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Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction Design Methods Results Conclusion Clinical Relevance The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI‐integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms.The scoping review followed the Preferred Reporting Items for Systematic Review and Meta‐Analysis Extension for Scoping Reviews (PRISMA‐ScR) guidelines.A structured search was performed across seven databases according to PRISMA‐ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI).Twenty‐six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate‐ to high‐quality studies. The most used ML algorithm was Random Forest. The four second‐most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s).The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains.The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research. [ABSTRACT FROM AUTHOR]

Objective: NeoBase 2 Non-derivatized MSMS assay kit (NeoBase 2 kit) was used for newborn screening, the performance of the NeoBase 2 kit should be validated before its implementation in clinical diagnostic laboratories. Methods: Leftover dried blood spot samples, quality control materials in the NeoBase 2 kit, and proficiency testing materials received from the NSQAP were used. Precision, accuracy, LOD, LLOQ, recovery, and stability were carried out to verify the performance of the Waters ACQUITY TQD MS/MS system with the NeoBase 2 kit for newborn screening. Cutoffs were determined and analytes requiring different cutoffs in preterm neonates were investigated. Results: Within-run and between-run precisions ranged from 3.95% to 14.41%. The accuracy and stability were within 15%. All analytes demonstrated acceptable LOD, LLOQ, and recoveries. Cutoffs for term and preterm neonates were established. Conclusions: The performance of the NeoBase 2 kit is acceptable and can be implemented in clinical diagnostic laboratories. [ABSTRACT FROM AUTHOR]

An acoustic comparison coupler has been designed by the acoustic and vibration research group, National Research and Innovation Agency (BRIN). This research prototype is developed to be utilized by a private and industrial laboratory to comply with periodic tests of a sound level meter (SLM) for frequency weighting as the primary parameter. Further, the performance has been demonstrated in the previous paper. Nevertheless, the comparison test among the acoustic calibration systems associated with this mandatory parameter test has not been conducted yet. Therefore, the purpose of this work is to conduct the validation of this prototype as the SLM calibration system using the two established methods consists of the free field method by using free field facilities and insert voltage technique, and the second method by utilizing the multifunction acoustic calibrator in a pressure field condition. The measurement procedure is carried out following the standard IEC 61672–3. Therefore, the results are compared to the acceptance limit stated by this standard. This work also quantifies the uncertainty measurements that are provided by these methods. From the results, the measured frequency weighting is obtained using these methods. The deviation to the reference values and the expanded uncertainty measurement are also reported. [ABSTRACT FROM AUTHOR]

This study examines the relationship between the topology of stable time‐delay regions and the stability analysis of an linear multiple time delay system. To analyze the topology of stable time‐delay regions, we construct a function with a value equal to zero for the time‐delay points on the boundaries of stable time‐delay regions. The function is continuous and differentiable in the whole defining field with a global minimum of zero so that we can locate the boundaries by minimizing the value of the function. Based on the topology analysis, we proposed a performance validation approach for controllers that are designed to stabilize the system using feedback signals with time delays. The method based on the topology analysis is simple and reliable so that can deal with a linear time‐invariant system with high order and multiple time delays (more than 3). The example case study shows that the above method is reliable and enables ultra‐low latency coordination and control for the future power grid with ubiquitous power electronics. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies have evaluated metagenomic next-generation sequencing (mNGS) of cell-free DNA (cfDNA) for pathogen detection in blood and body fluid samples. However, no study has assessed the diagnostic efficacy of mNGS using cellular DNA. Objectives: This is the first study to systematically evaluate the efficacy of cfDNA and cellular DNA mNGS for pathogen detection. Methods: A panel of seven microorganisms was used to compare cfDNA and cellular DNA mNGS assays concerning limits of detection (LoD), linearity, robustness to interference, and precision. In total, 248 specimens were collected between December 2020 and December 2021. The medical records of all the patients were reviewed. These specimens were analysed using cfDNA and cellular DNA mNGS assays, and the mNGS results were confirmed using viral qPCR, 16S rRNA, and internal transcribed spacer (ITS) amplicon next-generation sequencing. Results: The LoD of cfDNA and cellular DNA mNGS was 9.3 to 149 genome equivalents (GE)/mL and 27 to 466 colony-forming units (CFU)/mL, respectively. The intra- and inter-assay reproducibility of cfDNA and cellular DNA mNGS was 100%. Clinical evaluation revealed that cfDNA mNGS was good at detecting the virus in blood samples (receiver operating characteristic (ROC) area under the curve (AUC), 0.9814). In contrast, the performance of cellular DNA mNGS was better than that of cfDNA mNGS in high host background samples. Overall, the diagnostic efficacy of cfDNA combined with cellular DNA mNGS (ROC AUC, 0.8583) was higher than that of cfDNA (ROC AUC, 0.8041) or cellular DNA alone (ROC AUC, 0.7545). Conclusion: Overall, cfDNA mNGS is good for detecting viruses, and cellular DNA mNGS is suitable for high host background samples. The diagnostic efficacy was higher when cfDNA and cellular DNA mNGS were combined.

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare.

The current regulations on building envelope requirements are no longer related only to aspects of energy efficiency or guarantee of durability over time, but also to new performance scenarios concerning technological systems in relation to adaptation, mitigation and resilience to increasingly frequent extreme climate events. The research aims to analyse causes, effects and potential strategies to increase the resilience of the building envelope in case of extreme events, proposing solutions to reduce the consequences of the impact of flying debris on the building envelope. The contribution is the result of a research activity conducted within the HORIZON METABUILDING LABS Innovation research project. [ABSTRACT FROM AUTHOR]

[Display omitted] • Laboratory validation of cell-free DNA (cfDNA) and cellular DNA mNGS assay was conducted. • The clinical performance of cfDNA and cellular DNA mNGS was compared in different body fluids, various host backgrounds, and pathogen types. • cfDNA mNGS assay was good at detecting viruses. • Cellular DNA mNGS assay was suitable for high host background samples. • Diagnostic efficacy was higher when cfDNA and cellular DNA mNGS were combined. Previous studies have evaluated metagenomic next-generation sequencing (mNGS) of cell-free DNA (cfDNA) for pathogen detection in blood and body fluid samples. However, no study has assessed the diagnostic efficacy of mNGS using cellular DNA. This is the first study to systematically evaluate the efficacy of cfDNA and cellular DNA mNGS for pathogen detection. A panel of seven microorganisms was used to compare cfDNA and cellular DNA mNGS assays concerning limits of detection (LoD), linearity, robustness to interference, and precision. In total, 248 specimens were collected between December 2020 and December 2021. The medical records of all the patients were reviewed. These specimens were analysed using cfDNA and cellular DNA mNGS assays, and the mNGS results were confirmed using viral qPCR, 16S rRNA, and internal transcribed spacer (ITS) amplicon next-generation sequencing. The LoD of cfDNA and cellular DNA mNGS was 9.3 to 149 genome equivalents (GE)/mL and 27 to 466 colony-forming units (CFU)/mL, respectively. The intra- and inter-assay reproducibility of cfDNA and cellular DNA mNGS was 100%. Clinical evaluation revealed that cfDNA mNGS was good at detecting the virus in blood samples (receiver operating characteristic (ROC) area under the curve (AUC), 0.9814). In contrast, the performance of cellular DNA mNGS was better than that of cfDNA mNGS in high host background samples. Overall, the diagnostic efficacy of cfDNA combined with cellular DNA mNGS (ROC AUC, 0.8583) was higher than that of cfDNA (ROC AUC, 0.8041) or cellular DNA alone (ROC AUC, 0.7545). Overall, cfDNA mNGS is good for detecting viruses, and cellular DNA mNGS is suitable for high host background samples. The diagnostic efficacy was higher when cfDNA and cellular DNA mNGS were combined. [ABSTRACT FROM AUTHOR]

Building energy simulation analysis plays an important supporting role in the conservation of building energy. Since the early 1980s, researchers have focused on the development and validation of building energy modeling programs (BEMPs) and have basically formed a set of systematic validation methods for BEMPs, mainly including analytical, comparative, and empirical methods. Based on related papers in this field, this study systematically analyzed the application status of validation methods for BEMPs from three aspects, namely, sources of validation cases, comparison parameters, and evaluation indicators. The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized. Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators. In addition, the current development trend of BEMPs and the challenges faced by validation methods were investigated, as well as the existing progress of current validation methods under this trend was analyzed. Subsequently, the development direction of the validation method was clarified. [ABSTRACT FROM AUTHOR]

Next-generation sequencing (NGS) has revolutionized the field of genomics and created new opportunities for basic research. We described the strategy for the NGS validation of the "dysglycaemia panel" composed by 44 genes related to glucose metabolism disorders (MODY, Wolfram syndrome) and familial renal glycosuria using Ion AmpliSeq technology combined with Ion-PGM. Anonymized DNA of 32 previously genotyped cases with 33 different variants were used to optimize the methodology. Standard protocol was used to generate the primer design, library, template preparation, and sequencing. Ion Reporter tool was used for data analysis. In all the runs, the mean coverage was over 200×. Twenty-nine out of thirty three variants (96.5%) were detected; four frameshift variants were missed. All point mutations were detected with high sensitivity. We identified three further variants of unknown significance in addition to pathogenic mutations previously identified by Sanger sequencing. The NGS panel allowed us to identify pathogenic variants in multiple genes in a short time. This could help to identify several defects in children and young adults that have to receive the genetic diagnosis necessary for optimal treatment. In order not to lose any pathogenic variants, Sanger sequencing is included in our analytical protocol to avoid missing frameshift variants. [ABSTRACT FROM AUTHOR]

Technological innovations have resulted in a digital transformation in a variety of fields, including culture and tourism. We propose an innovative and personalized solution to benefit art and cultural heritage in indoor and outdoor environments by combining Internet of Things-enabled technologies and deep learning-based approaches. A recent Convolutional Neural Network (CNN) architecture to jointly perform local feature detection and description has been adapted and exploited for the first time for image matching in the cultural heritage application context. The performance validation of the proposed system shows that the proposed modular architecture ensures a very low error rate and excellent response time up to 2000 user visits in 700 seconds. The validation of the computer vision module shows as the proposed CNN based feature extraction approach improves image matching performance, especially in poorly textured object areas reaching a F1-Score of 0.9907 (against the 0.9679 obtained by traditional gradient based approaches) on the challenging dataset of images taken from 4 different historical sites and a F1-Score of 0.9807 (against the 0.9798 obtained by traditional approaches) on a public benchmark dataset of artworks.

Objective A novel corona virus(SARS-CoV-2) nucleic acid detection kit was developed and performance had been optimized for large-scale population screening in China. The effect of mixed detection of multiple samples was analyzed aiming at providing theoretical guidance for clinical use. Methods The primers and probe sequences for ORF1ab and N announced by China Center for Disease Control and prevention and the primers and probe sequences for RNase P as control gene were synthesized. Single variable optimization and orthogonal experiment were used to get the best concentration ratio of primers and probes for ORF1ab, N and RNase P in fluorescent RT-qPCR detection system. The accuracy, specificity and sensitivity of the kit were investigated. The positive samples and negative samples were collected and mixed according to different proportions, and the SARS-CoV-2 was detected by this kit and the commercial reference kit respectively. The detection concentration for different targets and the amplification Ct value were compared. Results The positive coincidence rate, negative coincidence rate and specificity of the kit were 100%, respectively。And the sensitivity was 1×103 copies/mL. The detection sensitivity of this kit for ORF1ab target gene was 1/20, higher than that of reference kit (1/10), and the amplification Ct value with same template concentration was lower than that of reference kit (P＜0.05). Conclusions This kit has great advantages in screening multi samples detection of new corona virus nucleic acids because of high sensitivity to ORF1ab target gene, and suitable for in large-scale population.

As ultrasound-compatible flow phantoms are devised for performance testing and calibration, there is a practical need to obtain independent flow measurements for validation using a gold-standard technique such as particle image velocimetry (PIV). In this paper, we present the design of a new dual-modality flow phantom that allows ultrasound and PIV measurements to be simultaneously performed. Our phantom's tissue mimicking material is based on a novel hydrogel formula that uses propylene glycol to lower the freezing temperature of an ultrasound-compatible poly(vinyl) alcohol cryogel and, in turn, maintain the solution's optical transparency after thermocycling. The hydrogel's optical attenuation {1.56 dB/cm with 95% confidence interval (CI) of [1.512 1.608]}, refractive index {1.337, CI: [1.340 1.333]}, acoustic attenuation {0.038 dB/(cm*MHz), CI: [0.0368 0.0403]; frequency dependent factor of 1.321, CI: [1.296 1.346]}, and speed of sound {1523.6 m/s, CI: [1523.8 1523.4]} were found to be suitable for PIV and ultrasound flow measurements. As an application demonstration, a bimodal flow phantom with spiral lumen was fabricated and used in simultaneous flow measurements with PIV and ultrasound color flow imaging (CFI). Velocity fields and profiles were compared between the two modalities under a constant flow rate (2.5 mL/s). CFI was found to overestimate flow speed compared to the PIV measurements, with a 14%, 10%, and 6% difference between PIV and ultrasound for the 60°, 45°, and 30° angles measured. These results demonstrate the new phantom's feasibility in enabling performance validation of ultrasound flow mapping tools., (Creative Commons Attribution license.)

Background: Mucocutaneous separation (MCS) is a common postoperative complication in enterostomy patients, potentially leading to significant morbidity. Early identification of risk factors is crucial for preventing this condition. However, predictive models for MCS remain underdeveloped., Aim: To construct a risk prediction model for MCS in enterostomy patients and assess its clinical predictive accuracy., Methods: A total of 492 patients who underwent enterostomy from January 2019 to March 2023 were included in the study. Patients were divided into two groups, the MCS group ( n = 110), and the non-MCS ( n = 382) based on the occurrence of MCS within the first 3 weeks after surgery. Univariate and multivariate analyses were used to identify the independent predictive factors of MCS and the model constructed. Receiver operating characteristic curve analysis was used to assess the model's performance., Results: The postoperative MCS incidence rate was 22.4%. Suture dislodgement ( P < 0.0001), serum albumin level ( P < 0.0001), body mass index (BMI) ( P = 0.0006), hemoglobin level ( P = 0.0409), intestinal rapture ( P = 0.0043), incision infection ( P < 0.0001), neoadjuvant therapy ( P = 0.0432), stoma site ( P = 0.0028) and elevated intra-abdominal pressure ( P = 0.0395) were potential predictive factors of MCS. Suture dislodgement [ P < 0.0001, OR: 28.0075 95%CI: (11.0901-82.1751)], serum albumin level ( P = 0.0008, OR: 0.3504, 95%CI: [0.1902-0.6485]), BMI [ P = 0.0045, OR: 2.1361, 95%CI: (1.2660-3.6235)], hemoglobin level [ P = 0.0269, OR: 0.5164, 95%CI: (0.2881-0.9324)], intestinal rapture [ P = 0.0351, OR: 3.0694, 95%CI: (1.0482-8.5558)], incision infection [ P = 0.0179, OR: 0.2885, 95%CI: (0.0950-0.7624)] and neoadjuvant therapy [ P = 0.0112, OR: 1.9769, 95%CI: (1.1718-3.3690)] were independent predictive factors and included in the model. The model had an area under the curve of 0.827 and good clinical utility on decision curve analysis., Conclusion: The mucocutaneous separation prediction model constructed in this study has good predictive performance and can provide a reference for early warning of mucocutaneous separation in enterostomy patients., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Objective To evaluate the performance and clinical application of a high-throughput nucleic acid blood screening detection system for SARS-CoV-2, so as to provide basis and technical means for its application in detection of plasma from recovered COVID-19 patients. Methods The SARS-CoV-2 nucleic acid was detected by real-time fluorescence quantitative PCR, and the sensitivity, precision, anti-interference and other parameters were evaluated. Blood donor samples collected during COVID-19 epidemic period were screened using the detection system to evaluate its applicability. Results The detection limits of gene N and ORF 1ab were 3.98 copies/mL and 9.38 copies/mL, respectively. The CV of high and low concentration samples were both less than 5%. Hemoglobin at 500 mg/dL and triglyceride at 3g/dL had little effect on the results. The detection system can effectively prevent carryover, thus avoiding false positive results. The SARS-CoV-2 nucleic acid blood screening was carried out in a total of 39 306 blood samples, and all samples were negative. Conclusion The established method can meet the needs of SARS-CoV-2 nucleic acid screening therefore ensure the safe application of plasma from recovered COVID-19 patients.

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies.

For testing ventilation rate, a reusable control sample was designed for cigarette test station. The control sample was composed of a wrapper layer of elastic material and a filler layer of porous material. The critical flow tubes used for draw resistance and ventilation rate testing were adopted to simulate the draw resistance and ventilation rate of cigarette. A quality control chart was used to monitor the testing process and effectively identify the problems in the testing process. The performance and applicability of the control sample were assessed and the effects in major application scenarios were analyzed. The results showed that: 1) The simulative testing of all cigarette physical indexes, including weight, circumference, draw resistance, hardness, length and ventilation rate, were realized. 2) The consistency and uniformity of the control sample was significantly better than those of cigarette. The coefficient of variation of all indexes was obviously lower than that of cigarette, that of hardness was lower than 2% and that of the other indexes was lower than 1%. The variation range of draw resistance was 9 Pa and that of ventilation rate was 1.7% during five successive years, and the means and standard deviations of the other indexes did not change basically. It suggested that designed control sample might be adaptable to cigarette test stations of different types. 3) The control sample performed very well in inter-laboratory collaborative testing, testing process monitoring, the repeatability and reproducibility evaluation of standard measurement methods and measurement auditing, it guaranteed the effectiveness and comparability of test results. [ABSTRACT FROM AUTHOR]

Technological innovations have resulted in a digital transformation in a variety of fields, including culture and tourism. We propose an innovative and personalized solution to benefit art and cultural heritage in indoor and outdoor environments by combining Internet of Things-enabled technologies and deep learning-based approaches. A recent Convolutional Neural Network (CNN) architecture to jointly perform local feature detection and description has been adapted and exploited for the first time for image matching in the cultural heritage application context. The performance validation of the proposed system shows that the proposed modular architecture ensures a very low error rate and excellent response time up to 2000 user visits in 700 seconds. The validation of the computer vision module shows as the proposed CNN based feature extraction approach improves image matching performance, especially in poorly textured object areas reaching a F1-Score of 0.9907 (against the 0.9679 obtained by traditional gradient based approaches) on the challenging dataset of images taken from 4 different historical sites and a F1-Score of 0.9807 (against the 0.9798 obtained by traditional approaches) on a public benchmark dataset of artworks. [ABSTRACT FROM AUTHOR]

Blind source separation, surface EMG, motor unit, EMG decomposition, performance validation In fact, most surface EMG decomposition algorithms based on blind source separation (BSS) use a parallel search strategy, i.e., they repeatedly search for a number of motor units from the original signal and then deal with duplicates at the end. Keywords: surface EMG; motor unit; EMG decomposition; blind source separation; performance validation EN surface EMG motor unit EMG decomposition blind source separation performance validation 1 4 4 05/30/22 20220526 NES 220526 Introduction Significant progress has been achieved in decomposition of surface electromyographic (EMG) signals, particularly with advances in surface electrode array recording and processing techniques (Holobar and Zazula, [6]; Chen and Zhou, [3]; Negro et al., [11], among others). The agreement in discharge timing of the common motor units from both types of recordings can be viewed as a performance index of the surface EMG decomposition, given that intramuscular EMG decomposition has been well established (Parsaei et al., [12]). [Extracted from the article]

DNA N4-methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN (Escherichia coli), DNA4mC-LIP (Arabidopsis thaliana), iDNA-MS (Fragaria vesca), DNA4mC-LIP and 4mCCNN (Drosophila melanogaster), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus, Geobacter pickeringii, and Mus musculus, thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.

In order to obtain a safe and economical pumping house structure design, this article established a mathematical model for topology optimization of pumping house. Also, the two-stage pumping house structure topology optimization solution strategy was proposed based on the structural topology optimization theory. Firstly, the topology of the pumping house was optimized on the ANSYS platform, and the optimal topology of the structure was obtained. Then the topological form was adaptively processed and a qualitative validation that met the strength, stiffness, and stability of the project was performed. Compared to the original scheme, the optimized design shows a decrease of 13. 7%· in terms of the volume of the pumping house. Indicators in the safety of pumping station design meet the qualification. This work is expected to promote the application of topology optimization tedrnicp1e in pumping house. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Historically, the skin temperature has been used as a functional indicator of the processes undergoing inside the body, where the anomalous change in temperature gives indication of illness. Infrared thermography (IRT) is a non-invasive approach which radiometrically measures the temperature distribution on the object's surface based on emanating IR radiation. In medical field, IRT has been used to diagnose the various problems associated with superficial body parts since 1960s. The clinical abnormalities can be diagnosed by visual and subjective analysis of the thermograms, but human based diagnoses are likely to be influenced by narcissus effect, negligence, visual exhaustion and mental workload. Apart, the deep and objective diagnostic information, like severity and type of a disease can only be obtained by statistically analyse the region of interest (ROI). So, the segmentation of ROI has often been an initial step in medical diagnosis, where the automated approaches lead to fast and highly reproducible analysis. Recent advancement in deep learning opens a new way for effective computer-aided medical diagnosis, several studies have proposed deep learning based automatic segmentation and data classification. This review focuses on the wide range of image processing techniques adopted for automatic segmentation of various clinically significant ROIs, like breast, face, foot, hand etc. In addition, the present efforts are targeted on the selection of characteristic points for an effective automatic segmentation and identification of new areas to carry out the further research. Besides, the basics of medical IRT, need of automatic segmentation and factors which hinder the automatic segmentation in IRT are also discussed. [ABSTRACT FROM AUTHOR]

Heterozygous carriers of the survival of motor neuron 1 (SMN1) gene deletion in parents account for approximately 95% of neonatal spinal muscular atrophy cases. Given the severity of the disease, professional organizations have recommended periconceptional spinal muscular atrophy carrier screening to all couples, regardless of race or ethnicity. However, the prevalence of screening activities in mainland China remains suboptimal, mainly attributed to the limitations of the existing carrier screening methods. Herein, we aimed to develop a low-cost, accessible, and accurate carrier screening method based on duplex droplet digital PCR (ddPCR), to cover a wider population in developing countries, including China. The receiver operating characteristic curve was used to determine the cut-off value of SMN1 copy numbers. Performance validation was conducted for linearity, precision, and accuracy. In total, 482 cases were considered to validate the concordance between the developed ddPCR assay and multiplex ligation-dependent probe amplification. Linear correlations were excellent between the expected concentration of the reference gene and the observed values (R2 > 0.99). Both the intra- and inter-assay precision of our ddPCR assays were less than 6.0%. The multiplex ligation-dependent probe amplification and ddPCR results were consistent in 480 of the 482 cases (99.6%). Two cases with multiplex ligation-dependent probe amplification, suggestive of two copies of SMN1 exon 7, were classified into three copies by ddPCR analysis. The overall correct classification of the samples included in our ddPCR assay was 100%. This study demonstrates that an appropriate cut-off value is an important prerequisite for establishing a semi-quantitative method to determine the SMN1 copy numbers. Compared to conventional methods, our ddPCR assay is low-cost, highly accurate, and has full potential for application in population spinal muscular atrophy carriers screening. • Spinal muscular atrophy (SMA) is a common lethal monogenetic disorder. • A ddPCR assay for determining SMN1 copy numbers was developed. • The assay improved the accessibility and reproducibility of SMA carrier screening. • The developed ddPCR assay has the advantages of rapidity, high accuracy, and affordability. [ABSTRACT FROM AUTHOR]

• A series elastic actuator (SEA) using a hexagonal tensile springs array is designed. • The working principle of the spring's array is illustrated and theoretically analyzed. • A linear and consistent rotary stiffness of the SEA is verified experimentally. • The proposed SEA presents good torque control performance and compliance. Series elastic actuator (SEA) has already been used in robotics, especially the human-robot interaction field, to acquire compliance and force sensing ability. However, achieving a SEA design with linear and consistent elastic properties while having low friction, minor hysteresis, and good compliance is still challenging. In addition, the inherent conflict between good compliance and high bandwidth must be appropriately handled in the design. This paper proposes a newly designed compliant actuator with a novel elastic component using a tensile springs array. The unique geometry of the spring array enables SEA's consistent rotary stiffness within the whole working range while having trivial friction and hysteresis. We adopt PID and sliding mode control to evaluate the device's performance. It can be verified that the SEA has a constant low rotary stiffness of 1.13 Nm/deg bringing good compliance while having a torque tracking bandwidth of 7 Hz. These desirable properties enable our device to provide compliant, smooth, and predictable output to meet human-robot interaction requirements. [ABSTRACT FROM AUTHOR]

The leap motion controller is a commercial low-cost marker-less optical sensor that can track the motion of a human hand by recording various parameters. Upper limb rehabilitation therapy is the treatment of people having upper limb impairments, whose recovery is achieved through continuous motion exercises. However, the repetitive nature of these exercises can be interpreted as boring or discouraging while patient motivation plays a key role in their recovery. Thus, serious games have been widely used in therapies for motivating patients and making the therapeutic process more enjoyable. This paper explores the feasibility, accuracy, and repeatability of a leap motion controller (LMC) to be applied in combination with a serious game for upper limb rehabilitation. Experimental feasibility tests are carried out by using an industrial robot that replicates the upper limb motions and is tracked by using an LMC. The results suggest a satisfactory performance in terms of tracking accuracy although some limitations are identified and discussed in terms of measurable workspace.

The antigen rapid diagnostic test (Ag-RDT) is an immunodiagnostic test that detects the presence of viral proteins (antigens) expressed by the COVID-19 virus in a sample from a patient’s respiratory tract. This study focused on evaluating the performance of self-conduct buccal and nasal swabs RTK-antigen test compared to nasopharyngeal swab RTK-based COVID-19 diagnostic assays, Panbio™ COVID-19 Ag Rapid Test Device (Nasopharyngeal) (Abbott Rapid Diagnostics Jena GmbH, Jena, Germany) used in hospitals for first-line screening. The sensitivity and specificity of the paired RTK-Ag test in detecting the an-tigen were calculated at 96.4% and 100%, respectively. Fisher exact tests showed the association between nasopharyngeal swabs RTK-Ag assay and buccal-nasal swabs RTK-Ag from ProdetectTM is significant (p-values < 0.001). The result showed that a self-conducted buccal and nasal RTK-antigen rapid test by the patients is comparable to the results obtained from a rapid test device conducted by trained medical personnel using a nasopharyngeal swab.

Respiratory rate (RR) is typically the first vital sign to change when a patient decompensates. Despite this, RR is often monitored infrequently and inaccurately. The Circadia Contactless Breathing Monitor™ (model C100) is a novel device that uses ultra-wideband radar to monitor RR continuously and un-obtrusively. Performance of the Circadia Monitor was assessed by direct comparison to manually scored reference data. Data were collected across a range of clinical and non-clinical settings, considering a broad range of user characteristics and use cases, in a total of 50 subjects. Bland–Altman analysis showed high agreement with the gold standard reference for all study data, and agreement fell within the predefined acceptance criteria of ±5 breaths per minute (BrPM). The 95% limits of agreement were −3.0 to 1.3 BrPM for a nonprobability sample of subjects while awake, −2.3 to 1.7 BrPM for a clinical sample of subjects while asleep, and −1.2 to 0.7 BrPM for a sample of healthy subjects while asleep. Accuracy rate, using an error margin of ±2 BrPM, was found to be 90% or higher. Results demonstrate that the Circadia Monitor can effectively and efficiently be used for accurate spot measurements and continuous bedside monitoring of RR in low acuity settings, such as the nursing home or hospital ward, or for remote patient monitoring.

The study deals with intelligible analytics for performance modelling of vehicle platooning. Knowledge extraction with rules is targeted to understand safety regions (collision avoidance) of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms. Safety regions are evidenced on test data with statistical error very close to zero.

Multiphase pump is a cost-effective option for subsea gas and oil fields development. The ability of handling different gas volume fractions with high rotational speed is necessary to optimize the operating performance of a multiphase pump. Thus meeting the demand for increasing gas and oil production of undersea wells especially for those brownfields. This study aims to validate the performance of a three-stage helico-axial multiphase pump with new designed impellers and high rotational speed in both single and two-phase experiments. Then establish a numerical analysis method to predict the pump performance and evaluate the characteristics of multiphase flow with high reliability by using commercial CFD packages. The design volume flowrate of the pump is 100 m 3 /h and rotational speed is 4500 rpm. The experiment results show that the highest efficiency is 45% in water condition at the design parameters. There is not much difference on the pump performance at low inlet gas volume fractions (GVF) such as 5% and 10%, which indicates that multi-stage pump can prevent large performance degradation at low inlet GVFs. Numerical simulations are implemented by solving the three-dimensional steady Reynolds-Averaged Navier-Stokes (RANS) equations. The effects of various turbulence models, wall roughness, gas bubble sizes and interphase drag models on the hydraulic performance of multiphase are investigated. The results show that the predicted performance with Shear Stress Transport ( SST ) turbulence model at the optimal conditions agree well with the experiment data. Wall roughness should be considered since it has a significant effect on pump efficiency. Gas bubble size varies according to different inlet GVFs and rotational speed. The in-situ α g is high in diffuser and it varies with different interphase drag force models. The reliability of the numerical analysis method is proven by comparing the numerical results and experiments data. [ABSTRACT FROM AUTHOR]

There has been a large volume of research on structural health monitoring since the 1970s but this research effort has yielded relatively few routine industrial applications. Structural health monitoring can include applications on very different structures with very different requirements; this article splits the subject into four broad categories: rotating machine condition monitoring, global monitoring of large structures (structural identification), large area monitoring where the area covered is part of a larger structure, and local monitoring. The capabilities and potential applications of techniques in each category are discussed. Condition monitoring of rotating machine components is very different to the other categories since it is not strictly concerned with structural health. However, it is often linked with structural health monitoring and is a relatively mature field with many routine applications, so useful lessons can be read across to mainstream structural health monitoring where there are many fewer industrial applications. Reasons for the slow transfer from research to practical application of structural health monitoring include lack of attention to the business case for monitoring, insufficient attention to how the large data flows will be handled and the lack of performance validation on real structures in industrial environments. These issues are discussed and ways forward proposed; it is concluded that given better focused research and development considering the key factors identified here, structural health monitoring has the potential to follow the path of rotating machine condition monitoring and become a widely deployed technology. [ABSTRACT FROM AUTHOR]

An effective reduction in power prediction error profile and an improved battery management system design for photovoltaic (PV) based microgrid application are presented in this study, where battery life and power loss are considered to be effectiveness measures. For local energy management the prediction error has a direct influence on distributed generator (DG) control reference calculation and thus in system stability. The silent effect of prediction error in battery energy storage life deterioration is highlighted in terms of battery temperature and power losses. The PV power prediction challenge (null versus positive volatility nature) is addressed with effective error reduction by kernel‐based feature mapping function. To obtain fast prediction (operational references to DG primary control) in an online manner, a new fast reduced Morlet kernel‐based online sequential extreme learning machine is proposed in this study. The battery (lithium‐ion) temperature effect is addressed by introducing a new secondary controller, which comprises battery temperature reference model (model reference) along with rule‐based temperature tolerance switching of stacks. The effectiveness of the proposed design is presented by rigorous case studies (MATLAB and TMS320 C6713), where extreme performance is achieved by simultaneous prediction error and local uncertainty. [ABSTRACT FROM AUTHOR]

Multiphase pumps are now in high demand as a result of undersea wells. The resulting sharp increase in the demand for multiphase pumps has made it necessary to optimize the operating performance of these pumps. The primary shortcoming of multiphase flow transport technology is that the characteristics of the internal flow change according to the gas volume fraction (GVF). This research aimed to establish a numerical analysis method that is used commercial CFD packages to evaluate the multiphase flow with high reliability and proposes a numerical method to investigate the effect of different GVFs on the flow characteristics. An appropriate combination of bubble size, closure interphase force model, and coefficient of force acting on the interface between the water and air phases is important for predicting the performance of the multiphase flow. The reliability of the new numerical analysis method was proven by comparing the numerical and experimental results. A model pump was produced and the structural stability of the prototype was secured through an analysis according to the API610 11th standard (Modal, Pressure Load). The model pump was made by AL7075 and the experiment device is connected with a 150A SUS pipe flange. The error range of the reliability verification of the multi-phase flow was about 0.5–1.5% at the flow rate used in the design. The numerical results obtained for pressure performance generally corresponded to high reliability within the operating range. Advanced computational fluid dynamics based on the three-dimensional steady and unsteady Reynolds-averaged Navier–Stokes equations were solved to analyze the detailed dynamic flow phenomenon with various GVFs. The effects of various interphase forces acting between the liquid and gas phase such as drag, lift, virtual mass, wall lubrication and turbulent dispersion forces, and the diameter of bubbles in the gas phase on the hydraulic performance were investigated. [ABSTRACT FROM AUTHOR]

The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies. [ABSTRACT FROM AUTHOR]

Most multi-phase pumps used in crude oil production have been developed to satisfy certain pressure specifications. In the design of these pumps, the flow characteristics of the posterior stage are different from those of the prior stage. For this reason, the design of the second stage needs to be supplemented. To optimize performance in this stage, multi-objective optimization to simultaneously increase pressure and efficiency is reported in this article. Flow analyses of the single and multiple phases of the multi-phase pump were conducted by solving three-dimensional steady Reynolds-averaged Navier-Stokes equations. For the numerical optimization, two design variables related to the blade inlet angle were selected. The impeller and the diffuser blades were optimized using a systematic optimization technique combined with a central composite method and a hybrid multi-objective evolutionary algorithm coupled with a surrogate model. The selected optimal model yielded better hydrodynamic performance than the base model, and reasons for this are investigated through internal flow field analysis. [ABSTRACT FROM AUTHOR]

Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component. [ABSTRACT FROM AUTHOR]