Real-time Platform Research Articles

Positive sequence component based directional relaying for lines integrated with solar photovoltaic plant

Different fault current characteristics of converter interfaced solar photovoltaic plants as compared to the synchronous generator based sources impose challenges to the commonly used directional relaying methods. In this work, the issues with phasor based directional relay algorithms are analysed for the solar plant connected lines. A directional relaying method is proposed for such connectivity using the phase angle difference between positive sequence voltage and the line current measured at the local bus relay. Using PSCAD/EMTDC simulations the proposed method is tested for solar plant connected 39 bus system and validated using field data. The accuracy of the proposed method is also evaluated on a real-time simulation platform. Comparative analysis with the conventional directional relaying methods reveals the superiority of the proposed method.

IRTIDP: A simple integrated real-time isolation and detection platform for small extracellular vesicles Glypican-1 in pancreatic cancer patients

Glypican-1 (GPC-1) protein-positive small extracellular vesicles (GPC-1+-sEV) have been proposed as potential biomarkers for early diagnosis of pancreatic cancer. In this study, we present an integrated real-time isolation and detection platform (IRTIDP) to capture and analyze GPC-1+-sEV directly from sera of pancreatic cancer patients. First, CD63 antibody-modified metal-organic framework (MOF) materials were utilized to enrich sEVs with a capture efficiency of 93.93 %. Second, a SERS probe was constructed by Raman reporter 4-MBA and GPC-1 antibody modified SERS active silver nanoparticles (AgNPs), which formed a sandwich complex structure of "MOFs@GPC-1+-sEV@AgNPs-4-MBA" with MOFs-enriched sEVs. The IRTSDP can complete the capture and detection process within 35 min, with a detection limit for 1 GPC-1+-sEV/μL, and linear range between 105∼109 GPC-1+-sEV/mL. Furthermore, this approach has been applied to quantify serum sEV GPC-1 in clinical pancreatic cancer patients. Based on the SERS intensity analysis, pancreatic cancer patients can be distinguished from pancreatic cystadenoma patients and healthy individuals effectively using this innovative platform that provides highly specific and sensitive means for early diagnosis of pancreatic cancer as well as other tumor types.

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Initial treatment experience obtained with the real-time predictive motion tracking radiotherapy platform Synchrony: A pilot study.

This pilot study presents initial experience obtained with a real-time predictive motion tracking platform called Synchrony mounted on the Radixact radiotherapy device. Synchrony radiotherapy treatments were offered as an alternative to surgical excision for primary pulmonary carcinomas as well as in dogs in a suspected oligometastatic disease state. All dogs were treated with three fractions of 8 Gy. Six dogs with pulmonary targets were successfully treated, while we were unable to treat abdominal targets with implanted fiducials. Cranial targets showed minimal movement, while targets located adjacent to the diaphragm showed a large amplitude of movement. No acute or late clinically apparent side effects were noted in any of the dogs that received radiation therapy. A strong partial response with minimal pneumonitis was seen in follow-up imaging of the one dog where imaging was available. Synchrony motion tracking will continue to be investigated for efficacy.

DC fault current clearance coordinated control strategy for DC grid with hybrid MMC

For DC grid based on the hybrid modular multilevel converter (MMC), the traditional DC fault current clearance scheme takes a long time and greatly affects the active power transmission. A novel DC fault current clearance coordinated control strategy is proposed, which can quickly interrupt the fault current and reduce the impact of DC fault on the DC grid and AC system. For fault-line connected MMC (FLMMC), a negative DC voltage control is employed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault-line connected MMC (NFLMMC), the active current-limiting control (ACLC) based on the virtual reactor is adopted, which can reduce the current flow to the fault location and further shorten fault isolation time. To reduce the impact on the AC system during the DC fault, a short-time active power support control is designed. Finally, a four-terminal DC grid simulation model is built based on the RT-LAB OP5600 real-time digital simulation platform. The simulation results show that the proposed coordinated control strategy for the DC grid can quickly clear DC fault current, shorten DC fault isolation time, and strengthen active power support capability.

A self-powered sensor based on hollow bimetallic sulfide and double-strand TSDR amplification achieves sensitive miRNA-21 detection

MicroRNA (miRNA) sensitive detection is significant for cancer diagnosis. Currently, self-powered biosensors in clinical miRNA detection show the advantages of no external power supply, mild reaction conditions, low cost and portability. In this work, the bimetallic sulfide heterojunction hollow nanocubes are prepared as the substrate material, which can significantly improve the electrochemical reaction rate and increase the enzyme load. Meanwhile, we design the independent duplex-chain assisted toehold mediated strand displacement reaction (TSDR) strategy. The two substable double chains trigger a continuous chain displacement reaction, and the current signal amplification is realized based on molecular recognition. The constructed sensor has a linear response range of 0.05–10000 fM and a detection limit (LOD) of 8.3 aM. In addition, we use commercial apps to present signals, fundamentally changing the signal output mode of electrochemical sensors, and facilitating the construction of a portable real-time sensor platform. The self-powered biosensor has the characteristics of good biocompatibility, real-time monitoring and economic efficiency in miRNA detection, which provides a new idea for sensitive detection of miRNA. Moreover, it has far-reaching reference significance for the development of other sensing technologies, which is expected to extend from the medical field to environmental monitoring, food safety and other fields.

Development of an event-specific PCR method to quantify genetically modified soybean DBN8002 on both real-time and digital PCR platforms

The genetically modified (GM) soybean DBN8002 has been approved for commercial planting in China. For enforcing GMO labeling policy, an event-specific real-time quantitative PCR (qPCR) method was developed to target the junction fragment between the T-DNA left border and the flanking genomic DNA, yielding a 104 base pair (bp) amplicon. This event-specific qPCR method can identify and quantify the DBN8002 event with high specificity, satisfactory linearity, and acceptable accuracy. Furthermore, the DBN8002-event specific primer/probe set was successfully transferred to a droplet digital PCR (ddPCR) platform for quantification. The quantitative results from qPCR were found to be comparable to those obtained from ddPCR, with a P-value exceeding 0.05, indicating no significant difference. The limit of detection (LOD) for both qPCR and ddPCR methods was determined to be 10 copies per reaction, while the limit of quantification (LOQ) was estimated to be 20 copies per reaction for qPCR and 40 copies per reaction for ddPCR. The collaborative validation demonstrated that the DBN8002 event-specific qPCR method had satisfactory repeatability and reproducibility. Both the event-specific qPCR and ddPCR methods are suitable for quantifying the DBN8002 content in samples. Additionally, ddPCR can be utilized for the characterization of DBN8002 reference materials.

Real-Time Tunable Gas Sensing Platform Based on SnO2 Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

HighlightsBlue micro-light-emitting diodes (μLED)-integrated gas sensors were fabricated as monolithic structure by directly loading sensing materials onto the μLED.SnO2 nanoparticles are activated by blue μLED and exhibit outstanding sensitivity to NO2 at μ-Watt power levels.Noble metal (Au, Pd, Pt)-decorated SnO2 showed the tunable gas selectivity for 4 target gases under blue light illumination.

Evaluation of the LightCycler® PRO Instrument as a Platform for Rhesus D Typing.

Rapid and reliable Rhesus D typing is crucial for blood donation centers. In instances of massive blood transfusion or reduced antigen expression, DNA-based phenotype prediction becomes mandatory. Our molecular RHD typing approach involves an initial real-time PCR for the most common aberrant RHD types in our region, RHD*01W.1 (weak D type 1), RHD*01W.2 (weak D type 2), RHD*01W.3 (weak D type 3), and RHD*07.01 (DVII). For comprehensive coverage, Sanger sequencing of RHD coding regions is performed in the case of PCR target-negative results. We evaluated the specificity and accuracy of these methods using the recently launched LightCycler® PRO real-time platform. All findings demonstrated remarkable accuracy. Notably, the LightCycler® PRO instrument offers a distinct advantage in data interpretation and integration via the HL7 interface. This study underlines the importance of including advanced molecular techniques in blood typing protocols, especially in scenarios where conventional serological methods may be insufficient.

Comparison of microbiological and molecular diagnosis for identification of respiratory secondary infections in COVID-19 patients and their antimicrobial resistance patterns

We report the use of a new multiplex Real-Time PCR platform to simultaneously identify 24 pathogens and 3 antimicrobial-resistance genes directly from respiratory samples of COVID-19 patients. Results were compared to culture-based diagnosis.Secondary infections were detected in 60% of COVID-19 patients by molecular analysis and 73% by microbiological assays, with no significant differences in accuracy, indicating Gram-negative bacteria as the predominant species. Among fungal superinfections, Aspergillus spp. were detected by both methods in more than 7% of COVID-19 patients. Oxacillin-resistant S. aureus and carbapenem-resistant K. pneumoniae were highlighted by both methods.Secondary microbial infections in SARS-CoV-2 patients are associated with poor outcomes and an increased risk of death. Since PCR-based tests significantly reduce the turnaround time to 4 hours and 30 minutes (compared to 48 hours for microbial culture), we strongly support the routine use of molecular techniques, in conjunction with microbiological analysis, to identify co/secondary infections.

IVF laboratory management through workflow-based RFID tag witnessing and real-time information entry

BackgroundDual-person inspection in IVF laboratories cannot fully avoid mix-ups or embryo transfer errors, and data transcription or entry is time-consuming and redundant, often leading to delays in completing medical records.MethodsThis study introduced a workflow-based RFID tag witnessing and real-time information entry platform for addressing these challenges. To assess its potential in reducing mix-ups, we conducted a simulation experiment in semen preparation to analyze its error correction rate. Additionally, we evaluated its impact on work efficiency, specifically in operation and data entry. Furthermore, we compared the cycle costs between paper labels and RFID tags. Finally, we retrospectively analyzed clinical outcomes of 20,424 oocyte retrieval cycles and 15,785 frozen embryo transfer cycles, which were divided into paper label and RFID tag groups.ResultsThe study revealed that comparing to paper labels, RFID tag witnessing corrected 100% of tag errors, didn’t affect gamete/embryo operations, and notably shorten the time of entering data, but the cycle cost of RFID tags was significantly higher. However, no significant differences were observed in fertilization, embryo quality, blastocyst rates, clinical pregnancy, and live birth rates between two groups.ConclusionsRFID tag witnessing doesn’t negatively impact gamete/embryo operation, embryo quality and pregnancy outcomes, but it potentially reduces the risk of mix-ups or errors. Despite highly increased cost, integrating RFID tag witnessing with real-time information entry can remarkably decrease the data entry time, substantially improving the work efficiency. This workflow-based management platform also enhances operational safety, ensures medical informational integrity, and boosts embryologist’s confidence.

Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.

Evaluation of two real-time PCR methods to detect Yersinia enterocolitica in bivalve molluscs collected in Campania region

Yersinia enterocolitica (Ye) is a foodborne pathogen isolated from humans, food, animals, and the environment. Yersiniosis is the third most frequently reported foodborne zoonosis in the European Union. Ye species are divided into six biotypes 1A, 1B, 2, 3, 4, and 5, based on biochemical reactions and about 70 serotypes. Biotype 1A is non-pathogenic, 1B is highly pathogenic, and biotypes 2–5 have moderate or low pathogenicity. The reference analysis method for detecting pathogenic Ye species underestimates the presence of the pathogen due to similarities between Yersinia enterocolitica-like species and other Yersiniaceae and/or Enterobacteriaceae, low concentrations of distribution pathogenic strains and the heterogeneity of Yersinia enterocolitica species.In this study, the real-time PCR method ISO/TS 18867 to identify pathogenic biovars of Ye in bivalve molluscs was validated. The sensitivity, specificity and accuracy of the molecular method were evaluated using molluscs experimentally contaminated. The results fully agree with those obtained with the ISO 10273 method. Finally, we evaluated the presence of Ye in seventy commercial samples of bivalve molluscs collected in the Gulf of Naples using ISO/TS 18867. Only one sample tested resulted positive for the ail gene, which is considered the target gene for detection of pathogenic Ye according to ISO/TS 18867. Additionally, the presence of the ystB gene, used as target for Ye biotype 1A, was assessed in all samples using a real-time PCR SYBR Green platform. The results showed amplification ystB gene aim two samples.

Evaluation of the analytical and clinical performance of the Abbott Alinity m instrument for the quantification of HIV-1 RNA plasma viral load in a reference laboratory in Rio de Janeiro, Brazil.

We sought to evaluate the analytical performance of the Alinity m system (Abbott Molecular) and to compare the clinical performance of HIV-1 assays on the Alinity m and m2000 RealTime platforms (Abbott Molecular). The sensitivity, precision, and accuracy of the Alinity m instrument were determined using a panel of standard samples (n = 46). The carryover effect was assessed by analyzing HIV-negative clinical samples (n = 20). Clinical performance of the Alinity m and m2000 RealTime platforms was compared using surplus HIV-positive patient plasma samples (n = 39). The Alinity m HIV-1 assay demonstrated 100% sensitivity, a high precision (coefficient of variation (s/x̄) × 100 ≤1.5% [SD ≤ 0.05] logarithm to base 10 [log10] copies/mL), and partial accuracy over the quantification range. Analysis of clinical samples suggested that the Alinity m HIV-1 assay does not cause carryover effect and produced a mean bias of 0.209 log10 copies/mL (95% CI, 0.153-0.265) compared with the m2000 RealTime System. The Alinity m instrument's performance correlated to that of the m2000 RealTime platform and showed excellent sensitivity, precision, and accuracy, despite producing overquantification not clinically relevant for disease management. Furthermore, use of the Alinity m platform can reduce turnaround time.

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array

Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In this work, biosensors with remarkable gas-sensing characteristics were developed using a self-assembly method consisting of peptides and MXene. Based on these biosensors, a mimetic biosensor array (MBA) was fabricated and integrated into a real-time testing platform (RTP). In addition, machine learning (ML) algorithms were introduced to improve the RTP's detection and identification capabilities of exhaled gas signals. The synthesized biosensor, with the ability to specifically bind to targeted gas molecules, demonstrated higher performance than the pristine MXene, with a response up to 150% greater. Besides, the MBA successfully detected 15 odor molecules affiliated with five categories of alcohols, ketones, aldehydes, esters, and acids by pattern recognition algorithms. Furthermore, with the ML assistance, the RTP detected the breath odor samples from volunteers of four categories, including healthy populations, patients of lung cancer, upper digestive tract cancer, and lower digestive tract cancer, with accuracies of 100%, 94.1%, 90%, and 95.2%, respectively. In summary, we have developed a cost-effective and precise model for non-invasive tumor diagnosis. Furthermore, this prototype also offers a versatile solution for diagnosing other diseases like nephropathy, diabetes, etc.

AI-Assisted Real-Time Immunoassay Improves Clinical Sensitivity and Specificity.

Real-time biosensing systems can interrogate the association between the analyte and the biorecognition element across time. Typically, the resulting data are preprocessed to offer valuable bioanalytical information obtained at a single optimal point of such a real-time response; for instance, a diagnosis of certain medical conditions can be established depending on a biomarker (analyte) concentration measured at an optimal time, that is, a threshold. Exploiting this conventional approach, we previously developed a nanophotonic immunoassay for bacterial vaginosis diagnosis exhibiting a clinical sensitivity and specificity of ca. 96.29% (n = 162). Herein, we demonstrate that a real-time biosensing platform assisted by artificial intelligence not only obviates biomarker concentration (i.e., a threshold) determination but also increases sensitivity and specificity in the targeted diagnostic, thereby reaching values of up to 100%.

MiPRIME: an integrated and intelligent platform for mining primer and probe sequences of microbial species.

Accurately detecting pathogenic microorganisms requires effective primers and probe designs. Literature-derived primers are a valuable resource as they have been tested and proven effective in previous research. However, manually mining primers from published texts is time-consuming and limited in species scop. To address these challenges, we have developed MiPRIME, a real-time Microbial Primer Mining platform for primer/probe sequences extraction of pathogenic microorganisms with three highlights: (i) comprehensive integration. Covering >40 million articles and 548942 organisms, the platform enables high-frequency microbial gene discovery from a global perspective, facilitating user-defined primer design and advancing microbial research. (ii) Using a BioBERT-based text mining model with 98.02% accuracy, greatly reducing information processing time. (iii) Using a primer ranking score, PRscore, for intelligent recommendation of species-specific primers. Overall, MiPRIME is a practical tool for primer mining in the pan-microbial field, saving time and cost of trial-and-error experiments. The web is available at {{https://www.ai-bt.com}}.

Near Real-time Sentiment Analysis using ChatGPT

Abstract: Sentiment analysis, also known as opinion mining, analyses people's opinions, sentiments, attitudes, and emotions from written language. With the rapid growth of social media and other real-time communication platforms, the demand for real-time sentiment analysis has surged. This paper explores the application of OpenAI's ChatGPT, a state-of-the-art language model, in conducting near real-time sentiment analysis. The study investigates the model's capabilities, performance, and potential limitations, proposing a framework for integrating ChatGPT into real-time sentiment analysis systems.

Efektivitas Penerapan Sistem Informasi Manajemen di SMPS Galih Agung Deli Serdang dalam Peningkatan Kualitas Pendidikan

This study aims to describe and evaluate the effectiveness of the implementation of the Management Information System (MIS) at SMPS Galih Agung Deli Serdang in improving the quality of education. The research employs a descriptive qualitative method with a case study approach, collecting data through interviews, observations, and documentation studies. The results indicate that the implementation of the school website and the Academic Information System (Dapodik) has enhanced information accessibility and management efficiency. The school website provides a real-time platform for students, parents, and teachers to access important information, while Dapodik speeds up academic administrative processes such as recording student attendance and grades. The implementation of these systems has facilitated information access, improved administrative efficiency, and positively impacted lesson planning and parental involvement in education. The study also identified challenges in resource management and monitoring, such as a lack of guidelines for activity planning and an imbalance in staff workload. The training provided has improved staff understanding of planning, and the redistribution of teaching duties has reduced workload-related complaints. The implications of this research suggest that effective management information system implementation can enhance educational quality by facilitating information access, improving administrative efficiency, and supporting a more structured learning process.