Score was also linked to myofibroblast-secreted COL5A1, a key marker for mechanical stress. Patient tissues with low SVM_Scores exhibited higher COL5A1 expression, enhanced EMT propensity, and increased internal mechanical stress. Sorafenib, by inhibiting COL5A1 secretion, attenuated the pro-tumor effects of myofibroblasts, inhibiting proliferation and migration of LUAD cells, and rendering LUAD cells more sensitive to chemotherapy. Conclusion: This comprehensive study unveils the intricate relationship between mechanical stress, ECM alterations, and LUAD progression. SVM_Score emerges as a potent prognostic tool, reflects tumor mechanical characteristics. Sorafenib intervention targeting COL5A1 secretion provides a potential therapeutic strategy to mitigate LUAD aggressiveness. These findings contribute to a deeper understanding of the biomechanical aspects of LUAD, offering insights for future research and clinical applications. Citation Format: Guangsheng Zhu, Yanan Wang, Yingjie Wang, Chen, Yongwen Li, Hongyu Liu, Jun Chen. Type V collagen promotes lung adenocarcinoma metastasis by regulating tumor mechanical stress and the implication for prognosis and therapeutic strategies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6219.

Dietary Iron Overload Triggers Hepatic Metabolic Disorders and Inflammation in Laying Hen.

Iron, an essential trace element, is involved in various physiological processes; however, consumption of excessive iron possesses detrimental effects. In practical feed production, the iron content added to feeds often far exceeds the actual demand, resulting in an excess of iron in the body. The liver as a central regulator of iron homeostasis is susceptible to damage caused by disorders in iron metabolism. A model of hepatic iron overload in laying hens was developed in this study by incorporating iron into their diet, and the specific mechanisms underlying iron overload-induced hepatic injury were investigated. Firstly, this study revealed that a high-iron diet resulted in hepatic iron overload, accompanied by impaired liver function. Next, assessment of oxidative stress markers indicated a decrease in activities of T-SOD and CAT, coupled with an increase in MDA content, pointing to the iron-overloaded liver oxidative stress. Thirdly, the impact of iron overload on hepatic glycolipid and bile acid metabolism-related gene expressions were explored, including PPAR-α, GLUT2, and CYP7A1, highlighting disruptions in hepatic metabolism. Subsequently, analyses of inflammation-related genes such as iNOS and IL-1β at both protein and mRNA levels demonstrated the presence of inflammation in the liver under conditions of dietary iron overload. Overall, this study provided comprehensive evidence that dietary iron overload contributed to disorders in glycolipid and bile acid metabolism, accompanied by inflammatory responses in laying hens. Further detailing the specific pathways involved and the implications of these findings could offer valuable insights for future research and practical applications in poultry nutrition.

A Critical Review on Room-Temperature Sodium-Sulfur Batteries: From Research Advances to Practical Perspectives.

Room-temperature sodium-sulfur (RT-Na/S) batteries are promising alternatives for next-generation energy storage systems with high energy density and high power density. However, some notorious issues are hampering the practical application of RT-Na/S batteries. Besides, the working mechanism of RT-Na/S batteries under practical conditions such as high sulfur loading, lean electrolyte, and low capacity ratio between the negative and positive electrode (N/P ratio), is of essential importance for practical applications, yet the significance of these parameters has long been disregarded. Herein, it is comprehensively reviewed recent advances on Na metal anode, S cathode, electrolyte, and separator engineering for RT-Na/S batteries. The discrepancies between laboratory research and practical conditions are elaborately discussed, endeavors toward practical applications are highlighted, and suggestions for the practical values of the crucial parameters are rationally proposed. Furthermore, an empirical equation to estimate the actual energy density of RT-Na/S pouch cells under practical conditions is rationally proposed for the first time, making it possible to evaluate the gravimetric energy density of the cells under practical conditions. This review aims to reemphasize the vital importance of the crucial parameters for RT-Na/S batteries to bridge the gaps between laboratory research and practical applications.

Nitrogen-Doped Cellulose-Derived Porous Carbon Fibers for High Mass-Loading Aqueous Supercapacitors.

Biomass-based porous carbon with renewability and flexible structure tunability is a promising electrode material for supercapacitors. However, there is a huge gap between experimental research and practical applications. How to maintain good electrochemical performance of high mass-loading electrodes and suppress the self-discharge of supercapacitors is a key issue that urgently needs to be addressed. The structure regulation of electrode materials such as heteroatom doping is a promising optimization strategy for high mass-loading electrodes. In this work, nitrogen-doped cellulose-derived porous carbon fibers (N-CHPCs) were prepared by a facile bio-template method using cotton cellulose as raw material and urea as dopant. The prepared N-CHPCs have high specific surface area, excellent hierarchical porous structure, partial graphitization properties and suitable heteroatom content. The assembled high mass-loading (12.8 mg cm-2; 245 μm) aqueous supercapacitor has excellent electrochemical performance, i. e., low open-circuit voltage attenuation rate (21.39 mV h-1), high voltage retention rate (78.81 %), high specific capacitance (295.8 F g-1 at 0.1 A g-1), excellent area capacitance (3.79 F cm-2 at 0.1 A g-1), excellent cycling stability (97.28 % over 20,000 cycles at 1.0 A g-1). The excellent performance of high mass-loading N-CHPCs is of great significance for their practical applications in advanced aqueous supercapacitors.

Enhancing value creation of operational management for small to medium manufacturer: A conceptual data-driven analytical system

This paper aims to explore the challenges and opportunities for Small and Medium-sized Manufacturing Enterprises (SMMEs) in implementing data-driven techniques in their operations. SMMEs are often considered to be low and medium–low tech companies, even if they have machinery, as they still rely on traditional processes and manpower and lack any digital technology. Previous research has shown that medium–high and high-tech companies perform better, with higher rates of growth, than low and medium–low ones by a sustainable and significant margin. Therefore, there is a need for further research on the implementation of data-driven analytical methods and technologies in SMMEs that are both cost-effective and easy to use. This study proposes a conceptual analytical system that combines Integration Definition for Function Modeling 0 (IDEF0) and the Cross Industry Standard Process for Data Mining (CRISP-DM) business analytics method to develop a practical and widely applicable framework for data-driven techniques in manufacturing. We then developed a case study of an Indonesian company, where we collected real and direct information about specific objects, events, and activities related to particular aspects, including showing their key performance indicators (KPIs) through data dashboards, to evaluate the effectiveness of the proposed conceptual analytical system in improving operational management in SMMEs. The findings of this study provide valuable insights that can be used to develop effective solutions for SMMEs to leverage data-driven techniques and improve their operations. We also highlight implications of the findings for future research and practical applications. The final framework can be converted into a system that can be continuously and flexibly updated and customized, based on specific needs.

Evidence-Based Nursing Practice: A Different View: Book Review of Evidence-Based Practice for Nurses: Appraisal and Application of Research by Nola A. Schmidt and Janet M. Brown (2022). Jones & Bartlett Learning.

This is a review of the 5th edition of Evidence-Based Practice for Nurses: Appraisal and Application of Research edited by Nola A. Schmidt and Janet M. Brown (2022). The reviewed book provides an exploration of evidence-based nursing practice, viewed through the lens of traditional perspectives that position the nursing discipline as applied science and approach to nursing practice from the standpoint of the medical model. In this column, the meaning of evidence-based nursing practice is reconsidered within the context of the theory-guided nursing practice arising from the distinctive perspective of the nursing discipline as a human science.